Edge Service Caching with Delayed Hits: Minimizing Latency and Reducing Cost

Multi-access Edge Computing (MEC) networks reduce service latency by caching services at the edge. A service request may be forwarded to the cloud for a response (Request Forwarding (R)), increasing Latency (L), and the service may be optionally downloaded, incurring a cost. If a download is in progress, the request may be buffered (a Delayed Hit (D)) if the remaining download time is less than the response time, adding to latency and previously overlooked. We argue that latency and cost must be considered separately, as the goal of MEC is to reduce latency. For the first time, we jointly consider Delayed Hits, Request Forwarding, resource constraints, download costs, and service caching, and introduce the DRL problem: minimizing Latency without assuming any request arrival pattern. We formulate this as an offline optimization problem (OPT). We propose an online algorithm called Online-DRL, derive its competitive ratio, and evaluate it using Google, SHARCNet and synthetic datasets. Compared to a simple baseline method, Online-DRL has up to 7.41% higher latency, but 92.54% lower cost, for Google traces. The results demonstrate that Online-DRL is a viable online algorithm for service caching