Multitiered Worker-Oriented Resource Allocation: Mitigating Worker Attrition at the Extreme Edge

Democratizing Edge Computing (EC) by harnessing the underused computational resources of Extreme Edge Devices (EEDs) can revolutionize a broad spectrum of IoT applications. However, studying the impact of EED/worker attrition on the Quality of Service (QoS) at the extreme edge has been overlooked. In this paper, we propose the Multitiered Worker-Oriented Resource Allocation (MWORA) scheme. MWORA is the first scheme that studies the impact of worker attrition on the QoS and mitigates its risk by optimizing resource allocation to ensure that workers receive a satisfactory profit to maintain their participation in the service. Furthermore, MWORA accounts for the fact that EEDs are user-owned devices, and are thus subject to a dynamic user access behavior, which can affect the level of computational resources they are willing to endow. MWORA accounts for such dynamicity by pioneering the notion of enabling multitiered computational capabilities to be solicited from each worker based on the profit gained from the assigned tasks. We formulate the problem as an Integer Linear Program (ILP) to maximize the QoS while abiding by certain worker satisfaction, deadline, and budget constraints. We also propose the MWORA-Weighted Sum (MWORA-WS) scheme to derive an analytical solution using the Karush–Kuhn–Tucker (KKT) conditions and Lagrangian analysis. Extensive simulations show that MWORA outperforms prominent resource allocation schemes by up to 31%, 52%, and 85% in terms of response delay, service capacity, and worker satisfaction, respectively. Additionally, MWORA-WS yields a small gap of 7% and 8% with MWORA in terms of response delay and worker satisfaction, respectively.