Heuristic Distribution of Latency-Sensitive Tasks in Multi-Access Edge Computing Systems

Multi-Access Edge Computing (MEC) paradigm has been widely studied as a potential solution to cope with the challenges emerging from new generations of mobile networks. By processing applications’ data closer to the users, service providers are able to offload origin servers and their underlying network infrastructure, which consequently reduces users’ experienced latency. In this paper, we consider internet-based applications with strict latency tolerance which are primarily enabled by the MEC architecture. Moreover, nodes at the edge may host application-related tasks as well as assist in their provision. We address the Task Distribution Problem (TDP), where the objective is to maximize the overall Quality of Service (QoS) based on the achieved throughput while ensuring that tasks’ latency requirements are satisfied. The TDP is modeled as an Integer Programming problem, taking into account three components: (i) tasks’ priority assignment, (ii) placement and (iii) routing through the MEC network. We propose to approach the problem through two different heuristics: a greedy replacement algorithm and a streaming algorithm. In our experiments, we evaluate the algorithms’ performance by showing numerical results across different experimental settings. We observe that, for the tested scenarios, our techniques provide a good trade-off between run time and high performance.