LaSS: Running Latency Sensitive Serverless Computations at the Edge

TL;DR

LaSS is a platform designed to efficiently run latency-sensitive serverless functions at the edge by using model-driven resource allocation, auto-scaling, and resource reclamation techniques to handle dynamic workloads.

Contribution

LaSS introduces a model-driven, queuing-based approach for resource management and auto-scaling of serverless functions at the edge, ensuring low latency and fair resource distribution.

Findings

Accurately predicts resource needs under dynamic workloads

Reprovisions container capacity within hundreds of milliseconds

Maintains fair share resource guarantees

Abstract

Serverless computing has emerged as a new paradigm for running short-lived computations in the cloud. Due to its ability to handle IoT workloads, there has been considerable interest in running serverless functions at the edge. However, the constrained nature of the edge and the latency sensitive nature of workloads result in many challenges for serverless platforms. In this paper, we present LaSS, a platform that uses model-driven approaches for running latency-sensitive serverless computations on edge resources. LaSS uses principled queuing-based methods to determine an appropriate allocation for each hosted function and auto-scales the allocated resources in response to workload dynamics. LaSS uses a fair-share allocation approach to guarantee a minimum of allocated resources to each function in the presence of overload. In addition, it utilizes resource reclamation methods based on container deflation and termination to reassign resources from over-provisioned functions to under-provisioned ones. We implement a prototype of our approach on an OpenWhisk serverless edge cluster and conduct a detailed experimental evaluation. Our results show that LaSS can accurately predict the resources needed for serverless functions in the presence of highly dynamic workloads, and reprovision container capacity within hundreds of milliseconds while maintaining fair share allocation guarantees.