  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.
