Resource Allocation Techniques in Edge/Fog Computing

Majumder, Darpan; Kumar, Siddhartha; Ashoka, D. V.; Nargunam, A. Shajin

doi:10.1109/icaect49130.2021.9392422

Cited by 2 publications

(1 citation statement)

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“…While there are many challenges associated with task offloading, we focus primarily on optimizing the resource allocation process across this multi-layered IoT architecture. A number of research efforts in the area of optimization of resource allocation have been conducted which include algorithms that allocate resources on a predetermined schedule or manual resource allocation [16][17][18][19]. Additionally, some work has been conducted on calculating the resource requirements for a task and preemptively allocating resources.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS

et al. 2022

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Existing approaches for allocating resources on edge environments are inefficient and lack the support of heterogeneous edge devices, which in turn fail to optimize the dependency on cloud infrastructures or datacenters. To this extent, we propose in this paper OpERA, a multi-layered edge-based resource allocation optimization framework that supports heterogeneous and seamless execution of offloadable tasks across edge, fog, and cloud computing layers and architectures. By capturing offloadable task requirements, OpERA is capable of identifying suitable resources within nearby edge or fog layers, thus optimizing the execution process. Throughout the paper, we present results which show the effectiveness of our proposed optimization strategy in terms of reducing costs, minimizing energy consumption, and promoting other residual gains in terms of processing computations, network bandwidth, and task execution time. We also demonstrate that by optimizing resource allocation in computation offloading, it is then possible to increase the likelihood of successful task offloading, particularly for computationally intensive tasks that are becoming integral as part of many IoT applications such robotic surgery, autonomous driving, smart city monitoring device grids, and deep learning tasks. The evaluation of our OpERA optimization algorithm reveals that the TOPSIS MCDM technique effectively identifies optimal compute resources for processing offloadable tasks, with a 96% success rate. Moreover, the results from our experiments with a diverse range of use cases show that our OpERA optimization strategy can effectively reduce energy consumption by up to 88%, and operational costs by 76%, by identifying relevant compute resources.

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Section: Introductionmentioning

confidence: 99%