SLA for Sequential Serverless Chains: A Machine Learning Approach

Despite its vast potential, a challenge facing serverless computing’s wide-scale adoption is the lack of Service Level Agreements (SLAs) for serverless platforms. This challenge is compounded when composition technologies are employed to construct large applications using chains of functions. Due to the dependency of a chain’s performance on each function forming it, a single function’s sub-optimal performance can result in performance degradations of the entire chain. This paper sheds light on this problem and provides a categorical classification of the factors that impact a serverless function execution performance. We discuss the challenge of serverless chains’ SLA and present the results of leveraging our proposed serverless SLA framework to define SLAs for fixed-size and variable-size sequential serverless chains. The validation results demonstrate high accuracy in detecting sub-optimal executions exceeding 79%.