Improving the energy efficiency of virtual data centers in an IT service provider through proactive fuzzy rules-based multicriteria decision making

A proactive multicriteria mechanism for virtual data center optimization through server consolidation is proposed. In contrast with previous works where heuristic mechanisms were designed using expert knowledge, the new proactive approach uses multiobjective evolutionary algorithms to learn fuzzy rule-based systems that determine optimal reallocation decisions according to the preferences of the data center operator and a prediction of the load. Experimental evaluations based on an actual IT service provider show that the proactive mechanism is capable of improving energy savings compared to commercial hypervisors while complying with service provider’s preferences and constraints.

[1]  Zoltán Ádám Mann.  Modeling the virtual machine allocation problem , 2015 .

[2]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[3]  Michael I. Jordan,et al.  Forward Models: Supervised Learning with a Distal Teacher , 1992, Cogn. Sci..

[4]  Albert Y. Zomaya,et al.  Author manuscript, published in "Journal of Parallel and Distributed Computing (2011)" A Parallel Bi-objective Hybrid Metaheuristic for Energy-aware Scheduling for Cloud Computing Systems , 2011 .

[5]  John J. Liptak,et al.  Administrator ’ s Guide , 1999 .

[6]  Yi Mei,et al.  A NSGA-II-based approach for service resource allocation in Cloud , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[7]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Barbara Panicucci,et al.  Energy-Aware Autonomic Resource Allocation in Multitier Virtualized Environments , 2012, IEEE Transactions on Services Computing.

[9]  Philip Robinson,et al.  Dynamic SLA management with forecasting using multi-objective optimization , 2013, 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013).

[10]  Feng Xia,et al.  A survey on virtual machine migration and server consolidation frameworks for cloud data centers , 2015, J. Netw. Comput. Appl..

[11]  Zoltán Ádám Mann,et al.  Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms , 2015, ACM Comput. Surv..

[12]  Ajay Gulati.  VMware distributed resource Management : design , Implementation , and lessons learned , 2022 .

[13]  Jing Zhang,et al.  The placement method of resources and applications based on request prediction in cloud data center , 2014, Inf. Sci..

[14]  Nagarajan Kandasamy,et al.  On the application of predictive control techniques for adaptive performance management of computing systems , 2009, IEEE Transactions on Network and Service Management.

[15]  Abbas Horri,et al.  Novel resource allocation algorithms to performance and energy efficiency in cloud computing , 2014, The Journal of Supercomputing.

[16]  Hannu Tenhunen,et al.  Utilization Prediction Aware VM Consolidation Approach for Green Cloud Computing , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[17]  Li Xu,et al.  Multi-objective Optimization Based Virtual Resource Allocation Strategy for Cloud Computing , 2012, 2012 IEEE/ACIS 11th International Conference on Computer and Information Science.

[18]  Prashant Dahiwale,et al.  An efficient dynamic resource allocation strategy for VM environment in cloud , 2015, 2015 International Conference on Pervasive Computing (ICPC).

[19]  Zhen Xiao,et al.  Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment , 2013, IEEE Transactions on Parallel and Distributed Systems.

[20]  Nagarajan Kandasamy,et al.  A control-based framework for self-managing distributed computing systems , 2004, WOSS '04.

[21]  Rajkumar Buyya,et al.  Multi-objective planning for workflow execution on Grids , 2007, 2007 8th IEEE/ACM International Conference on Grid Computing.

[22]  Djamal Zeghlache,et al.  Energy Efficient VM Scheduling for Cloud Data Centers: Exact Allocation and Migration Algorithms , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[23]  Pasi Liljeberg,et al.  LiRCUP: Linear Regression Based CPU Usage Prediction Algorithm for Live Migration of Virtual Machines in Data Centers , 2013, 2013 39th Euromicro Conference on Software Engineering and Advanced Applications.

[24]  Benjamín Barán,et al.  Multi-objective Virtual Machine Placement with Service Level Agreement: A Memetic Algorithm Approach , 2013, 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing.

[25]  Rajkumar Buyya,et al.  Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers , 2012, Concurr. Comput. Pract. Exp..

[26]  MannZoltán Ádám.  Allocation of Virtual Machines in Cloud Data CentersA Survey of Problem Models and Optimization Algorithms , 2015 .

[27]  Mohsen Guizani,et al.  Energy-Efficient Resource Allocation and Provisioning Framework for Cloud Data Centers , 2015, IEEE Transactions on Network and Service Management.

[28]  Jie Lu,et al.  A multi-objective optimization model for virtual machine mapping in cloud data centres , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[29]  Hisao Ishibuchi,et al.  Classification and modeling with linguistic information granules - advanced approaches to linguistic data mining , 2004, Advanced information processing.

[30]  Erhan Kozan,et al.  Profile-based application assignment for greener and more energy-efficient data centers , 2017, Future Gener. Comput. Syst..

[31]  T. Bittman,et al.  Magic Quadrant for x 86 Server Virtualization Infrastructure , 2010 .

[32]  Hai Jin,et al.  Lifetime or energy: Consolidating servers with reliability control in virtualized cloud datacenters , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.