scholarly journals Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing

2020 ◽  
Vol 13 (1) ◽  
pp. 496 ◽  
Author(s):  
Boonhatai Kruekaew ◽  
Warangkhana Kimpan
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Machine Scheduling ◽  
Bee Colony ◽  
Virtual Machine Scheduling

High-efficient energy saving processing of big data of communication under mobile cloud computing

2019 ◽  
Vol 10 (04) ◽  
pp. 1950025 ◽  
Author(s):  
Yazhen Liu ◽  
Pengfei Fan ◽  
Jiyang Zhu ◽  
Liping Wen ◽  
Xiongfei Fan
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Migration Strategy ◽  
Bee Colony

From 21st century, it is hard for traditional storage and algorithm to provide service with high quality because of big data of communication which grows rapidly. Thus, cloud computing technology with relatively low cost of hardware facilities is created. However, to guarantee the quality of service in the situation of the rapid growth of data volume, the energy consumption cost of cloud computing begins to exceed the hardware cost. In order to solve the problems mentioned above, this study briefly introduced the virtual machine and its energy consumption model in the mobile cloud environment, introduced the basic principle of the virtual machine migration strategy based on the artificial bee colony algorithm and then simulated the performance of processing strategy to big data of communication based on artificial bee colony algorithm in mobile cloud computing environment by CloudSim3.0 software, which was compared with the performance of two algorithms, resource management (RM) and genetic algorithm (GA). The results showed that the power consumption of the migration strategy based on the artificial bee colony algorithm was lower than the other two strategies, and there were fewer failed virtual machines under the same number of requests, which meant that the service quality was higher.

Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing

2014 ◽  
Vol 1046 ◽  
pp. 508-511
Author(s):  
Jian Rong Zhu ◽  
Yi Zhuang ◽  
Jing Li ◽  
Wei Zhu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Machine Scheduling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Virtual Machine Scheduling ◽  
Set Up

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.

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