Scheduling and data redistribution strategies on star platforms

TL;DR

This paper investigates scheduling and data redistribution on star platforms, proving NP-completeness for some cases, offering optimal algorithms for specific scenarios, and proposing heuristics validated by simulations.

Contribution

It introduces new complexity results, optimal algorithms for special platform types, and heuristics for general cases in data redistribution on star platforms.

Findings

NP-completeness of the general scheduling problem

Optimal algorithms for specific platform types

Heuristics validated through simulations

Abstract

In this work we are interested in the problem of scheduling and redistributing data on master-slave platforms. We consider the case were the workers possess initial loads, some of which having to be redistributed in order to balance their completion times. We examine two different scenarios. The first model assumes that the data consists of independent and identical tasks. We prove the NP-completeness in the strong sense for the general case, and we present two optimal algorithms for special platform types. Furthermore we propose three heuristics for the general case. Simulations consolidate the theoretical results. The second data model is based on Divisible Load Theory. This problem can be solved in polynomial time by a combination of linear programming and simple analytical manipulations.