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Wang, Yue, Choonhwa Lee, Shuyang Ren, Eunsam Kim, and Sungwook Chung. "Enabling Role-Based Orchestration for Cloud Applications." Applied Sciences 11, no. 14 (July 20, 2021): 6656. http://dx.doi.org/10.3390/app11146656.
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With the rapidly growing popularity of cloud services, the cloud computing faces critical challenges to orchestrate the deployment and operation of cloud applications on heterogenous cloud platforms. Cloud applications are built on a platform model that abstracts away underlying platform-specific details, so that their orchestration can benefit from the abstract view and flexibility of the underlying platform configuration. However, considerable efforts are still required to properly manage complicated cloud applications. This paper proposes a model-driven approach to cloud application orchestration which promotes the concerns of distinct roles for cloud system provisioning and operation. By establishing a set of capabilities as modeling constructs, our approach allows TOSCA-based application topology itself and its orchestration needs to be specified in a way to provide a more targeted support for different needs and concerns of application developers and operators. With novel orchestration features like application topology description, platform capability modeling, and role-awareness for cloud application orchestration, it can significantly reduce the complexity of application orchestration in diverse cloud environments. To show the feasibility and effectiveness of our proposal for cloud application orchestration, we present a proof-of-concept orchestration system implementation and evaluate its deployment and orchestration results in a Kubernetes cluster.
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Phani Sheetal, A., and K. Ravindranath. "Software metric evaluation on cloud based applications." International Journal of Engineering & Technology 7, no. 1.5 (December 31, 2017): 13. http://dx.doi.org/10.14419/ijet.v7i1.5.9071.
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Unbound growth in the cloud computing service models have motivated the companies building traditional software to be migrated into the clouds. During the high demand of the traditional applications, the performance and quality of the software were evaluated by the popular and globally accepted metrics. Nevertheless, after the migration of the same applications into the cloud, the expectation and definition of performance and quality has been changed. The beneficiaries of these applications are setting new milestones for the applications. Hence, the recent demand of the research trend is to build new software metric models to match the trade of between the new expectations from the beneficiaries and the software quality policies for organization or individual or state. Thus this work makes an attempt to understand the traditional software quality metrics and try to justify the applicability of these parameters in the trend of cloud based software applications. This work also proposes a novel metric method for performance evaluation for the migrated applications into the cloud, with the intension of formalizing and standardizing the cloud based metric methods unlike the recent trends.
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LEYMANN, FRANK, CHRISTOPG FEHLING, RALPH MIETZNER, ALEXANDER NOWAK, and SCHAHRAM DUSTDAR. "MOVING APPLICATIONS TO THE CLOUD: AN APPROACH BASED ON APPLICATION MODEL ENRICHMENT." International Journal of Cooperative Information Systems 20, no. 03 (September 2011): 307–56. http://dx.doi.org/10.1142/s0218843011002250.
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In this paper we describe a method and corresponding tool chain that allows moving an application to the cloud. In particular, we support to split an application such that various parts of it are moved to different clouds. This split can be done manually or by support of optimization algorithms. The split application is then automatically provisioned in the different target clouds. A metamodel for such applications supporting the proposed method is introduced. The architecture of a supporting tool is described. Experiences from the usage of the proposed method are reported.
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Li, Zhaojian, Tianshu Chu, Ilya V. Kolmanovsky, Xiang Yin, and Xunyuan Yin. "Cloud resource allocation for cloud-based automotive applications." Mechatronics 50 (April 2018): 356–65. http://dx.doi.org/10.1016/j.mechatronics.2017.10.010.
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Abuzrieq, Yara, Amro Al-Said Ahmad, and Maram Bani Younes. "An Experimental Performance Evaluation of Cloud-API-Based Applications." Future Internet 13, no. 12 (December 13, 2021): 314. http://dx.doi.org/10.3390/fi13120314.
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Cloud Application Programming Interfaces (APIs) have been developed to link several cloud computing applications together. API-based applications are widely used to provide flexible and reliable services over cloud platforms. Recently, a huge number of services have been attached to cloud platforms and widely utilized during a very short period of time. This is due to the COVID-19 lockdowns, which forced several businesses to switch to online services instantly. Several cloud platforms have failed to support adequate services, especially for extended and real-time-based applications. Early testing of the available platforms guarantees a level of suitability and reliability for the uploaded services. In this work, we first selected two different API-based applications from education and professional taxonomies, the two most recently used applications that have switched to the cloud environment. Then, we aimed to evaluate the performance of different API-based applications under different cloud platforms, in order to measure and validate the ability of these platforms to support these services. The advantages and drawbacks of each platform were experimentally investigated for each application.
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Gonidis, Fotis, Iraklis Paraskakis, and Anthony J. H. Simons. "Rapid Development of Service-based Cloud Applications." International Journal of Systems and Service-Oriented Engineering 5, no. 4 (October 2015): 1–25. http://dx.doi.org/10.4018/ijssoe.2015100101.
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Cloud application platforms gain popularity and have the potential to alter the way service-based cloud applications are developed involving utilisation of platform basic services. A platform basic service provides certain functionality and is usually offered via a web API. However, the diversification of the services and the available providers increase the challenge for the application developers to integrate them and deal with the heterogeneous providers' web APIs. Therefore, a new approach of developing applications should be adopted in which developers leverage multiple platform basic services independently from the target application platforms. To this end, the authors present a development framework assisting the design of service-based cloud applications. The objective of the framework is to enable the consistent integration of the services, and to allow the seamless use of the concrete providers. The optimal service provider each time can vary depending on criteria such as pricing, quality of service and can be determined based upon Big Data analysis approaches.
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Jaybhaye, Sangita M., and Dr Vahida Z. Attar. "Resource Allocation and Optimization in Cloud for Workflow based Applications." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (November 20, 2019): 1190–98. http://dx.doi.org/10.5373/jardcs/v11sp11/20193151.
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Hagos, Desta Haileselassie. "Software-Defined Networking for Scalable Cloud-based Services to Improve System Performance of Hadoop-based Big Data Applications." International Journal of Grid and High Performance Computing 8, no. 2 (April 2016): 1–22. http://dx.doi.org/10.4018/ijghpc.2016040101.
