Energy, network, and application-aware virtual machine placement model in SDN-enabled large scale cloud data centers

Rawas, Soha

doi:10.1007/s11042-021-10616-6

Cited by 24 publications

(15 citation statements)

References 19 publications

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“…Therefore, how to reduce energy consumption while ensuring service quality is a problem to be solved. In a cloud computing environment, the optimal VM resource distribution scheme can effectively save energy by properly deploying VM resources on physical nodes [ 15 , 16 ]. Virtual machine deployment strategy is mainly considering the distribution and use of computing resources in data center, so as to select the best distribution scheme, according to the distribution plan to make sure the virtual machine and the mapping relations between physical nodes.…”

Section: Related Workmentioning

confidence: 99%

Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm

Shi

Lin

2022

Computational Intelligence and Neuroscience

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Cloud computing is an important milestone in the development of distributed computing as a commercial implementation, and it has good prospects. Infrastructure as a service (IaaS) is an important service mode in cloud computing. It combines massive resources scattered in different spaces into a unified resource pool by means of virtualization technology, facilitating the unified management and use of resources. In IaaS mode, all resources are provided in the form of virtual machines (VM). To achieve efficient resource utilization, reduce users’ costs, and save users’ computing time, VM allocation must be optimized. This paper proposes a new multiobjective optimization method of dynamic resource allocation for multivirtual machine distribution stability. Combining the current state and future predicted data of each application load, the cost of virtual machine relocation and the stability of new virtual machine placement state are considered comprehensively. A multiobjective optimization genetic algorithm (MOGANS) was designed to solve the problem. The simulation results show that compared with the genetic algorithm (GA-NN) for energy saving and multivirtual machine redistribution overhead, the virtual machine distribution method obtained by MOGANS has a longer stability time. Aiming at this shortage, this paper proposes a multiobjective optimization dynamic resource allocation method (MOGA-C) based on MOEA/D for virtual machine distribution. It is illustrated by experimental simulation that moGA-D can converge faster and obtain similar multiobjective optimization results at the same calculation scale.

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Section: Related Workmentioning

confidence: 99%

Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm

Shi

Lin

2022

Computational Intelligence and Neuroscience

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“…According to the given taxonomy, the objective of our research can be defined as improving performance in heterogeneously configured inter-Cloud data center architecture that will focus on joint optimization of resources for batch processing. Further, an adaptive Genetic Algorithm for resource allocation and VM placement in SDN-based Cloud data centers was proposed in [44]. The proposed model considered energy, VMs, and intra-data center communication costs.…”

Section: Related Workmentioning

confidence: 99%

Scalable Management of Heterogeneous Cloud Resources Based on Evolution Strategies Algorithm

Lončar

Loncar

2022

IEEE Access

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The emergence and rapid development of Cloud computing have intensively changed the Information Technology paradigm. Cloud computing found its wide application in science and engineering. Cloud-based systems provide new solutions for scientific experiments and access to data distributed across data centers. Optimization of Cloud resource management is becoming a significant area of research. This research focuses on scheduling tasks and processing scientific data in a heterogeneous Cloud environment where most jobs require a large number of resources and computing power. Our approach proposes a strategy for optimizing task scheduling across different virtual machines and data centers based on the metaheuristic Evolution Strategies algorithm. The Evolution Strategies algorithm was tested, which has not been used in this domain. As an essential property of the system, scalability has played an important role in selecting the algorithm. We created a model and added a Longest Job First broker policy. Compared to the standard Genetic Algorithm, our approach has shown improvements in measured metrics. After testing under different loads, the proposed strategy gave promising results and achieved a better makespan, larger average resource utilization, better throughput, less average execution time, a smaller degree of imbalance, and scalability.

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“…D.c. Hardy, a senior economist of the International Monetary Fund, believes that financial risk indicators are composed of the index system of the banking sector and the index system of the macro-economy sector. e authors of [17] constructed a financial risk early-warning indicator system mainly from 34 indicators reflecting the real economy indicators, such as operating soundness indicators. Paper [18] considered that the financial risk index system suitable for China is constructed from the following five aspects: general operation of national economy, finance, finance, foreign economy, and bubble economy.…”

Section: Related Workmentioning

confidence: 99%

Research on Risk Prediction Model of Internet Finance Based on Cloud Computing

Liu

2022

Journal of Mathematics

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With the rapid development of the Internet, the traditional Internet financial risk prediction methods can no longer meet the needs of individuals and enterprises, so the concept of cloud computing arises at the historic moment. Cloud computing has subverted the traditional financial risk prediction method and has been widely studied and applied for its distributed, dynamic and autonomous characteristics. How to efficiently and reasonably schedule the resources of cloud data center and improve the accuracy of financial risk prediction is the focus of current research. How to quantify financial risk and financial risk early warning is one of the urgent problems to be solved. Under the framework of cloud computing, this paper combines the feature extraction and data weighting to study the user’s basic attribute data and a large number of downloaded APP types. After that, linear regression with penalty is used to construct the prediction model to improve insolvency. The accuracy of customer default judgment can realize local optimization, so as to improve the prediction and control of hidden risks of customer commercial bank loans and greatly reduce the default risk of bank loans.

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Energy, network, and application-aware virtual machine placement model in SDN-enabled large scale cloud data centers

Cited by 24 publications

References 19 publications

Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm

Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm

Scalable Management of Heterogeneous Cloud Resources Based on Evolution Strategies Algorithm

Research on Risk Prediction Model of Internet Finance Based on Cloud Computing

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