Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing

Xiong, Fei; Zhao, Qiuyan; Wang, Junchang; Zhang, Lin; Qiao, Lei

doi:10.1155/2018/9471356

Cited by 12 publications

(11 citation statements)

References 15 publications

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“…3 Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt. 4 University of Bisha, Bisha, Kingdom of Saudi Arabia. 5 Department of Computer, Damietta University, Damietta, Egypt.…”

Section: Resultsmentioning

confidence: 99%

“…In this experiment, we investigate the influence of different parameters on the performance of the MOSSASCA. Table 5 illustrates the results of the proposed MOSSASCA based on different values of a = [1,4] , N S = [50,200], and g max = [50,200]. The proposed method at a = 1.5 is better than that at a = 0.5 , indicating that the performance of the MOSSASCA increases with increasing a.…”

Section: Influence the Parameters Of Proposed Methodsmentioning

confidence: 99%

“…Therefore, if energy consumption is reduced, the operating costs of datacenters will be reduced. An effective technique to achieve this goal is to apply and optimize the VM placement (VMP) process [4].…”

Section: Introductionmentioning

confidence: 99%

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Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Alresheedi

Elaziz

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOS-SASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the

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Section: Resultsmentioning

confidence: 99%

Section: Influence the Parameters Of Proposed Methodsmentioning

confidence: 99%

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Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Alresheedi

Elaziz

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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“…For mining data value, CC is very efficient, valuable, and inexpensive. It creates a significant effect on social and economic development [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Samriya

Patel

Khurana

et al. 2021

Mathematical Problems in Engineering

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Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service.

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“…High energy consumption causes an increase in cost and resource utilization of data centers. So the virtual machine placement (VMP) process is used to reduce energy consumption 3 . VMP allocates each VM to the appropriate physical machine (PM).…”

Section: Introductionmentioning

confidence: 99%

Hybrid swarm optimization algorithm based on task scheduling in a cloud environment

Eldesokey

El-atty

El‐Shafai

et al. 2021

Int J Communication

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Summary Cloud computing is the current computing standard, which provides information technology (IT) services over the Internet on demand. In the cloud environment, a task is mapped with an available resource to attain a good result. Task scheduling is the technique that is used to allocate tasks on virtual machines (VMs) of a server based on its capacity of workload. Tasks are scheduled to the server in such a way to minimize traffic and time delay. Particle swarm optimization (PSO) is the best existing algorithm used to schedule a task to an existing resource on the environment of the cloud. By PSO, the task is scheduled for an existing resource to reduce computational cost. In this paper, a hybrid swarm optimization (HSO) algorithm, which is the combination of PSO and salp swarm optimization (SSO), is proposed to resolve task scheduling issues in the cloud environment. The main goal of HSO is to schedule the task to the available resource in such a way to reduce the execution time and computation cost. Multilayer logistic regression (MLR) is an approach used to detect the overloaded VMs, so that a task can be scheduled to a VM according to its capacity of workload. The proposed HSO algorithm with MLR is simulated on the cloudsim toolkit, and the results reveal the efficiency of the proposed algorithm in terms of cost, execution time, and makespan. Compared to the existing algorithms such as the genetic algorithms (GAs), the improved efficiency evolution (IDEA), and the PSO, the proposed algorithm reveals superiority in terms of efficiency, resource utilization, and speed.

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Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing

Cited by 12 publications

References 15 publications

Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Hybrid swarm optimization algorithm based on task scheduling in a cloud environment

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