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The rapid growth of Cloud Computing has brought with it major new challenges in the automated manageability, dynamic network reconfiguration, provisioning, scalability and flexibility of virtual networks. OpenFlow-enabled Software-Defined Networking (SDN) alleviates these key challenges through the abstraction of lower level functionality that removes the complexities of the underlying hardware by separating the data and control planes. SDN has an efficient, dynamic, automated network management, higher availability and application provisioning through programmable interfaces which are very critical for flexible and scalable cloud-based services. In this study, the author explores broadly useful open technologies and methodologies for applying an OpenFlow-enabled SDN to scalable cloud-based services and a variety of diverse applications. The approach in this paper introduces new research challenges in the design and implementation of advanced techniques for bringing an SDN-enabled components and big data applications into a cloud environment in a dynamic setting. Some of these challenges become pressing concerns to cloud providers when managing virtual networks and data centers, while others complicate the development and deployment of cloud-hosted applications from the perspective of developers and end users. However, the growing demand for manageable, scalable and flexible clouds necessitates that effective solutions to these challenges be found. Hence, through real-world research validation use cases, this paper aims at exploring useful mechanisms for the role and potential of an OpenFlow-enabled SDN and its direct benefit for scalable cloud-based services. Finally, it demonstrates the impact of an OpenFlow-enabled SDN that fully embraces the opportunities and challenges of cloud infrastructures to improve the system performance of Hadoop-based big data applications by utilizing the network control capabilities of an OpenFlow to solve network congestion.
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Bi, Zhen Bo, and Hui Qin Wang. "BIM Application Research Based on Cloud Computing." Applied Mechanics and Materials 170-173 (May 2012): 3565–69. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3565.
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Applications based on CC (cloud computing) has the potential efficient and low-cost advantages, while there are the lack of computing power, the limited range of applications and the higher cost of BIM (Building Information Model) applications in the traditional desktop mode. According to BIM application features and the actual situation, the paper starts from the concept of CC and discusses the advantages of BIM applications using CC. The system framework, the key technologies and the implementation methods of the BIM application platform based on CC have been put forward on the basis of the above discussion.
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Xue-Jun Liu, Xue-Jun Liu, Wen-Hui Wang Xue-Jun Liu, Yong Yan Wen-Hui Wang, Zhong-Ji Cui Yong Yan, Yun Sha Zhong-Ji Cui, and Yi-Nan Jiang Yun Sha. "A Point Cloud Classification Method and Its Applications Based on Multi-Head Self-Attention." 電腦學刊 34, no. 4 (August 2023): 163–73. http://dx.doi.org/10.53106/199115992023083404014.
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<p>In the monitoring the safety status of hazardous chemical warehouses by three-dimensional re-construction of deep camera point clouds, there are classification difficulties such as large space, sparse distribution of point clouds in cargo images, and similar distribution in low dimensions. Based on the above problem, a point cloud recognition method based on multi-head attention mechanism is proposed. The algorithm first normalizes the distribution of the point cloud data set through the affine transformation algorithm to solve the problem of sparse distribution. Then, the high-dimensional feature map is obtained by fusing the data down-sampling and curve feature aggregation algorithms to solve the problem of low-dimensional distribution approximation. The feature map is then encoded using a multi-head self-attention encoder to obtain features under different heads, which are then merged into a feature map. Finally, a multi-layer fully connected neural network is used as the decoder to decode the feature map into the final object classification. Comparative experiments were performed on the ModelNet40 dataset and the self-built dataset of warehouse goods, and the results showed that the accuracy of this paper was improved by 0.5% to 7.8% compared with that of other classification algorithms.</p> <p> </p>
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Malawski, Maciej, Kamil Figiela, Marian Bubak, Ewa Deelman, and Jarek Nabrzyski. "Scheduling Multilevel Deadline-Constrained Scientific Workflows on Clouds Based on Cost Optimization." Scientific Programming 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/680271.
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This paper presents a cost optimization model for scheduling scientific workflows on IaaS clouds such as Amazon EC2 or RackSpace. We assume multiple IaaS clouds with heterogeneous virtual machine instances, with limited number of instances per cloud and hourly billing. Input and output data are stored on a cloud object store such as Amazon S3. Applications are scientific workflows modeled as DAGs as in the Pegasus Workflow Management System. We assume that tasks in the workflows are grouped into levels of identical tasks. Our model is specified using mathematical programming languages (AMPL and CMPL) and allows us to minimize the cost of workflow execution under deadline constraints. We present results obtained using our model and the benchmark workflows representing real scientific applications in a variety of domains. The data used for evaluation come from the synthetic workflows and from general purpose cloud benchmarks, as well as from the data measured in our own experiments with Montage, an astronomical application, executed on Amazon EC2 cloud. We indicate how this model can be used for scenarios that require resource planning for scientific workflows and their ensembles.
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Harfoushi, Osama. "Influence of Cloud Based Mobile Learning Applications on User Experiences: A Review Study in the Context of Jordan." International Journal of Interactive Mobile Technologies (iJIM) 11, no. 4 (May 22, 2017): 202. http://dx.doi.org/10.3991/ijim.v11i4.6938.
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Beside the increasing trend of cloud computing and mobile applications, the use of cloud based mobile learning applications is also mounting. Almost every e-commerce service provider offers cloud based mobile learning applications so that they can target more visitors and ultimately increase their sales. The usability of cloud based mobile applications is not only grounded in e-commerce platforms but it also ease out mobile learning processes. Most of the educational institutes are now offering cloud based mobile applications so their students can navigate to their knowledge portal more easily and download relevant material or submit assignments respectively. The main research area of this article is to explore how cloud based mobile learning applications can be utilized more effectively and what impact they imply on its users. Also, this research compares the mobile learning methods versus traditional learning methods. The study is evidence from Jordan and the major part of the research will be carried out through surveying literature, reports, content, and national and international databases in order to critically discuss the interactions between clouds based mobile learning application and user experiences. Published researches, published reports, books and articles has been included in the review. Review of literature shows that mobile cloud computing is rising in Jordan and have significant impact on mobile learning of Jordanian students. Further, M-learning indeed an innovative tool for learning and it helps the users in many ways. In traditional learning, students used to spend their money on books and other written content. Findings of this study are helpful for the educational institution so they will come to know about user experiences of utilizing these cloud based mobile applications.
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C, Berin Jones, and Murugamani C. "Plant Disease Detection System based IoT for Agricultural Applications Using Cloud." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 738–50. http://dx.doi.org/10.5373/jardcs/v11/20192628.
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Kaur, Gurleen, and Anju Bala. "A survey of prediction-based resource scheduling techniques for physics-based scientific applications." Modern Physics Letters B 32, no. 25 (September 5, 2018): 1850295. http://dx.doi.org/10.1142/s0217984918502950.
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The state-of-the-art physics alliances have augmented various opportunities to solve complex real-world problems. These problems require both multi-disciplinary expertise as well as large-scale computational experiments. Therefore, the physics community needs a flexible platform which can handle computational challenges such as volume of data, platform heterogeneity, application complexity, etc. Cloud computing provides an incredible amount of resources for scientific users on-demand, thus, it has become a potential platform for executing scientific applications. To manage the resources of Cloud efficiently, it is required to explore the resource prediction and scheduling techniques for scientific applications which can be deployed on Cloud. This paper discusses an extensive analysis of scientific applications, resource predictions and scheduling techniques for Cloud computing environment. Further, the trend of resource prediction-based scheduling and the existing techniques have also been studied. This paper would be helpful for the readers to explore the significance of resource prediction-based scheduling techniques for physics-based scientific applications along with the associated challenges.
