Journal articles: 'Virtualized Data Center'

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Karamat Khan, Tehmina, Mohsin Tanveer, and Asadullah Shah. "Energy Efficiency in Virtualized Data Center." International Journal of Engineering & Technology 7, no. 4.15 (October 7, 2018): 315. http://dx.doi.org/10.14419/ijet.v7i4.15.23019.

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Industrial and academic communities have been trying to get more computational power out of their investments. Data centers have recently received huge attention due to its increased business value and achievable scalability on public/private clouds. Infra-structure and applications of modern data center is being virtualized to achieve energy efficient operation on servers. Despite of data center advantages on performance, there is a tradeoff between power and performance especially with cloud data centers. Today, these cloud application-based organizations are facing many energy related challenges. In this paper, through survey it has been analyzed how virtualization and networking related challenges affects energy efficiency of data center with suggested optimization strategies.

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Erickson, David, Brandon Heller, Shuang Yang, Jonathan Chu, Jonathan Ellithorpe, Scott Whyte, Stephen Stuart, Nick McKeown, Guru Parulkar, and Mendel Rosenblum. "Optimizing a virtualized data center." ACM SIGCOMM Computer Communication Review 41, no. 4 (October 22, 2011): 478–79. http://dx.doi.org/10.1145/2043164.2018530.

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Tan, Xiao Long, Wen Bin Wang, and Yu Qin Yao. "Research of Network Virtualization in Data Center." Applied Mechanics and Materials 644-650 (September 2014): 2961–64. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2961.

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With the rapid grow of the volume of data and internet application, as an efficient and promising infrastructure, data center has been widely deployed .data center provide a variety of perform for network services, applications such as video stream, cloud compute and so on. All this services and applications call for volume, compute, bandwidth, and latency. Existing data centers lacks enough flexible so they provide poor support in QOS, deployability, manageability, and defense when facing attacks. Virtualized data centers are a good solution to these problems. Compared to existing data centers, virtualized data centers do better in resource utilization, scalability, and flexibility.

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Kanniga Devi R., Murugaboopathi Gurusamy, and Vijayakumar P. "An Efficient Cloud Data Center Allocation to the Source of Requests." Journal of Organizational and End User Computing 32, no. 3 (July 2020): 23–36. http://dx.doi.org/10.4018/joeuc.2020070103.

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A Cloud data center is a network of virtualized resources, namely virtualized servers. They provision on-demand services to the source of requests ranging from virtual machines to virtualized storage and virtualized networks. The cloud data center service requests can come from different sources across the world. It is desirable for enhancing Quality of Service (QoS), which is otherwise known as a service level agreement (SLA), an agreement between cloud service requester and cloud service consumer on QoS, to allocate the cloud data center closest to the source of requests. This article models a Cloud data center network as a graph and proposes an algorithm, modified Breadth First Search where the source of requests assigned to the Cloud data centers based on a cost threshold, which limits the distance between them. Limiting the distance between Cloud data centers and the source of requests leads to faster service provisioning. The proposed algorithm is tested for various graph instances and is compared with modified Voronoi and modified graph-based K-Means algorithms that they assign source of requests to the cloud data centers without limiting the distance between them. The proposed algorithm outperforms two other algorithms in terms of average time taken to allocate the cloud data center to the source of requests, average cost and load distribution.

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Tan, Tingxi, Rob Simmonds, Bradley Arlt, Martin Arlitt, and Bruce Walker. "Image management in a virtualized data center." ACM SIGMETRICS Performance Evaluation Review 36, no. 2 (August 31, 2008): 4–9. http://dx.doi.org/10.1145/1453175.1453177.

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Hanane Aznaoui and Canan Batur Şahin. "A Virtual Data Center Comparison Of Different Firewalls' Performance." Journal of Advancement in Computing 1, no. 1 (April 18, 2023): 1–8. http://dx.doi.org/10.36755/jac.v1i1.46.

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Whether virtual or real, every data centre depends on its network, and the firewall is a crucial component of that network for secure communication. Different types of firewalls, such as software firewalls, physical firewalls, virtual appliance firewalls, and kernel-integrated firewalls, can secure data centre connections. When choosing a firewall, there are many things to take into account, especially in a virtualized data centre where each firewall behaves differently depending on the situation. Reduced costs, effective administration extensibility, greater resource usage, scalability, and energy resilience are just a few benefits that virtualized data centres are supposed to produce. In this study, the use of firewalls is examined in relation to virtualized data centres. The effectiveness of several kinds of firewalls, including virtual firewalls, physical firewalls, and software .The performance of various types of firewalls, such as software firewalls, physical firewalls, virtual appliance firewalls, and kernel-integrated firewalls, is being analysed. Virtual data centre firewall implementation and performance comparisons explain how to design and which firewall type provides the best performance. In all conditions, it was shown that kernel-integrated firewalls worked properly. Virtual machine IP addresses and networks can vary, and the kernel-based firewall can dynamically update its rules to keep pace with such changes.

