Virtual machine placement based on degradation factor ant colony algorithm

Rational use of physical resources is the most important challenge in cloud computing. To accomplish this challenge, the system must take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a traveling salesman problem (TSP) and propose to apply degradation factor ant colony algorithm technique for optimal placing of virtual machines in the physical servers. The value of degradation factor has been calculated by resource waste model. Experimental results on Cloudsim simulation platform demonstrate that the proposed approach can achieve superior performance than the standard ant colony algorithm.

[1]  Jianhua Gu,et al.  A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment , 2010, 2010 3rd International Symposium on Parallel Architectures, Algorithms and Programming.

[2]  Jing Xu,et al.  Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[3]  Renu Saini.  A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement , 2017 .

[4]  Medhat A. Tawfeek,et al.  Virtual Machine Placement Based on Ant Colony Optimization for Minimizing Resource Wastage , 2014, AMLTA.

[5]  Cheng-Ming Zou,et al.  A Task Scheduling Algorithm Based on Genetic Algorithm and Ant Colony Optimization in Cloud Computing , 2014, 2014 13th International Symposium on Distributed Computing and Applications to Business, Engineering and Science.

[6]  Rajkumar Buyya,et al.  Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic , 2014, Euro-Par.

[7]  Xin Lu,et al.  A load-adapative cloud resource scheduling model based on ant colony algorithm , 2011, 2011 IEEE International Conference on Cloud Computing and Intelligence Systems.

[8]  Takahiro Hara,et al.  A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing , 2015, IEEE Access.

[9]  Zhi-hui Zhan,et al.  Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach , 2014, GECCO.

[10]  Liang Liu,et al.  A multi-objective ant colony system algorithm for virtual machine placement in cloud computing , 2013, J. Comput. Syst. Sci..

[11]  Yuansheng Lou,et al.  A Task Scheduling Algorithm Based on Genetic Algorithm and Ant Colony Optimization Algorithm with Multi-QoS Constraints in Cloud Computing , 2015, 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[12]  Sun Lei.  Research on Extended Ant Colony Optimization Based Virtual Machine Deployment in Infrastructure Clouds , 2012 .

[13]  Xiao-Dong Fu,et al.  A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform , 2014, TheScientificWorldJournal.