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Chen, Shanjing, Wenjuan Zhang, Zhen Li, Yuxi Wang, and Bing Zhang. "Cloud Removal with SAR-Optical Data Fusion and Graph-Based Feature Aggregation Network." Remote Sensing 14, no. 14 (July 13, 2022): 3374. http://dx.doi.org/10.3390/rs14143374.
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In observations of Earth, the existence of clouds affects the quality and usability of optical remote sensing images in practical applications. Many cloud removal methods have been proposed to solve this issue. Among these methods, synthetic aperture radar (SAR)-based methods have more potential than others because SAR imaging is hardly affected by clouds, and can reflect ground information differences and changes. While SAR images used as auxiliary information for cloud removal may be blurred and noisy, the similar non-local information of spectral and electromagnetic features cannot be effectively utilized by traditional cloud removal methods. To overcome these weaknesses, we propose a novel cloud removal method using SAR-optical data fusion and a graph-based feature aggregation network (G-FAN). First, cloudy optical images and contemporary SAR images are concatenated and transformed into hyper-feature maps by pre-convolution. Second, the hyper-feature maps are inputted into the G-FAN to reconstruct the missing data of the cloud-covered area by aggregating the electromagnetic backscattering information of the SAR image, and the spectral information of neighborhood and non-neighborhood pixels in the optical image. Finally, post-convolution and a long skip connection are adopted to reconstruct the final predicted cloud-free images. Both the qualitative and quantitative experimental results from the simulated data and real data experiments show that our proposed method outperforms traditional deep learning methods for cloud removal.
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Yue, Zhiguo, Daniel Rosenfeld, Guihua Liu, Jin Dai, Xing Yu, Yannian Zhu, Eyal Hashimshoni, Xiaohong Xu, Ying Hui, and Oliver Lauer. "Automated Mapping of Convective Clouds (AMCC) Thermodynamical, Microphysical, and CCN Properties from SNPP/VIIRS Satellite Data." Journal of Applied Meteorology and Climatology 58, no. 4 (April 2019): 887–902. http://dx.doi.org/10.1175/jamc-d-18-0144.1.
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AbstractThe advent of the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi NPP (SNPP) satellite made it possible to retrieve a new class of convective cloud properties and the aerosols that they ingest. An automated mapping system of retrieval of some properties of convective cloud fields over large areas at the scale of satellite coverage was developed and is presented here. The system is named Automated Mapping of Convective Clouds (AMCC). The input is level-1 VIIRS data and meteorological gridded data. AMCC identifies the cloudy pixels of convective elements; retrieves for each pixel its temperature T and cloud drop effective radius re; calculates cloud-base temperature Tb based on the warmest cloudy pixels; calculates cloud-base height Hb and pressure Pb based on Tb and meteorological data; calculates cloud-base updraft Wb based on Hb; calculates cloud-base adiabatic cloud drop concentrations Nd,a based on the T–re relationship, Tb, and Pb; calculates cloud-base maximum vapor supersaturation S based on Nd,a and Wb; and defines Nd,a/1.3 as the cloud condensation nuclei (CCN) concentration NCCN at that S. The results are gridded 36 km × 36 km data points at nadir, which are sufficiently large to capture the properties of a field of convective clouds and also sufficiently small to capture aerosol and dynamic perturbations at this scale, such as urban and land-use features. The results of AMCC are instrumental in observing spatial covariability in clouds and CCN properties and for obtaining insights from such observations for natural and man-made causes. AMCC-generated maps are also useful for applications from numerical weather forecasting to climate models.
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Zeginis, Chrysostomos, Kyriakos Kritikos, and Dimitris Plexousakis. "Event Pattern Discovery in Multi-Cloud Service-Based Applications." International Journal of Systems and Service-Oriented Engineering 5, no. 4 (October 2015): 78–103. http://dx.doi.org/10.4018/ijssoe.2015100104.
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The adoption of Cloud computing in the Service Oriented Architecture (SOA) world is continuously increasing. However, as developers try to optimize their application deployment cost and performance, they may also deploy application parts redundantly on different VMs. In such heterogeneous and distributed environments, it is important to have a clear view of the system's state and its components' interrelationships. This paper aims at proposing a novel monitoring and adaptation framework for Service-based Applications (SBAs) deployed on multiple Clouds. The main functionality of this framework is the discovery of critical event patterns within monitoring event streams, leading to specific Service Level Objective (SLO) violations. Furthermore, two main meta-models are proposed for describing the SBA's components and their dependencies, and the supported adaptation actions in a specific context respectively. The proposed approach is empirically evaluated based on a real-world traffic management application.
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Xie, Feng, and Cai Zhi Liang. "Research of Internet of Things Based on Cloud Computing." Applied Mechanics and Materials 443 (October 2013): 589–93. http://dx.doi.org/10.4028/www.scientific.net/amm.443.589.
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This paper discusses the application of cloud computing in the logistics information platform, First introduced the basic concept and advantages of cloud computing, cloud computing architecture, cloud computing applications in the logistics information platform mode and the basic framework.
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Schmidt, Albrecht. "Cloud-Based AI for Pervasive Applications." IEEE Pervasive Computing 15, no. 1 (January 2016): 14–18. http://dx.doi.org/10.1109/mprv.2016.11.
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Bonchi, Filippo, Antonio Brogi, Andrea Canciani, and Jacopo Soldani. "Simulation-based matching of cloud applications." Science of Computer Programming 162 (September 2018): 110–31. http://dx.doi.org/10.1016/j.scico.2017.06.001.
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Margulies, Jonathan. "Securing Cloud-Based Applications, Part 1." IEEE Security & Privacy 13, no. 5 (September 2015): 96–98. http://dx.doi.org/10.1109/msp.2015.117.
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Jain, Jaishree, and Ajit Singh. "Quantum-based Rivest–Shamir–Adleman (RSA) approach for digital forensic reports." Modern Physics Letters B 34, no. 06 (February 14, 2020): 2050085. http://dx.doi.org/10.1142/s0217984920500852.
