2022
Avgeris, M.; Spatharakis, D.; Dechouniotis, D.; Leivadeas, A.; Karyotis, V.; Papavassiliou, S.
ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge Journal Article
In: Sensors, vol. 22, no. 2, 2022, ISSN: 14248220, (cited By 17).
Abstract | Links | BibTeX | Tags: article; energy consumption; Markov random field; mobile application; prediction; resource allocation; simulation; workload, Cloud infrastructures; Computational requirements; Distributed energies; Edge computing; Energy aware; Energy optimization; Markov Random Fields; Mobile applications; Resources allocation; Task offloading, Dynamics; Economic and social effects; Edge computing; Energy utilization; Markov processes; Metals; Mobile computing; Power management, Resource allocation
@article{Avgeris2022,
title = {ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge},
author = {M. Avgeris and D. Spatharakis and D. Dechouniotis and A. Leivadeas and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123731074&doi=10.3390%2fs22020660&partnerID=40&md5=3f622c6a52a9e5935515a33023f22b4b},
doi = {10.3390/s22020660},
issn = {14248220},
year = {2022},
date = {2022-01-01},
journal = {Sensors},
volume = {22},
number = {2},
publisher = {MDPI},
abstract = {Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.},
note = {cited By 17},
keywords = {article; energy consumption; Markov random field; mobile application; prediction; resource allocation; simulation; workload, Cloud infrastructures; Computational requirements; Distributed energies; Edge computing; Energy aware; Energy optimization; Markov Random Fields; Mobile applications; Resources allocation; Task offloading, Dynamics; Economic and social effects; Edge computing; Energy utilization; Markov processes; Metals; Mobile computing; Power management, Resource allocation},
pubstate = {published},
tppubtype = {article}
}
Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.