Criticality-Awareness Edge User Allocation for Public Safety

Edge computing provides a novel computing paradigm by deploying services on edge servers deployed at base stations to serve nearby end-users with low service latency. In this regard, a suitable allocation strategy is crucial that maximizes the number of users served at minimum overall cost, which is referred to as the Edge User Allocation (EUA) problem. However, when edge computing meets public safety, there are critical issues that have not been fully considered by existing EUA approaches. Among these issues, the levels of danger to individual users quantitatively indicate that users are in danger or not in an emergency scenario like fire. Hence, the inclusion of these levels impacts the priority for allocating resources to serve individual users in the EUA problem. In this article, these levels are first defined as
individual criticalities
formally. Then, we take them into account to formulate the novel CRiticality-EUA (CR-EUA) problem, and prove its NP-hardness. To solve this problem, an optimal approach, named CR-EUA-O, is proposed by utilizing the Integer Programming technique. Furthermore, we propose an approximation approach with a proven approximation ratio, referred to as CR-EUA-H, as an effective and efficient solution. Extensive experiments are conducted on a widely-used real-world dataset to evaluate our approaches against four representative approaches. The results show the superior performance of our approaches in the overall criticality and execution time in emergency scenarios.