A Cost-Sensitive Adaptation Engine for Server Consolidation of Multitier Applications

Virtualization-based server consolidation requires runtime resource reconfiguration to ensure adequate application isolation and performance, especially for multitier services that have dynamic, rapidly changing workloads and responsiveness requirements. While virtualization makes reconfiguration easy, indiscriminate use of adaptations such as VM replication, VM migration, and capacity controls has performance implications. This paper demonstrates that ignoring these costs can have significant impacts on the ability to satisfy response-time-based SLAs, and proposes a solution in the form of a cost-sensitive adaptation engine that weighs the potential benefits of runtime reconfiguration decisions against their costs. Extensive experimental results based on live workload traces show that the technique is able to maximize SLA fulfillment under typical time-of-day workload variations as well as flash crowds, and that it exhibits significantly improved transient behavior compared to approaches that do not account for adaptation costs.

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

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

[3]  Jerome A. Rolia,et al.  An integrated approach to resource pool management: Policies, efficiency and quality metrics , 2008, 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN).

[4]  Alan Gillies,et al.  Software Quality , 1993 .

[5]  John Dilley Hewlett-Packard Web Server Workload Characterization , 1996 .

[6]  Sameh Elnikety,et al.  Performance Comparison of Middleware Architectures for Generating Dynamic Web Content , 2003, Middleware.

[7]  Qi Zhang,et al.  A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

[8]  Jing Xu,et al.  On the Use of Fuzzy Modeling in Virtualized Data Center Management , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

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

[10]  Andrew Warfield,et al.  Live migration of virtual machines , 2005, NSDI.

[11]  Brian D. Noble,et al.  Mobile network estimation , 2001, MobiCom '01.

[12]  Vijay K. Naik,et al.  Efficient Resource Virtualization and Sharing Strategies for Heterogeneous Grid Environments , 2007, 2007 10th IFIP/IEEE International Symposium on Integrated Network Management.

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

[14]  Renato J. O. Figueiredo,et al.  A case for grid computing on virtual machines , 2003, 23rd International Conference on Distributed Computing Systems, 2003. Proceedings..

[15]  Anand Sivasubramaniam,et al.  Xen and co.: communication-aware CPU scheduling for consolidated xen-based hosting platforms , 2007, VEE '07.

[16]  Dennis F. Galletta,et al.  Web Site Delays: How Tolerant are Users? , 2004, J. Assoc. Inf. Syst..

[17]  Rajarshi Das,et al.  A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation , 2006, 2006 IEEE International Conference on Autonomic Computing.

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

[19]  Arun Venkataramani,et al.  Black-box and Gray-box Strategies for Virtual Machine Migration , 2007, NSDI.

[20]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

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

[22]  Ben Shneiderman,et al.  Determining Causes and Severity of End-User Frustration , 2004, Int. J. Hum. Comput. Interact..

[23]  Prashant J. Shenoy,et al.  Dynamic resource allocation for shared data centers using online measurements , 2003, IWQoS'03.

[24]  Gautam Kar,et al.  Application Performance Management in Virtualized Server Environments , 2006, 2006 IEEE/IFIP Network Operations and Management Symposium NOMS 2006.

[25]  Calton Pu,et al.  Generating Adaptation Policies for Multi-tier Applications in Consolidated Server Environments , 2008, 2008 International Conference on Autonomic Computing.

[26]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[27]  Varsha Apte,et al.  A methodology and tool for performance analysis of distributed server systems , 2006, ICSE '06.

[28]  Daniel A. Menascé,et al.  Resource Allocation for Autonomic Data Centers using Analytic Performance Models , 2005, Second International Conference on Autonomic Computing (ICAC'05).

[29]  Benny Rochwerger,et al.  Oceano-SLA based management of a computing utility , 2001, 2001 IEEE/IFIP International Symposium on Integrated Network Management Proceedings. Integrated Network Management VII. Integrated Management Strategies for the New Millennium (Cat. No.01EX470).

[30]  F. ArlittMartin,et al.  Web server workload characterization , 1996 .