Bargaining towards maximized resource utilization in video streaming datacenters

Datacenters can be used to host large-scale video streaming services with better operational efficiency, as the multiplexing achieved by virtualization technologies allows different videos to share resources at the same physical server. Live migration of videos from servers that are overloaded to those that are under-utilized may be a solution to handle a flash crowd of requests, in the form of virtual machines (VMs). However, such migration has to be performed with a well-designed mechanism to fully utilize available resources in all three resource dimensions: storage, bandwidth and CPU cycles. In this paper, we show why the challenge of maximizing resource utilization in a video streaming datacenter is equivalent to maximizing the joint profit in the context of Nash bargaining solutions, by defining utility functions properly. Having servers participating as players to bargain with each other, and VMs as commodities in the game, trades conducted after bargaining govern VM migration decisions in each server. With extensive simulations driven by real-world traces from UUSee Inc., we show that our new VM migration algorithm based on such Nash bargaining solutions increases both the resource utilization ratio and the number of video streaming requests handled by the datacenter, yet achievable in a lightweight fashion.

[1]  Philippe Solal,et al.  Finding a Nash equilibrium in spatial games is an NP-complete problem , 2004 .

[2]  S. Griffis EDITOR , 1997, Journal of Navigation.

[3]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[4]  Aameek Singh,et al.  Coupled placement in modern data centers , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[5]  Albert G. Greenberg,et al.  The cost of a cloud: research problems in data center networks , 2008, CCRV.

[6]  Kelvin Kian Loong Wong,et al.  A Geometrical Perspective for the Bargaining Problem , 2010, PloS one.

[7]  Yellu Sreenivasulu,et al.  FAST TRANSPARENT MIGRATION FOR VIRTUAL MACHINES , 2014 .

[8]  Aameek Singh,et al.  Server-storage virtualization: Integration and load balancing in data centers , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[9]  John K. Karlof,et al.  Integer programming : theory and practice , 2005 .

[10]  Rittwik Jana,et al.  Exploiting virtualization for delivering cloud-based IPTV services , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[11]  Borko Furht,et al.  A study of transcoding on cloud environments for video content delivery , 2010, MCMC '10.

[12]  M. Dufwenberg Game theory. , 2011, Wiley interdisciplinary reviews. Cognitive science.

[13]  J. Nash THE BARGAINING PROBLEM , 1950, Classics in Game Theory.

[14]  Gang Wang,et al.  Appliance-Based Autonomic Provisioning Framework for Virtualized Outsourcing Data Center , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

[15]  Vasileios Pappas,et al.  Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement , 2010, 2010 Proceedings IEEE INFOCOM.