An Adaptive Virtual Machine Location Selection Mechanism in Distributed Cloud

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

[1]  Priyanka Sharma,et al.  Survey of virtual machine placement in federated clouds , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[2]  Maoguo Gong,et al.  Clone Selection Algorithm to Solve Preference Multi-Objective Optimization: Clone Selection Algorithm to Solve Preference Multi-Objective Optimization , 2010 .

[3]  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.

[4]  David Breitgand,et al.  Improving consolidation of virtual machines with risk-aware bandwidth oversubscription in compute clouds , 2012, 2012 Proceedings IEEE INFOCOM.

[5]  Wang Xingwei.  Optimizing Multi-Dimensional QoS Cloud Resource Scheduling by Immune Clonal with Preference , 2011 .

[6]  Odej Kao,et al.  Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud , 2011, IEEE Transactions on Parallel and Distributed Systems.

[7]  Ai Hao-ju.  Research of Cloud Computing Virtual Machine Allocated Strategy on Multi-objective Evolutionary Algorithm , 2014 .

[8]  Jianhua Gu,et al.  A New Resource Scheduling Strategy Based on Genetic Algorithm in Cloud Computing Environment , 2012, J. Comput..

[9]  Yuan Aipin.  Virtual machine deployment strategy based on improved genetic algorithm in cloud computing environment , 2014 .

[10]  Wang Shuo.  Resources Scheduling Strategy Based on Ant Colony Optimization Algorithms in Cloud Computing , 2011 .

[11]  Chuang Lin,et al.  Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction , 2011, J. Netw. Comput. Appl..

[12]  Li Qiang,et al.  Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing: Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing , 2012 .

[13]  B Xu.  Virtual Machine Resource Scheduling Multi-objective Optimization in Cloud Computing , 2014 .

[14]  Henri Casanova,et al.  Resource allocation algorithms for virtualized service hosting platforms , 2010, J. Parallel Distributed Comput..

[15]  Nicola Beume,et al.  SMS-EMOA: Multiobjective selection based on dominated hypervolume , 2007, Eur. J. Oper. Res..

[16]  Gregg Hamilton,et al.  Distributed virtual machine migration for cloud data centre environments , 2014 .

[17]  Nicola Beume,et al.  An EMO Algorithm Using the Hypervolume Measure as Selection Criterion , 2005, EMO.

[18]  T. V. Lakshman,et al.  Online Allocation of Virtual Machines in a Distributed Cloud , 2017, IEEE/ACM Transactions on Networking.

[19]  R. Srikant,et al.  Stochastic models of load balancing and scheduling in cloud computing clusters , 2012, 2012 Proceedings IEEE INFOCOM.

[20]  T. V. Lakshman,et al.  Optimizing data access latencies in cloud systems by intelligent virtual machine placement , 2013, 2013 Proceedings IEEE INFOCOM.

[21]  Shailesh Sawant,et al.  A Genetic Algorithm Scheduling Approach for Virtual Machine Resources in a Cloud Computing Environment , 2011 .

[22]  Masaki Samejima,et al.  Dynamic optimization of virtual machine placement by resource usage prediction , 2013, 2013 11th IEEE International Conference on Industrial Informatics (INDIN).

[23]  Ender Özcan,et al.  A Grouping Genetic Algorithm Using Linear Linkage Encoding for Bin Packing , 2008, PPSN.

[24]  Minghua Chen,et al.  Joint VM placement and routing for data center traffic engineering , 2012, 2012 Proceedings IEEE INFOCOM.

[25]  Guofei Jiang,et al.  Effective VM sizing in virtualized data centers , 2011, 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops.