Combined Communication and Computing Resource Scheduling in Sliced 5G Multi-Access Edge Computing Systems - Seah, Winston K.G.; Lee, Chung-Hau; Lin, Ying-Dar; Lai, Yuan-Cheng (IEEE transactions on vehicular technology, 2022)
The fifth generation (5G) cellular networks aim to deliver data with low/ultra-low latency to users and support diverse services which require two main types of resources: communication and computing resources. These motivated the development of the Multi-access Edge Computing (MEC) paradigm because it can execute tasks' computation closer to users to reduce latency. To satisfy quality of services (QoS) requirements of different applications, both communication and computing resources in 5G MEC systems can be hard partitioned or soft partitioned among multiple network slices where each slice supports one or more services. A typical soft partition approach is using scheduling. Furthermore, packets in the multi-resource system can either be scheduled individually for each resource using a discrete resource scheduling approach or scheduled collectively only once by combined resource scheduling. In this paper, we propose a combined resource scheduler, called Extended Weighted Fair Queueing with Latency Constraint (EWFQ/LC), to schedule packets among network slices subject to system fairness and their latency constraints. By exploiting the virtual finish time feature inherent in weight fair queueing, EWFQ/LC schedules packets according to the fairness when their latency constraints can be met while according to these latency constraints when they are possibly violated. Based on simulations of realistic heterogeneous traffic scenarios, we show that EWFQ/LC reduces latency by as much as 73% compared to scheduling each resource discretely and maintains latency satisfaction ratio above 90%. More importantly, EWFQ/LC is able to accomplish these improvements with greater fairness in resource consumption especially under heavy traffic conditions, i.e., traffic with stricter latency constraints do not excessively over-consume resources of other traffic classes.