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Cloud computing is a model that permits usage of a distributed resource for cloud users using the pay-as-you-use method. It offers many advantages to users and companies, in terms of various resources and applications as a service. In spite of the existence of these advantages, there are a few limitations that place constraints on the utilization of a cloud computing environment. Security is an important concern in a cloud computing environment as it probes various security attacks. Therefore, in this work, a novel quantum-based Rivest–Shamir–Adleman (RSA) model is proposed for encryption of forensic reports during storage or data sharing on clouds. To evaluate the effectiveness of the proposed approach, a suitable simulation environment is designed for a multi-cloud environment. Experimental results reveal the proposed approach can efficiently encrypt and store data on multiple clouds without introducing potential overheads. Therefore, the proposed approach is more efficient for real-time applications.
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Shuang, Liang. "The Design and Realization of Cloud Computing Framework Model Based on SOA." Advanced Materials Research 171-172 (December 2010): 696–701. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.696.
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Cloud computing is a system which provide hardware services, infrastructure services, platform services, software services, storage services to a variety of Internet applications, and SOA is a component model, it will rely on well-defined interfaces between services and linked contract applications. In this paper, combined cloud computing and SOA together closely to form a cloud computing framework model based on SOA, So that the clouds provide in a simple and flexible way to offer services. And it gives a realization of the model. Experiments show that the model is simple, practical and fully reflects the cloud computing and service-oriented architecture advantages.
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Pflanzner, Tamas, Hamza Baniata, and Attila Kertesz. "Latency Analysis of Blockchain-Based SSI Applications." Future Internet 14, no. 10 (September 29, 2022): 282. http://dx.doi.org/10.3390/fi14100282.
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Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, we can find many other application fields in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source solution for realizing permissioned BC systems for SSI projects. SSI applications usually require short response times from the underlying BC network, which may vary highly depending on the application type, the used BC software, and the actual BC deployment parameters. To support the developers and users of SSI applications, we present a detailed latency analysis of a private permissioned BC system built with Indy and Aries. To streamline our experiments, we developed a Python application using containerized Indy and Aries components from official Hyperledger repositories. We deployed our experimental application on multiple virtual machines in the public Google Cloud Platform and on our local, private cloud using a Docker platform with Kubernetes. We evaluated and compared their performance with the metrics of reading and writing response latency. We found that the local Indy ledger reads 30–50% faster, and writes 65–85% faster than the Indy ledger running on the Google Cloud Platform.
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Li, Bao, Zhe Li, Jun Luo, Yusong Tan, and Pingjing Lu. "µFuncCache: A User-Side Lightweight Cache System for Public FaaS Platforms." Electronics 12, no. 12 (June 13, 2023): 2649. http://dx.doi.org/10.3390/electronics12122649.
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Building cloud-native applications based on public “Function as a Service” (FaaS) platforms has become an attractive way to improve business roll-out speed and elasticity, as well as reduce cloud usage costs. Applications based on FaaS are usually designed with multiple different cloud functions based on their functionality, and there will be call relationships between cloud functions. At the same time, each cloud function may depend on other services provided by cloud providers, such as object storage services, database services, and file storage services. When there is a call relationship between cloud functions, or between cloud functions and other services, a certain delay will occur, and the delay will increase with the length of the call chain, thereby affecting the quality of application services and user experience. Therefore, we introduce μFuncCache, a user-side lightweight caching mechanism to speed up data access for public FaaS services, fully utilizing the container delay destruction mechanism and over-booked memory commonly found in public FaaS platforms, to reduce function call latency without the need to perceive and modify the internal architecture of public clouds. Experiments in different application scenarios have shown that μFuncCache can effectively improve the performance of FaaS applications by consuming only a small amount of additional resources, while achieving a maximum reduction of 97% in latency.
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G, Sandhya, and Dr H. S. Guruprasad. "Security Monitoring for Multi-Cloud Native Network Service Based Functions." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 1473–78. http://dx.doi.org/10.22214/ijraset.2022.46634.
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Abstract: Nowadays, enterprises are adopting a cloud- native to provide rapid change, large scale, and resilience in their application. The applications were built as independent services and packaged as self-contained, lightweight containers.And in order to provide leading applications, better performance, and avoid getting locked into a particular cloud provider's infrastructure. They choose to deploy a cloud-native application on a multi-cloud Infrastructure. While this native multi-cloud strategy has many benefits, it definitely adds more management complexity. We have proposed a framework by creating an abstraction layer that provides security and visibility across these multi-cloud services. We have visualized the metrics like request per second, status code, bandwidth, andlatencies of two sample API services (Users, and Products) which are publicly available and deployed on Google cloudfunction and on a CloudFlare.
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Bhardwaj, Akashdeep, and Sam Goundar. "Unique Taxonomy for Evaluating Fog Computing Services." International Journal of E-Business Research 14, no. 4 (October 2018): 78–90. http://dx.doi.org/10.4018/ijebr.2018100105.
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Cloud computing has slowly but surely become the foremost service provider for information technology applications and platform delivery. However, Cloud issues continue to exist, like cyberattacks, slow last mile latency, and clouds lack client-centric and location-aware applications to process real time data for efficient and customized application delivery. As an alternative, Fog Computing has the potential to resolve these issues by extending the Cloud service provider's reach to the edge of the Cloud network model, right up to the Cloud service consumer. This enables a whole new state of applications and services which increases the security, enhances the cloud experience and keeps the data close to the user. This research article presents a review on the academic literature research work on Fog Computing, introduces a novel taxonomy to classify cloud products based on Fog computing elements and then determine the best fit Fog Computing product to choose for the Cloud service consumer.
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Kampars, Jānis, and Krišjānis Pinka. "AUTO-SCALING AND ADJUSTMENT PLATFORM FOR CLOUD-BASED SYSTEMS." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (June 15, 2017): 52. http://dx.doi.org/10.17770/etr2017vol2.2591.
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For customers of cloud-computing platforms it is important to minimize the infrastructure footprint and associated costs while providing required levels of Quality of Service (QoS) and Quality of Experience (QoE) dictated by the Service Level Agreement (SLA). To assist with that cloud service providers are offering: (1) horizontal resource scaling through provisioning and destruction of virtual machines and containers, (2) vertical scaling through changing the capacity of individual cloud nodes. Existing scaling solutions mostly concentrate on low-level metrics like CPU load and memory consumption which doesn’t always correlate with the level of SLA conformity. Such technical measures should be preprocessed and viewed from a higher level of abstraction. Application level metrics should also be considered when deciding upon scaling the cloud-based solution. Existing scaling platforms are mostly proprietary technologies owned by cloud service providers themselves or by third parties and offered as Software as a Service. Enterprise applications could span infrastructures of multiple public and private clouds, dictating that the auto-scaling solution should not be isolated inside a single cloud infrastructure.
