Energy-Aware Resource Scheduling in FoG Environment for IoT-Based Applications

Tiwari, Rajeev; Mittal, Mamta; Garg, Sachin; Kumar, Sumit

doi:10.1007/978-981-16-3448-2_1

Cited by 15 publications

(8 citation statements)

References 52 publications

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“…In order to save energy, each energy module subsystem estimates its energy efficiency using an energy-aware scheduling approach. Now the bit-error-rate (q a ) has the following, q a ∀R a > L a (7) q a ∀ min…”

Section: Optimized Resource Allocationmentioning

confidence: 99%

“…5G's higher bandwidth facilitates faster internet access and new tech applications. These systems also improve global data transfer, benefitting businesses and rural areas [7]. The emergence of 5G green communication systems brings vastly increased speed and capacity, drastically improving energy efficiency and supporting numerous applications [8].…”

Section: Introductionmentioning

confidence: 99%

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Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Sangeetha,

Logeshwaran,

Faheem

et al. 2024

The Journal of Engineering

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This paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy‐aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization.

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Section: Optimized Resource Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Sangeetha,

Logeshwaran,

Faheem

et al. 2024

The Journal of Engineering

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“…In another study, researchers in Reference 21 presented a review article focusing on Resource Scheduling techniques in edge computing, to identify energy‐efficient methods. The method of article selection is not described in their research.…”

Section: Related Workmentioning

confidence: 99%

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Hashemi,

Sahafi,

Rahmani

et al. 2023

Softw Pract Exp

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The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

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“…Some of the QoS parameters are mentioned in Figure 2 like cost, makespan, time, response time, etc. Efficient calculation of these parameters eventually depends on how resources are distributed to tasks [27,28]. Discussion on QoS parameters cost, makespan, time, and response time are done below:…”

Section: Task Management Optimization Techniques To Attain Qos Parame...mentioning

confidence: 99%

Task management in IoT-Fog-Cloud environment employing static scheduling Techniques

Goel

Tiwari

2022

ENPESJ

Self Cite

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In a distributed computing system, there are limited resources, which needs to be utilized effectively. Then for improving QoS Fog computing paradigm is an effective way, with suitable allocations. Thus, different resource scheduling and optimization algorithms exist. However, still, there is a scope to improve bandwidth, latency, energy consumption, and total communication cost in the Fog environment. In this work investigation is done to show significance of task management in such resource constrained environment. Various heuristics and meta-heuristic algorithms are evaluated using simulations, to show the task placement and their impacts by using 5 different Montage datasets from work flow sim tool kit for Fog-Computing environment. Then QoS parameters like cost, makespan, and energy consumptions are computed for various state-of-the-art techniques like Min-max, PSO, GA, ACO, and BLA. This shows the behaviour of these techniques with such different tasks and allocation environment configurations. Evaluated result parameters are collected and presented in the result section. This work shows the effectiveness of heuristics and meta-heuristics techniques to manage the tasks and their allocations in the Fog environment.

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Energy-Aware Resource Scheduling in FoG Environment for IoT-Based Applications

Cited by 15 publications

References 52 publications

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Task management in IoT-Fog-Cloud environment employing static scheduling Techniques

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