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Serverless computing relieves developers from the burden of resourcemanagement, thus providing ease-of-use to the users and the opportunity tooptimize resource utilization for the providers. However, today's serverlesssystems lack performance guarantees for function invocations, thus limitingsupport for performance-critical applications: we observed severe performancevariability (up to 6x). Providers lack visibility into user functions and hencefind it challenging to right-size them: we observed heavy resourceunderutilization (up to 80%). To understand the causes behind the performancevariability and underutilization, we conducted a measurement study of commonlydeployed serverless functions and learned that the function performance andresource utilization depend crucially on function semantics and inputs. Our keyinsight is to delay making resource allocation decisions until after thefunction inputs are available. We introduce Shabari, a resource managementframework for serverless systems that makes decisions as late as possible toright-size each invocation to meet functions' performance objectives (SLOs) andimprove resource utilization. Shabari uses an online learning agent toright-size each function invocation based on the features of the function inputand makes cold-start-aware scheduling decisions. For a range of serverlessfunctions and inputs, Shabari reduces SLO violations by 11-73% while notwasting any vCPUs and reducing wasted memory by 64-94% in the median case,compared to state-of-the-art systems, including Aquatope, Parrotfish, andCypress.

Shabari: Delayed Decision-Making for Faster and Efficient Serverless Functions