The goal of this paper is to address the challenges above by presenting the architecture of Auto-scaling and Adjustment Platform for Cloud-based Systems (ASAPCS). It is based on open-source technologies and supports integration of various low and high level performance metrics, providing higher levels of abstraction for design of scaling algorithms. ASAPCS can be used with any cloud service provider and guarantees that move from one cloud platform to another will not result in complete redesign of the scaling algorithm. ASAPCS itself is horizontally scalable and can process large amounts of real-time data which is particularly important for applications developed following the microservices architectural style. ASAPCS approaches the scaling problem in a nonstandard way by considering real-time adjustments of the application logic to be part of the scalability strategy if it can result in performance improvements.
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Wu, Weichao, Zhong Xie, Yongyang Xu, Ziyin Zeng, and Jie Wan. "Point Projection Network: A Multi-View-Based Point Completion Network with Encoder-Decoder Architecture." Remote Sensing 13, no. 23 (December 3, 2021): 4917. http://dx.doi.org/10.3390/rs13234917.
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Recently, unstructured 3D point clouds have been widely used in remote sensing application. However, inevitable is the appearance of an incomplete point cloud, primarily due to the angle of view and blocking limitations. Therefore, point cloud completion is an urgent problem in point cloud data applications. Most existing deep learning methods first generate rough frameworks through the global characteristics of incomplete point clouds, and then generate complete point clouds by refining the framework. However, such point clouds are undesirably biased toward average existing objects, meaning that the completion results lack local details. Thus, we propose a multi-view-based shape-preserving point completion network with an encoder–decoder architecture, termed a point projection network (PP-Net). PP-Net completes and optimizes the defective point cloud in a projection-to-shape manner in two stages. First, a new feature point extraction method is applied to the projection of a point cloud, to extract feature points in multiple directions. Second, more realistic complete point clouds with finer profiles are yielded by encoding and decoding the feature points from the first stage. Meanwhile, the projection loss in multiple directions and adversarial loss are combined to optimize the model parameters. Qualitative and quantitative experiments on the ShapeNet dataset indicate that our method achieves good results in learning-based point cloud shape completion methods in terms of chamfer distance (CD) error. Furthermore, PP-Net is robust to the deletion of multiple parts and different levels of incomplete data.
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Hiden, Hugo, Simon Woodman, Paul Watson, and Jacek Cala. "Developing cloud applications using the e-Science Central platform." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1983 (January 28, 2013): 20120085. http://dx.doi.org/10.1098/rsta.2012.0085.
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This paper describes the e-Science Central (e-SC) cloud data processing system and its application to a number of e-Science projects. e-SC provides both software as a service (SaaS) and platform as a service for scientific data management, analysis and collaboration. It is a portable system and can be deployed on both private (e.g. Eucalyptus) and public clouds (Amazon AWS and Microsoft Windows Azure). The SaaS application allows scientists to upload data, edit and run workflows and share results in the cloud, using only a Web browser. It is underpinned by a scalable cloud platform consisting of a set of components designed to support the needs of scientists. The platform is exposed to developers so that they can easily upload their own analysis services into the system and make these available to other users. A representational state transfer-based application programming interface (API) is also provided so that external applications can leverage the platform's functionality, making it easier to build scalable, secure cloud-based applications. This paper describes the design of e-SC, its API and its use in three different case studies: spectral data visualization, medical data capture and analysis, and chemical property prediction.
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Shakya, Dr Subarna. "Survey on Cloud Based Robotics Architecture, Challenges and Applications." Journal of Ubiquitous Computing and Communication Technologies 2, no. 1 (March 11, 2020): 10–18. http://dx.doi.org/10.36548/jucct.2020.1.002.
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The emergence of the cloud computing, and the other advanced technologies has made possible the extension of the computing and the data distribution competencies of the robotics that are networked by developing an cloud based robotic architecture by utilizing both the centralized and decentralized cloud that is manages the machine to cloud and the machine to machine communication respectively. The incorporation of the robotic system with the cloud makes probable the designing of the cost effective robotic architecture that enjoys the enhanced efficiency and a heightened real- time performance. This cloud based robotics designed by amalgamation of robotics and the cloud technologies empowers the web enabled robots to access the services of cloud on the fly. The paper is a survey about the cloud based robotic architecture, explaining the forces that necessitate the robotics merged with the cloud, its application and the major concerns and the challenges endured in the robotics that is integrated with the cloud. The paper scopes to provide a detailed study on the changes influenced by the cloud computing over the industrial robots.
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Zhao, X., Z. Gao, W. Sun, and F. Wen. "A COARSE-TO-FINE BAND REGISTRATION FRAMEWORK FOR MULTI/HYPERSPECTRAL REMOTE SENSING IMAGES CONSIDERING CLOUD INFLUENCE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 201–8. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-201-2020.
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Abstract. Band registration is one of the most critical steps in the production of multi/hyperspectral images and determines the accuracy of applications directly. Because of the characteristics of imaging devices in some multi/hyperspectral satellites, there may be a time difference between bands during push-broom imaging, which leads to displacements of moving clouds with respect to the ground. And a large number of feature points may gather around cloud contours due to the high contrast and rich texture, resulting in building a transformation more suitable for moving clouds and making ground objects ghosted and blurred. This brings a big challenge for registration methods based on feature extraction and matching. In this paper, we propose a novel coarse-to-fine band registration framework for multi/hyperspectral images containing moving clouds. In the coarse registration stage, a cloud mask is generated by grayscale stretching, morphology and other operations. Based on this mask, a coarse matching of cloud-free regions is performed to eliminate large misalignment between bands. In the refinement stage, low-rank analysis and RASL (Robust Alignment by Sparse and Low-rank decomposition) are used to optimize the rank of coarse results to achieve fine registration between bands. After experiments on a total of 102 images (83 cloudy images and 19 cloud-free images with all 32 bands) from Zhuhai-1 hyperspectral satellite, our method can achieve a registration accuracy of 0.6 pixels in cloudy images, 0.41 pixels in cloud-free images, which is enough for subsequent applications.
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Molthan, Andrew L., Jonathan L. Case, Jason Venner, Richard Schroeder, Milton R. Checchi, Bradley T. Zavodsky, Ashutosh Limaye, and Raymond G. O’Brien. "Clouds in the Cloud: Weather Forecasts and Applications within Cloud Computing Environments." Bulletin of the American Meteorological Society 96, no. 8 (August 1, 2015): 1369–79. http://dx.doi.org/10.1175/bams-d-14-00013.1.
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Abstract Cloud computing offers new opportunities to the scientific community through cloud-deployed software, data-sharing and collaboration tools, and the use of cloud-based computing infrastructure to support data processing and model simulations. This article provides a review of cloud terminology of possible interest to the meteorological community, and focuses specifically on the use of infrastructure as a service (IaaS) concepts to provide a platform for regional numerical weather prediction. Special emphasis is given to developing countries that may have limited access to traditional supercomputing facilities. Amazon Elastic Compute Cloud (EC2) resources were used in an IaaS capacity to provide regional weather simulations with costs ranging from $40 to $75 per 48-h forecast, depending upon the configuration. Simulations provided a reasonable depiction of sensible weather elements and precipitation when compared against typical validation data available over Central America and the Caribbean.
