Function Offloading and Data Migration for Stateful Serverless Edge Computing

Serverless computing and, in particular, Function-as-a-Service (FaaS) have emerged as valuable paradigms to deploy applications without the burden of managing the computing infrastructure. While initially limited to the execution of stateless functions in the cloud, serverless computing is steadily evolving. The paradigm has been increasingly adopted at the edge of the network to support latency-sensitive services. Moreover, it is not limited to stateless applications, with functions often recurring to external data stores to exchange partial computation outcomes or to persist their internal state. To the best of our knowledge, several policies to schedule function instances to distributed hosts have been proposed, but they do not explicitly model the data dependency of functions and its impact on performance. In this paper, we study the allocation of functions and associated key-value state in geographically distributed environments. Our contribution is twofold. First, we design a heuristic for function offloading that satisfies performance requirements. Then, we formulate the state migration problem via Integer Linear Programming, taking into account the heterogeneity of data, its access patterns by functions, and the network resources. Extensive simulations demonstrate that our policies allow FaaS providers to effectively support stateful functions and also lead to improved response times.