Journal articles on the topic 'Server computing Evaluation'
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Selvam, S. "An Effective Techniques Using Apriori and Logistic Methods in Cloud Computing." IARS' International Research Journal 11, no. 2 (August 29, 2021): 35–39. http://dx.doi.org/10.51611/iars.irj.v11i2.2021.167.
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This paper presents a creativity data prefetching scheme on the loading servers in distributed file systems for cloud computing. The server will get and piggybacked the frequent data from the client system, after analyzing the fetched data is forward to the client machine from the server. To place this technique to work, the data about client nodes is piggybacked onto the real client I/O requests, and then forwarded to the relevant storage server. Next, dual prediction algorithms have been proposed to calculation future block access operations for directing what data should be fetched on storage servers in advance. Finally, the prefetching data can be pressed to the relevant client device from the storage server. Over a series of evaluation experiments with a group of application benchmarks, we have demonstrated that our presented initiative prefetching technique can benefit distributed file systems for cloud environments to achieve better I/O performance. In particular, configuration-limited client machines in the cloud are not answerable for predicting I/O access operations, which can certainly contribute to preferable system performance on them.
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Ahuja, Sanjay P., and Suganya Sridharan. "Performance Evaluation of Hypervisors for Cloud Computing." International Journal of Cloud Applications and Computing 2, no. 3 (July 2012): 26–67. http://dx.doi.org/10.4018/ijcac.2012070102.
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The virtualization of IT infrastructure enables consolidation and pooling of IT resources so they are shared over diverse applications to offset the limitation of shrinking resources and growing business needs. Virtualization provides a logical abstraction of physical computing resources and creates computing environments that are not restricted by physical configuration or implementation. Virtualization is very important for cloud computing because the delivery of services simplified by providing a platform for optimizing complex IT resources in a scalable manner, which makes cloud computing more cost effective. Hypervisor plays an important role in the virtualization of hardware. This software provides a virtual hardware to support running multiple operating systems concurrently using one physical server. This research quantitatively and qualitatively compares the performance of VMware ESXi 4.1, Citrix Systems Xen Server 5.6 and KVM Hypervisors using standard benchmark SPECvirt_sc2010v1.01 formulated by Standard Performance Evaluation Committee (SPEC), which generate various Workloads simulating real life situations.
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Singh, Harikesh, and Shishir Kumar. "Dispatcher Based Dynamic Load Balancing on Web Server System." International Journal of System Dynamics Applications 1, no. 2 (April 2012): 15–27. http://dx.doi.org/10.4018/ijsda.2012040102.
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The traffic increasing in the network creates bulk congestion while the bulk transfer of data evolves. Performance evaluation and high availability of servers are important factors to resolve this problem using various cluster based systems. There are several low-cost servers using the load sharing cluster system which are connected to high speed networks, and apply load balancing technique between servers. It offers high computing power and high availability. A distributed website server can provide scalability and flexibility to manage with emergent client demands. Efficiency of a replicated web server system will depend on the way of distributed incoming requests among these replicas. A distributed Web-server architectures schedule client requests among the multiple server nodes in a user-transparent way that affects the scalability and availability. The aim of this paper is the development of a load balancing techniques on distributed Web-server systems.
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Kotyk, Vladyslav, and Yevhenii Vavruk. "Comparative Analysis of Server and Serverless Cloud Computing Platforms Vladyslav Kotyk, Yevhenii Vavruk." Advances in Cyber-Physical Systems 7, no. 2 (December 16, 2022): 115–20. http://dx.doi.org/10.23939/acps2022.02.115.
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Cloud computing is emerging as a powerful computing paradigm for the efficient use of resources. However, decisions to move to cloud computing always remain risky from the customer's point of view, considering the benefits they get from it. Existing research on cloud computing is more focused on technical aspects such as security, quality, efficiency, etc. However, research on the implementation of cloud computing is at an early stage. Thus, in this article, an attempt is made to create a model for cost analysis and advantages for deciding on the application of cloud computing. It takes into account various organizational parameters, designing server and serverless architectures using Microsoft Azure Portal cloud platform services and policies of this organization. Also, it makes a comparative characterization of these services according to power and price criteria. A comparative description of these services according to capacity and price criteria is also given. It shows the structure of the test tool for assessment. Evaluation parameters and metrics are defined. In addition, this article contains information about approaches to evaluating cloud platforms according to various criteria that are most important for a developer.
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Salau, Ayodeji Olalekan, Tsehay Admassu Assegie, Adedeji Tomide Akindadelo, and Joy Nnenna Eneh. "Evaluation of Bernoulli Naive Bayes model for detection of distributed denial of service attacks." Bulletin of Electrical Engineering and Informatics 12, no. 2 (April 1, 2023): 1203–8. http://dx.doi.org/10.11591/eei.v12i2.4020.
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Distributed denial of service is a form of cyber-attack that involves sending several network traffic to a target system such as DHCP, domain name server (DNS), and HTTP server. The attack aims to exhaust computing resources such as memory and the processor of a target system by blocking the legitimate users from getting access to the service provided by the server. Network intrusion prevention ensures the security of a network and protects the server from such attacks. Thus, this paper presents a predicitive model that identifies distributed denial of service attacks (DDSA) using Bernoulli-Naive Bayes. The developed model is evaluated on the publicly available Kaggle dataset. The method is tested with a confusion matrix, receiver operating characteristics (ROC) curve, and accuracy to measure its performance. The experimental results show an 85.99% accuracy in detecting DDSA with the proposed method. Hence, Bernoulli-Naive Bayes-based method was found to be effective and significant for the protection of network servers from malicious attacks.
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Kadhim, Abrar Saad, and Mehdi Ebady Manaa. "Hybrid load-balancing algorithm for distributed fog computing in internet of things environment." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3462–70. http://dx.doi.org/10.11591/eei.v11i6.4127.
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Fog computing is a novel idea created by Cisco that provides the same capabilities as cloud computing but close to objects to improve performance, such as by minimizing latency and reaction time. Packet failure can happen on a single fog server across a large number of messages from internet of things (IoT) sensors due to several variables, including inadequate bandwidth and server queue capacity. In this paper, a fog-to-server architecture based on the IoT is proposed to solve the problem of packet loss in fog and servers using hybrid load balancing and a distributed environment. The proposed methodology is based on hybrid load balancing with least connection and weighted round robin algorithms combined together in fog nodes to take into consideration the load and time to distribute requests to the active servers. The results show the proposed system improved network evaluation parameters such as total response time of 131.48 ms, total packet loss rate of 15.670%, average total channel idle of 99.55%, total channel utilization of 77.44%, average file transfer protocol (FTP) file transfer speed (256 KB to 15 MB files) of 260.77 KB/sec, and average time (256 KB to 15 MB) of 19.27 sec.
