Ricerc@Sapienza

The goal of this work is to propose an energy-efficient algorithm for dynamic computation offloading, in a multi-access edge computing scenario, where multiple mobile users compete for a common pool of radio and computational resources. We focus on delay-critical applications, incorporating an upper bound on the probability that the overall time required to send the data and process them exceeds a prescribed value. In a dynamic setting, the above constraint translates into preventing the sum of the communication and computation queues' lengths from exceeding a given value. Ultra-reliable low latency communications (URLLC) are also taken into account using finite blocklengths and reliability constraints. The proposed algorithm, based on stochastic optimization, strikes an optimal balance between the service delay and the energy spent at the mobile device, while guaranteeing a target out-of-service probability. Starting from a long-term average optimization problem, our algorithm is based on the solution of a convex problem in each time slot, which is provided with a very fast iterative strategy. Finally, we extend the approach to mobile devices having energy harvesting capabilities, typical of Internet of Things scenarios, thus devising an energy efficient dynamic offloading strategy that stabilizes the battery level of each device around a prescribed operating level.