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Optimizing cloud caches for free: A case for autonomic systems with a serverless computing approach

Abstract:

While significant advances have been made towards realizing self-tuning cloud caches, existing products still require manual tuning. These systems are built to serve requests extremely fast and anything that consumes resources not directly related to the request-serving control path is avoided. We show that severless computing platforms can be leveraged to solve complex optimization problems that arise during self-tuning loops, and thus can be used to optimize resources in cloud caches, for free. To show that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDis), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. Through this case study and cost analysis, we make a case for implementing the controller of autonomic systems using a serverless computing approach.