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Alghamdi, Ibrahim, Christos Anagnostopoulos, and Dimitrios P. Pezaros. "Delay-Tolerant Sequential Decision Making for Task Offloading in Mobile Edge Computing Environments." Information 10, no. 10 (October 12, 2019): 312. http://dx.doi.org/10.3390/info10100312.
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In recent years, there has been a significant increase in the use of mobile devices and their applications. Meanwhile, cloud computing has been considered as the latest generation of computing infrastructure. There has also been a transformation in cloud computing ideas and their implementation so as to meet the demand for the latest applications. mobile edge computing (MEC) is a computing paradigm that provides cloud services near to the users at the edge of the network. Given the movement of mobile nodes between different MEC servers, the main aim would be the connection to the best server and at the right time in terms of the load of the server in order to optimize the quality of service (QoS) of the mobile nodes. We tackle the offloading decision making problem by adopting the principles of optimal stopping theory (OST) to minimize the execution delay in a sequential decision manner. A performance evaluation is provided using real world data sets with baseline deterministic and stochastic offloading models. The results show that our approach significantly minimizes the execution delay for task execution and the results are closer to the optimal solution than other offloading methods.
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Hiraga, Kohei, Osamu Tatebe, and Hideyuki Kawashima. "Scalable Distributed Metadata Server Based on Nonblocking Transactions." JUCS - Journal of Universal Computer Science 26, no. 1 (January 28, 2020): 89–106. http://dx.doi.org/10.3897/jucs.2020.006.
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Metadata performance scalability is critically important in high-performance computing when accessing many small files from millions of clients. This paper proposes a design of a scalable distributed metadata server, PPMDS, for parallel file systems using multiple key-value servers. In PPMDS, hierarchical namespace of a file system is efficiently managed by multiple servers. Multiple entries can be atomically updated using a nonblocking distributed transaction based on an algorithm of dynamic software transactional memory. This paper also proposes optimizations to further improve the metadata performance by introducing a server-side transaction processing, multiple readers, and a shared lock mode, which reduce the number of remote procedure calls and prevent unnecessary blocking. Performance evaluation shows the scalable performance up to 3 servers, and achieves 62,000 operations per second, which is 2.58x performance improvement compared to a single metadata performance.
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Ye, Kong, Penglin Dai, Xiao Wu, Yan Ding, Huanlai Xing, and Zhaofei Yu. "Bandwidth-Aware Traffic Sensing in Vehicular Networks with Mobile Edge Computing." Sensors 19, no. 16 (August 14, 2019): 3547. http://dx.doi.org/10.3390/s19163547.
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Traffic sensing is one of the promising applications to guarantee safe and efficient traffic systems in vehicular networks. However, due to the unique characteristics of vehicular networks, such as limited wireless bandwidth and dynamic mobility of vehicles, traffic sensing always faces high estimation error based on collected traffic data with missing elements and over-high communication cost between terminal users and central server. Hence, this paper investigates the traffic sensing system in vehicular networks with mobile edge computing (MEC), where each MEC server enables traffic data collection and recovery in its local server. On this basis, we formulate the bandwidth-constrained traffic sensing (BCTS) problem, aiming at minimizing the estimation error based on the collected traffic data. To tackle the BCTS problem, we first propose the bandwidth-aware data collection (BDC) algorithm to select the optimal uploaded traffic data by evaluating the priority of each road segment covered by the MEC server. Then, we propose the convex-based data recovery (CDR) algorithm to minimize estimation error by transforming the BCTS into an l 2 -norm minimization problem. Last but not the least, we implement the simulation model and conduct performance evaluation. The comprehensive simulation results verify the superiority of the proposed algorithm.
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Špeťko, Matej, Ondřej Vysocký, Branislav Jansík, and Lubomír Říha. "DGX-A100 Face to Face DGX-2—Performance, Power and Thermal Behavior Evaluation." Energies 14, no. 2 (January 12, 2021): 376. http://dx.doi.org/10.3390/en14020376.
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Nvidia is a leading producer of GPUs for high-performance computing and artificial intelligence, bringing top performance and energy-efficiency. We present performance, power consumption, and thermal behavior analysis of the new Nvidia DGX-A100 server equipped with eight A100 Ampere microarchitecture GPUs. The results are compared against the previous generation of the server, Nvidia DGX-2, based on Tesla V100 GPUs. We developed a synthetic benchmark to measure the raw performance of floating-point computing units including Tensor Cores. Furthermore, thermal stability was investigated. In addition, Dynamic Frequency and Voltage Scaling (DVFS) analysis was performed to determine the best energy-efficient configuration of the GPUs executing workloads of various arithmetical intensities. Under the energy-optimal configuration the A100 GPU reaches efficiency of 51 GFLOPS/W for double-precision workload and 91 GFLOPS/W for tensor core double precision workload, which makes the A100 the most energy-efficient server accelerator for scientific simulations in the market.
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Lei, Ming Yue, Zheng Huang, Qiao Yan Wen, and Chun Ye Hu. "A Secure Cloud Computing Scaling Model." Applied Mechanics and Materials 411-414 (September 2013): 60–66. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.60.
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Since many Internet enterprises employ the computing resources provided by Cloud Service Provider (CSP), how to dynamically adjust the number of rented servers and improve service quality becomes a crucial subject. A lot of efforts have been made to achieve intelligent energy saving capacity adjustment but scarcely any of them takes the security issue of cloud data into account. In this paper, we focus on the cloud storage security in the cloud computing capacity scaling. We design the security part of the whole model based on the idea of Proofs of Retrievability (POR). In the scaling part of the model, we determine the scaling strategy according to email server instances CPU utilization. With simulation and performance evaluation, we conclude that the designed model is able to enable verifier to check the integrity of the information in the cloud storage and maintain satisfied response time target within Service Level Agreement (SLA).
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Ahuja, Sanjay P., and Niharika Deval. "On the Performance Evaluation of IaaS Cloud Services With System-Level Benchmarks." International Journal of Cloud Applications and Computing 8, no. 1 (January 2018): 80–96. http://dx.doi.org/10.4018/ijcac.2018010104.
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Infrastructure-as-a-service is a cloud service model that allows customers to outsource computing resources such as servers and storage. This article evaluates four IaaS cloud services - Amazon EC2, Microsoft Azure, Google Compute Engine and Rackspace Cloud in a vendor-neutral approach with regards to system parameter usage including server, file I/O and network utilization. Thus, system-level benchmarking provides objective comparison of cloud providers from performance standpoint. Unixbench, Dbench and Iperf are the System-level benchmarks chosen to test the performance of server, file I/O and network respectively. In order to capture the variation in performance, the tests were performed at different times on weekdays and weekends. With each offering, the benchmarks are tested on different configurations to provide an insight to the cloud users in selection of provider followed by appropriate VM sizing according to the workload requirement. In addition to the performance evaluation, price-per-performance value of all the providers is also examined and compared.
