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Journal articles on the topic 'Virtualization: Scheduling'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Hong, Cheol-Ho, Ivor Spence, and Dimitrios S. Nikolopoulos. "GPU Virtualization and Scheduling Methods." ACM Computing Surveys 50, no. 3 (2017): 1–37. http://dx.doi.org/10.1145/3068281.

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2

Sharma, Gourav Prateek, Wouter Tavernier, Didier Colle, and Mario Pickavet. "Scheduling for Media Function Virtualization." Future Internet 13, no. 7 (2021): 167. http://dx.doi.org/10.3390/fi13070167.

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Broadcasters are building studio architectures based on commercial off-the-shelf (COTS) IT hardware because of advantages such as cost reduction, ease of management, and upgradation. Media function virtualization (MFV) leverages IP networking to transport media streams between virtual media functions (VMFs), where they are processed. Media service deployment in an MFV environment entails solving the VMF-FG scheduling problem to ensure that the required broadcast quality guarantees are fulfilled. In this paper, we formulate the VMF-FG scheduling problem and propose a greedy-based algorithm to s

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3

Kumari, Shabnam, Shabnam Kumari, and Shahdab Payami. "Virtualization and Scheduling in Real Time OS: A Survey." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (2017): 434–39. http://dx.doi.org/10.31142/ijtsrd172.

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4

Boutcher, David, and Abhishek Chandra. "Does virtualization make disk scheduling passé?" ACM SIGOPS Operating Systems Review 44, no. 1 (2010): 20–24. http://dx.doi.org/10.1145/1740390.1740396.

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5

Yang, Jungwoo, Hyungseok Kim, Sangwon Park, Changki Hong, and Insik Shin. "Implementation of compositional scheduling framework on virtualization." ACM SIGBED Review 8, no. 1 (2011): 30–37. http://dx.doi.org/10.1145/1967021.1967025.

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6

Lan, Li, Chen Jianya, Cui Hongyan, Huang Tao, and Liu Yunjie. "Resource scheduling virtualization in service-oriented future Internet architecture." Journal of China Universities of Posts and Telecommunications 22, no. 4 (2015): 92–100. http://dx.doi.org/10.1016/s1005-8885(15)60672-0.

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7

Zhang, Yujie, Jiabin Yuan, Xiangwen Lu, and Xingfang Zhao. "Multi-GPU Parallel Computing and Task Scheduling under Virtualization." International Journal of Hybrid Information Technology 8, no. 7 (2015): 253–66. http://dx.doi.org/10.14257/ijhit.2015.8.7.24.

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8

Wang, Bei, Yuxia Cheng, Wenzhi Chen, et al. "Efficient consolidation-aware VCPU scheduling on multicore virtualization platform." Future Generation Computer Systems 56 (March 2016): 229–37. http://dx.doi.org/10.1016/j.future.2015.08.007.

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9

Liu, Jun Jun. "Construction on Virtualized Environment for Digital Library Based on Cloud Computing Technology." Applied Mechanics and Materials 687-691 (November 2014): 3019–22. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3019.

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Cloud computing technology is emerging technology in the field of information technology, and its technical advantage has brought new opportunities and challenges for the development and service of digital library in the Internet era. Virtualization is the key technology of cloud computing, and the paper discusses the virtualization of digital library in the cloud computing environment from technical level. Firstly, the paper introduced cloud computing and virtualization technology; then created a virtualized environment for digital library based on the cloud computing technology, and describe

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10

Zhou, Guo Hong. "Study of Cloud Computing and Advanced Manufacturing Technology under Information Technology Development." Applied Mechanics and Materials 484-485 (January 2014): 227–30. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.227.

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Cloud computing can be regarded as the integration of grid computing and virtualization technology: namely the use of grid distributed computing processing power of IT resources and build into a resource pool, plus on a mature server virtualization, storage virtualization technology make users can real-time monitoring and deployment of resources. Cloud computing and advanced manufacturing technology is facing many challenges during the process of development in the future, including security and privacy will become a primary issue. This study focused on relations with the important areas of em

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11

Thirumala Rao, B. "Scheduling Data Intensive Workloads through Virtualization on MapReduce based Clouds." International Journal of Distributed and Parallel systems 3, no. 4 (2012): 99–110. http://dx.doi.org/10.5121/ijdps.2012.3411.

