Adaptive Policies for Scheduling with Reconfiguration Delay: An End-to-End Solution for All-Optical Data Centers

TL;DR

This paper introduces an adaptive scheduling framework for all-optical data center networks with nonzero reconfiguration delay, ensuring throughput optimality and improved delay performance without prior traffic load knowledge.

Contribution

It proposes a general adaptive scheduling approach that inherits throughput optimality from any zero-delay policy, specifically applying it to MaxWeight, for optical networks with reconfiguration delays.

Findings

Adaptive MaxWeight (AMW) achieves throughput optimality.

AMW demonstrates better delay performance than prior methods.

The framework applies to any scheduling policy with near-MaxWeight schedule weights.

Abstract

All-optical switching networks have been considered a promising candidate for the next generation data center networks thanks to its scalability in data bandwidth and power efficiency. However, the bufferless nature and the nonzero recon- figuration delay of optical switches remain great challenges in deploying all-optical networks. This paper considers the end-to- end scheduling for all-optical data center networks with no in- network buffer and nonzero reconfiguration delay. A framework is proposed to deal with the nonzero reconfiguration delay. The proposed approach constructs an adaptive variant of any given scheduling policy. It is shown that if a scheduling policy guarantees its schedules to have schedule weights close to the MaxWeight schedule (and thus is throughput optimal in the zero reconfiguration regime), then the throughput optimality is inherited by its adaptive variant (in any nonzero reconfiguration delay regime). As a corollary, a class of adaptive variants of the well known MaxWeight policy is shown to achieve throughput optimality without prior knowledge of the traffic load. Further- more, through numerical simulations, the simplest such policy, namely the Adaptive MaxWeight (AMW), is shown to exhibit better delay performance than all prior work.