A Framework for Automatic Resource Provisioning for Private Clouds

A private cloud is maintained by an enterprise forits internal use. In such a scenario instead of buying the resources the enterprise can acquire the resources from a public cloud such as the ones provided by Amazon and Microsoft. On conventional systems rigorous analysis of the system and its workload is performed for determining the appropriate number of resources to be deployed on the private cloud. This paper presents a middleware framework that avoids this step of a priori capacity analysis and allows such private cloud owners to provision resources automatically such that a specified grade of service is maintained. The proposed framework performs dynamic resource provisioning that also leads to a reduction of operational cost. Additional resources are acquired during high traffic periods and released during low traffic periods such that the desired grade of service is always maintained. The paper describes the architecture of the framework and the experience gained from a prototype implementation including a preliminary analysis of its performance.

[1]  Warren Smith,et al.  Scheduling with advanced reservations , 2000, Proceedings 14th International Parallel and Distributed Processing Symposium. IPDPS 2000.

[2]  Bernd Freisleben,et al.  On-Demand Resource Provisioning for BPEL Workflows Using Amazon's Elastic Compute Cloud , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[3]  Ivona Brandic,et al.  SLA-Aware Application Deployment and Resource Allocation in Clouds , 2011, 2011 IEEE 35th Annual Computer Software and Applications Conference Workshops.

[4]  Jie Jin,et al.  On a novel property of the earliest deadline first algorithm , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[5]  Eric Bouillet,et al.  Efficient resource provisioning in compute clouds via VM multiplexing , 2010, ICAC '10.

[6]  Rajkumar Buyya,et al.  SLA-oriented resource provisioning for cloud computing: Challenges, architecture, and solutions , 2011, 2011 International Conference on Cloud and Service Computing.

[7]  Rajkumar Buyya,et al.  Grid Simulation Infrastructure Supporting Advance Reservation , 2004 .

[8]  Chunlin Li,et al.  Optimization decomposition approach for layered QoS scheduling in grid computing , 2007, J. Syst. Archit..

[9]  Jose Orlando Melendez,et al.  Matchmaking on Clouds and Grids , 2012 .

[10]  S. Majumdar,et al.  Dynamic scheduling of lightpaths in lambda grids , 2005, 2nd International Conference on Broadband Networks, 2005..

[11]  Rajkumar Buyya,et al.  Introduction to Cloud Computing , 2011, CloudCom 2011.

[12]  Omkhar Arasaratnam,et al.  Introduction to Cloud Computing , 2011 .

[13]  Shikharesh Majumdar,et al.  The “Any-Schedulability” Criterion for Providing QoS Guarantees through Advance Reservation Requests , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[14]  Thomas J. Hacker,et al.  A Methodology for Account Management in Grid Computing Environments , 2001, GRID.

[15]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[16]  Klara Nahrstedt,et al.  A distributed resource management architecture that supports advance reservations and co-allocation , 1999, 1999 Seventh International Workshop on Quality of Service. IWQoS'99. (Cat. No.98EX354).

[17]  Bu-Sung Lee,et al.  Optimization of Resource Provisioning Cost in Cloud Computing , 2012, IEEE Transactions on Services Computing.

[18]  Rajkumar Buyya,et al.  Cloud Resource Provisioning to Extend the Capacity of Local Resources in the Presence of Failures , 2012, 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems.