Scheduling Coflows in Multi-Core OCS Networks with Performance Guarantee

TL;DR

This paper addresses coflow scheduling in multi-core optical circuit switching networks, proposing an approximation algorithm with performance guarantees that improves job completion times in data centers.

Contribution

It introduces a novel approximation algorithm for coflow scheduling in multi-core OCS networks, considering cross-core coupling and reconfiguration delays, with proven performance bounds.

Findings

Algorithm reduces weighted coflow completion time in simulations.

Effective in minimizing tail coflow completion times.

Applicable to multi-core EPS scenarios with similar guarantees.

Abstract

Coflow provides a key application-layer abstraction for capturing communication patterns, enabling the efficient coordination of parallel data flows to reduce job completion times in distributed systems. Modern data center networks (DCNs) are employing multiple independent optical circuit switching (OCS) cores operating concurrently to meet the massive bandwidth demands of application jobs. However, existing coflow scheduling research primarily focuses on the single-core setting, with multi-core fabrics only for EPS (electrical packet switching) networks. To address this gap, this paper studies the coflow scheduling problem in multi-core OCS networks under the not-all-stop reconfiguration model in which one circuit's reconfiguration does not interrupt other circuits. The challenges stem from two aspects: (i) cross-core coupling induced by traffic assignment across heterogeneous cores; and (ii) per-core OCS scheduling constraints, namely port exclusivity and reconfiguration delay. We propose an approximation algorithm that jointly integrates cross-core flow assignment and per-core circuit scheduling to minimize the total weighted coflow completion time (CCT) and establish a provable worst-case performance guarantee. Furthermore, our algorithm framework can be directly applied to the multi-core EPS scenario with the corresponding approximation ratio under packet-switched fabrics. Trace-driven simulations using real Facebook workloads demonstrate that our algorithm effectively reduces weighted CCT and tail CCT.