Edge computing extends cloud capabilities to the proximity of end-users, offering ultra-low latency, which is essential for real-time applications. Unlike traditional cloud systems that suffer from latency and reliability constraints due to distant datacenters, edge computing employs a distributed model, leveraging local edge datacenters to process and store data.
This talk explores key challenges in edge computing across three domains: workloads, storage, and service allocation.
The first part focuses on the absence of comprehensive edge workload datasets. Current datasets do not accurately reflect the unique attributes of edge systems. To address this, we propose a workload composition methodology and introduce WoW-IO, an open-source trace generator. The second part examines aspects of edge storage. Edge datacenters are significantly smaller than their cloud counterparts and require dedicated solutions. We analyze the applicability of a promising mathematical model for edge storage systems and raise inherent gaps between theory and practice. The final part addresses the virtual network embedding problem (VNEP). In VNEP, given a set of requests for deploying virtualized applications, the edge provider has to deploy a maximum number of them to the underlying physical network, subject to capacity constraints. We propose novel solutions, including a proactive service allocation strategy for mobile users, a flexible algorithm for service allocation that is adaptable to the underlying physical topology, and an algorithm for scalable online service allocation.