Streaming Algorithms for Multitasking Scheduling with Shared Processing

TL;DR

This paper introduces the first streaming algorithms for multitasking scheduling on parallel machines with shared processing, enabling approximate solutions in one or two passes, advancing big data scheduling methods.

Contribution

It presents novel streaming approximation algorithms for multitasking scheduling, providing the first solutions capable of approximating the optimal makespan in a streaming context.

Findings

One-pass streaming approximation schemes for makespan estimation.

Two-pass algorithms can produce schedules close to optimal.

Provides insights into streaming algorithm design for scheduling problems.

Abstract

In this paper, we design the first streaming algorithms for the problem of multitasking scheduling on parallel machines with shared processing. In one pass, our streaming approximation schemes can provide an approximate value of the optimal makespan. If the jobs can be read in two passes, the algorithm can find the schedule with the approximate value. This work not only provides an algorithmic big data solution for the studied problem, but also gives an insight into the design of streaming algorithms for other problems in the area of scheduling.