Order Optimal Task Allocation in Distributed Computing via Interweaved Cliques

TL;DR

This paper introduces an interweaved clique design for distributed computing that nearly optimally minimizes communication and balances computational load, extending beyond Steiner system limitations.

Contribution

It proposes a universal, deterministic allocation framework that relaxes Steiner system constraints, achieving near-optimal communication and order-optimal computation costs.

Findings

IC design achieves within a constant factor of the optimal communication cost.

IC design maintains order-optimal computational load.

The framework generalizes distributed task allocation beyond Steiner systems.

Abstract

We consider a distributed computing system in which a master node coordinates $N$ workers to evaluate a function over $n$ input files, where this function accepts general decomposition. In particular, we focus on the general case where the requested function admits a $d$-uniform decomposition, meaning that it can be decomposed into a set of subfunctions that each depends on a unique $d$-tuple of the $n$ files. Our objective is to design file and task allocations that minimize the worst-case communication from the master to any worker and the worst-case computational load across workers. We first show that the optimal file and task allocation with minimum communication and computation costs admits a natural characterization within combinatorial design theory: it corresponds to a Steiner system $S(t, k, v)$ with $t=d$, $v=n$, and $k \approx \frac{n}{N^{1/d}}$. However, Steiner systems are known to exist only for very restricted parameter regimes. To overcome this limitation, we propose the information-theoretic-inspired \emph{Interweaved Clique (IC) design}, a universal and deterministic allocation framework that relaxes the strict structure of Steiner systems by allowing slight variations in worker file loads. Although slightly suboptimal, the IC design achieves a communication cost within a constant factor $4e$ from our converse, while also maintaining an order-optimal computation cost, thus allowing this work to derive the fundamental scaling laws of this general distributed computing problem for a large range of parameters.