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Nine, Md S. Q. Zulkar, Abul Kalam Azad, Saad Abdullah, and Rashedur M. Rahman. "Fuzzy Dynamic Load Balancing in Virtualized Data Centers of SaaS Cloud Provider." International Journal of Fuzzy System Applications 4, no. 3 (July 2015): 50–71. http://dx.doi.org/10.4018/ijfsa.2015070104.

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Cloud computing provides a robust infrastructure that can facilitate computing power as a utility service. All the virtualized services are made available to end users in a pay-as-you-go basis. Serving user requests using distributed network of Virtualized Data Centers is a challenging task as response time increases significantly without a proper load balancing strategy. As the parameters involved in generating load in the Virtualized Data Center has imprecise effect on the overall load of Virtual Machine, a fuzzy load balancing strategy is required. This paper proposes two efficient fuzzy load balancing methods - Fuzzy Active Monitoring Load Balancer (FAM-LB) and Fuzzy Throttled Load Balancer (FT-LB) for the distributed SaaS cloud provider. The authors implemented a cloud model in simulation environment and compared the results of otheir novel approach with the existing techniques. Among them FT-LB has provided better performance compared to other scheduling algorithms.

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Sinha, RIshabh. "Understanding In-Depth About Data Centre Security: Core Concepts & Market Growth." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 623–26. http://dx.doi.org/10.22214/ijraset.2022.42265.

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Abstract: Enterprises often perceive Data Centers as a key resource and demand dedicated security initiatives. Further, the emergence of security of the Data Center is playing a critical role in security-critical applications and data in a virtualized environment through optimal safeguarding. The providers of the Data Center have increased their focus. They have shown their participation in the market by enhancing products to augment the increasing demand for Data Center security solutions to address the rising needs of growing industries. This paper discusses various concepts of Data Center security along with its projected market growth on a global level. Keywords: Data Centers, Data Center Security, Data Center Security Importance, Data Center Security Market Growth, Data Center Security Practices

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Assaf, Eshrak, Amr Badr, and Ibrahim Farag. "Application of Computational Intelligence to Virtualized Data Center Management." International Journal of Computer Applications 55, no. 10 (October 20, 2012): 42–49. http://dx.doi.org/10.5120/8793-2780.

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Islam, Mohammad A., Shaolei Ren, Niki Pissinou, A. Hasan Mahmud, and Athanasios V. Vasilakos. "Distributed temperature-aware resource management in virtualized data center." Sustainable Computing: Informatics and Systems 6 (June 2015): 3–16. http://dx.doi.org/10.1016/j.suscom.2014.03.002.

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Umm e Khadija and Iqra Saqib. "Comparison Of Different Firewalls Performance In A Virtual For Cloud Data Center." Journal of Advancement in Computing 1, no. 1 (March 15, 2023): 21–28. http://dx.doi.org/10.36755/jac.v1i1.49.

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Every data centre, whether virtual or physical, relies on its network, and the firewall is an essential part of that network for safe communication. Data centre connection can be protected by a variety of firewall types, including software firewalls, physical firewalls, virtual appliance firewalls, and kernel-integrated firewalls. There are several factors to consider when selecting a firewall, especially in a virtualized data centre, where each firewall works differently in different situations. Virtualized data centres are intended to yield lower budgets, efficient management extensibility, better utilization of available resources, scalability, and energy resilience, among several other advantages. Virtualized data centres are the topic of this study, which examines the application of firewalls. The performance of various types of firewalls, such as software firewalls, physical firewalls, virtual appliance firewalls, and kernel-integrated firewalls, is being analysed. Virtual data centre firewall implementation and performance comparisons explain how to design and which firewall type provides the best performance. In all conditions, it was shown that kernel-integrated firewalls worked properly. Virtual machine IP addresses and networks can vary, and the kernel-based firewall can dynamically update its rules to keep pace with such changes. With its distributed firewall functionality, virtual machines can travel across hypervisors with no disruption to their security settings, if their policies remain the same. A kernel-based, distributed firewall is the best way to protect against viruses.

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Al-Karawi, Yassir, Raad S. Alhumaima, Khalid Hussein Khudair, and Abdulmunem Ahmed. "Optimizing the placement of cloud data center in virtualized environment." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (June 1, 2022): 3276. http://dx.doi.org/10.11591/ijece.v12i3.pp3276-3286.

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<p><span>In cloud mobile networks, precise assessment for the position of the virtualization powered cloud center would improve the capacity limit, latency and energy efficiency (EEf). This paper utilized the Monte Carlo oriented particle swarm optimization (PSO) and genetic algorithm (GA) to first, obtain the optimal number of virtual machines (VMs) that maximize the EEf of the mobile cloud center, second, optimize the position of the mobile data center. To fulfil such examination, a power evaluation framework is proposed to shape the power utilization of a virtualized server while hosting an amount of VMs. In addition, the total power consumption of the network is examined, including data center and radio units (RUs). This evaluation is based on linear modelling of the network parameters, such as resource blocks, number of VMs, transmitted and received powers, and overhead power consumption. Finally, the EEf is constrained to many quality of service (QoS) metrics, including number of resource blocks, total latency and minimum user's data rate.</span></p>

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Islam, Mohammad A., Shaolei Ren, A. Hasan Mahmud, and Gang Quan. "Online Energy Budgeting for Cost Minimization in Virtualized Data Center." IEEE Transactions on Services Computing 9, no. 3 (May 1, 2016): 421–32. http://dx.doi.org/10.1109/tsc.2015.2390231.

