Context-aware Container Orchestration in Serverless Edge Computing

TL;DR

This paper introduces a context-aware learning framework for resource orchestration in serverless edge computing, significantly reducing convergence time and maintaining low latency amid heterogeneous resources.

Contribution

It proposes a novel adaptive framework that jointly allocates wireless bandwidth and computing resources in edge networks for serverless functions.

Findings

Reduces convergence time by over 95%.

Maintains comparable end-to-end delay to existing methods.

Effectively manages heterogeneous resources in edge environments.

Abstract

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art.