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Bulla, Suneetha, Chirra Venkata Rami Reddy, Panguluri Padmavathi, and Turaka Padmasri. "Analytical Evaluation of Resource Estimation in Web Application Services." Ingénierie des systèmes d information 25, no. 5 (November 10, 2020): 683–90. http://dx.doi.org/10.18280/isi.250516.
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Cloud computing for web application is ubiquitous in the global market and represents a generic pattern because rapid elasticity and infrastructure scaling naturally lends itself to the needs of a virtual data center. Server requirement analysis depending on the workload play a very important role in web app development and it leads to availability of service to customer at any cost and cost analysis to the application provider. To achieve proper infrastructure scaling the minimal number of servers are have to satisfy and determine SLO. Thus this paper evaluates an analytical model to formulate prediction or estimation of required servers has to satisfy the QoS performance metrics such as throughput, utilization of cloud datacenter, request loss and required number of servers. The experimental model is used to validate correctness of the analytical model that was hosted on AWS cloud platform. Finally results have presented and conclusions are drawn.
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Xie, Yong, and Yi Lai Zhang. "Research on Cloud Service Leasing Method Based on Maximum Benefit." Applied Mechanics and Materials 641-642 (September 2014): 1291–94. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.1291.
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In order to promote mutual benefit of service provider and requestor under the cloud computing environment, a new method to obtain maximum utility during cloud service leasing progress was proposed. Cloud service providers decide to tender or not according to their actual conditions and tender bid, and at the same time, it’s evaluation and other service providers’ recommends are considered during the bid evaluation by the service management center, the maximum utility will be computed among the servers provider and server requestor to determine which services bid. It showed great effectiveness of this method through a large number of simulation experiments.
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Ding, Wei Min, Benjamin Ghansah, and Yan Yan Wu. "Research on the Virtualization Technology in Cloud Computing Environment." International Journal of Engineering Research in Africa 21 (December 2015): 191–96. http://dx.doi.org/10.4028/www.scientific.net/jera.21.191.
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Virtualization and Cloud computing are two popular research directions in recent times. Today, Virtualization is being used by a growing number of organizations to reduce power consumption, Server Consolidation, Testing and Development, Dynamic Load Balancing and Disaster Recovery, Virtual Desktops and Improved System Reliability and Security. Virtualization also provides high availability for critical applications, and streamlines application deployment and migrations. Through cloud computing, Information Technology resources can be delivered as services over the Internet to the end user. Virtualization is one of such important core technologies of cloud computing. In this paper, we present a detailed review on virtualization. Furthermore, three technologies for x86 CPU virtualization and the architecture of Xen are introduced. Specifically, we propose an architecture of the cloud computing platform based on virtualization. Finally, we discuss the performance evaluation of server virtualization in saving cost, time and energy consumption.
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Pan, Wenwen, Jianzhi Wang, and Jingsheng Ji. "Evaluation and Analysis of Traditional Physical Training by Using Mobile Edge Computing and Software-Defined Networking." Mobile Information Systems 2021 (March 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/5596291.
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The body health plays an important metric in people’s everyday life, and it directly determines whether people have the ability to preferably contribute to the society. In fact, the physical training is a universal sport to enhance the body health. Therefore, the evaluation and analysis of physical training become particularly significant. With the rapid development and emerging of new techniques and networking paradigms, the traditional offline physical training evaluation and analysis cannot be performed well. Instead, this paper uses Mobile Edge Computing (MEC) and Software-Defined Networking (SDN) to implement the evaluation and analysis of physical training, shortened for MSPT, where MEC is the new computing technique and SDN is the new networking paradigm. The proposed MSPT includes two parts. At first, the physical training data from different mobile devices are migrated into the edge server for computing according to the current condition, in which the game theory is used to complete the task scheduling. Then, SDN is responsible for the global scheduling in the centralized control manner, in which the multigranularity scheduling strategy is used to handle the traffic between the SDN controller and edge computing server. The experiments are driven by OMNet, including three aspects of evaluation, i.e., task offloading of MEC, traffic scheduling of SDN, and performance analysis of physical training, and the results show that the proposed MSPT has better performance than the corresponding baselines.
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Md Sahabul Hossain, Nafis Irtija, Maria Diamanti, Fisayo Sangoleye, Eirini Eleni Tsiropoulou, and Symeon Papavassiliou. "Reconfigurable intelligent surfaces-enabled edge computing: A location-aware task offloading framework." ITU Journal on Future and Evolving Technologies 3, no. 3 (December 9, 2022): 830–43. http://dx.doi.org/10.52953/fltj9889.
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In this paper, an energy efficient task offloading mechanism in a Multiaccess Edge Computing (MEC) environment is introduced, based on the principles of contract theory. The technology of Reconfigurable Intelligent Surfaces (RISs) is adopted and serves as the enabler for energy efficient task offloading, from the perspective of location-awareness and improved communication environment. Initially a novel positioning, navigation, and timing solution is designed, based on the RIS technology and an artificial intelligent method that selects a set of RISs to perform the multilateration technique and determine the Internet of Things (IoT) nodes' positions in an efficient and accurate manner is introduced. Being aware of the nodes' positions, a maximization problem of the nodes' sum received signal strength at the MEC server where the nodes offload their computing tasks is formulated and solved, determining each RIS element's optimal phase shifts. Capitalizing on these enhancements, a contract-theoretic task offloading mechanism is devised enabling the MEC server to incentivize the IoT nodes to offload their tasks to it for further processing in an energy efficient manner, while accounting for the improved nodes' communications and computing characteristics. The performance evaluation of the proposed framework is obtained via modeling and simulation under different operation scenarios.
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Tan, Qingyan. "English Teaching Evaluation Combined with End-User Computing and Neural Network." Mobile Information Systems 2022 (February 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/4018269.
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A neural network model and English teaching evaluation of university with end-user computing are the focus of this paper. The main research contributions are as follows: (1) Propose an ADA-BP neural network. It applies the adaptive learning rate as well as the momentum term to promote the BP network. Experiments show that the model solves the problems of the existing methods and models such as difficulty in determining weights, prone to overfitting, slow convergence, and prone to local minimums, which verifies the effectiveness in evaluating college English teaching. (2) Propose the DA-SVR network. The model has multiple hidden layers and performs multiple feature conversions to optimize the network. The output layer of the model uses SVR as a predictor to achieve evaluation. (3) Deploy the designed evaluation network on the server, and end users can use the API interface to directly access and test the model functions. Massive experiments have verified the effectiveness of the designed method.
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Hossen, Md Rajib, and Mohammad A. Islam. "Mobile Task Offloading Under Unreliable Edge Performance." ACM SIGMETRICS Performance Evaluation Review 48, no. 4 (May 17, 2021): 29–32. http://dx.doi.org/10.1145/3466826.3466838.
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Offloading resource-hungry tasks from mobile devices to an edge server has been explored recently to improve task com- pletion time as well as save battery energy. The low la- tency computing resource from edge servers are a perfect companion to realize such task offloading. However, edge servers may su er from unreliable performance due to its rapid workload variation and reliance on intermittent re- newable energy. Further, batteries in mobile devices make online optimum offloading decisions challenging since it in- tertwines offloading decisions across di erent tasks. In this paper, we propose a deep Q-learning based task offloading solution, DeepTO, for online task offloading. DeepTO learns edge server performance in a model-free manner and takes future battery needs of the mobile device into account. Us- ing a simulation-based evaluation, we show that DeepTO can perform close to the optimum solution that has com- plete future knowledge.
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Wang, Zhao En, and Jia Liang Liu. "A QoS Evaluation Model in the Environment of Cloud Computing." Advanced Materials Research 488-489 (March 2012): 1094–100. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1094.
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As the name implies, the quality of service（QoS） often reflects the service consumer satisfaction with the services provided by the service provider. In computer systems, particularly in the computer network system, as the providers of the computing and information services, It is necessary to consider the cloud user’s suitability in the process of different physical implementation in the same functional service when constructing the server cluster in the cloud. This paper is for the cloud, build a service quality model oriented user request and the implementation process, completed the choice for the physical implementation with the best users fit service.
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Akki, Praveena, and V. Vijayarajan. "An efficient mobility prediction model for resource allocation in mobile cloud computing." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 1 (April 9, 2021): 149–57. http://dx.doi.org/10.3233/kes-210060.
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Mobile cloud computing (MCC) is a technology which provides cloud server resources to mobile users with optimized latency. MCC allows mobile device to access cloud resources and to offload tasks to cloud let servers at any time and from anywhere. The cloud let servers are attached to wireless Access points. But mobility plays an important role which leads to the loss of connectivity of mobile devices because of varying signal strengths. On the other hand, optimal code execution is a challenge. In this paper, a connectivity base mobility prediction method is proposed to assign the cloud resources to the user without loss in the connection. The past accessing history of the users and path loss factors are taken into consideration to predict proper access point. From the performance evaluation the performance of the proposed method is increased by 15.23% when compared to other existing methods.
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Amo-Asuah, Charles, Samuel Akyeramfo-Sam, and Isaac Kofi Nti. "Evaluation of Server Performance in Cloud Computing On Virtual Network Infrastructure: Using Riverbed Simulator." International Journal of Science and Engineering Applications 6, no. 3 (March 22, 2017): 95–103. http://dx.doi.org/10.7753/ijsea0603.1005.
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Bai, Wei-Hua, Jian-Qing Xi, Jia-Xian Zhu, and Shao-Wei Huang. "Performance Analysis of Heterogeneous Data Centers in Cloud Computing Using a Complex Queuing Model." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/980945.
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Performance evaluation of modern cloud data centers has attracted considerable research attention among both cloud providers and cloud customers. In this paper, we investigate the heterogeneity of modern data centers and the service process used in these heterogeneous data centers. Using queuing theory, we construct a complex queuing model composed of two concatenated queuing systems and present this as an analytical model for evaluating the performance of heterogeneous data centers. Based on this complex queuing model, we analyze the mean response time, the mean waiting time, and other important performance indicators. We also conduct simulation experiments to confirm the validity of the complex queuing model. We further conduct numerical experiments to demonstrate that the traffic intensity (or utilization) of each execution server, as well as the configuration of server clusters, in a heterogeneous data center will impact the performance of the system. Our results indicate that our analytical model is effective in accurately estimating the performance of the heterogeneous data center.
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Liyanage, Mohan, Chii Chang, and Satish Narayana Srirama. "Adaptive mobile Web server framework for Mist computing in the Internet of Things." International Journal of Pervasive Computing and Communications 14, no. 3/4 (September 3, 2018): 247–67. http://dx.doi.org/10.1108/ijpcc-d-18-00023.
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Purpose The distant data centre-centric Internet of Things (IoT) systems face the latency issue especially in the real-time-based applications, such as augmented reality, traffic analytics and ambient assisted living. Recently, Fog computing models have been introduced to overcome the latency issue by using the proximity-based computational resources, such as the computers co-located with the cellular base station, grid router devices or computers in local business. However, the increasing users of Fog computing servers cause bottleneck issues and consequently the latency issue arises again. This paper aims to introduce the utilisation of Mist computing (Mist) model, which exploits the computational and networking resources from the devices at the very edge of the IoT networks. Design/methodology/approach This paper proposes a service-oriented mobile-embedded Platform as a Service (mePaaS) framework that allows the mobile device to provide a flexible platform for proximal users to offload their computational or networking program to mePaaS-based Mist computing node. Findings The prototype has been tested and performance has been evaluated on the real-world devices. The evaluation results have shown the promising nature of mePaaS. Originality/value The proposed framework supports resource-aware autonomous service configuration that can manage the availability of the functions provided by the Mist node based on the dynamically changing hardware resource availability. In addition, the framework also supports task distribution among a group of Mist nodes.
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Teng, Rui, and Kenya Sato. "Two-Level Sharing and Extraction of Sensing Information with Hybrid V2X Communications." Applied Sciences 12, no. 9 (May 3, 2022): 4603. http://dx.doi.org/10.3390/app12094603.
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Vehicle-to-Everything (V2X) communications provide opportunities for information sharing among vehicles, edge servers, and cloud services. By the collection and extraction of sensing information from vehicles, such as communication quality or free space size, the edge server in V2X communications can improve its sensing and perception coverage. However, the collection of sensing data from vehicles consumes a large amount of wireless resources and computing resources at the edge server. The objective of this study is to extract object sensing information from vehicles, including the minimum or maximum of the sensing values, with low resource consumption and with high scalability. We propose a method that transforms the extraction of sensing information into a two-level procedure that includes (1) the local sharing and extraction of sensing information among vehicles and (2) the efficient extraction of sensing information at the edge server. Moreover, hybrid communication methods are employed at vehicles, with a short range of communication between vehicles to reduce the consumption of wireless resources for the local sharing of sensing data. The evaluation results show that the proposed method highly reduces the number of reports from the vehicles to the edge server, with a small amount of network resource consumption and scalability.
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Badr, Aymen Mudheher, Yi Zhang, and Hafiz Gulfam Ahmad Umar. "Dual Authentication-Based Encryption with a Delegation System to Protect Medical Data in Cloud Computing." Electronics 8, no. 2 (February 1, 2019): 171. http://dx.doi.org/10.3390/electronics8020171.
