Bayesian network-based Virtual Machines consolidation method

Efficient Virtual Machines (VMs) consolidation, as one of the primary methods for balancing between guaranteeing Quality of Service (QoS) and saving energy, is critical for data centers. Most existing VMs consolidation methods reallocate physical resources by adopting live VM migration. Therefore, VMs consolidation can be cast into estimating the physical resource utilization in Physical Machines (PMs) and predicting the migration probability of VMs. In this paper, we develop a Bayesian network-based estimation model (BNEM) for live VM migration, allowing a comprehensive treatment of nine actual factors in real data centers. A selection criterion of VMs to be migrated and a VM placement criterion are presented. By combining three algorithms corresponding to different phases in VMs consolidation, a hybrid Bayesian network-based VMs consolidation (BN-VMC) method is proposed. We have validated our approach by conducting a performance evaluation study using CloudSim toolkit, and the trace-driven comparison experiments are also performed. The simulation results show that the method can significantly degrade energy consumption, avoid inefficient VM migrations, and improve QoS.

[1]  Sadok Bouamama,et al.  Solving bin Packing Problem with a Hybrid Genetic Algorithm for VM Placement in Cloud , 2015, KES.

[2]  Helmut Hlavacs,et al.  A Cooperative Multi Agent Learning Approach to Manage Physical Host Nodes for Dynamic Consolidation of Virtual Machines , 2015, 2015 IEEE Fourth Symposium on Network Cloud Computing and Applications (NCCA).

[3]  Hannu Tenhunen,et al.  Using Ant Colony System to Consolidate VMs for Green Cloud Computing , 2015, IEEE Transactions on Services Computing.

[4]  Meng Wang,et al.  Consolidating virtual machines with dynamic bandwidth demand in data centers , 2011, 2011 Proceedings IEEE INFOCOM.

[5]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[6]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[7]  Helmut Hlavacs,et al.  Dynamic Virtual Machine Consolidation: A Multi Agent Learning Approach , 2015, 2015 IEEE International Conference on Autonomic Computing.

[8]  Christine Morin,et al.  Energy-Aware Ant Colony Based Workload Placement in Clouds , 2011, 2011 IEEE/ACM 12th International Conference on Grid Computing.

[9]  Pasi Liljeberg,et al.  Energy Aware Consolidation Algorithm Based on K-Nearest Neighbor Regression for Cloud Data Centers , 2013, 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing.

[10]  Quanwang Wu,et al.  Heterogeneous Virtual Machine Consolidation Using an Improved Grouping Genetic Algorithm , 2015, 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems.

[11]  Irene Moser,et al.  The Effects of Hotspot Detection and Virtual Machine Migration Policies on Energy Consumption and Service Levels in the Cloud , 2015, ICCS.

[12]  Nagarajan Kandasamy,et al.  Power and performance management of virtualized computing environments via lookahead control , 2008, 2008 International Conference on Autonomic Computing.

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

[14]  Lin Wang,et al.  Power-efficient assignment of virtual machines to physical machines , 2016, Future Gener. Comput. Syst..

[15]  Qinghua Zheng,et al.  Multi-objective Optimization Algorithm Based on BBO for Virtual Machine Consolidation Problem , 2015, 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS).

[16]  Helmut Hlavacs,et al.  An Intelligent and Adaptive Threshold-Based Schema for Energy and Performance Efficient Dynamic VM Consolidation , 2013, EE-LSDS.

[17]  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..

[18]  Evgenia Smirni,et al.  Data Centers in the Cloud: A Large Scale Performance Study , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[19]  Guofeng Zhu,et al.  Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing , 2015, Computing.

[20]  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.

[21]  Zoltán Ádám Mann.  Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center , 2015, Future Gener. Comput. Syst..

[22]  Anil Kumar Singh,et al.  Use of proactive and reactive hotspot detection technique to reduce the number of virtual machine migration and energy consumption in cloud data center , 2015, Comput. Electr. Eng..

[23]  Jie Xu,et al.  Analysis, Modeling and Simulation of Workload Patterns in a Large-Scale Utility Cloud , 2014, IEEE Transactions on Cloud Computing.

[24]  Hannes Hartenstein,et al.  Confidential database-as-a-service approaches: taxonomy and survey , 2014, Journal of Cloud Computing.

[25]  Rajkumar Buyya,et al.  Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation , 2009, CloudCom.

[26]  Guangjie Han,et al.  An Efficient Virtual Machine Consolidation Scheme for Multimedia Cloud Computing , 2016, Sensors.

[27]  Sarabjit Kaur,et al.  Cuckoo search approach for virtual machine consolidation in cloud data centre , 2015, International Conference on Computing, Communication & Automation.

[28]  KyoungSoo Park,et al.  CoMon: a mostly-scalable monitoring system for PlanetLab , 2006, OPSR.

[29]  Rashedur M. Rahman,et al.  Implementation and performance analysis of various VM placement strategies in CloudSim , 2015, Journal of Cloud Computing.

[30]  Feng Zhao,et al.  Energy aware consolidation for cloud computing , 2008, CLUSTER 2008.

[31]  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.

[32]  Alexandru Iosup,et al.  Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters , 2015, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[33]  Mor Harchol-Balter,et al.  Optimal power allocation in server farms , 2009, SIGMETRICS '09.

[34]  Samuel P. Midkiff,et al.  Workload-Driven VM Consolidation in Cloud Data Centers , 2015, 2015 IEEE International Parallel and Distributed Processing Symposium.

[35]  Haiying Shen,et al.  Distributed Autonomous Virtual Resource Management in Datacenters Using Finite-Markov Decision Process , 2014, IEEE/ACM Transactions on Networking.

[36]  Akshat Verma,et al.  pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems , 2008, Middleware.

[37]  Anirudha Sahoo,et al.  On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[38]  Antti Ylä-Jääski,et al.  Virtual Machine Consolidation with Usage Prediction for Energy-Efficient Cloud Data Centers , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[39]  Norman E. Fenton,et al.  Making decisions: using Bayesian nets and MCDA , 2001, Knowl. Based Syst..