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Rofoee, Bijan Rahimzadeh, Georgios Zervas, Yan Yan, Markos Anastasopoulos, Anna Tzanakaki, Shuping Peng, Reza Nejabati, and Dimitra Simeonidou. "Hardware Virtualized Flexible Network for Wireless Data-Center Optical Interconnects [Invited]." Journal of Optical Communications and Networking 7, no. 3 (February 16, 2015): A526. http://dx.doi.org/10.1364/jocn.7.00a526.

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Asghari, Vahid, Reza Farrahi Moghaddam, and Mohamed Cheriet. "Performance analysis of modified BCube topologies for virtualized data center networks." Computer Communications 96 (December 2016): 52–61. http://dx.doi.org/10.1016/j.comcom.2016.10.001.

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Zharikov, E. V. "A method of two-tier storage management in virtualized data center." PROBLEMS IN PROGRAMMING, no. 4 (December 2018): 003–14. http://dx.doi.org/10.15407/pp2018.04.003.

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Bellavista, Paolo, Antonio Corradi, Luca Foschini, Sabato Luciano, and Michele Solimando. "A Simulation Framework for Virtualized Resources in Cloud Data Center Networks." IEEE Journal on Selected Areas in Communications 37, no. 8 (August 2019): 1808–19. http://dx.doi.org/10.1109/jsac.2019.2927066.

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Koorapati, Koundinya, P. Rubini, Prem Kumar Ramesh, and Sairam Veeraswamy. "Ontology Based Power Profiling for Internet of Things Deployed with Software Defined Data Center." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 479–87. http://dx.doi.org/10.1166/jctn.2020.8694.

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Cost effective, secure and environment friendly data centers are crucial for the success of embracing the adoption of Internet of Things (IoT). Regardless of the potential benefits of IoT, there are still many hurdles to be overcome to leverage the wide growth of the IoT technology. One challenging vector is the efficient power profiling of the entities involved in an IoT ecosystem. Software-Defined Data Center (SDDC) virtualizes the key data center infrastructure such as compute, storage and network. IoT fabric with SDDC, due to virtualization and the reduction in the hardware footprint, an extent of reduced power consumption is achieved when compared to a non-virtualized traditional data center. Today, in the end-to-end IoT and IT fabric, deficiency of intelligent power profiling techniques merits the need to supervise power in such an ecosystem, especially from an OT/IT convergence point of view where SDDC represents IT (Information Technology) and IoT forms OT (Operational Technology). To that end, this paper looks at power profiling for IoT fabric with SDDC by modeling the IoT ecosystem using semantic approaches. Thus, we bring in a notion of power awareness to the software defined IoT fabric. Specifically, this paper models a power profile ontology for the IoT fabric with SDDC and monitor such an ecosystem at run time and derive useful value by bridging the OT/IT convergence gap with respect to end-to-end power profiling.

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Fang, Fang, and Xiao Feng Yu. "Building the Next-Generation Data Centers Infrastructure Cloud." Applied Mechanics and Materials 610 (August 2014): 601–5. http://dx.doi.org/10.4028/www.scientific.net/amm.610.601.

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In order to adapt to the development trend of cloud computing, next-generation data center virtualized computing, storage and other resources to provide users with dynamic deployment of resources through the network, Equipment and technology such as servers, storage, security and software to connect together to form the infrastructure cloud platforms play a key supporting role. This paper focuses on network bandwidth, cabling systems, network protocols, and explores the next-generation data center infrastructure construction. The important idea is how to deploy more energy-efficient and low-cost, on-demand service-oriented infrastructure cloud.

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Wadhwa, Amit, and Ankit Garg. "Studying and Analyzing Virtualization While Transition from Classical to Virtualized Data Center." International Journal of Computer Applications 117, no. 14 (May 20, 2015): 10–14. http://dx.doi.org/10.5120/20620-3341.

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Feng, Danqing, Zhibo Wu, Decheng Zuo, and Zhan Zhang. "Auto-Scaling Provision Basing on Workload Prediction in the Virtualized Data Center." International Journal of Grid and High Performance Computing 12, no. 1 (January 2020): 53–69. http://dx.doi.org/10.4018/ijghpc.2020010104.