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The increasing use of cloud computing, especially in commercial, government and healthcare institutions, started with the use of computerized clouds. Clouds store important data, which reduces the cost of management and ensures easy access. To protect this data, cryptographic methods are used to ensure confidentiality of the data, as well as to secure access to user data and increase trust in cloud technology. In our paper, we suggest a new scheme to support an attribute-based encryption system (ABE) that involves multiple parties such as data owners, data users, cloud servers and authority. A verified and authenticated decryption process for the cloud environment is the imperative feature of our proposed architecture. The data owner encrypts their data and sends it to the cloud. The cloud server performs partial decryption and the final decrypted data are shared for users as per their privileges. Thus, the data owner reduces complexity of productivity by delegating the decryption process to the cloud server. Analysis of the experimental results confirms that data access in the electronic cloud atmosphere is safer due to a controlled multiple-users-rights scheme. Our performance evaluation results show that the proposed model condensed the communication overhead and made Digital Imaging and Communications in Medicine (DICOM) more secure.
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dos Anjos, Julio C. S., João L. G. Gross, Kassiano J. Matteussi, Gabriel V. González, Valderi R. Q. Leithardt, and Claudio F. R. Geyer. "An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture." Sensors 21, no. 9 (April 21, 2021): 2914. http://dx.doi.org/10.3390/s21092914.
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Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to cloud computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in mobile edge computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among cloud or mobile edge computing (MEC) server or local IoT devices to achieve better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.
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Cui, Yunhe, Qing Qian, Guowei Shen, Chun Guo, and Saifei Li. "REVERT: A Network Failure Recovery Method for Data Center Networks." Electronics 9, no. 8 (July 23, 2020): 1187. http://dx.doi.org/10.3390/electronics9081187.
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As a repository that holds computing facilities, storage facilities, network facilities and other facilities, the Software Defined Data Center (SDDC) can provide computing and storage resources for users. For a SDDC, it is important to provide continuous services for users. Hence, in order to achieve high reliability in Software Defined Data Center Networks (SDDCNs), a network failure recovery method for software defined data center networks (REVERT) is proposed to recover failures in SDDCNs. In REVERT, the network failures that occurred in SDDCNs are classified into three types, which are switch failure, failure of links among switches and failure of links between switches and servers. Specially, except recovering the switch failure and failure of links between switches, REVERT can also recover the failures of links between the switches and servers. To achieve that, a failure preprocessing method used to classify the network failures, a data structure for storing and finding the affected flows, a server cluster agent for communicating with the server clustering algorithm and a routing path calculation method are designed in REVERT. Meanwhile, REVERT has been implemented and evaluated on RYU controller and Mininet using three routing algorithms. Compared with the link usage before recovering the network failures, when there are more than 200 flows in the network, the mean link usages only slightly increase at about 1.83 percent. More importantly, the evaluation results also demonstrate that except recovering switch failures, intra-topo link failures, REVERT has the ability of recovering failures of links between servers and edge switches successfully.
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Xu, Xin, and Huiqun Yu. "A Game Theory Approach to Fair and Efficient Resource Allocation in Cloud Computing." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/915878.
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On-demand resource management is a key characteristic of cloud computing. Cloud providers should support the computational resource sharing in a fair way to ensure that no user gets much better resources than others. Another goal is to improve the resource utilization by minimizing the resource fragmentation when mapping virtual machines to physical servers. The focus of this paper is the proposal of a game theoretic resources allocation algorithm that considers the fairness among users and the resources utilization for both. The experiments with an FUGA implementation on an 8-node server cluster show the optimality of this algorithm in keeping fairness by comparing with the evaluation of the Hadoop scheduler. The simulations based on Google workload trace demonstrate that the algorithm is able to reduce resource wastage and achieve a better resource utilization rate than other allocation mechanisms.
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Mo, Jiaqing, Zhongwang Hu, Hang Chen, and Wei Shen. "An Efficient and Provably Secure Anonymous User Authentication and Key Agreement for Mobile Cloud Computing." Wireless Communications and Mobile Computing 2019 (February 4, 2019): 1–12. http://dx.doi.org/10.1155/2019/4520685.
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Nowadays, due to the rapid development and wide deployment of handheld mobile devices, the mobile users begin to save their resources, access services, and run applications that are stored, deployed, and implemented in cloud computing which has huge storage space and massive computing capability with their mobile devices. However, the wireless channel is insecure and vulnerable to various attacks that pose a great threat to the transmission of sensitive data. Thus, the security mechanism of how the mobile devices and remote cloud server authenticate each other to create a secure session in mobile cloud computing environment has aroused the interest of researchers. In this paper, we propose an efficient and provably secure anonymous two-factor user authentication protocol for the mobile cloud computing environment. The proposed scheme not only provides mutual authentication between mobile devices and cloud computing but also fulfills the known security evaluation criteria. Moreover, utilization of ECC in our scheme reduces the computing cost for mobile devices that are computation capability limited and battery energy limited. In addition, the formal security proof is given to show that the proposed scheme is secure under random oracle model. Security analysis and performance comparisons indicate that the proposed scheme has reasonable computation cost and communication overhead at the mobile client side as well as the server side and is more efficient and more secure than the related competitive works.
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Huang, Wei, Zhen Wang, Mianxiong Dong, and Zhuzhong Qian. "A Two-Tier Energy-Aware Resource Management for Virtualized Cloud Computing System." Scientific Programming 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/4386362.
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The economic costs caused by electric power take the most significant part in total cost of data center; thus energy conservation is an important issue in cloud computing system. One well-known technique to reduce the energy consumption is the consolidation of Virtual Machines (VMs). However, it may lose some performance points on energy saving and the Quality of Service (QoS) for dynamic workloads. Fortunately, Dynamic Frequency and Voltage Scaling (DVFS) is an efficient technique to save energy in dynamic environment. In this paper, combined with the DVFS technology, we propose a cooperative two-tier energy-aware management method including local DVFS control and global VM deployment. The DVFS controller adjusts the frequencies of homogenous processors in each server at run-time based on the practical energy prediction. On the other hand, Global Scheduler assigns VMs onto the designate servers based on the cooperation with the local DVFS controller. The final evaluation results demonstrate the effectiveness of our two-tier method in energy saving.
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Surantha, Nico, and Noffal A. Putra. "Integrated SDN-NFV 5G Network Performance and Management-Complexity Evaluation." Future Internet 14, no. 12 (December 14, 2022): 378. http://dx.doi.org/10.3390/fi14120378.
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Digitalization is one of the factors that affects the acceleration of the application of telecommunications technologies such as 5G. The 5G technology that has been developed today does not yet meet different performance and manageability standards, particularly for data center networks as a supportive technology. Software-defined networking (SDN) and network function virtualization (NFV) are two complementary technologies that are currently used by almost all data centers in the telecommunications industry to rectify performance and manageability issues. In this study, we deliver an integrated SDN-NFV architecture to simplify network management activities in telecommunication companies. To improve network performance at the computing level, we performed a modification of a networking system at the computing level, underlying NFV devices by replacing the default virtual switch with a data plane development kit (DPDK) and single root I/O virtualization (SR-IOV). This study evaluated the proposed architecture design in terms of network performance and manageability. Based on 30 days of observation in prime time, the proposed solution increased throughput up to 200 Mbps for the server leaf and 1.6 Gbps for the border leaf compared to the legacy architecture. Meanwhile, the latency decreased to 12 ms for the server leaf and 17 ms for the border leaf. For manageability, we tested three different scenarios and achieved savings of 13 min for Scenario 1, 22 min for Scenario 2 and 9 min for Scenario 3.
