A Trace-driven Performance Evaluation of Hash-based Task Placement Algorithms for Cache-enabled Serverless Computing

Data-driven interactive computation is widely used for business analytics, search-based decision-making, and log mining. These applications' short duration and bursty nature makes them a natural fit for serverless computing. Data processing serverless applications are composed of many small tasks. Application tasks that use remote storage encounter bottlenecks in the form of high latency, performance variability, and throttling. Caching has been used to mitigate this bottleneck for intermediate data. However, the use of caching for input data, albeit widely used in industry, has yet to be studied. We present the first performance study of scaling, a key feature of serverless computing, on serverless clusters with input data caches. We compare 8 task placement algorithms and quantify their impact on task slowdown and resource usage before and after scaling. We quantify the consequences of using work stealing. We quantify the performance impact of scaling in the buffer period immediately after scaling. We find up to a 420% increase in task slowdown after scaling without work stealing and a 22% slowdown with work stealing. We also find that cache misses after scaling can lead to an additional 21% resource usage.