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12

Ye, Deshi, and Guochuan Zhang. "On-line scheduling of multi-core processor tasks with virtualization." Operations Research Letters 38, no. 4 (2010): 307–11. http://dx.doi.org/10.1016/j.orl.2010.04.002.

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13

Qu, Long, Chadi Assi, and Khaled Shaban. "Delay-Aware Scheduling and Resource Optimization With Network Function Virtualization." IEEE Transactions on Communications 64, no. 9 (2016): 3746–58. http://dx.doi.org/10.1109/tcomm.2016.2580150.

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14

Uchiteleva, Elena, Abdallah Shami, and Ahmed Refaey. "Virtualization of Wireless Sensor Networks Through MAC Layer Resource Scheduling." IEEE Sensors Journal 17, no. 5 (2017): 1562–76. http://dx.doi.org/10.1109/jsen.2016.2643670.

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15

Sarddar, Debabrata, Sougata Chakraborty, and Priyajit Sen. "Parallelism in CPU virtualization and Scheduling using Cuckoo Search Algorithm." International Journal of Grid and Distributed Computing 10, no. 6 (2017): 1–10. http://dx.doi.org/10.14257/ijgdc.2017.10.6.01.

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16

Shukur, Hanan, Subhi Zeebaree, Rizgar Zebari, Diyar Zeebaree, Omar Ahmed, and Azar Salih. "Cloud Computing Virtualization of Resources Allocation for Distributed Systems." Journal of Applied Science and Technology Trends 1, no. 3 (2020): 98–105. http://dx.doi.org/10.38094/jastt1331.

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Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques. The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and schedulin

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17

Lee, Jaehak, and Heonchang Yu. "I/O Strength-Aware Credit Scheduler for Virtualized Environments." Electronics 9, no. 12 (2020): 2107. http://dx.doi.org/10.3390/electronics9122107.

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With the evolution of cloud technology, the number of user applications is increasing, and computational workloads are becoming increasingly diverse and unpredictable. However, cloud data centers still exhibit a low I/O performance because of the scheduling policies employed, which are based on the degree of physical CPU (pCPU) occupancy. Notably, existing scheduling policies cannot guarantee good I/O performance because of the uncertainty of the extent of I/O occurrence and the lack of fine-grained workload classification. To overcome these limitations, we propose ISACS, an I/O strength-aware

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18

Ijaz, Qaiser, El-Bay Bourennane, Ali Kashif Bashir, and Hira Asghar. "Revisiting the High-Performance Reconfigurable Computing for Future Datacenters." Future Internet 12, no. 4 (2020): 64. http://dx.doi.org/10.3390/fi12040064.

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Modern datacenters are reinforcing the computational power and energy efficiency by assimilating field programmable gate arrays (FPGAs). The sustainability of this large-scale integration depends on enabling multi-tenant FPGAs. This requisite amplifies the importance of communication architecture and virtualization method with the required features in order to meet the high-end objective. Consequently, in the last decade, academia and industry proposed several virtualization techniques and hardware architectures for addressing resource management, scheduling, adoptability, segregation, scalabi

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19

Chai, Zhilei, Wei Liu, Qin Wu, Qunfang He, and Wenjie Chen. "FPGA Virtualization Mechanism Based on Heterogeneous Zynq Platforms." Journal of Circuits, Systems and Computers 28, no. 12 (2019): 1950199. http://dx.doi.org/10.1142/s0218126619501998.

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FPGA (Field Programmable Gate Array) has the advantages of parallelism and reconfigurability, therefore, it is widely used in areas such as image processing, robotics and artificial intelligence. However, the development of FPGA currently involves too many hardware details, so it lacks extensibility for different platforms and flexibility for system level management and scheduling. In this paper, we propose an FPGA Virtualization Mechanism (FVM), which divides physical resources into pages (virtual resources). We use the technology of PR (Partial Reconfiguration) and the method of intermediate

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20

Loganathan, Shyamala, and Saswati Mukherjee. "Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter." Scientific World Journal 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/983018.

