Optimal Proactive Resource Allocation at the Extreme Edge

Khatib, Rawan F. El; Elsayed, Sara A.; Hassanein, Hossam S.

doi:10.1109/icc45855.2022.9838897

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…resource allocation schemes aim to improve the response delay; as in [36], while 5% aim to improve the execution delay. The remaining 72% are distributed among the remaining factors, including the recruitment cost [37].…”

Section: Resource Allocation Schemes Based On Performance Indicators 15mentioning

confidence: 99%

Multitiered Worker-Oriented Resource Allocation at the Extreme Edge

De'bas

Elsayed

Hassanein

2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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Democratizing Edge Computing (EC) by leveraging the prolific yet underutilized computational resources of IoT devices, also referred to as Extreme Edge Devices (EEDs), has gained significant momentum lately. In such edge computing paradigms, fair resource allocation is a major concern. However, fairness is typically considered from the requester's perspective, whereas fairness for workers is mostly overlooked. In this thesis, we propose the Multitiered Worker-oriented Resource Allocation (MWORA) framework. MWORA aims to minimize the drop rate and task response delay while enabling fair resource allocation that maintains a specific satisfactory profit for workers. Such a satisfactory profit is maintained to prevent workers from leaving the system and ensure their recurrent subscription to the service provider. MWORA also 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 endowed by workers. MWORA considers this by enabling multitiered computational resources to be granted by each worker depending on the price of the allocated task.MWORA formulates the resource allocation problem as an Integer Linear Program (ILP). 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

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Section: Resource Allocation Schemes Based On Performance Indicators 15mentioning

confidence: 99%