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Zaineldeen, Samar, and Abdelrahim Ate. "Improved cloud data transfer security using hybrid encryption algorithm." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (October 1, 2020): 521. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp521-527.
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Cloud computing is a model of sophisticated computing which has a strong effect on data innovation. Cloud computing offers remote access to shared computerized assets in the stored cloud. Operationally cloud servers utilize Web services that give enormus advantage to the user in a variety of applications such as banking and finance, storage, social networking and e-mail. Cloud computing accomplishes many of the features interrelated to elasticity, ease of utility, efficiency and performance with low cost. There are a number of potential concerns related to security and privacy since the requirement to protect cloud computing expanded, the encryption algorithms play the key part in data and information security systems, on side these algorithms consume a considerable quantity of computing resource.This paper,presenting a new hybrid encryption algorithm emphasising on AES and Enhanced Homomorphic Cryptosystem (EHC) as a hybrid encryption to guarantee the secure exchange of data between the user and the cloud server, and compression study for two proficient homomorphic encryption techniques for encoding Data Encryption Standard (DES), Advance Encryption Stander (AES).With The proposed techniques an evaluation has been conducted for those encryption algorithms at diverse file sizes of data, to evaluate time taking for encryption and decryption, throughput, memory consumption and power consumption. The major finding was thats the proposed method has the extremity throughput; memory consumption and our proposed work took advantage of the least time taken in sec for encryption and decryption.
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Tao, Qian, Bo Pan, and Wen Quan Cui. "Task Scheduling of Cloud Computing in Weapon Network System." Applied Mechanics and Materials 610 (August 2014): 695–98. http://dx.doi.org/10.4028/www.scientific.net/amm.610.695.
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In recent years, the rapid development of cloud computing brings significant innovation in the whole IT industry. For the local tasks scheduling on each computational node of the top model of weapon network, an open task scheduling framework was introduced a task accept control scheme based on the tasks based on load balancing, quality of service (QoS) and an improved constant bandwidth server algorithm was presented. The result of simulation shows that the scheduling policies can improve the schedule speed when the number of tasks increases and can meet the demand better for the real time requirementsof the tactical training evaluation system for complexity and Large-scale.
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Gao, Kai, Shuo Huang, Farong Han, Shuo Li, Wenguang Wu, and Ronghua Du. "An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario." Applied Sciences 10, no. 6 (March 19, 2020): 2078. http://dx.doi.org/10.3390/app10062078.
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Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm.
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Rocha, Murilo Silveira, Guilherme Serpa Sestito, Andre Luis Dias, Alfonso Celso Turcato, Dennis Brandão, and Paolo Ferrari. "On the performance of OPC UA and MQTT for data exchange between industrial plants and cloud servers." ACTA IMEKO 8, no. 2 (June 27, 2019): 80. http://dx.doi.org/10.21014/acta_imeko.v8i2.648.
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The Internet of Things (IoT) is a key technology in the development of Industry 4.0. An increasing number of new industrial devices are expected to communicate with each other by means of local (edge) and cloud computing servers. In this article, two well-known protocols used for IoT and Industrial IoT (IIoT) are compared in terms of their performance when they are used to send/receive data to/from cloud servers. Due to their wide diffusion and suitability, the considered protocols are open platform communication-unified architecture publisher-subscriber (OPC UA PubSub) (purposely developed and maintained by industrial consortia) and message queuing telemetry transport (MQTT), the most well-known message protocol originally developed by IBM. The performance comparison is carried out considering the overall quantity of the data transferred (user payload plus overhead) and the roundtrip time required to send in data and receive a feedback message in return. The experimental results include the evaluation of several cloud computing server and application scenarios, highlighting how each protocol is particularly suitable for certain situations. Finally, conclusions about the best choice for data exchange between devices are given.
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Revathi, M., and R. Priya. "Security Issues in Cloud Computing and Existing Solutions- a Survey." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 200. http://dx.doi.org/10.14419/ijet.v7i3.27.17761.
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Cloud computing is turning into an outstanding fluff word these days. It is an appealing innovation for long-haul benefits through cost diminishment and change in business results. It additionally tackles numerous issues of stereotyped processing, including taking care of more volume of burdens, introducing programming updates, and, utilizing overabundance figuring cycles. Regardless, the contemporary advancement has new troubles, for instance, data security, information inborn, and trans-code data storing. Cloud computing is consumed and compared to the modern transformation. Cloud administrations are conveyed from server farms, for example, Amazon, Google, Microsoft et cetera, all through the world. The quick blast in the territory of "Cloud computing" moreover expands outrageous wellbeing concerns. This paper review cloud structure and the monstrous evaluation of Cloud computing with the essential consideration on holes and security issues. We perceive the best security dangers and their present arrangements.
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Meng, Xiang Lei, Ming Shun Ai, and Peng Peng Yu. "An Improved Data Possession Checking Scheme in Cloud Computing." Advanced Materials Research 760-762 (September 2013): 1733–37. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1733.
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Remotely data storage in cloud computing faces great challenge. Data possession checking ensures the data integrity and availability. In this paper, a cloud data possession checking model has been presented. Based on Paillier cryptosystem and elliptic curve cryptograph (ECC),we designed a data possession checking scheme, where the user asks the server to compute a checking value of randomly appointed data blocks, and then makes a decision by proof verification. The homomorphic verification tag (HVT) generated based on Paillier cryptosystem to achieve unlimited queried times. Extensive evaluation and analysis shows the proposed scheme is efficient and secure with the features: a high confidence level, un-forged HVTs, a small constant overhead, etc.
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Su Jeong, Yoon, Yong Tae Kim, and Gil Cheol Park. "Efficient key distribution protocol for mobile devices in cloud environments." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 18. http://dx.doi.org/10.14419/ijet.v7i2.12.11027.
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Background/Objectives: Recently, as the fourth industrial revolution has emerged, cloud computing services have been attracting attention for efficient use of Internet-based computing resources. Among the cloud computing services, even if the data processed by the mobile device is encrypted after being stored in the server, the confidential information can be leaked. Therefore, there is a need for the key generation for data encryption and decryption.Methods/Statistical analysis: In this paper, we propose a key distribution protocol that enables mobile devices to securely encrypt and decrypt keys in an efficient manner in a cloud environment. The main purpose of the proposed protocol is to maximize the efficiency and cost reduction of key generation, which can securely transmit and receive data, in a situation where the size of data used in the cloud environment and the storage location are increasing. As a result of the performance evaluation, the proposed method improved the authentication processing time by 4.1% on average compared with the existing protocol, and the average throughput rate of the server per unit time was 6.5%. In addition, the communication delay time between the authentication server and the mobile device improved by 9.3% on average, and the authentication overhead of the server was 11.5% lower than that of the conventional method.Findings: In order to solve this problem, the proposed protocol can solve the security problem of the mobile device because it can receive the authentication through the one - way hash function and the XOR operation using the encrypted data using the session key.Improvements/Applications: In future studies, we will apply the proposed protocol to the actual environment based on the results of this study and compare it with the results obtained from the theoretical studies.
