Photons: Lambdas on a diet

Abstract

Serverless computing allows users to create short, stateless functions and invoke hundreds of them concurrently to tackle massively parallel workloads. We observe that even though most of the footprint of a serverless function is fixed across its invocations - - language runtime, libraries, and other application state - - today's serverless platforms do not exploit this redundancy. Such an inefficiency has cascading negative impacts: longer startup times, lower throughput, higher latency, and higher cost. To mitigate these problems, we have built Photons, a framework leveraging workload parallelism to co-locate multiple instances of the same function within the same runtime. Concurrent invocations can then share the runtime and application state transparently, without compromising execution safety. Photons reduce function's memory consumption by 25% to 98% per invocation, with no performance degradation compared to today's serverless platforms. We also show that our approach can reduce the overall memory utilization by 30%, and the total number of cold starts by 52%.