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With the development in the Cloud datacenters, the purpose of the efficient resource allocation is to meet the demand of the users instantly with the minimum rent cost. Thus, the elastic resource allocation strategy is usually combined with the prediction technology. This article proposes a novel predict method combination forecast technique, including both exponential smoothing (ES) and auto-regressive and polynomial fitting (PF) model. The aim of combination prediction is to achieve an efficient forecast technique according to the periodic and random feature of the workload and meet the application service level agreement (SLA) with the minimum cost. Moreover, the ES prediction with PSO algorithm gives a fine-grained scaling up and down the resources combining the heuristic algorithm in the future. APWP would solve the periodical or hybrid fluctuation of the workload in the cloud data centers. Finally, experiments improve that the combined prediction model meets the SLA with the better precision accuracy with the minimum renting cost.

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Kim, Hwantae, and Hwangnam Kim. "Control Algorithm for Virtual Machine-Level Fairness in Virtualized Cloud Data center." Journal of Korean Institute of Communications and Information Sciences 38C, no. 6 (June 30, 2013): 512–20. http://dx.doi.org/10.7840/kics.2013.38c.6.512.

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Chaudhry, Muhammad Tayyab, Chun Yong Chon, T. C. Ling, Saim Rasheed, and Jongwon Kim. "Thermal Prediction Models for Virtualized Data center Servers by using Thermal-profiles." Malaysian Journal of Computer Science 29, no. 1 (March 1, 2016): 1–14. http://dx.doi.org/10.22452/mjcs.vol29no1.1.

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Lin, An-dee, Hubertus Franke, Chung-sheng Li, and Wanjiun Liao. "Toward performance optimization with CPU offloading for virtualized multi-tenant data center networks." IEEE Network 30, no. 3 (May 2016): 59–63. http://dx.doi.org/10.1109/mnet.2016.7474345.

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Chen, Congjie, Dan Li, Jun Li, and Konglin Zhu. "SVDC: A Highly Scalable Isolation Architecture for Virtualized Layer-2 Data Center Networks." IEEE Transactions on Cloud Computing 6, no. 4 (October 1, 2018): 1178–90. http://dx.doi.org/10.1109/tcc.2016.2586047.

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Bi, Jing, Haitao Yuan, Wei Tan, MengChu Zhou, Yushun Fan, Jia Zhang, and Jianqiang Li. "Application-Aware Dynamic Fine-Grained Resource Provisioning in a Virtualized Cloud Data Center." IEEE Transactions on Automation Science and Engineering 14, no. 2 (April 2017): 1172–84. http://dx.doi.org/10.1109/tase.2015.2503325.

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Lin, Wei-Wei, Liang Tan, and James Z. Wang. "Novel Resource Allocation Algorithm for Energy-Efficient Cloud Computing in Heterogeneous Environment." International Journal of Grid and High Performance Computing 6, no. 1 (January 2014): 63–76. http://dx.doi.org/10.4018/ijghpc.2014010104.

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Energy efficiency is one of the most important design considerations for a cloud data center. Recent approaches to the energy-efficient resource management for data centers usually model the problem as a bin packing problem with the goal of minimizing the number of physical machines (PMs) employed. However, minimizing the number of PMs may not necessarily minimize the energy consumption in a heterogeneous cloud environment. To address the problem, this paper models the resource allocation problem in a heterogeneous cloud data center as a constraint satisfaction problem (CSP). By solving this constraint satisfaction problem, an optimal resource allocation scheme, which includes a virtual machine provision algorithm and a virtual machine packing algorithm, is designed to minimize the energy consumption in a virtualized heterogeneous cloud data center. Performance studies show that this proposed new scheme outperforms the existing bin-packing based approaches in terms of energy consumption in heterogeneous cloud data centers.

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Uddin, Mueen, Mohammed Hamdi, Abdullah Alghamdi, Mesfer Alrizq, Mohammad Sulleman Memon, Maha Abdelhaq, and Raed Alsaqour. "Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership." International Journal of Distributed Sensor Networks 17, no. 3 (March 2021): 155014772199721. http://dx.doi.org/10.1177/1550147721997218.

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Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

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Zhang, Yueping, Ao-Jan Su, and Guofei Jiang. "Understanding data center network architectures in virtualized environments: A view from multi-tier applications." Computer Networks 55, no. 9 (June 2011): 2196–208. http://dx.doi.org/10.1016/j.comnet.2011.03.001.

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Huang, Kun. "Applied-Information Technology in Virtualized Cloud Storage on Campus." Applied Mechanics and Materials 685 (October 2014): 571–74. http://dx.doi.org/10.4028/www.scientific.net/amm.685.571.

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With the constant development of campus network informationization construction, the amount of information on campus exponentially increases, thus requiring more storage on campus. This paper discusses about the transformation of storage architecture in campus data center based on current situations of campus network storage as well as storage virtualization technology, with an aim to provide an efficient and transparent storage environment to users.

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Yin, Chunxia, Jian Liu, and Shunfu Jin. "An Energy-Efficient Task Scheduling Mechanism with Switching On/Sleep Mode of Servers in Virtualized Cloud Data Centers." Mathematical Problems in Engineering 2020 (February 18, 2020): 1–11. http://dx.doi.org/10.1155/2020/4176308.

