Mitigating Multi-tenant Interference in Continuous Mobile Offloading

Offloading computation to resource-rich servers is effective in improving application performance on resource constrained mobile devices. Despite a rich body of research on mobile offloading frameworks, most previous works are evaluated in a single-tenant setting, i.e., a server is assigned to a single client. In this paper we consider that multiple clients offload various continuous mobile sensing applications with end-to-end delay constraints, to a cluster of machines as the server. Contention for shared computing resources on a server can unfortunately result in delays and application malfunctions. We present a two-phase Plan-Schedule approach to mitigate multi-tenant resource contention, thus to reduce offloading delays. The planning phase predicts future workloads from all clients, estimates contention, and devises offloading schedule to remove or reduce contention. The scheduling phase dispatches arriving offloaded workloads to the server machine that minimizes contention, according to the running workloads on each machine. We implement the methods into ATOMS (Accurate Timing prediction and Offloading for Mobile Systems), a framework that adopts prediction of workload computing times, estimation of network delays, and mobile-server clock synchronization techniques. Using several mobile vision applications, we evaluate ATOMS under diverse configurations and prove its effectiveness.