Ship Compute or Ship Data? Why Not Both?

How cloud applications should interact with their data remains an active area of research. Over the last decade, many have suggested relying on a key-value (KV) interface to interact with data stored in remote storage servers, while others have vouched for the benefits of using remote procedure call (RPC). Instead of choosing one over the other, in this paper, we observe that an ideal solution must adaptively combine both of them in order to maximize throughput while meeting application latency requirements. To this end, we propose a new system called Kayak that proactively adjusts the rate of requests and the fraction of requests to be executed using RPC or KV, all in a fully decentralized and self-regulated manner. We theoretically prove that Kayak can quickly converge to the optimal parameters. We implement a system prototype of Kayak. Our evaluations show that Kayak achieves sub-second convergence and improves overall throughput by 32.5%-63.4% for compute-intensive workloads and up to 12.2% for non-compute-intensive and transactional workloads over the state-of-the-art.