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In recent years, the energy consumption of cloud data centers has continued to increase. A large number of servers run at a low utilization rate, which results in a great waste of power. To save more energy in a cloud data center, we propose an energy-efficient task-scheduling mechanism with switching on/sleep mode of servers in the virtualized cloud data center. The key idea is that when the number of idle VMs reaches a specified threshold, the server with the most idle VMs will be switched to sleep mode after migrating all the running tasks to other servers. From the perspective of the total number of tasks and the number of servers in sleep mode in the system, we establish a two-dimensional Markov chain to analyse the proposed energy-efficient mechanism. By using the method of the matrix-geometric solution, we mathematically estimate the energy consumption and the response performance. Both numerical and simulated experiments show that our proposed energy-efficient mechanism can effectively reduce the energy consumption and guarantee the response performance. Finally, by constructing a cost function, the number of VMs hosted on each server is optimized.

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Li, Zhen, Bin Chen, Xiaocheng Liu, Dandan Ning, and Xiaogang Qiu. "QoS-aware simulation job scheduling algorithm in virtualized cloud environment." International Journal of Modeling, Simulation, and Scientific Computing 11, no. 05 (September 25, 2020): 2050048. http://dx.doi.org/10.1142/s1793962320500488.

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Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center. These applications are submitted to the cloud in the form of simulation jobs. Meanwhile, the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service. In this paper, we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center, named simulation execution as a service (SimEaaS). It aims at releasing users from complex simulation running settings, while guaranteeing the QoS requirements adaptively. Furthermore, a novel job scheduling algorithm named adaptive deadline-aware job size adjustment (ADaSA) algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS. ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively, while guaranteeing that jobs’ deadline requirements are not violated. Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA. The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time (up to 90%) and bounded slow down (up to 95%), while obtains approximately equivalent deadline-missed rate. ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate (up to 60%).

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Domínguez-Dorado, Manuel, Jesús Calle-Cancho, Jesús Galeano-Brajones, Francisco-Javier Rodríguez-Pérez, and David Cortés-Polo. "Detection and Mitigation of Security Threats Using Virtualized Network Functions in Software-Defined Networks." Applied Sciences 14, no. 1 (December 31, 2023): 374. http://dx.doi.org/10.3390/app14010374.

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The evolution of interconnected systems and the evolving demands in service requirements have led to data centers integrating multiple heterogeneous technologies that must coexist. Consequently, the resource management and the security of the infrastructure are becoming more complex than in traditional scenarios. In this context, technologies such as Software-Defined Networking (SDN) or Network Function Virtualization (NFV) are being embraced as mechanisms that facilitate communication management. The integration of both technologies into a single framework, termed Software-Defined NFV (SDNFV) introduces a multitude of tools for managing the security of the data center’s resources. This work delineates the primary characteristics of the evolution of these communication networks and their application to information security and communications within a data center. It presents an illustrative use case demonstrating the application of these next-generation technologies to detect and mitigate a security issue through virtualized network functions deployed in containers.

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Manikandan, J., and Sri Lakshmi Uppalapati. "Critical Analysis on Detection and Mitigation of Security Vulnerabilities in Virtualization Data Centers." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (March 13, 2023): 238–46. http://dx.doi.org/10.17762/ijritcc.v11i3s.6187.

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There is an increasing demand for IT resources in growing business enterprises. Data center virtualization helps to meet this increasing demand by driving higher server utilization and utilizing un-used CPU cycles without causes much increase in new servers. Reduction in infrastructure complexities, Optimization of cost of IT system management, power and cooling are some of the additional benefits of virtualization. Virtualization also brings various security vulnerabilities. They are prone to attacks like hyperjacking, intrusion, data thefts, denial of service attacks on virtualized servers and web facing applications etc. This works identifies the security challenges in virtualization. A critical analysis on existing state of art works on detection and mitigation of various vulnerabilities is presented. The aim is to identify the open issues and propose prospective solutions in brief for these open issues.

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Babitha, M. N., and M. Siddappa. "An approach for data integrity authentication and protection in fog computing." Multiagent and Grid Systems 18, no. 2 (August 30, 2022): 87–105. http://dx.doi.org/10.3233/mgs-220210.

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The data integrity verification process in cloud has become more promising research area in several Internet of Things (IoT) applications. The traditional data verification approaches use encryption in order to preserve data. Moreover, fog computing is considered as extensively employed virtualized platform and it affords various services including storage as well as services interconnected to computing and networking between user and data center based on standard cloud computing. Moreover, fog computing is an extensive description of cloud computing. Thus, fog servers effectively decrease the latency by integrating fog servers. In this paper, novel model for data integrity authentication and protection is designed in IoT cloud-fog model. This method mainly comprises fog nodes, cloud server, IoT nodes, and key distribution center. Here, dynamic and secure key is produced based on the request to key distribution center based on hashing, Exclusive OR (XOR), homomorphic encryption and polynomial. The fog nodes are employed to encrypt the data gathered from IoT nodes as well as allocate the nearby nodes based on Artificial Bee Colony-based Fuzzy-C-Means (ABC FCM) – based partitioning approach. The proposed data integrity authentication approach in IoT fog cloud system outperformed than other existing methods with respect to detection rate, computational time and memory usage of 0.8541, 34.25 s, and 54.8 MB, respectively.

