ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

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.

Mobility-Aware Offloading Decision for Multi-Access Edge Computing in 5G Networks

“…In [ 13 ], a mobility-aware hybrid flow rule cache scheme is presented for tackling the problem of forwarding node. From another point of view, edge computing can provide the solutions for the cloud limitations in the current communication systems [ 14 ].…”

An Optimized Approach for Industrial IoT Based on Edge Computing

“…This paper considers an edge computing network with N = 50 nodes, and the maximum computing power p i of each node in the initial stage of network repair is independent of each other and evenly distributed in (10,25) Gbit. Considering the matching of computing power and computing requirements, the computing requirements r i of nodes in the initial stage of network repair also obey the uniform distribution on (10,25) Gbit.…”

“…This paper considers an edge computing network with N = 50 nodes, and the maximum computing power p i of each node in the initial stage of network repair is independent of each other and evenly distributed in (10,25) Gbit. Considering the matching of computing power and computing requirements, the computing requirements r i of nodes in the initial stage of network repair also obey the uniform distribution on (10,25) Gbit. Without loss of generality, it is assumed that the number of wired links in the original topology of the edge network is jEj = 2N, the network topology is the same as the random network [4], and the data transmission between nodes is reachable everywhere.…”

“…Problem P is a mixed-integer problem, NP-hard problem, and its data scale is large, and there is no known polynomial-time algorithm to solve the above problem. The current methods for solving the above problems can be divided into three categories: Heuristic algorithm [25], approximate algorithm [26], and exact algorithm [27]. The Heuristic algorithms are fast and easy to apply, but they lack rigorous theoretical proofs, and the results often deviate significantly from the optimal solution.…”

Performance Evaluation Analysis of Spark Streaming Backpressure for Data-Intensive Pipelines

“…Stream Processing (SP) is a trending topic that represents a remarkable milestone for data-intensive processing and analysis in both industry and research fields [1,2]. Moreover, SP systems have provided near or real-time data analysis for numerous network-based applications and services in the most varied areas and domains, such as financial services, healthcare, education, manufacturing, retail, social media, and sensor networks [3,4].…”