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Yin, Zuozhong, Jihong Liu, Bin Chen, and Chuanjun Chen. "A Delivery Robot Cloud Platform Based on Microservice." Journal of Robotics 2021 (February 18, 2021): 1–10. http://dx.doi.org/10.1155/2021/6656912.
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Delivery robots face the problem of storage and computational stress when performing immediate tasks, exceeding the limits of on-board computing power. Based on cloud computing, robots can offload intensive tasks to the cloud and acquire massive data resources. With its distributed cluster architecture, the platform can help offload computing and improve the computing power of the control center, which can be considered the external “brain” of the robot. Although it expands the capabilities of the robot, cloud service deployment remains complex because most current cloud robot applications are based on monolithic architectures. Some scholars have proposed developing robot applications through the microservice development paradigm, but there is currently no unified microservice-based robot cloud platform. This paper proposes a delivery robot cloud platform based on microservice, providing dedicated services for autonomous driving of delivery robot. The microservice architecture is adopted to split the monomer robot application into multiple services and then implement automatic orchestration and deployment of services on the cloud platform based on components such as Kubernetes, Docker, and Jenkins. This enables containerized CI/CD (continuous integration, continuous deployment, and continuous delivery) for the cloud platform service, and the whole process can be visualized, repeatable, and traceable. The platform is prebuilt with development tools, and robot application developers can use these tools to develop in the cloud, without the need for any customization in the background, to achieve the rapid deployment and launch of robot cloud service. Through the cloud migration of traditional robot applications and the development of new APPs, the platform service capabilities are continuously improved. This paper verifies the feasibility of the platform architecture through the delivery scene experiment.
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Tsagkaropoulos, Andreas, Yiannis Verginadis, Maxime Compastié, Dimitris Apostolou, and Gregoris Mentzas. "Extending TOSCA for Edge and Fog Deployment Support." Electronics 10, no. 6 (March 20, 2021): 737. http://dx.doi.org/10.3390/electronics10060737.
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The emergence of fog and edge computing has complemented cloud computing in the design of pervasive, computing-intensive applications. The proximity of fog resources to data sources has contributed to minimizing network operating expenditure and has permitted latency-aware processing. Furthermore, novel approaches such as serverless computing change the structure of applications and challenge the monopoly of traditional Virtual Machine (VM)-based applications. However, the efforts directed to the modeling of cloud applications have not yet evolved to exploit these breakthroughs and handle the whole application lifecycle efficiently. In this work, we present a set of Topology and Orchestration Specification for Cloud Applications (TOSCA) extensions to model applications relying on any combination of the aforementioned technologies. Our approach features a design-time “type-level” flavor and a run time “instance-level” flavor. The introduction of semantic enhancements and the use of two TOSCA flavors enables the optimization of a candidate topology before its deployment. The optimization modeling is achieved using a set of constraints, requirements, and criteria independent from the underlying hosting infrastructure (i.e., clouds, multi-clouds, edge devices). Furthermore, we discuss the advantages of such an approach in comparison to other notable cloud application deployment approaches and provide directions for future research.
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Priyadarshan, Prasuryya. "NLP Applications as Cloud Based Services in Understanding Human Emotions." International Journal of Research in Engineering, Science and Management 3, no. 11 (November 10, 2020): 47–49. http://dx.doi.org/10.47607/ijresm.2020.371.
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This paper aims to throw light on the purpose of natural language processing applications and their future scope as cloud based services. It highlights certain features of cloud, along with a unique model of cloud hosted natural language processing application concept, to make the most out of emotional expressions in a person’s text or speech, which will prove effective to tackle diminishing human emotional interaction.
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Shaharkar, Bhushan B., and Darshan P. Pandit. "ML Based Authentication Scheme for Data Storage in Cloud Based IoT." International Journal of Engineering and Advanced Technology 11, no. 6 (August 30, 2022): 123–27. http://dx.doi.org/10.35940/ijeat.f3767.0811622.
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Today, organizations are using IoT devices to accurately collect real data and make better business decisions to increase customer satisfaction. The data collected should be stored and stored in a well-designed storage system, which encourages companies to review their data storage infrastructure. The company needs to store data created by the Internet of Things, and that data grows exponentially, forcing IoT to think about cloud storage for data storage. Security issues are a major concern when handling and processing data in DI and cloud environments. Secure integration of IoT and cloud computing, and introduced a model to ensure this integration. The secure database of any IoT operating system was suffers from poorly protected read and write functions, which limits data storage on any IoT platform. In addition, clouds can provide space to store a wide variety of data that plays an important role in the world of cyber security. However, large centralized systems operating in the cloud are also very vulnerable due to their power, so they can be transformed into a kind of double-edged sword. In this paper, we propose a novel secure lightweight authentication scheme for data storage (SLA-DS) in IoT and cloud server. The SLA-DS integrates IoT and cloud technology combination which mainly focuses on security issues.
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Bugliaro, L., T. Zinner, C. Keil, B. Mayer, R. Hollmann, M. Reuter, and W. Thomas. "Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI." Atmospheric Chemistry and Physics Discussions 10, no. 9 (September 21, 2010): 21931–88. http://dx.doi.org/10.5194/acpd-10-21931-2010.
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Abstract. Validation of cloud properties retrieved from passive spaceborne imagers is essential for cloud and climate applications but complicated due to the large differences in scale and observation geometry between the satellite footprint and the independent ground based or airborne observations. Here we illustrate and demonstrate an alternative approach: starting from the output of the COSMO-EU weather model of the German Weather Service realistic three-dimensional cloud structures at a spatial scale of 2.33 km are produced by statistical downscaling and microphysical properties are associated to them. The resulting data sets are used as input to the one-dimensional radiative transfer model libRadtran to simulate radiance observations for all eleven low resolution channels of MET-8/SEVIRI. At this point, both cloud properties and satellite radiances are known such that cloud property retrieval results can be tested and tuned against the objective input "truth". As an example, we validate a cloud property retrieval of the Institute of Atmospheric Physics of DLR and that of EUMETSAT's Climate Monitoring Science Application Facility CMSAF. Cloud detection and cloud phase assignment perform well. By both retrievals 88% of the pixels are correctly classified as clear or cloudy. The DLR algorithm assigns the correct thermodynamic phase to 95% of the cloudy pixels and the CMSAF retrieval to 79%. Cloud top temperature is slightly overestimated by the DLR code (+3.1 K mean difference with a standard deviation of 10.6 K) and underestimated by the CMSAF code (−16.4 K with a standard deviation of 37.3 K). Both retrievals account reasonably well for the distribution of optical thickness for both water and ice clouds, with a tendency to underestimation for the DLR and to overestimation for the CMSAF algorithm. Cloud effective radii are most difficult to evaluate and not always the algorithms are able to produce realistic values. The CMSAF cloud water path, which is a combination of the last two quantities, is particularly accurate for ice clouds, while water clouds are overestimated, mainly because of the effective radius overestimation for water clouds.
