Centralized versus distributed schedulers for multiple bag-of-task applications

  • Authors:

  • Olivier Beaumont;Larry Carter;Jeanne Ferrante;Arnaud Legrand;Loris Marchal;Yves Robert

  • Affiliations:

  • Laboratoire LaBRI, CNRS, INRIA Bordeaux, France;Dept. of Computer Science and Engineering, University of California, San Diego;Dept. of Computer Science and Engineering, University of California, San Diego;Laboratoire ID-IMAG, CNRS, INRIA, Grenoble, France;Laboratoire LIP, CNRS, INRIA, École Normale Supérieure de Lyon, France;Laboratoire LIP, CNRS, INRIA, École Normale Supérieure de Lyon, France

  • Venue:
  • IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
  • Year:
  • 2006

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Abstract

Multiple applications that execute concurrently on heterogeneous platforms compete for CPU and network resources. In this paper we consider the problem of scheduling applications to ensure fair and efficient execution on a distributed network of processors. We limit our study to the case where communication is restricted to a tree embedded in the network, and the applications consist of a large number of independent tasks that originate at the tree's root. The tasks of a given application all have the same computation and communication requirements, but these requirements can vary for different applications. Each application is given a weight that quantifies its relative value. The goal of scheduling is to maximize throughput while executing tasks from each application in the same ratio as their weights. We can find the optimal asymptotic rates by solving a linear program that expresses all necessary problem constraints, and we show how to construct a periodic schedule. For single-level trees, the solution is characterized by processing tasks with larger communication-to-computation ratios at children with larger bandwidths. For multi-level trees, this approach requires global knowledge of all application and platform parameters. For large-scale platforms, such global coordination by a centralized scheduler may be unrealistic. Thus, we also investigate decentralized schedulers that use only local information at each participating resource. We assess their performance via simulation, and compare to a centralized solution obtained via linear programming. The best of our decentralized heuristics achieves the same performance on about two-thirds of our test cases, but is far worse in a few cases. While our results are based on simplistic assumptions and do not explore all parameters (such as buffer size), they provide insight into the important question of fairly and optimally co-scheduling heterogeneous applications on heterogeneous grids.