ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Sensors

Dimitrios Spatharakis¹  Dimitrios Dechouniotis¹  Marios Avgeris¹  Symeon Papavassiliou¹  Vasileios Karyotis²  Aris Leivadeas³ 

[1] Department of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece;Department of Informatics, Ionian University, 49100 Corfu, Greece;École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada;

关键词: task offloading;    edge computing;    energy optimization;    resource allocation;    Markov Random Fields;   

DOI  :  10.3390/s22020660

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown