Exploiting the Computational Path Diversity with In-network Computing for MEC

With Computing in the Network technologies, Mobile Edge Computing (MEC) has expanded the resource distribution and tightly integrated computing-network capabilities from the end-devices, through the edge, to the cloud infrastructure, including at points in between. Thus, edge computing is able to deliver a more collaborative processing, better service responding to the increasing application needs in low latency processing. In the presence of integrated computing-network resources and their increased capacity, current proximity-to-data methods in edge computing lead to sub-optimal performance in terms of processing latency. Addressing this issue, this paper presents a Low-latency Adaptive Workload Allocation framework (LAWA) to harness the growing in-network computing resources to deliver low latency processing capabilities for emerging latency-constrained applications. LAWA defines an application by its computational source and destination. Considering the diversity of computing and network resources, we try to find an optimal computational path and its workload allocation. We model the problem as a mixed integer programming problem. To solve this problem, we propose the computational pathfinding and workload allocation algorithms with optimality guarantees. Experimental results show that, comparing with the state-of-the-art methods, our method achieves up to 8.04x speedup, in terms of end-to-end latency.