Offloading work to mobile devices
Ansuman Banerjee, Arijit Mukherjee, Himadri Sekhar Paul, Swarnava Dey · 2013
In the context of Internet of Things (IoT), data acquisition, management, and analysis for knowledge extraction has given rise to a new generation of services. The heterogeneity of services being offered today on the enormous expanse of data as available from sensors and smart devices, needs a distributed infrastructure for analysis and computation. We consider a scenario where the computing infrastructure of an IoT framework is distributed and is capable of harnessing the computing power of mobile devices connected to the network in a heterogeneous grid. Edge devices like smart-phones, gateways, etc. can potentially contribute to the infrastructure. However, such devices and communication channels to such devices are intermittently unavailable, which may be due to network failure, planned shut-down, high workload on the devices, etc. In addition to this, we consider a task offloading context where the computing infrastructure inside the IoT invites bids from connected and available devices from the network to offload a part of the computation on part of the input data-set. We expect the overall completion time to improve in this setting. We investigate the data partitioning problem under the scenario where unavailability of communication and computation are advertised a priori and we pose the problem as a scheduling problem where cost of execution of an analysis job is to be minimized. We present a constraint-based model, evaluate the same on various scenarios and present some of the results.