Package-Aware Scheduling of FaaS Functions

Abstract

We consider the problem of scheduling small cloud functions on serverless computing platforms. Fast deployment and execution of these functions is critical, for example, for microservices architectures. However, functions that require large packages or libraries are bloated and start slowly. A solution is to cache packages at the worker nodes instead of bundling them with the functions. However, existing FaaS schedulers are vanilla load balancers, agnostic of any packages that may have been cached in response to prior function executions, and cannot reap the benefits of package caching (other than by chance). To address this problem, we propose a package-aware scheduling algorithm that tries to assign functions that require the same package to the same worker node. Our algorithm increases the hit rate of the package cache and, as a result, reduces the latency of the cloud functions. At the same time, we consider the load sustained by the workers and actively seek to avoid imbalance beyond a configurable threshold. Our preliminary evaluation shows that, even with our limited exploration of the configuration space so-far, we can achieve 66% performance improvement at the cost of a (manageable) higher node imbalance.