Evaluation of Selection Policy with Various Virtual Machine Instances in Dynamic VM Consolidation for Energy Efficient at Cloud Data Centers

Various VM instances in Cloud Infrastructure provide flexibility for user to meet their computation requirements. However, this condition leads to the complex infrastructure that require numerous resources and consumes massive electricity due to the flexibility of VM instances. This paper concern in evaluate VM selection policy in Dynamic VM Consolidation. The study would evaluate our proposed method Constant Position Selection Policy (CPS) that compared with other VM Selection Policy such as Minimum Migration Time (MMT),Random Choice(RC) and Maximum Correlation (MC).Evaluation process of this study, measured the performance of Energy Consumption, SLAV, SLATAH, and PDMwith real workload trace data from PlanetLab VMs in various VM instances. Result the proposed method able to minimizing energy consumption of cloud data center in various VM instances with acceptable SLA

[1]  Loet Leydesdorff,et al.  Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks , 2011, J. Informetrics.

[2]  M. Yue.  A simple proof of the inequality FFD (L) ≤ 11/9 OPT (L) + 1, ∀L for the FFD bin-packing algorithm , 1991 .

[3]  Zhifei Zhang,et al.  PA-NEMO: Proxy Mobile IPv6-aided Network Mobility Management Scheme for 6LoWPAN , 2014 .

[4]  Terrill L. Frantz,et al.  Robustness of centrality measures under uncertainty: Examining the role of network topology , 2009, Comput. Math. Organ. Theory.

[5]  Huey-Ing Liu.  Mobile domain name system: an alternative for mobile IP , 2002, The 8th International Conference on Communication Systems, 2002. ICCS 2002..

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

[7]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[8]  Gerhard Weiss,et al.  Effects of Evolution on the Emergence of Scale Free Networks , 2014, ALIFE.

[9]  Christine Morin,et al.  A case for fully decentralized dynamic VM consolidation in clouds , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.

[10]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[11]  Jim Dowling,et al.  Usurp: distributed NAT traversal for overlay networks , 2011, DAIS'11.

[12]  Rolf Stadler,et al.  Gossip-based resource allocation for green computing in large clouds , 2011, 2011 7th International Conference on Network and Service Management.

[13]  Rolf Stadler,et al.  Gossip-based resource management for cloud environments , 2010, 2010 International Conference on Network and Service Management.

[14]  Jon M. Kleinberg,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World [Book Review] , 2013, IEEE Technol. Soc. Mag..

[15]  W. Cleveland,et al.  Smoothing by Local Regression: Principles and Methods , 1996 .

[16]  Hanan Lutfiyya,et al.  An analysis of first fit heuristics for the virtual machine relocation problem , 2012, 2012 8th international conference on network and service management (cnsm) and 2012 workshop on systems virtualiztion management (svm).

[17]  Ernesto Estrada,et al.  The Structure of Complex Networks: Theory and Applications , 2011 .

[18]  Peter L. Brooks,et al.  Visualizing data , 1997 .

[19]  Gade Krishna,et al.  A scalable peer-to-peer lookup protocol for Internet applications , 2012 .

[20]  Ahmad Ashari,et al.  Efficiency Energy Consumption in Cloud Computing Based on Constant Position Selection Policy in Dynamic Virtual Machine Consolidation , 2014 .

[21]  Paul Vixie,et al.  What DNS is not , 2009, Commun. ACM.

[22]  G. B. A. Barab'asi.  Competition and multiscaling in evolving networks , 2000, cond-mat/0011029.

[23]  Stefan Bornholdt,et al.  Handbook of Graphs and Networks: From the Genome to the Internet , 2003 .

[24]  Daniel Ortiz-Arroyo,et al.  Centrality Robustness and Link Prediction in Complex Social Networks , 2012 .

[25]  Arkady B. Zaslavsky,et al.  Context Aware Computing for The Internet of Things: A Survey , 2013, IEEE Communications Surveys & Tutorials.

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

[27]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[28]  Artemis Moroni,et al.  Vision and Challenges for Realising the Internet of Things , 2010 .

[29]  Sasu Tarkoma,et al.  The internet of things program: the finnish perspective , 2013, IEEE Commun. Mag..

[30]  Rajkumar Buyya,et al.  Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges , 2010, PDPTA.

[31]  Gargi Dasgupta,et al.  Server Workload Analysis for Power Minimization using Consolidation , 2009, USENIX Annual Technical Conference.

[32]  S. Hill,et al.  Dynamic model of time-dependent complex networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  M. Nijsse.  Multiple correlation coefficient. , 1991, Biometrics.

[34]  Jamie Walters,et al.  MediaSense - an Internet of Things Platform for Scalable and Decentralized Context Sharing and Control , 2012, ICDT 2012.