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Cloud computing is an on-demand computing model, which uses virtualization technology to provide cloud resources to users in the form of virtual machines through internet. Being an adaptable technology, cloud computing is an excellent alternative for organizations for forming their own private cloud. Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal. For that, efficient scheduling is needed. This research reports on an efficient data structure for resource management and resource

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21

Cao, Wenzhi, Hai Jin, and Xia Xie. "Proportional Allocation of Resources on Shared Ring Buffer for Virtualization." International Journal of Cloud Applications and Computing 2, no. 2 (2012): 12–30. http://dx.doi.org/10.4018/ijcac.2012040102.

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In virtualization, without proper scheduling and resource management, a load surge in one VM may unacceptably degrade the performance of another. Key requirement of IO performance virtualization is performance isolation. The current state of performance isolation for virtualization is more rudimentary. This paper presents a resources allocation framework, based on abstracted Xen IO model, that provides both throughput and fairness guarantees for network and storage device via 2-level resource control. The high-level tier makes guest domains perceive the states of the resource through resource

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22

Sultanpure, Kavita, and Lakkam Reddy. "Job Scheduling for Energy Efficiency Using Artificial Bee Colony through Virtualization." International Journal of Intelligent Engineering and Systems 11, no. 3 (2018): 138–48. http://dx.doi.org/10.22266/ijies2018.0630.15.

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23

Badoi, Cornelia-Ionela, Neeli Prasad, and Ramjee Prasad. "Virtualization and Scheduling Methods for 5G Cognitive Radio Based Wireless Networks." Wireless Personal Communications 89, no. 2 (2016): 599–619. http://dx.doi.org/10.1007/s11277-016-3295-8.

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24

Kamaleswari, A., and P. Thangaraj. "An Efficient Packet Priority Scheduling Algorithm for IO Virtualization of Cloud." Journal of Computational and Theoretical Nanoscience 12, no. 12 (2015): 5984–94. http://dx.doi.org/10.1166/jctn.2015.4747.

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25

Zhang, Jianyong, Anand Sivasubramaniam, Alma Riska, Qian Wang, and Erik Riedel. "An interposed 2-Level I/O scheduling framework for performance virtualization." ACM SIGMETRICS Performance Evaluation Review 33, no. 1 (2005): 406–7. http://dx.doi.org/10.1145/1071690.1064277.

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26

Zhang, Yue, and Yun Xia Pei. "An Effective Grid Resource Management Model Based on Cloud Computing Platform." Applied Mechanics and Materials 50-51 (February 2011): 358–62. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.358.

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Resource management model was presented based on cloud computing using virtualization technologies. According to this strategy, a resource scheduling model was designed. This strategy can really effectively improve resource utilization rate. This algorithm met the needs of cloud computing more than others for grid environment with shorter response time and better performance, which were proved by the simulation results in the Gridsim environment.

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27

Huang, Shu, Jing Bo Yang, and Hui Jie Ding. "Research on GA-DPSO Virtual Machine Scheduling Algorithm Based on Comprehensive Utilization of Host Resource." Advanced Materials Research 791-793 (September 2013): 1373–76. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1373.

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Virtualization technology has been widely used. Modern data center uses different equipments to support a large number of business systems. Whether the resources scheduling of virtual machine can be realized has become a very important issue, which will reflect the data centers management level of intelligence, intensiveness and automation. On the basis of comprehensive utilization of the physical machines various resources, the algorithm combines the advantages of genetic algorithm with particle swarm optimization algorithm, with reference to model of load balancing scheduling system, constan

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28

Chydzinski, Andrzej. "Analysis of the scheduling mechanism for virtualization of links with partial isolation." Applied Mathematics and Computation 281 (April 2016): 39–54. http://dx.doi.org/10.1016/j.amc.2016.01.047.

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29

Du, Guangyu, Hong He, and Qinggang Meng. "Energy-Efficient Scheduling for Tasks with Deadline in Virtualized Environments." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/496843.

