Reservoir: Named Data for Pervasive Computation Reuse at the Network Edge

TL;DR

Reservoir leverages Named Data Networking and Locality Sensitive Hashing to enable efficient, scalable computation reuse at the network edge, significantly reducing task completion times in edge computing scenarios.

Contribution

This paper introduces Reservoir, a novel framework that implements pervasive computation reuse across distributed edge nodes using NDN and LSH, addressing scalability and fault tolerance.

Findings

Achieves up to 21.34x reduction in task completion times.

Reuses computation with nearly perfect accuracy.

Operates with minimal overhead on devices and network infrastructure.

Abstract

In edge computing use cases (e.g., smart cities), where several users and devices may be in close proximity to each other, computational tasks with similar input data for the same services (e.g., image or video annotation) may be offloaded to the edge. The execution of such tasks often yields the same results (output) and thus duplicate (redundant) computation. Based on this observation, prior work has advocated for "computation reuse", a paradigm where the results of previously executed tasks are stored at the edge and are reused to satisfy incoming tasks with similar input data, instead of executing these incoming tasks from scratch. However, realizing computation reuse in practical edge computing deployments, where services may be offered by multiple (distributed) edge nodes (servers) for scalability and fault tolerance, is still largely unexplored. To tackle this challenge, in this paper, we present Reservoir, a framework to enable pervasive computation reuse at the edge, while imposing marginal overheads on user devices and the operation of the edge network infrastructure. Reservoir takes advantage of Locality Sensitive Hashing (LSH) and runs on top of Named-Data Networking (NDN), extending the NDN architecture for the realization of the computation reuse semantics in the network. Our evaluation demonstrated that Reservoir can reuse computation with up to an almost perfect accuracy, achieving 4.25-21.34x lower task completion times compared to cases without computation reuse.