On Optimal Allocation in a Distributed Processing Environment

Amitava Dutta, Gary Koehler and Andrew Whinston
Additional contact information
Amitava Dutta: University of Rochester
Gary Koehler: Micro Data Base Systems, Inc., Lafayette, Indiana
Andrew Whinston: Purdue University

Management Science, 1982, vol. 28, issue 8, 839-853

Abstract: Distributed processing has been motivated by many objectives. Among these are a desire to share resources, reduce communications costs (as compared to a centralized processing scheme), increase performance and decrease response time by partitioning tasks and achieve higher system availability (fault tolerance). Typically, processes (tasks) within a single processor and across processors will communicate among themselves in such a distributed environment. It is desirable to be able to assign tasks to processors in some optimal manner. In this paper, a quadratic partitioning model is developed to represent this interaction of tasks in a distributed processing environment. Initially, no capacity constraints are considered at any processor. An optimal solution (in a probabilistic sense) is found for this uncapacitated problem using a probabilistic branch and bound technique. Capacity constraints for processors are then introduced. Three intuitively appealing heuristics are developed to obtain good, though not necessarily optimal, solutions to the capacitated problem.

Keywords: distributed processing; quadratic assignment problem; warehouse location; branch and bound (search for similar items in EconPapers)
Date: 1982
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