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Data centers, as resource providers, take advantage of virtualization technology to achieve excellent resource utilization, scalability, and high availability. However, large numbers of computing servers containing virtual machines of data centers consume a tremendous amount of energy. Thus, it is necessary to significantly improve resource utilization. Among the many issues associated with energy, scheduling plays a very important role in successful task execution and energy consumption in virtualized environments. This paper seeks to implement an energy-efficient task scheduling algorithm fo

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30

Kang, Jihun, and Heonchang Yu. "GPGPU Task Scheduling Technique for Reducing the Performance Deviation of Multiple GPGPU Tasks in RPC-Based GPU Virtualization Environments." Symmetry 13, no. 3 (2021): 508. http://dx.doi.org/10.3390/sym13030508.

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In remote procedure call (RPC)-based graphic processing unit (GPU) virtualization environments, GPU tasks requested by multiple-user virtual machines (VMs) are delivered to the VM owning the GPU and are processed in a multi-process form. However, because the thread executing the computing on general GPUs cannot arbitrarily stop the task or trigger context switching, GPU monopoly may be prolonged owing to a long-running general-purpose computing on graphics processing unit (GPGPU) task. Furthermore, when scheduling tasks on the GPU, the time for which each user VM uses the GPU is not considered

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31

Kouidri, Siham, and Belabbas Yagoubi. "Dynamic Data Replication Based on Tasks scheduling for Cloud Computing Environment." International Journal of Strategic Information Technology and Applications 8, no. 4 (2017): 40–51. http://dx.doi.org/10.4018/ijsita.2017100104.

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Cloud computing provides IT resources (e.g., CPU, memory, network, storage, etc.) based on virtualization concepts and a pay-as-you-go principle. It comprises an accumulation of inter-related plus virtualized calculating resources which are managed by one or more amalgamated calculating resources. With the development of a computerized scientific workflow, the amount of data is increasing exponentially. Workflow scheduling and data replication have been considered the major challenges in cloud computing. Nevertheless, many researchers focus on scheduling or data replication separately. In this

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32

Huang, Qiulan, Haibo Li, Yaodong Cheng, Jingyan Shi, Wei Zheng, and Qingbao Hu. "Evolution of the LHAASO Distributed Computing System based Cloud." EPJ Web of Conferences 245 (2020): 07043. http://dx.doi.org/10.1051/epjconf/202024507043.

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In this paper we will describe the LHAASO distributed computing system based on virtualization and cloud computing technologies. Particularly, we discuss the key points of integrating distributed resources. A solution of integrating cross-domain resources is proposed, which adopt the Openstack+HTCondor to make the distributed resources work as a whole resource pool. A flexible resource scheduling strategy and a job scheduling policy are presented to realize the resource expansion on demand and the efficient job scheduling to remote sites transparently, so as to improve the overall resource uti

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33

Strumberger, Ivana, Nebojsa Bacanin , Milan Tuba, and Eva Tuba. "Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm." Applied Sciences 9, no. 22 (2019): 4893. http://dx.doi.org/10.3390/app9224893.

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The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its

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34

Zhang, Youhui, Peng Qu, Jiang Cihang, and Weimin Zheng. "A Cloud Gaming System Based on User-Level Virtualization and Its Resource Scheduling." IEEE Transactions on Parallel and Distributed Systems 27, no. 5 (2016): 1239–52. http://dx.doi.org/10.1109/tpds.2015.2433916.

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35

Ranjan, Rohit, Ishan Singh Thakur, Gagangeet Singh Aujla, Neeraj Kumar, and Albert Y. Zomaya. "Energy-Efficient Workflow Scheduling Using Container-Based Virtualization in Software-Defined Data Centers." IEEE Transactions on Industrial Informatics 16, no. 12 (2020): 7646–57. http://dx.doi.org/10.1109/tii.2020.2985030.

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36

Zhang, Zhe, and Ying Li. "User Utility Oriented Queuing Model for Resource Allocation in Cloud Environment." Journal of Electrical and Computer Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/246420.