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El-Gharib, Najah Mary, and Daniel Amyot. "Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs." Algorithms 15, no. 6 (May 25, 2022): 180. http://dx.doi.org/10.3390/a15060180.
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Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.
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Benas, Nikos, Stephan Finkensieper, Martin Stengel, Gerd-Jan van Zadelhoff, Timo Hanschmann, Rainer Hollmann, and Jan Fokke Meirink. "The MSG-SEVIRI-based cloud property data record CLAAS-2." Earth System Science Data 9, no. 2 (July 10, 2017): 415–34. http://dx.doi.org/10.5194/essd-9-415-2017.
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Abstract. Clouds play a central role in the Earth's atmosphere, and satellite observations are crucial for monitoring clouds and understanding their impact on the energy budget and water cycle. Within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF), a new cloud property data record was derived from geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements for the time frame 2004–2015. The resulting CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) data record is publicly available via the CM SAF website (https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002). In this paper we present an extensive evaluation of the CLAAS-2 cloud products, which include cloud fractional coverage, thermodynamic phase, cloud top properties, liquid/ice cloud water path and corresponding optical thickness and particle effective radius. Data validation and comparisons were performed on both level 2 (native SEVIRI grid and repeat cycle) and level 3 (daily and monthly averages and histograms) with reference datasets derived from lidar, microwave and passive imager measurements. The evaluation results show very good overall agreement with matching spatial distributions and temporal variability and small biases attributed mainly to differences in sensor characteristics, retrieval approaches, spatial and temporal samplings and viewing geometries. No major discrepancies were found. Underpinned by the good evaluation results, CLAAS-2 demonstrates that it is fit for the envisaged applications, such as process studies of the diurnal cycle of clouds and the evaluation of regional climate models. The data record is planned to be extended and updated in the future.
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Kim, Dong Kwan. "Development of Mobile Cloud Applications using UML." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 1 (February 1, 2018): 596. http://dx.doi.org/10.11591/ijece.v8i1.pp596-604.
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With the proliferation of cloud computing technologies, smartphone users are able to use a variety of cloud computing-based mobile services such as games, education, entertainment, and social networking. Despite the popularity of such a mobile cloud computing, the complicated multi-tier system configuration of the mobile application must be one of the major impediments to develop mobile cloud applications. This paper presents development processes and procedures for developing mobile cloud applications by effectively applying Unified Modeling Language (UML), a representative object-oriented modeling language. The paper is intended to enhance the development productivity of the mobile cloud application and to improve the effectiveness of communication between software developers. In addition, we used the Android mobile platform and Amazon Web Service for cloud computing in order to demonstrate the applicability of the proposed approach to systematically apply the UML profiles and diagrams for cloud-based mobile applications.
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Li, Zijun, Hoiio Kong, and Chan-Seng Wong. "Neural Network-Based Identification of Cloud Types from Ground-Based Images of Cloud Layers." Applied Sciences 13, no. 7 (March 31, 2023): 4470. http://dx.doi.org/10.3390/app13074470.
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Clouds are a significant factor in regional climates and play a crucial role in regulating the Earth’s water cycle through the interaction of sunlight and wind. Meteorological agencies around the world must regularly observe and record cloud data. Unfortunately, the current methods for collecting cloud data mainly rely on manual observation. This paper presents a novel approach to identifying ground-based cloud images to aid in the collection of cloud data. However, there is currently no publicly available dataset that is suitable for this research. To solve this, we built a dataset of surface-shot images of clouds called the SSC, which was overseen by the Macao Meteorological Society. Compared to previous datasets, the SSC dataset offers a more balanced distribution of data samples across various cloud genera and provides a more precise classification of cloud genera. This paper presents a method for identifying cloud genera based on cloud texture, using convolutional neural networks. To extract cloud texture effectively, we apply Gamma Correction to the images. The experiments were conducted on the SSC dataset. The results show that the proposed model performs well in identifying 10 cloud genera, achieving an accuracy rate of 80% for the top three possibilities.
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Balashov, Nikita, Maxim Bashashin, Pavel Goncharov, Ruslan Kuchumov, Nikolay Kutovskiy, Kiril Kulikov, Mikhail Matveev, et al. "Service for parallel applications based on JINR cloud and HybriLIT resources." EPJ Web of Conferences 214 (2019): 07012. http://dx.doi.org/10.1051/epjconf/201921407012.
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Cloud computing has become a routine tool for scientists in many fields. The JINR cloud infrastructure provides JINR users with computational resources to perform various scientific calculations. In order to speed up achievements of scientific results the JINR cloud service for parallel applications has been developed. It consists of several components and implements a flexible and modular architecture which allows to utilize both more applications and various types of resources as computational backends. An example of using the Cloud&HybriLIT resources in scientific computing is the study of superconducting processes in the stacked long Josephson junctions (LJJ). The LJJ systems have undergone intensive research because of the perspective of practical applications in nano-electronics and quantum computing. In this contribution we generalize the experience in application of the Cloud&HybriLIT resources for high performance computing of physical characteristics in the LJJ system.
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Berenberg, Anna, and Brad Calder. "Deployment Archetypes for Cloud Applications." ACM Computing Surveys 55, no. 3 (April 30, 2023): 1–48. http://dx.doi.org/10.1145/3498336.
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This is a survey article that explores six Cloud-based deployment archetypes for Cloud applications and the tradeoffs between them to achieve high availability, low end-user latency, and acceptable costs. These are (1) Zonal, (2) Regional, (3) Multi-regional, (4) Global, (5) Hybrid, and (6) Multi-cloud deployment archetypes. The goal is to classify cloud applications into a set of deployment archetypes and deployment models that tradeoff their needs around availability, latency, and geographical constraints with a focus on serving applications. This enables application owners to better examine the tradeoffs of each deployment model and what is needed for achieving the availability and latency goals for their application.
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Kim, Dongmin, Hanif Muhammad, Eunsam Kim, Sumi Helal, and Choonhwa Lee. "TOSCA-Based and Federation-Aware Cloud Orchestration for Kubernetes Container Platform." Applied Sciences 9, no. 1 (January 7, 2019): 191. http://dx.doi.org/10.3390/app9010191.
