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.

scholarly journals Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2724 ◽  
Author(s):  
Yuan ◽  
Sun
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machines ◽  
Ant Colony ◽  
Virtual Machine Migration ◽  
Server Consolidation ◽  
Cloud Data ◽  
Cloud Data Centers

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.

scholarly journals A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement in Traffic Intense Data Centers

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 58912-58923 ◽  
Author(s):  
Yao Qin ◽  
Hua Wang ◽  
Fangjin Zhu ◽  
Linbo Zhai
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Ant Colony System ◽  
Multi Objective

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.

Study of Complex Network Optimization in Wireless Sensor Networks

2011 ◽  
Vol 55-57 ◽  
pp. 1305-1309
Author(s):  
Zheng Yao ◽  
Zhao Hua Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Complex Network ◽  
Ant Colony Algorithm ◽  
Transition Probability ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Local Optimum ◽  
Network Systems

Energy consumption is a critical problem in operation of wireless sensor networks. For the sake of avoiding the data abundance and balancing the energy consumption in wireless sensor networks, this paper makes a research on network nodes Optimization in wireless sensor network based on ant colony algorithm and WIA-PA protocol stack. The novel design improved on hardware and software to control consumption of the energy and used transition probability of ant colony algorithm from one node to the other to calculate and determine the optimal path of network node in traversal of these locations. The results of the examples show that this method has lower energy consumption, computational briefness and higher positioning accuracy; it can not easily run into the local optimum, and also be applied to other tracking of complex network systems.

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