Adaptive Virtual Machine Provisioning in Elastic Multi-tier Cloud Platforms

Virtual machines are allocated on demand in virtualized cloud platforms to provide flexible and reliable services. The major difficulty lies in satisfying the conflicting objectives of reducing response time while lowering resource costs. In this paper, a mathematical multi-tier framework for virtual machine allocation is proposed, which can be used to capture the performance of the cloud platform. We first use simulations to derive virtual resource allocation policies, and later use real benchmarking applications to verify the effectiveness of this framework. Experimental results show that the model can be simply and effectively used to satisfy the response time requirement as well as lowering the cost of using the virtual machine resources.

[1]  Prashant Shenoy,et al.  Cataclysm: Handling Extreme Overloads in Internet Services , 2004, PODC 2004.

[2]  S. Wittevrongel,et al.  Queueing Systems , 2019, Introduction to Stochastic Processes and Simulation.

[3]  G. J. A. Stern,et al.  Queueing Systems, Volume 2: Computer Applications , 1976 .

[4]  Lakshmi Sobhana Kalli,et al.  Market-Oriented Cloud Computing : Vision , Hype , and Reality for Delivering IT Services as Computing , 2013 .

[5]  Dan Rubenstein,et al.  Provisioning servers in the application tier for e-commerce systems , 2004, Twelfth IEEE International Workshop on Quality of Service, 2004. IWQOS 2004..

[6]  Asser N. Tantawi,et al.  Performance management for cluster-based web services , 2005, IEEE Journal on Selected Areas in Communications.

[7]  Martin Arlitt,et al.  A workload characterization study of the 1998 World Cup Web site , 2000, IEEE Netw..

[8]  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..

[9]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[10]  Kai Hwang,et al.  Adaptive Workload Prediction of Grid Performance in Confidence Windows , 2010, IEEE Transactions on Parallel and Distributed Systems.

[11]  Louis P. Slothouber,et al.  A Model of Web Server Performance , 1996 .

[12]  Martin Arlitt,et al.  Workload Characterization of the 1998 World Cup Web Site , 1999 .

[13]  Daniel A. Menascé.  Web Server Software Architectures , 2003, IEEE Internet Comput..

[14]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[15]  Jeffrey S. Chase,et al.  Energy management for server clusters , 2001, Proceedings Eighth Workshop on Hot Topics in Operating Systems.

[16]  Jeffrey S. Chase,et al.  Balance of Power: Energy Management for Server Clusters , 2001 .

[17]  S. Ranjan,et al.  QoS-driven server migration for Internet data centers , 2002, IEEE 2002 Tenth IEEE International Workshop on Quality of Service (Cat. No.02EX564).

[18]  D. Tirupati,et al.  Technology choice with stochastic demands and dynamic capacity allocation: A two‐product analysis , 1995 .

[19]  Daniel C. Stanzione,et al.  Dynamic Virtual Clustering with Xen and Moab , 2006, ISPA Workshops.

[20]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[21]  Erich M. Nahum,et al.  Yaksha: a self-tuning controller for managing the performance of 3-tiered Web sites , 2004, Twelfth IEEE International Workshop on Quality of Service, 2004. IWQOS 2004..

[22]  Prashant J. Shenoy,et al.  Agile dynamic provisioning of multi-tier Internet applications , 2008, TAAS.

[23]  Pearl Brereton,et al.  Turning Software into a Service , 2003, Computer.