An experts learning approach to mobile service offloading

Mobile devices are more and more often called on to perform services which require too much computation power and battery energy. If delay is an important consideration, offloading to the cloud may be too slow and a better approach is to offload to a resource-rich machine in the proximity of the device. This paper develops a new approach to this problem in which the machines are viewed as a collection of experts - but experts that are coupled in space and in time: the current action at a given machine affects the future state of the given machine and of other machines to which the given machine is connected. At any time, given the state and unknown dynamics of the system, the experts available at that time should cooperatively pick the best available actions. Within this framework, we propose online learning algorithms that results in substantial savings in energy consumption.