Dynamic Clustering-based Task Orchestrator in Mobile Edge Computing

Multi-access Edge Computing (MEC) is an emerging paradigm designed to provide storage, computing and communication capabilities in the proximity of end-user devices. This approach facilitates the deployment of real-time on mobile devices with limited capabilities. To realize the MEC goals, it is essential to effectively manage and offload computing tasks to both edge and cloud-based resources. However, the dynamic nature, uncertainty and mobility within edge computing environments pose significant challenges to resource management. Furthermore, the inherent software and hardware heterogeneity, coupled with the distributed nature of architecture, complicates the development of efficient task offloading strategies that can adeptly manage resources across both edge and cloud platforms. In this paper, we propose a cluster-based task edge orchestrator, where edge servers are grouped based on service demands, resource ability and other factors to improve the overall service. Our proposed method leverages the K-Medoids clustering algorithm to dynamically form clusters of suitable edge servers for offloading computing tasks with minimum response time. To validate our proposed solution, we have orchestrated a comprehensive series of tests using EdgeCloudSim. Results show that our approach outperforms its competitor in terms of average service time by around 8%.