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Kubernetes, a container orchestration tool for automatically installing and managing Docker containers, has recently begun to support a federation function of multiple Docker container clusters. This technology, called Kubernetes Federation, allows developers to increase the responsiveness and reliability of their applications by distributing and federating container clusters to multiple service areas of cloud service providers. However, it is still a daunting task to manually manage federated container clusters across all the service areas or to maintain the entire topology of cloud applications at a glance. This research work proposes a method to automatically form and monitor Kubernetes Federation, given application topology descriptions in TOSCA (Topology and Orchestration Specification for Cloud Applications), by extending the orchestration tool that automatizes the modeling and instantiation of cloud applications. It also demonstrates the successful federation of the clusters according to the TOSCA specifications and verifies the auto-scaling capability of the configured system through a scenario in which the servers of a sample application are deployed and federated.
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Carvalho, Juliana, Dario Vieira, Christiano Rodrigues, and Fernando Trinta. "LM2K Model for Hosting an Application Based on Microservices in Multi-Cloud." Sensors 23, no. 9 (May 2, 2023): 4450. http://dx.doi.org/10.3390/s23094450.
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Cloud computing has become a popular delivery model service, offering several advantages. However, there are still challenges that need to be addressed when applying the cloud model to specific scenarios. Two of such challenges involve deploying and executing applications across multiple providers, each comprising several services with similar functionalities and different capabilities. Therefore, dealing with application distributions across various providers can be a complex task for a software architect due to the differing characteristics of the application components. Some works have proposed solutions to address the challenges discussed here, but most of them focus on service providers. To facilitate the decision-making process of software architects, we previously presented PacificClouds, an architecture for managing the deployment and execution of applications based on microservices and distributed in a multi-cloud environment. Therefore, in this work, we focus on the challenges of selecting multiple clouds for PacificClouds and choosing providers that best meet the microservices and software architect requirements. We propose a selection model and three approaches to address various scenarios. We evaluate the performance of the approaches and conduct a comparative analysis of them. The results demonstrate their feasibility regarding performance.
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Achar, Sandesh. "Enterprise SaaS Workloads on New-Generation Infrastructure-as-Code (IaC) on Multi-Cloud Platforms." Global Disclosure of Economics and Business 10, no. 2 (July 20, 2021): 55–74. http://dx.doi.org/10.18034/gdeb.v10i2.652.
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Cloud Computing has become the primary model used by DevOps practitioners and researchers to provision infrastructure in minimal time. But recently, the traditional method of using a single cloud provider has fallen out of favor due to several limitations regarding performance, compliance rules, geographical reach, and vendor lock-in. To address these issues, industry and academia are implementing multiple clouds (i.e., multi-cloud). However, managing the infrastructure provisioning of enterprise SaaS applications faces several challenges, such as configuration drift and the heterogeneity of cloud providers. This has seen Infrastructure-as-Code (IaC) technologies being used to automate the deployment of SaaS applications. IaC facilitates the rapid deployment of new versions of application infrastructures without degrading quality or stability. Therefore, this work presents a vision of uniformly managing the infrastructure provisioning of enterprise SaaS applications that utilize multiple cloud providers. Hence, we introduce an initial design for the IaC-based Multi-Cloud Deployment pattern and discuss how it addresses the relative challenges.
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Stupar, Ivana, and Darko Huljenić. "Model-Based Extraction of Knowledge about the Effect of Cloud Application Context on Application Service Cost and Quality of Service." Scientific Programming 2019 (September 2, 2019): 1–19. http://dx.doi.org/10.1155/2019/5075412.
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With the increased usage of cloud computing in production environments, both for scientific workflows and industrial applications, the focus of application providers shifts towards service cost optimisation. One of the ways to achieve minimised service execution cost is to optimise the placement of the service in the resource pool of the cloud data centres. An increasing number of research approaches is focusing on using machine learning algorithms to deal with dynamic cloud workloads by allocating resources to services in an adaptive way. Many of such solutions are intended for cloud infrastructure providers and deal only with specific types of cloud services. In this paper, we present a model-based approach aimed at the providers of applications hosted in the cloud, which is applicable in early phases of the service lifecycle and can be used for any cloud application service. Using several machine learning methods, we create models to predict cloud service cost and response times of two cloud applications. We also explore how to extract knowledge about the effect that the cloud application context has on both service cost and quality of service so that the gained knowledge can be used in the service placement decision process. The experimental results demonstrate the ability of providing relevant information about the impact of cloud application context parameters on service cost and quality of service. The results also indicate the relevance of our approach for applications in preproduction phase since application providers can gain useful insights regarding service placement decision without acquiring extensive training datasets.
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Kotarba, Andrzej Z. "Calibration of global MODIS cloud amount using CALIOP cloud profiles." Atmospheric Measurement Techniques 13, no. 9 (September 25, 2020): 4995–5012. http://dx.doi.org/10.5194/amt-13-4995-2020.
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Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOVs) as either “confident clear”, “probably clear”, “probably cloudy”, or “confident cloudy”. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by the MODIS Science Team assumes that confident clear and probably clear IFOVs are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this “best-guess” approach is unreliable, especially on a regional/local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with Aqua MODIS IFOV. Based on 33 793 648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in polar regions at night, and in selected locations over the Northern Hemisphere (e.g. China, the north-west coast of Africa, and eastern parts of the United States). Consequently, applications of MODIS data on a regional/local scale should first assess the extent of the uncertainty. We suggest using CALIPSO-based cloud fractions to improve MODIS cloud amount estimates. This approach can also be used for Terra MODIS data, and other passive cloud imagers, where the footprint is collocated with CALIPSO.
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Ra, Ho-Kyeong, Hee Jung Yoon, Sang Hyuk Son, John A. Stankovic, and JeongGil Ko. "HealthNode: Software Framework for Efficiently Designing and Developing Cloud-Based Healthcare Applications." Mobile Information Systems 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/6071580.
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With the exponential improvement of software technology during the past decade, many efforts have been made to design remote and personalized healthcare applications. Many of these applications are built on mobile devices connected to the cloud. Although appealing, however, prototyping and validating the feasibility of an application-level idea is yet challenging without a solid understanding of the cloud, mobile, and the interconnectivity infrastructure. In this paper, we provide a solution to this by proposing a framework called HealthNode, which is a general-purpose framework for developing healthcare applications on cloud platforms using Node.js. To fully exploit the potential of Node.js when developing cloud applications, we focus on the fact that the implementation process should be eased. HealthNode presents an explicit guideline while supporting necessary features to achieve quick and expandable cloud-based healthcare applications. A case study applying HealthNode to various real-world health applications suggests that HealthNode can express architectural structure effectively within an implementation and that the proposed platform can support system understanding and software evolution.