Optimal Resource Allocation in Clouds

Cloud platforms enable enterprises to lease computing power in the form of virtual machines. An important problem for such enterprise users is to understand how many and what kinds of virtual machines will be needed from clouds. We formulate demand for computing power and other resources as a resource allocation problem with multiplicity, where computations that have to be performed concurrently are represented as tasks and a later task can reuse resources released by an earlier task. We show that finding a minimized allocation is NP-complete. This paper presents an approximation algorithm with a proof of its approximation bound that can yield close to optimum solutions in polynomial time. Enterprise users can exploit the solution to reduce the leasing cost and amortize the administration overhead (e.g., setting up VPNs or configuring a cluster). Cloud providers may utilize the solution to share their resources among a larger number of users.

[1]  Laurence A. Wolsey,et al.  An analysis of the greedy algorithm for the submodular set covering problem , 1982, Comb..

[2]  Prashant J. Shenoy,et al.  The Case for Enterprise-Ready Virtual Private Clouds , 2009, HotCloud.

[3]  David E. Irwin,et al.  Sharing Networked Resources with Brokered Leases , 2006, USENIX Annual Technical Conference, General Track.

[4]  Themistocles Politof,et al.  Substitutes and complements in network flow problems , 1981, Discret. Appl. Math..

[5]  Ian T. Foster,et al.  DiPerF: an automated distributed performance testing framework , 2004, Fifth IEEE/ACM International Workshop on Grid Computing.

[6]  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).

[7]  Ian T. Foster.  The globus toolkit for grid computing , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[8]  Mihir Bellare,et al.  On Chromatic Sums and Distributed Resource Allocation , 1998, Inf. Comput..

[9]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[10]  Fangzhe Chang,et al.  Optimal Resource Allocation for Batch Testing , 2009, 2009 International Conference on Software Testing Verification and Validation.

[11]  Willy Zwaenepoel,et al.  Cluster reserves: a mechanism for resource management in cluster-based network servers , 2000, SIGMETRICS '00.

[12]  Andrew V. Goldberg,et al.  Finding minimum-cost circulations by canceling negative cycles , 1989, JACM.

[13]  Borja Sotomayor,et al.  Combining batch execution and leasing using virtual machines , 2008, HPDC '08.

[14]  Edward Walker,et al.  Creating personal adaptive clusters for managing scientific jobs in a distributed computing environment , 2006, 2006 IEEE Challenges of Large Applications in Distributed Environments.

[15]  Maciej Drozdowski,et al.  Scheduling multiprocessor tasks -- An overview , 1996 .

[16]  David E. Culler,et al.  PlanetLab: an overlay testbed for broad-coverage services , 2003, CCRV.

[17]  Partha Dasgupta,et al.  CALYPSO: a novel software system for fault-tolerant parallel processing on distributed platforms , 1995, Proceedings of the Fourth IEEE International Symposium on High Performance Distributed Computing.

[18]  Katarzyna Keahey,et al.  Contextualization: Providing One-Click Virtual Clusters , 2008, 2008 IEEE Fourth International Conference on eScience.

[19]  Uwe Schwiegelshohn,et al.  Theory and Practice in Parallel Job Scheduling , 1997, JSSPP.

[20]  David E. Irwin,et al.  Dynamic virtual clusters in a grid site manager , 2003, High Performance Distributed Computing, 2003. Proceedings. 12th IEEE International Symposium on.

[21]  John Fisher-Ogden,et al.  Hardware Support for Efficient Virtualization , 2006 .