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Titus Mukisa, Muhambe, and Daniel Orwa Ochien. "Post adoption evaluation model for cloud computing services utilization in universities in Kenya." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 5, no. 3 (October 15, 2013): 614–28. http://dx.doi.org/10.24297/ijmit.v5i3.4221.
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Cloud Computing, a recent technology development presents a paradigm shift in computing, a move away from personal computers and enterprise server systems, to a cloud of computers located on the internet. Applications and resources are accessed from the cloud. The introduction of cloud computing services in Kenya by some telecommunication company like Safari Cloud from Safaricom Limited is an indicator that cloud computing technology is gaining ground and popularity locally. Technology adoption studies, which include Cloud computing adoption, have mainly been carried out in United State of America, Europe, Japan and Australia. No previous study has analyzed the acceptance and use of cloud computing services in a university setting in Kenya. In addition, no model has been advanced to explain the factors that influence acceptance and use of cloud services in the same setting. A conceptual model derived from the Unified Theory of Acceptance and Use of Technology (UTAUT) was used as a guide in this study. Survey (n=217) and Focus Group Discussion (n=12) were used as data collection instruments in an attempt to understand cloud services adoption and use. The results of partial correlation showed that Performance Expectancy and Facilitating Condition were the two main factors that significantly influence cloud services acceptance and use in the universities in Kenya. The Focus Group Discussion results established that personal ego was a factor that prevented individuals from admitting that they were socially influenced towards adoption and use of cloud services. The findings will be useful to multiple stakeholders that include; academic researchers, technology adoption and perception researchers and cloud services providers and vendors.
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Hassan, Syed Rizwan, Ishtiaq Ahmad, Ateeq Ur Rehman, Seada Hussen, and Habib Hamam. "Design of Resource-Aware Load Allocation for Heterogeneous Fog Computing Environments." Wireless Communications and Mobile Computing 2022 (June 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/3543640.
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The architecture employed by most of the researchers for the deployment of latency-sensitive Internet of Things (IoT) applications is fog computing. Fog computing architecture offers less delay as compared to the cloud computing paradigm by providing resource constraint fog devices close to the edge of the network. Fog nodes process the incoming data by utilizing available resources which reduces the volume of data to be sent to the cloud server. Fog devices having dissimilar processing capabilities are present in a system. The connection of suitable sensor nodes to the parent fog node plays an essential role in achieving the optimum performance of the system. In this paper, we have designed an algorithm that dynamically assigns appropriate sensor devices to fog nodes to achieve a reduction in network utilization and latency. The proposed algorithm estimates the volume of information detected by an edge device from the rate of sensing frequency of the sensor attached to the edge device. The proposed policy while connecting the network nodes takes into account the heterogeneity and processing capability of the devices. Several evaluations are performed on multiple scales for the evaluation of the proposed algorithm. The outcomes of the evaluations confirm the effectiveness of the proposed algorithm in achieving a reduction in network consumption and end-to-end delay.
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Jaiganesh, M., and A. Vincent Antony Kumar. "B3: Fuzzy-Based Data Center Load Optimization in Cloud Computing." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/612182.
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Cloud computing started a new era in getting variety of information puddles through various internet connections by any connective devices. It provides pay and use method for grasping the services by the clients. Data center is a sophisticated high definition server, which runs applications virtually in cloud computing. It moves the application, services, and data to a large data center. Data center provides more service level, which covers maximum of users. In order to find the overall load efficiency, the utilization service in data center is a definite task. Hence, we propose a novel method to find the efficiency of the data center in cloud computing. The goal is to optimize date center utilization in terms of three big factors—Bandwidth, Memory, and Central Processing Unit (CPU) cycle. We constructed a fuzzy expert system model to obtain maximum Data Center Load Efficiency (DCLE) in cloud computing environments. The advantage of the proposed system lies in DCLE computing. While computing, it allows regular evaluation of services to any number of clients. This approach indicates that the current cloud needs an order of magnitude in data center management to be used in next generation computing.
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Lee, JoonYoung, MyeongHyun Kim, JiHyeon Oh, YoungHo Park, KiSung Park, and Sungkee Noh. "A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service." Applied Sciences 11, no. 19 (September 23, 2021): 8841. http://dx.doi.org/10.3390/app11198841.
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As the amount of data generated in various distributed environments is rapidly increasing, cloud servers and computing technologies are attracting considerable attention. However, the cloud server has privacy issues, including personal information and requires the help of a Trusted Third Party (TTP) for data sharing. However, because the amount of data generated and value increases, the data owner who produces data must become the subject of data sharing. In this study, we use key aggregate searchable encryption (KASE) technology, which enables keyword search, to efficiently share data without using TTP. The traditional KASE scheme approach only discusses delegation of authority from the data owner to another user. However, if the delegated entity cannot perform time-critical tasks because the shared data are unavailable, the delegate must further delegate the rights given to other users. Consequently, this paper proposes a new KASE scheme that enables multi-delegation without TTP and includes an authentication technique between the user and the server. After that, we perform informal and formal analysis using BAN logic and AVISPA for security evaluation, and compare the security and performance aspects with existing schemes.
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Shen, Yu. "Analysis and Research on the Characteristics of Modern English Classroom Learners’ Concentration Based on Deep Learning." Scientific Programming 2022 (May 21, 2022): 1–11. http://dx.doi.org/10.1155/2022/2211468.
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There are some problems in modern English education, such as difficulties in classroom teaching quality evaluation, lack of objective evaluation basis in teaching process management, and quality monitoring. The development of artificial intelligence technology provides a new idea for classroom teaching evaluation, but the existing classroom evaluation scheme based on artificial intelligence technology has a series of problems such as high system cost, low evaluation accuracy, and incomplete evaluation. In view of the above problems, this paper proposes a solution of English classroom concentration evaluation system based on deep learning. The program studies the evaluation methods of students’ class concentration, class activity, and enrichment degree of teaching links, and constructs an information evaluation system of students’ learning process and class teaching quality. Based on the edge computing system architecture, a hardware platform with cloud platform AI+ embedded visual edge computing devices managed by an FPGA deep learning accelerated server was built. The design, debugging, and testing of classroom evaluation and student behavior statistics-related functions were completed. This scheme uses edge computing hardware architecture to solve the problem of high system cost. Deep learning technology is used to solve the problem of low accuracy of classroom evaluation. It mainly evaluates the classroom objectively by extracting indicators such as the students' attention in the classroom, and solves the problems of the students’ inattentiveness in the classroom. After the test, the classroom evaluation system designed by the paper runs stably and all functions run normally. The test results show that the system can basically meet the requirements of classroom teaching evaluation application.
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Shu, Chang, Yinhui Luo, and Fang Liu. "Exploiting Duplications for Efficient Task Offloading in Multi-User Edge Computing." Electronics 11, no. 14 (July 18, 2022): 2244. http://dx.doi.org/10.3390/electronics11142244.
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The proliferation of IoT applications has pushed the horizon of edge computing, which provides processing ability at the edge of networks. Task offloading is one of the most important issues in edge computing and has attracted continuous research attention in recent years. With task offloading, end devices can offload the entire task or only subtasks to the edge servers to meet the delay and energy requirements. Most existing offloading schemes are limited by the increasing complexity of task topologies, as considerable time is wasted for local/edge subtasks to wait for their precedent subtasks being executed at the edge/local device. This problem becomes even worse when the dependencies among subtasks become complex and the number of end-users increases. To address this problem, our key methodology is to exploit subtask duplications to reduce the inter-subtask delay and shorten the task completion time. Based on this, we propose a Duplication-based and Energy-aware Task Offloading scheme (DETO), which duplicates critical subtasks that have a large impact on the completion time and thus enhances the parallelism between local and edge computing. In addition, among numerous choices of subtask duplications, DETO evaluates the gain/cost ratio for each possible duplication and chooses the most efficient ones. As a result, the extra resource for duplications is greatly reduced. We also design a distributed DETO algorithm to support multi-user, multi-server edge computing. Extensive evaluation results show that DETO can effectively reduce the task completion time (by 12.22%) and improve the resource utilization (by 15.17%), in particular for multi-user edge computing networks.
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Jiao, Yu, and Ali R. Hurson. "Performance Analysis of Mobile Agents in Mobile Distributed Information Retrieval System – A Quantitative Case Study." Journal of Interconnection Networks 05, no. 03 (September 2004): 351–72. http://dx.doi.org/10.1142/s0219265904001210.
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The traditional client/server-based distributed system design paradigm demonstrates deficiencies in the mobile computing environment because it relies on network connectivity and reliable communication. The advances in mobile agent technology alleviate these problems. The execution autonomy of mobile agents relaxes the requirement on network connectivity and provides opportunities for conserving energy. To the best of our knowledge, in spite of the large body of research in the application and exploitation of agent-based technology, a quantitative comparison of systems implemented using the client/server and agent-based models is still missing and issues related to the energy efficiency of mobile agents remain unexplored. In this paper, we present a quantitative performance evaluation of these two models in terms of response time, energy consumption, and communication cost.
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Park, Jaehyoung, and Hyuk Lim. "Privacy-Preserving Federated Learning Using Homomorphic Encryption." Applied Sciences 12, no. 2 (January 12, 2022): 734. http://dx.doi.org/10.3390/app12020734.
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Federated learning (FL) is a machine learning technique that enables distributed devices to train a learning model collaboratively without sharing their local data. FL-based systems can achieve much stronger privacy preservation since the distributed devices deliver only local model parameters trained with local data to a centralized server. However, there exists a possibility that a centralized server or attackers infer/extract sensitive private information using the structure and parameters of local learning models. We propose employing homomorphic encryption (HE) scheme that can directly perform arithmetic operations on ciphertexts without decryption to protect the model parameters. Using the HE scheme, the proposed privacy-preserving federated learning (PPFL) algorithm enables the centralized server to aggregate encrypted local model parameters without decryption. Furthermore, the proposed algorithm allows each node to use a different HE private key in the same FL-based system using a distributed cryptosystem. The performance analysis and evaluation of the proposed PPFL algorithm are conducted in various cloud computing-based FL service scenarios.
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Somula, Ramasubbareddy, and Sasikala R. "A Load and Distance Aware Cloudlet Selection Strategy in Multi-Cloudlet Environment." International Journal of Grid and High Performance Computing 11, no. 2 (April 2019): 85–102. http://dx.doi.org/10.4018/ijghpc.2019040105.
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Day to day the usage of mobile devices (MD) is growing in people's lives. But still the MD is limited in terms of memory, battery life time, processing capacity. In order to overcome these issues, the new emerging technology named mobile cloud computing (MCC) has been introduced. The offloading mechanism execute the resource intensive application on the remote cloud to save both the battery utilization and execution time. But still the high latency challenges in MCC need to be addressed by executing resource intensive task at nearby resource cloud server. The key challenge is to find optimal cloudlet to execute task to save computation time. In this article, the authors propose a Round Robin algorithm based on cloudlet selection in heterogeneous MCC system. This article considers both load and distance of server to find optimal cloudlet and minimize waiting time of the user request at server queue. Additionally, the authors provide mathematical evaluation of the algorithm and compare with existing load balancing algorithms.
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Lv, Mengjie, Shuming Zhou, Xueli Sun, Guanqin Lian, and Jiafei Liu. "Reliability Evaluation of Data Center Network DCell." Parallel Processing Letters 28, no. 04 (December 2018): 1850015. http://dx.doi.org/10.1142/s0129626418500159.
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With the rapid development of cloud computing, many large-scale data centers are being built to provide increasingly popular online application services, such as search, e-mails, WeChat, and microblog, etc. The reliability of a massive data center network is the likelihood that it performs its expected functions consistently well under the given conditions within a specified time interval. A typical approach to measure the reliability of the system is to count the mean time to failure (MTTF), which shows the time that the appearance of a certain number of faulty subsystem costs. The higher the MTTF, the more reliable the system is. In this paper, we explore the reliability of data center network DCell when it is decomposed into smaller ones along the last dimension under server (node) failure model and link failure model, respectively.
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Goswami, Veena, Sudhansu Shekhar Patra, and G. B. Mund. "Performance Analysis of Cloud Computing Centers for Bulk Services." International Journal of Cloud Applications and Computing 2, no. 4 (October 2012): 53–65. http://dx.doi.org/10.4018/ijcac.2012100104.
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Cloud is a service oriented platform where all kinds of virtual resources are treated as services to users. Several cloud service providers have offered different capabilities for a variety of market segments over the past few years. The most important aspects of cloud computing are resource scheduling, performance measures, and user requests. Sluggish access to data, applications, and web pages spoils employees and customers alike, as well as cause application crashes and data losses. In this paper, the authors propose an analytical queuing model for performance evaluation of cloud server farms for processing bulk data. Some important performance measures such as mean number of tasks in the queue, blocking probability, and probability of immediate service, and waiting-time distribution in the system have also been discussed. Finally, a variety of numerical results showing the effect of model parameters on key performance measures are presented.