Network aware virtual machine and image placement in a cloud

Optimal resource allocation is a key ingredient in the ability of cloud providers to offer agile data centers and cloud computing services at a competitive cost. In this paper we study the problem of placing images and virtual machine instances on physical containers in a way that maximizes the affinity between the images and virtual machine instances created from them. This reduces communication overhead and latency imposed by the on-going communication between the virtual machine instances and their respective images. We model this problem as a novel placement problem that extends the class constrained multiple knapsack problem (CCMK) previously studied in the literature, and present a polynomial time local search algorithm for the case where all the relevant images have the same size. We prove that this algorithm has an approximation ratio of (3 + ∈) and then evaluate its performance in a general setting where images and virtual machine instances are of arbitrary sizes, using production data from a private cloud. The results indicate that our algorithm can obtain significant improvements (up to 20%) compared to the greedy approach, in cases where local image storage or main memory resources are scarce.

[1]  Feng Zhao,et al.  Energy aware consolidation for cloud computing , 2008, CLUSTER 2008.

[2]  Amin Vahdat,et al.  A scalable, commodity data center network architecture , 2008, SIGCOMM '08.

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

[4]  Yasuhiro Ajiro,et al.  Improving Packing Algorithms for Server Consolidation , 2007, Int. CMG Conference.

[5]  Vahab S. Mirrokni,et al.  Tight approximation algorithms for maximum general assignment problems , 2006, SODA '06.

[6]  Samir Khuller,et al.  Approximation algorithms for data placement on parallel disks , 2000, SODA '00.

[7]  Chunqiang Tang,et al.  FVD: A High-Performance Virtual Machine Image Format for Cloud , 2011, USENIX Annual Technical Conference.

[8]  Zhe Zhang,et al.  VDN: Virtual machine image distribution network for cloud data centers , 2012, 2012 Proceedings IEEE INFOCOM.

[9]  Meng Wang,et al.  Consolidating virtual machines with dynamic bandwidth demand in data centers , 2011, 2011 Proceedings IEEE INFOCOM.

[10]  Hadas Shachnai,et al.  Approximation Schemes for Generalized 2-Dimensional Vector Packing with Application to Data Placement , 2003, RANDOM-APPROX.

[11]  Jeffrey O. Kephart,et al.  Multi-aspect hardware management in enterprise server consolidation , 2010, 2010 IEEE Network Operations and Management Symposium - NOMS 2010.

[12]  David Breitgand,et al.  Improving consolidation of virtual machines with risk-aware bandwidth oversubscription in compute clouds , 2012, 2012 Proceedings IEEE INFOCOM.

[13]  Sameep Mehta,et al.  ReCon: A tool to Recommend dynamic server Consolidation in multi-cluster data centers , 2008, NOMS 2008 - 2008 IEEE Network Operations and Management Symposium.

[14]  William H. Sanders,et al.  CPU gradients: Performance-aware energy conservation in multitier systems , 2010, International Conference on Green Computing.

[15]  Albert G. Greenberg,et al.  VL2: a scalable and flexible data center network , 2009, SIGCOMM '09.

[16]  Samir Khuller,et al.  Algorithms for non-uniform size data placement on parallel disks , 2003, J. Algorithms.

[17]  Hadas Shachnai,et al.  On Two Class-Constrained Versions of the Multiple Knapsack Problem , 2001, Algorithmica.

[18]  Hans Kellerer,et al.  A Polynomial Time Approximation Scheme for the Multiple Knapsack Problem , 1999, RANDOM-APPROX.

[19]  Sanjeev Khanna,et al.  A Polynomial Time Approximation Scheme for the Multiple Knapsack Problem , 2005, SIAM J. Comput..