Enabling Flexible and Efficient Remote Execution in Opportunistic Networks through Message-Oriented Middleware

Computation offloading has received much attention to improve the performance or energy efficiency of mobile systems that have usually limited and constrained resource capacities. Yet, applying the computation offloading technique in opportunistic networks that are highly dynamic and often become volatile still remains a challenge due to the following reasons: (1) technical difficulties in constructing efficient and reliable execution environment using commodity devices using WiFi, (2) a lack of runtime support for multiple clients that request diverse computational tasks and execute them concurrently with minimum performance impacts. In this paper, we introduce a new middleware system that provides an offloading framework operated in opportunistic networks. In particular, our middleware employs a publish-subscribe communication mechanism to provide multiple different communication models (e.g., one-to-one, one-to-many, many-to-one, and many-to-many) for different use cases. Furthermore, when distributing computational tasks to nearby nodes, our middleware takes their resource capabilities into consideration for efficient execution. Finally, since partial failure is an unavoidable artifact in highly dynamic and volatile opportunistic networks, we provide a simple, but effective failure handling mechanism. Our benchmarks and experimental results indicate that our approach enables programmers to easily apply computation offloading techniques in opportunistic networks when compared with the local execution.