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Liu, Xu, Peng Zhang, Hao Li, and Wenbing Sun. "Modular Data Plane Verification for Compositional Networks." Proceedings of the ACM on Networking 1, CoNEXT3 (November 27, 2023): 1–22. http://dx.doi.org/10.1145/3629145.

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Modern networks are increasingly using layering and bridging to form a compositional architecture. Layering protocols like VXLAN create multiple overlay networks on top of a single underlay network infrastructure. This makes network configurations even more complex, and error-prone. To check the correctness of such compositional networks, one needs to model the dependency across multiple layers (underlay and overlay) and multiple domains (different VPNs/VPCs). Existing verifiers, which are optimized to scale in single-layer single-domain networks, exhibit scalability limitations when applied to compositional networks. This paper proposes MNV, a modular network verifier that scales to large compositional networks. At its core is a new verification method termed decompose-merge reasoning, which decomposes the network into self-contained modules, verifies each module independently, and merges the verification results. Our experiments show that for a typical data center network virtualized with VXLAN, to check reachability for more than 100 million pairs of subnets, MNV is at least 100x faster than state-of-the-art tools.

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Shokeen, Vivek, and Lalit Malik. "Cloud Computing – Data Center – Access from Anywhere." International Journal of Computer Science and Mobile Computing 11, no. 6 (June 30, 2022): 212–19. http://dx.doi.org/10.47760/ijcsmc.2022.v11i06.015.

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Now days we are attached with our electronic gadgets either offline or online in such a way as we breathe, as without breathing we can’t alive, same like these electronics have become a vital part of our daily life. As of now by growing demand of these gadgets either in office work, or in research work or in various technical fields. As the use of computers increases as in day in and day out, the relevant resources that we need also go up. For companies like Oracle, IBM and Microsoft, utilizing the resources and establishing a huge infrastructure or network is not a big issue for these big giants. But for the new startup and small enterprises But when it comes to smaller enterprises, expensiveness becomes a huge factor. At the user or developer end we are facing a lot of issues like hardware disruption, various bugs in software, network errors. So this was such a big hassle for the large community of computer science in all over the world. Cloud Computing offers a quick fix to this situation. This technology has been completely transformed the way in which computing is moved up from PCs and as well as for the single user to enterprise via various servers to a ‘cloud’ of computers e.g: web servers, application servers, database servers and so on. A cloud is a virtualized server grid which can provide the different computing resources through IAAS (Infrastructure as a service),PAAS(platform as a service),SAAS(Software as a service) of their clients. The underlying details of how it is implemented which is not direct to end user. The data and the services provided reside in extensively scalable data centers so anyone can use or access it from any part of the globe. Various big giants like Google, Microsoft, Yahoo, IBM and Amazon have started providing cloud computing services from the individual user either paid or free upto some extent or to other small enterprises, big businesses through various models like B2B,B2C,C2C etc. Amazon, salesforce is the trailblazer in the field of cloud computing. Every big and small enterprise, companies which are doing their businesses online using cloud services for the storing information so that later on they will extract some meaning full insights for the growth of businesses. Cloud Computing is finding use in various areas like web hosting, parallel batch processing, graphics rendering, financial modeling, web crawling, genomics analysis, etc.

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Murugesan, M. "Virtual Machine Consolidation for Stochastic Load Balancing in Cloud Data Center Management." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 2593–602. http://dx.doi.org/10.17762/turcomat.v12i2.2227.

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Cloud computing is able to managing a massive quantity of growing work for the use of enterprise clients in a specified way Virtualization, which makes assumptions the network resources and makes it simple to control, is an important enabling technology for cloud computing. Computing is being used in the proposed work to distribute cloud services tailored to the needs and to promote the smart grid principle. “Skewness” concept was delivered here wherein equal was reducing to combine workloads to enhance the usage of the server. The complexities of on-demand allocation of resources arise from managing customer demands. As a result, the use of vms technologies has proved to be helpful in terms of resource provisioning. The use of virtualized environments is expected to reduce primarily consist connection speed while also executing tasks in accordance with cloud resource availability. This implementation can be use local negotiation based VM consolidation mechanism to predict each job request and reduce overloads to create virtual space at the time of multiple requests. The proposed system implement co-location approach to combine unused small spaces to create new virtual space for improves the performance of server. Also implement self-destruction approach to eliminate the invalid data based on time to live property. The proposed framework is executed in genuine time with effective asset allotment. In this system to begin with broaden a forecast show which will gauge the parcel sizes of decrease commitments at runtime. And it can detect information skewness in real time and allocate extra asses for mordant of large walls that help us complete faster.

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Dutta, Neha, and Pardeep Cheema. "Anaysis of Different Energy Efficiency Algorithms in Cloud Computing." International Journal of Engineering and Advanced Technology 11, no. 1 (October 30, 2021): 132–37. http://dx.doi.org/10.35940/ijeat.a3189.1011121.