[35]  Roozbeh Farahbod,et al.  Dynamic Resource Allocation in Computing Clouds Using Distributed Multiple Criteria Decision Analysis , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

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

[37]  Karsten Schwan,et al.  VirtualPower: coordinated power management in virtualized enterprise systems , 2007, SOSP.

[38]  Charles E. Perkins,et al.  Mobility support in IPv6 , 1996, MobiCom '96.

[39]  Richard E. Brown,et al.  Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431 , 2008 .

[40]  Basavaraj Patil,et al.  Proxy Mobile IPv6 , 2008, RFC.

[41]  Pekka Nikander,et al.  Hi3: An efficient and secure networking architecture for mobile hosts , 2008, Comput. Commun..

[42]  Katina Michael,et al.  Connected: To Everyone and Everything [Guest Editorial: Special Section on Sensors] , 2013, IEEE Technol. Soc. Mag..

[43]  Flavien Quesnel,et al.  Cooperative and reactive scheduling in large‐scale virtualized platforms with DVMS , 2013, Concurr. Comput. Pract. Exp..

[44]  G. Terrell.  Statistical theory and computational aspects of smoothing , 1997 .

[45]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[46]  Leonard Barolli,et al.  A Survey of Internet Mobility , 2009, 2009 International Conference on Network-Based Information Systems.

[47]  Andrei V. Gurtov,et al.  On scalability properties of the Hi3 control plane , 2006, Comput. Commun..

[48]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.

[49]  Saurabh Bagchi,et al.  Distributed mobility management for efficient video delivery over all-IP mobile networks: Competing approaches , 2013, IEEE Network.

[50]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[51]  M. Yue,et al.  A simple proof of the inequality MFFD(L)≤71/60 OPT(L) + 1,L for the MFFD bin-packing algorithm , 1991 .

[52]  Vasos Vassiliou,et al.  Inter-mobility support in controlled 6LoWPAN networks , 2010, 2010 IEEE Globecom Workshops.

[53]  Dror G. Feitelson,et al.  Workload Modeling for Computer Systems Performance Evaluation , 2015 .

[54]  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..

[55]  Imrich Chlamtac,et al.  Internet of things: Vision, applications and research challenges , 2012, Ad Hoc Networks.

[56]  Michael D. König,et al.  Network Evolution Based on Centrality , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[57]  Yuhua Liu,et al.  Research on Betweenness: The Model of Scale-Free Networks , 2009, 2009 Fourth International Conference on Internet Computing for Science and Engineering.

[58]  Massimo Riccaboni,et al.  Betweenness centrality of fractal and nonfractal scale-free model networks and tests on real networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[59]  Chavdar Dangalchev,et al.  Generation models for scale-free networks , 2004 .

[60]  M. A. Muñoz,et al.  Scale-free networks from varying vertex intrinsic fitness. , 2002, Physical review letters.

[61]  Mika Ratola.  Which Layer for Mobility ?-Comparing Mobile IPv 6 , HIP and SCTP , 2004 .

[62]  Thomas W. Valente,et al.  The stability of centrality measures when networks are sampled , 2003, Soc. Networks.

[63]  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..

[64]  Armin Dekorsy,et al.  M2M massive wireless access: Challenges, research issues, and ways forward , 2013, 2013 IEEE Globecom Workshops (GC Wkshps).

[65]  Rajkumar Buyya,et al.  Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers , 2010, MGC '10.

[66]  S. N. Dorogovtsev,et al.  Structure of growing networks with preferential linking. , 2000, Physical review letters.

[67]  Guido Caldarelli,et al.  Scale-Free Networks , 2007 .

[68]  Cohen,et al.  Resilience of the internet to random breakdowns , 2000, Physical review letters.

[69]  Akshat Verma,et al.  End-to-end disaster recovery planning: From art to science , 2010, 2010 IEEE Network Operations and Management Symposium - NOMS 2010.

[70]  Jerome A. Rolia,et al.  Resource pool management: Reactive versus proactive or let's be friends , 2009, Comput. Networks.

[71]  Martin G. Everett,et al.  A Graph-theoretic perspective on centrality , 2006, Soc. Networks.

[72]  S. Havlin,et al.  Breakdown of the internet under intentional attack. , 2000, Physical review letters.

[73]  Hui Wang,et al.  Multi-Tiered On-Demand Resource Scheduling for VM-Based Data Center , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[74]  J. Bolland,et al.  Sorting out centrality: An analysis of the performance of four centrality models in real and simulated networks , 1988 .

[75]  Reka Albert,et al.  Mean-field theory for scale-free random networks , 1999 .