Dynamic Heterogeneity-Aware Coded Cooperative Computation at the Edge

TL;DR

This paper introduces C3P, a dynamic coded cooperative computation protocol for edge devices that adapts to resource heterogeneity and variability, significantly reducing task delay and improving resource utilization.

Contribution

It proposes a novel adaptive coded computation framework, C3P, tailored for heterogeneous edge environments, with near-optimal delay and over 99% resource efficiency.

Findings

C3P achieves near-optimal task completion delay.

Resource utilization exceeds 99%.

Significant delay improvements over baselines in simulations and real testbeds.

Abstract

Cooperative computation is a promising approach for localized data processing at the edge, e.g. for Internet of Things (IoT). Cooperative computation advocates that computationally intensive tasks in a device could be divided into sub-tasks, and offloaded to other devices or servers in close proximity. However, exploiting the potential of cooperative computation is challenging mainly due to the heterogeneous and time-varying nature of edge devices. Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a coded cooperative computation framework, which we name Coded Cooperative Computation Protocol (C3P), by taking into account the heterogeneous resources of edge devices. C3P dynamically offloads coded sub-tasks to helpers and is adaptive to time-varying resources. We show that (i) task completion delay of C3P is very close to optimal coded cooperative computation solutions, (ii) the efficiency of C3P in terms of resource utilization is higher than $99\%$, and (iii) C3P improves task completion delay significantly as compared to baselines via both simulations and in a testbed consisting of real Android-based smartphones.