Centralized Congestion Control and Scheduling in a Datacenter

TL;DR

This paper proposes a centralized congestion control and scheduling policy for datacenter networks that guarantees bounded flow delays, emulating an optimal reversible queuing network to improve performance over traditional distributed approaches.

Contribution

It introduces a novel centralized policy that emulates a reversible queuing network, achieving optimal performance bounds in datacenter environments.

Findings

Guarantees per-flow delay bounds proportional to hop count and flow size.

Demonstrates emulation of reversible queuing networks with congestion control and scheduling.

Achieves baseline performance levels for datacenter traffic management.

Abstract

We consider the problem of designing a packet-level congestion control and scheduling policy for datacenter networks. Current datacenter networks primarily inherit the principles that went into the design of Internet, where congestion control and scheduling are distributed. While distributed architecture provides robustness, it suffers in terms of performance. Unlike Internet, data center is fundamentally a "controlled" environment. This raises the possibility of designing a centralized architecture to achieve better performance. Recent solutions such as Fastpass and Flowtune have provided the proof of this concept. This raises the question: what is theoretically optimal performance achievable in a data center? We propose a centralized policy that guarantees a per-flow end-to-end flow delay bound of $O$(#hops $\times$ flow-size $/$ gap-to-capacity). Effectively such an end-to-end delay will be experienced by flows even if we removed congestion control and scheduling constraints as the resulting queueing networks can be viewed as the classical reversible multi-class queuing network, which has a product-form stationary distribution. In the language of Harrison et al., we establish that baseline performance for this model class is achievable. Indeed, as the key contribution of this work, we propose a method to emulate such a reversible queuing network while satisfying congestion control and scheduling constraints. Precisely, our policy is an emulation of Store-and-Forward (SFA) congestion control in conjunction with Last-Come-First-Serve Preemptive-Resume (LCFS-PR) scheduling policy.