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. 2022 Jan 15;22(2):660.
doi: 10.3390/s22020660.

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Marios Avgeris  1 , Dimitrios Spatharakis  1 , Dimitrios Dechouniotis  1 , Aris Leivadeas  2 , Vasileios Karyotis  3 , Symeon Papavassiliou  1
Affiliations

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Marios Avgeris et al. Sensors (Basel). .

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.

Keywords: Markov Random Fields; edge computing; energy optimization; resource allocation; task offloading.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Example of Envisioned Edge Infrastructure.
Figure 2
Figure 2
Resource allocation optimization overview (Stage 1).
Figure 3
Figure 3
MRF inter-site load redistribution overview (Stage 2).
Figure 4
Figure 4
Relation of system slots, sweeps and update epochs.
Figure 5
Figure 5
Workload redistribution example: starting and final states. (a) starting state; (b) final state.
Figure 6
Figure 6
ART & QoS violations sensitivity to prediction error.
Figure 7
Figure 7
Dynamic resource allocation: Allocated cores (b), number of activated edge servers (c), power consumption (d) and ART (e) as a response to the number of predicted requests (a), for a single site.
Figure 8
Figure 8
MRF-based workload redistribution convergence.
Figure 9
Figure 9
MRF QoS improvements for various excess workloads in overloaded sites.
Figure 10
Figure 10
MRF energy savings for various excess workloads in underloaded sites.
Figure 11
Figure 11
MRF workload redistribution-induced delay minimizing for various excess workloads.
Figure 12
Figure 12
QoS violations and energy consumption during Experiment A. (a) QoS violations; (b) energy consumption.
Figure 13
Figure 13
QoS violations and energy consumption during Experiment B. (a) QoS violations; (b) energy consumption.
Figure 14
Figure 14
Comparison of the workload redistribution convergence.
See this image and copyright information in PMC

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