Virtual machine placement method for energy saving in cloud computing

2015 ◽  
Author(s):  
Pragan Wattanasomboon ◽  
Yuthapong Somchit
Keyword(s):  
Cloud Computing ◽  
Energy Saving ◽  
Virtual Machine ◽  
Virtual Machine Placement ◽  
Placement Method

scholarly journals Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Shanchen Pang ◽  
Kexiang Xu ◽  
Shudong Wang ◽  
Min Wang ◽  
Shuyu Wang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Virtual Machine ◽  
User Experience ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Local Optimum ◽  
Placement Method

Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers. Improving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption, but the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level agreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in unknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the improved ant colony algorithm to reduce the energy consumption and satisfy users’ experience, which achieves the balance between energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the traditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter the global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional ant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%, and 30% of energy consumption while satisfying user experience.

Chemical reaction optimization for virtual machine placement in cloud computing

2018 ◽  
Vol 49 (1) ◽  
pp. 220-232 ◽  
Author(s):  
Zhiyong Li ◽  
Yang Li ◽  
Tingkun Yuan ◽  
Shaomiao Chen ◽  
Shilong Jiang
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Chemical Reaction ◽  
Virtual Machine Placement ◽  
Chemical Reaction Optimization ◽  
Reaction Optimization

Location and Provisioning Problems in Cloud Computing Networks

2013 ◽  
pp. 22-45 ◽  
Author(s):  
Federico Larumbe ◽  
Brunilde Sansò
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Optimization Problems ◽  
Virtual Machine Placement ◽  
Future Research ◽  
Network Efficiency ◽  
Placement Problem ◽  
The Cost

This chapter addresses a set of optimization problems that arise in cloud computing regarding the location and resource allocation of the cloud computing entities: the data centers, servers, software components, and virtual machines. The first problem is the location of new data centers and the selection of current ones since those decisions have a major impact on the network efficiency, energy consumption, Capital Expenditures (CAPEX), Operational Expenditures (OPEX), and pollution. The chapter also addresses the Virtual Machine Placement Problem: which server should host which virtual machine. The number of servers used, the cost, and energy consumption depend strongly on those decisions. Network traffic between VMs and users, and between VMs themselves, is also an important factor in the Virtual Machine Placement Problem. The third problem presented in this chapter is the dynamic provisioning of VMs to clusters, or auto scaling, to minimize the cost and energy consumption while satisfying the Service Level Agreements (SLAs). This important feature of cloud computing requires predictive models that precisely anticipate workload dimensions. For each problem, the authors describe and analyze models that have been proposed in the literature and in the industry, explain advantages and disadvantages, and present challenging future research directions.

Virtual Machine Placement for Edge and Cloud Computing

2021 ◽  
pp. 53-64
Author(s):  
Behdad Partovi ◽  
Alireza Bagheri ◽  
Maryam Haddad Kazarji ◽  
Claus Pahl ◽  
Hamid R. Barzegar
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machine Placement
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