Community-Oriented Resource Allocation at the Extreme Edge.

The surging demand for Edge Computing (EC) to cope with the proliferation of latency-critical and data-intensive applications has inspired the notion of recycling ample yet underutilized computational resources of end devices, also referred to as Extreme Edge Devices (EEDs). Maintaining data privacy and cost efficiency remain core challenges for the viability of EED-enabled computing paradigms. In this context, we propose the Community-Oriented Resource Allocation (CORA) scheme. CORA exploits business, institutional, and social relationships to build clusters and communities of requesters and EEDs that can eliminate recruitment costs and preserve privacy. However, community-imposed constraints on resource allocation can lead to unbalanced work distribution. To address this issue, CORA considers community restrictions, minimizes flowtime and makespan for the allocated services, and retains a reasonable scheduler runtime for real-time resource allocation. Towards that end, CORA formulates the resource allocation problem as a Bipartite Graph Matching problem. Furthermore, CORA exposes tuneable parameters that allow prioritizing flowtime or makespan, making it suitable for different scenarios. Extensive simulations show that CORA outperforms six prominent heuristic-based resource allocation schemes by up to 24% in terms of average makespan while sustaining the same level of flowtime and runtime.