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Cloud computing is internet computing that offers metering-based services to customers. It implies accessing data from a consolidated pool of computer resources that may be requested and consumed on-demand. It also offers computer resources via virtualized over the internet. The data center is the most essential in cloud computing which includes a group of servers on which Business records are kept and applications operate. The data center which contains servers, cables, air conditioning units, networks, etc. uses more electricity and emits a large quantity of Carbon-di-oxide (CO2) to the atmosphere. One of the most significant problems encountered in cloud technology is the optimizing of Energy Usage. Hence the idea of cloud computing originated. It is a concept for allowing all over, on-demand access to a shared pool of customizable computer resources[ Wanneng Shu et. A.,2014].

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Ahmadi, Mohammad Reza. "Performance Evaluation of Virtualization Techniques for Control and Access of Storage Systems in Data Center Applications." Journal of Electrical Engineering 64, no. 5 (September 1, 2013): 272–82. http://dx.doi.org/10.2478/jee-2013-0040.

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Abstract Virtualization is a new technology that creates virtual environments based on the existing physical resources. This article evaluates effect of virtualization techniques on control servers and access method in storage systems [1, 2]. In control server virtualization, we have presented a tile based evaluation based on heterogeneous workloads to compare several key parameters and demonstrate effectiveness of virtualization techniques. Moreover, we have evaluated the virtualized model using VMotion techniques and maximum consolidation. In access method, we have prepared three different scenarios using direct, semi-virtual, and virtual attachment models. We have evaluated the proposed models with several workloads including OLTP database, data streaming, file server, web server, etc. Results of evaluation for different criteria confirm that server virtualization technique has high throughput and CPU usage as well as good performance with noticeable agility. Also virtual technique is a successful alternative for accessing to the storage systems especially in large capacity systems. This technique can therefore be an effective solution for expansion of storage area and reduction of access time. Results of different evaluation and measurements demonstrate that the virtualization in control server and full virtual access provide better performance and more agility as well as more utilization in the systems and improve business continuity plan.

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Bhutto, Afia, Aftab Ahmed Chandio, Kirshan Kumar Luhano, and Imtiaz Ali Korejo. "Analysis of Energy and Network Cost Effectiveness of Scheduling Strategies in Datacentre." Cybernetics and Information Technologies 23, no. 3 (September 1, 2023): 56–69. http://dx.doi.org/10.2478/cait-2023-0024.

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Abstract In parallel and distributed computing, cloud computing is progressively replacing the traditional computing paradigm. The cloud is made up of a set of virtualized resources in a data center that can be configured according to users’ needs. In other words, cloud computing faces the problem of a huge number of users requesting unlimited jobs for execution on a limited number of resources, which increases energy consumption and the network cost of the system. This study provides a complete analysis of classic scheduling techniques specifically for handling data-intensive workloads to see the effectiveness of the energy and network costs of the system. The workload is selected from a real-world data center. Moreover, this study offers the pros and cons of several classical heuristics-based job scheduling techniques that take into account the time and cost of transferring data from multiple sources. This study is useful for selecting appropriate scheduling techniques for appropriate environments.

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Girish, L., and Sridhar K. N. Rao. "Quantifying Sensitivity and Performance Degradation of Virtual Machines Using Machine Learning." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4055–60. http://dx.doi.org/10.1166/jctn.2020.9019.

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Virtualized data centers bring lot of benefits with respect to the reducing the high usage of physical hardware. But nowadays, as the usage of cloud infrastructures are rapidly increasing in all the fields to provide proper services on demand. In cloud data center, achieving efficient resource sharing between virtual machine and physical machines are very important. To achieve efficient resource sharing performance degradation of virtual machine and quantifying the sensitivity of virtual machine must be modeled, predicted correctly. In this work we use machine learning techniques like decision tree, K nearest neighbor and logistic regression to calculate the sensitivity of virtual machine. The dataset used for the experiment was collected using collected from open stack cloud environment. We execute two scenarios in this experiment to evaluate performance of the three mentioned classifiers based on precision, recall, sensitivity and specificity. We achieved good results using decision tree classifier with precision 88.8%, recall 80% and accuracy of 97.30%.

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Mahabaleshwar, Sneha, Shobha Gangadhar, and Sharath Krishnamurthy. "Anomaly detection for software defined datacenter networks using X-Pack." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 2 (August 1, 2023): 995. http://dx.doi.org/10.11591/ijeecs.v31.i2.pp995-1007.

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The global data center market is growing as more and more enterprises are increasingly adopting cloud computing services and applications. Data centers are evolving towards highly virtualized architectures where transformation to software defined network (SDN) based solutions provides benefits in terms of network programmability, automation, and flow visibility. With the benefits, the need for securing network becomes essential as many critical applications are hosted on to such networking platforms. Anomaly detection is a continuous process of monitoring the traffic pattern and alerting the user about the anomalies if detected. For such real time analysis NoSQL and relational databases are less efficient. This paper proposes a framework for anomaly detection and alerting system using Elasticsearch database for SDN. Traffic patterns generated from SDN devices are continuously monitored and predefined actions are taken immediately if an anomaly is detected. The proof of concept is implemented in NOKIAs Nuage Networks Laboratory and the results showed a real time anomaly detection and took relevant actions within minimum time.

