Optimal Scaling and Offloading for Sustainable Provision of Reliable V2N Services in Dynamic and Static Scenarios

The rising popularity of Vehicle-to-Network (V2N) applications is driven by the Ultra-Reliable Low-Latency Communications (URLLC) service offered by 5G. Distributed resources can help manage heavy traffic from these applications, but complicate traffic routing under URLLCfs strict delay requirements. In this paper, we introduce the V2N Computation Offloading and CPU Activation (V2N-COCA) problem, aiming at the monetary/energetic cost minimization via computation offloading and edge/cloud CPU activation decisions, under stringent latency constraints. Some challenges are the proven nonmonotonicity of the objective function and the no-existence of closed-formulas for the sojourn time of tasks. We present a provably tight approximation for the latter, and we design BiQui, a provably asymptotically optimal and computationally efficient algorithm for the V2N-COCA problem. We then study dynamic scenarios, introducing the Swap-Prevention problem, to account for changes in the traffic load and minimize the switching on/off of CPUs without incurring into overcosts.We prove the problemfs structural properties and exploit them to design Min-Swap, a provably correct and computationally effective algorithm for the Swap-Prevention Problem. We assess both BiQui and Min-Swap over real-world vehicular traffic traces, performing a sensitivity analysis and a stress-test. Results show that (i) BiQui is nearoptimal and significantly outperforms existing solutions; and (ii) Min-Swap reduces by a ≥90% the CPU swapping incurring into just ≤0.14% extra cost.