Offloading resource-intensive jobs to the cloud and nearby users is a promising approach to enhance mobile devices. This paper investigates a hybrid offloading system that takes both infrastructure-based networks and Ad-hoc networks into the scope. Specifically, we propose EDOS, an edge assisted offloading system that consists of two major components, an Edge Assistant (EA) and Offload Agent (OA). EA runs on the routers/towers to manage registered remote cloud servers and local service providers and OA operates on the users’ devices to discover the services in proximity. We present the system with a suite of protocols to collect the potential service providers and algorithms to allocate tasks according to user-specified constraints. To evaluate EDOS, we prototype it on commercial mobile devices and evaluate it with both experiments on a small-scale testbed and simulations. The results show that EDOS is effective and efficient for offloading jobs.
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Harvey, Hank H.; Mao, Ying; Hou, Yantian; and Sheng, Bo. (2017). "EDOS: Edge Assisted Offloading System for Mobile Devices". ICCN 2017: 2017 26th International Conference on Computer Communications and Networks (ICCCN): July 31-August 3, 2017 Vancouver, Canada, . http://dx.doi.org/10.1109/ICCCN.2017.8038421