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Resource allocation is one of the most important research topics in servers. In the cloud environment, there are massive hardware resources of different kinds, and many kinds of services are usually run on virtual machines of the cloud server. In addition, cloud environment is commercialized, and economical factor should also be considered. In order to deal with commercialization and virtualization of cloud environment, we proposed a user utility oriented queuing model for task scheduling. Firstly, we modeled task scheduling in cloud environment as anM/M/1 queuing system. Secondly, we classifi

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37

Zhang, Yi, and Yi Min Su. "Research on Resource Scheduling Algorithm in Cloud Computing Data Center." Advanced Materials Research 926-930 (May 2014): 2050–53. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2050.

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In recent years, with the rapid development of Internet and virtualization technology, cloud computing, which providing users with on-demand services, has become a research hotspot. Under the environment of cloud computing, the datacenter, consisted by hardware and software, is a loosely coupled resource sharing architecture. The existing cloud computing's inadequacies are as following three aspects: 1. For lacking of real adequate and effective transaction of global bidirectional-way selection, the revenue of most of cloud resource provider is too low. 2. Since not fully considering the sched

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38

K. Jasim Mohammad, Omer. "GALO:A New Intelligent Task Scheduling Algorithm in Cloud Computing Environment." International Journal of Engineering & Technology 7, no. 4 (2018): 2088. http://dx.doi.org/10.14419/ijet.v7i4.16486.

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Cloud communication technology is Internet-based computing, where shared resources, software, information, are provided to computers and devices on-demand. They guarantee a way to share distributed resources and services that belong to different organizations through virtualization technology. Cloud has announced a modern idea by deploying one application which offers variety and a lot of services to a number of cloud-users at the same time, however, it suffers from scheduling and workload problems. This paper proposed a cloud computing task scheduling algorithm based on greedy algorithm and A

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39

Beckert, Matthias, and Rolf Ernst. "Response Time Analysis for Sporadic Server Based Budget Scheduling in Real Time Virtualization Environments." ACM Transactions on Embedded Computing Systems 16, no. 5s (2017): 1–19. http://dx.doi.org/10.1145/3126559.

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40

Kang, Yongho, Kimoon Keum, Seongjong Kim, Kwangyoun Jin, and Jooman Kim. "Real-Time Scheduling Method to assign Virtual CPU in the Multocore Mobile Virtualization System." Journal of Digital Convergence 12, no. 3 (2014): 227–35. http://dx.doi.org/10.14400/jdc.2014.12.3.227.

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41

Islam, Md Motaharul, and Eui-Nam Huh. "A Novel Data Classification and Scheduling Scheme in the Virtualization of Wireless Sensor Networks." International Journal of Distributed Sensor Networks 9, no. 7 (2013): 546708. http://dx.doi.org/10.1155/2013/546708.

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42

Cao, Haotong, Jianbo Du, Haitao Zhao, et al. "Resource-Ability Assisted Service Function Chain Embedding and Scheduling for 6G Networks With Virtualization." IEEE Transactions on Vehicular Technology 70, no. 4 (2021): 3846–59. http://dx.doi.org/10.1109/tvt.2021.3065967.

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43

Soltanshahi, Minoo, and Aliakbar Niknafs. "A Study on Factors Contributing to Efficiency of Scheduling Algorithms in a Cloud Computing Environment; Overview of Several Algorithms." Ciência e Natura 37 (December 19, 2015): 427. http://dx.doi.org/10.5902/2179460x20804.

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Cloud computing is the latest distributed technology providing a rich environment of dynamically shared resources through virtualization, which can fulfill the requirements of users by allocating resources to programs. Any program in a cloud environment is delivered by workflows which are a series of interlinked tasks to accomplish a goal. One of the most important tasks in cloud computing is correct mapping of tasks onto resources. It is essential to schedule processes in distributed systems such as cloud, since it leaves a tremendous impact on the system performance. This is done by scheduli

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44

Xuan, Hejun, Xuelin Zhao, Lei You, Zhenghui Liu, and Yanling Li. "Multiobjective Model and Improved Artificial Raindrop Algorithm for Virtual Network Mapping." Mobile Information Systems 2021 (March 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/5542670.