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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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Wang, Liang, and Hong Xia Zhang. "Performance Analysis and Massive Concurrent Access Response Test of Sichuan Top IT Vocational Institute Data Center Based on Virtualized Cloud Computing." Procedia Computer Science 131 (2018): 102–7. http://dx.doi.org/10.1016/j.procs.2018.04.191.

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Zhao, Jia, Yan Ding, Gaochao Xu, Liang Hu, Yushuang Dong, and Xiaodong Fu. "A Location Selection Policy of Live Virtual Machine Migration for Power Saving and Load Balancing." Scientific World Journal 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/492615.

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Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

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Kant, Krishna, and Bhuvan Urgaonkar. "Virtualized Data Centers." Computer Networks 53, no. 17 (December 2009): 2871–72. http://dx.doi.org/10.1016/j.comnet.2009.10.003.

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Klinkowski, Mirosław. "Latency-Aware DU/CU Placement in Convergent Packet-Based 5G Fronthaul Transport Networks." Applied Sciences 10, no. 21 (October 22, 2020): 7429. http://dx.doi.org/10.3390/app10217429.

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The 5th generation mobile networks (5G) based on virtualized and centralized radio access networks will require cost-effective and flexible solutions for satisfying high-throughput and latency requirements. The next generation fronthaul interface (NGFI) architecture is one of the main candidates to achieve it. In the NGFI architecture, baseband processing is split and performed in radio (RU), distributed (DU), and central (CU) units. The mentioned entities are virtualized and performed on general-purpose processors forming a processing pool (PP) facility. Given that the location of PPs may be spread over the network and the PPs have limited capacity, it leads to the optimization problem concerning the placement of DUs and CUs. In the NGFI network scenario, the radio data between the RU, DU, CU, and a data center (DC)—in which the traffic is aggregated—are transmitted in the form of packets over a convergent packet-switched network. Because the packet transmission is nondeterministic, special attention should be put on ensuring the appropriate quality of service (QoS) levels for the latency-sensitive traffic flows. In this paper, we address the latency-aware DU and CU placement (LDCP) problem in NGFI. LDCP concerns the placement of DU/CU entities in PP nodes for a given set of demands assuming the QoS requirements of traffic flows that are related to their latency. To this end, we make use of mixed integer linear programming (MILP) in order to formulate the LDCP optimization problem and to solve it. To assure that the latency requirements are satisfied, we apply a reliable latency model, which is included in the MILP model as a set of constraints. To assess the effectiveness of the MILP method and analyze the network performance, we run a broad set of experiments in different network scenarios.

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Bouras, Christos, Anastasia Kollia, and Andreas Papazois. "Exploring SDN & NFV in 5G Using ONOS & POX Controllers." International Journal of Interdisciplinary Telecommunications and Networking 10, no. 4 (October 2018): 46–60. http://dx.doi.org/10.4018/ijitn.2018100103.

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This article describes how novel functionalities will take advantage of the cloud networking and will gradually replace the existing infrastructure of mobile networks with a virtualized one. Two technologies, namely software defined networking (SDN) and network function virtualization (NFV), offer their important benefits and a combination of them is an answer to the demands raised, such as central office re-architected as a data center (CORD). Open network operating system (ONOS) and POX are SDN controllers and offer an option to combine SDN and NFV addressing many ongoing problems in the field of mobile networks. In this paper, technologies and both controllers are compared and contrasted. Indicative cases of topologies are simulated and help evaluating both controllers. According to the experimental findings, ONOS is one of the most important controllers for practical, theoretical, research and educational purposes, while POX is a useful and simpler controller for other educative applications.

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Mohebbi, Keyvan, and Mohammad Reza Moslehi Takantapeh. "An Approach to Improve the Live Migration Using Asynchronized Cache and Prioritized IP Packets." Computer Engineering and Applications Journal 8, no. 2 (June 11, 2019): 93–104. http://dx.doi.org/10.18495/comengapp.v8i2.302.

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The live migration of a virtual machine is a method of moving virtual machines across hosts within a virtualized data center. Two main parameters should be considered for evaluation of live migration; total duration, and downtime of migration. This paper focuses on optimization of live migration in Xen environment where memory pages are dirtied rapidly. An approach is proposed to manage dirty pages during migration in the cache and prioritize the packets at the network level. According to the evaluations, when the system is under heavy workload or it is running within a stress tool, the virtual machines are intensively writing. The proposed approach outperforms the default method in terms of number of transferred pages, total migration time, and downtime. Experimental results showed that by increasing workload, the proposed approach reduced the number of sent pages by 47.4%, total migration time by 10%, and the downtime by 27.7% in live migration.

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Journal articles on the topic 'Virtualized Data Center'

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