On the Optimal Control of Parallel Processing Networks with Resource Collaboration and Multitasking

TL;DR

This paper investigates optimal scheduling in parallel processing networks with resource collaboration and multitasking, addressing synchronization constraints that impact capacity and delay, and proposes a dynamic policy that is asymptotically optimal.

Contribution

It introduces a novel dynamic prioritization policy that maximizes system capacity and achieves asymptotic optimality under heavy-traffic conditions.

Findings

The proposed policy retains maximum system capacity.

It is asymptotically optimal in diffusion and heavy-traffic regimes.

Synchronization constraints significantly influence system performance.

Abstract

We study scheduling control of parallel processing networks in which some resources need to simultaneously collaborate to perform some activities and some resources multitask. Resource collaboration and multitasking give rise to synchronization constraints in resource scheduling when the resources are not divisible, that is, when the resources cannot be split. The synchronization constraints affect the system performance significantly. For example, because of those constraints, the system capacity can be strictly less than the capacity of the bottleneck resource. Furthermore, the resource scheduling decisions are not trivial under those constraints. For example, not all static prioritization policies retain the maximum system capacity and the ones that retain the maximum system capacity do not necessarily minimize the delay (or in general the holding cost). We study optimal scheduling control of a class of parallel networks and propose a dynamic prioritization policy that retains the maximum system capacity and is asymptotically optimal in diffusion scale and conventional heavy-traffic regime with respect to the expected discounted total holding cost objective.