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Network virtualization (NV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of 5G communication. Resource scheduling in virtual network mapping is an important problem faced by NV technology. To enhance the performance of the network, optimal resource scheduling schemes should be determined. In general, maximum index of frequency slots used, energy consumption, and the ratio of used frequency slots are three very important indicators for the network. Based on the previous research studies, we first take these th

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45

Hong, Cheol-Ho, Kyungwoon Lee, Hyunchan Park, and Chuck Yoo. "ANCS: Achieving QoS through Dynamic Allocation of Network Resources in Virtualized Clouds." Scientific Programming 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4708195.

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To meet the various requirements of cloud computing users, research on guaranteeing Quality of Service (QoS) is gaining widespread attention in the field of cloud computing. However, as cloud computing platforms adopt virtualization as an enabling technology, it becomes challenging to distribute system resources to each user according to the diverse requirements. Although ample research has been conducted in order to meet QoS requirements, the proposed solutions lack simultaneous support for multiple policies, degrade the aggregated throughput of network resources, and incur CPU overhead. In t

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46

Kashyap, Rekha, and Deo Prakash Vidyarthi. "A Secured Real Time Scheduling Model for Cloud Hypervisor." International Journal of Cloud Applications and Computing 6, no. 4 (2016): 97–110. http://dx.doi.org/10.4018/ijcac.2016100106.

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Virtualization is critical to cloud computing and is possible through hypervisors, which maps the Virtual machines((VMs) to physical resources but poses security concerns as users relinquish physical possession of their computation and data. Good amount of research is initiated for resource provisioning on hypervisors, still many issues need to be addressed for security demanding and real time VMs. First work SRT-CreditScheduler (Secured and Real-time), maximizes the success rate by dynamically prioritizing the urgency and the workload of VMs but ensures highest security for all. Another work,

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47

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 (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 si

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48

R, Anitha, and C. Vidya Raj. "A Comprehensive Survey on SLA Compliant Energy Aware Resource Allocation in Cloud Datacenters." International Journal of Emerging Research in Management and Technology 6, no. 6 (2018): 335. http://dx.doi.org/10.23956/ijermt.v6i6.290.

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Cloud Computing has achieved immense popularity due to its unmatched benefits and characteristics. With its increasing popularity and round the clock demand, cloud based data centers often suffer with problems due to over-usage of resources or under-usage of capable servers that ultimately leads to wastage of energy and overall elevated cost of operation. Virtualization plays a key role in providing cost effective solution to service users. But on datacenters, load balancing and scheduling techniques remain inevitable to provide better Quality of Service to the service users and maintenance of

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49

M. Nandhini, J., and T. Gnanasekaran. "Review on failure forecast in cloud for a fault tolerant system." International Journal of Engineering & Technology 7, no. 3.3 (2018): 67. http://dx.doi.org/10.14419/ijet.v7i2.33.13857.

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Cloud Computing is an increasingly popular computer paradigm constituting a large infrastructure involving storage, memory, servers and applications accessible via computer network. The cloud system design aims to provide on-demand services with scalability on diverse resources to ensure efficient resource utilization in addition to effectiveness. As cloud is a service-oriented infrastructure, it is critically imperative that the system is highly reliable to meet the Service Level Agreement (SLA). To achieve reliability, cloud requires a very efficient fault tolerance mechanism. Serviceability

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50

Dad, Djouhra, and Ghalem Belalem. "Efficient Strategies of VMs Scheduling Based on Physicals Resources and Temperature Thresholds." International Journal of Cloud Applications and Computing 10, no. 3 (2020): 81–95. http://dx.doi.org/10.4018/ijcac.2020070105.

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Cloud computing offers a variety of services, including the dynamic availability of computing resources. Its infrastructure is designed to support the accessibility and availability of various consumer services via the Internet. The number of data centers allow the allocation of the applications, and the process of data in the cloud is increasing over time. This implies high energy consumption, thus contributing to large emissions of CO2 gas. For this reason, solutions are needed to minimize this power consumption, such as virtualization, migration, consolidation, and efficient traffic-aware v

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