Streaming Approximation Scheme for Minimizing Total Completion Time on Parallel Machines Subject to Varying Processing Capacity

TL;DR

This paper introduces a streaming approximation scheme for scheduling jobs on parallel machines with varying capacities, efficiently estimating the total completion time with minimal data passes.

Contribution

It presents the first streaming approximation algorithm for minimizing total completion time on parallel machines with capacity variations.

Findings

Algorithm computes approximate total completion time in one pass

Schedule output is achievable in two passes

Effective for massive data that cannot fit into memory

Abstract

We study the problem of minimizing total completion time on parallel machines subject to varying processing capacity. In this paper, we develop an approximation scheme for the problem under the data stream model where the input data is massive and cannot fit into memory and thus can only be scanned for a few passes. Our algorithm can compute the approximate value of the optimal total completion time in one pass and output the schedule with the approximate value in two passes.