Dynamic Scheduling of a Multiclass Fluid Model with Transient Overload

  • Authors:

  • Junxia Chang;Hayriye Ayhan;J. G. Dai;Cathy H. Xia

  • Affiliations:

  • School of ISyE, Georgia Institute of Technology, Atlanta, GA 30332-0205, USA junxiach@isye.gatech.edu;School of ISyE, Georgia Institute of Technology, Atlanta, GA 30332-0205, USA hayhan@isye.gatech.edu;School of ISyE, Georgia Institute of Technology, Atlanta, GA 30332-0205, USA dai@isye.gatech.edu;IBM T.J. Watson Research Center, P.O. Box 704, Yorktown Heights, NY 10598, USA cathyx@us.ibm.com

  • Venue:
  • Queueing Systems: Theory and Applications
  • Year:
  • 2004

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Abstract

We study the optimal dynamic scheduling of different requests of service in a multiclass stochastic fluid model that is motivated by recent and emerging computing paradigms for Internet services and applications. In particular, our focus is on environments with specific performance guarantees for each class under a profit model in which revenues are gained when performance guarantees are satisfied and penalties are incurred otherwise. Within the context of the corresponding fluid model, we investigate the dynamic scheduling of different classes of service under conditions where the workload of certain classes may be overloaded for a transient period of time. Specifically, we consider the case with two fluid classes and a single server whose capacity can be shared arbitrarily among the two classes. We assume that the class 1 arrival rate varies with time and the class 1 fluid can more efficiently reduce the holding cost. Under these assumptions, we characterize the optimal server allocation policy that minimizes the holding cost in the fluid model when the arrival rate function for class 1 is known. Using the insights gained from this deterministic case, we study the stochastic fluid system when the arrival rate function for class 1 is random and develop various policies that are optimal or near optimal under various conditions. In particular, we consider two different types of heavy traffic regimes and prove that our proposed policies are strongly asymptotically optimal. Numerical examples are also provided to demonstrate further that these policies yield good results in terms of minimizing the expected holding cost.