Coded Distributed Computing with Heterogeneous Function Assignments
Nicholas Woolsey, Rong-Rong Chen, Mingyue Ji

TL;DR
This paper extends coded distributed computing (CDC) to heterogeneous networks by proposing new schemes that assign functions variably across nodes, reducing communication load compared to traditional homogeneous CDC.
Contribution
It introduces two novel heterogeneous CDC approaches, including a scheme with variable function assignment and an expandable design for improved efficiency.
Findings
Heterogeneous CDC schemes can outperform homogeneous ones in communication load.
The proposed expandable scheme serves multiple nodes simultaneously in the Shuffle phase.
Heterogeneous function assignment reduces communication costs in certain scenarios.
Abstract
Coded distributed computing (CDC) introduced by Li et. al. is an effective technique to trade computation load for communication load in a MapReduce framework. CDC achieves an optimal trade-off by duplicating map computations at computing nodes to yield multicasting opportunities such that nodes are served simultaneously in the Shuffle phase. However, in general, the state-of-the-art CDC scheme is mainly designed only for homogeneous networks, where the computing nodes are assumed to have the same storage, computation and communication capabilities. In this work, we explore two novel approaches of heterogeneous CDC design. First, we study CDC schemes which operate on multiple, collaborating homogeneous computing networks. Second, we allow heterogeneous function assignment in the CDC design, where nodes are assigned a varying number of reduce functions. Finally, we propose an…
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Taxonomy
TopicsStochastic Gradient Optimization Techniques · Caching and Content Delivery · Image and Video Quality Assessment
