GA-IRACE: Genetic Algorithm-Based Improved Resource Aware Cost-Efficient Scheduler for Cloud Fog Computing Environment

Arshed, Jawad Usman; Ahmed, Masroor; Tufail, Muhammad; Afzal, Mehtab; Arif, Muhammad; Bazezew, Mekecha Banchigize

doi:10.1155/2022/6355192

Cited by 14 publications

(3 citation statements)

References 48 publications

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“…A genetic algorithm based improved Resource Aware Scheduler was proposed by [11] based on the optimized placement of tasks from fog to the loud in Cloud Fog computing environment. The author used the genetic algorithm and claim that the proposed approach has the better placement of task with improved execution time, less bandwidth consumption and Cloud resources monetary cost.…”

Section: Related Workmentioning

confidence: 99%

A Mobility Prediction Based Adaptive Task Migration in Mobile Edge Computing

Arshed,

Afzal,

Abbasi

et al. 2024

VFAST trans. softw. eng.

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During the past few years, mobile data traffic has exponentially increased due to emerging applications, such as social media, online gaming, and augmented/virtual reality. Although the capabilities of mobile devices are significantly improved, they are unable to execute computationally intensive tasks. To extend the computing capabilities of resource-constrained mobile devices, computation offloading is performed on edge servers. Due to user mobility, offloaded tasks often need to be migrated from one edge server to another. Mobility-aware task migration faces different challenges due to varying mobility characteristics of end-users. These challenges include latency, server utilization, and energy consumption. Existing techniques of task and machine (VM) migration do not consider the user movement trajectories while making migration decisions. Consequently, the task or VM is migrated to the edge server that may be far away from the mobile users' location that increases the response time. In this paper we proposed Mobility Migration Algorithm based on Linear Regression (MALR). After outsourcing the task, a recurrent neural network (RNN) and linear regression are used to forecast the user's present location. Using the distance between the user and the server, it gets a list of nearby servers, and then moves the task there. The proposed approach eliminates the job migration time with improvement in forecast accuracy as compared to the logistic regression and K-mean.

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Section: Related Workmentioning

confidence: 99%

A Mobility Prediction Based Adaptive Task Migration in Mobile Edge Computing

Arshed,

Afzal,

Abbasi

et al. 2024

VFAST trans. softw. eng.

Self Cite

View full text Add to dashboard Cite

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“…In the set case, the data set with 200 tasks is selected for the experiment, the population size is set to 40, the maximum number of iterations is 200 [40], the value of ω is from 0.1 to 0.9, and the experiment is run 20 times independently to take the average value. The results are shown in Table 4.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer

Tian,

Zhang,

Yuan

et al. 2023

Symmetry

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Due to the problem of large latency and energy consumption of fog computing in smart community applications, the fog computing task-scheduling method based on Hybrid Ant Lion Optimizer (HALO) is proposed in this paper. This method is based on the Ant Lion Optimizer (ALO. Firstly, chaotic mapping is adopted to initialize the population, and the quality of the initial population is improved; secondly, the Adaptive Random Wandering (ARW) method is designed to improve the solution efficiency; finally, the improved Dynamic Opposite Learning Crossover (DOLC) strategy is embedded in the generation-hopping stage of the ALO to enrich the diversity of the population and improve the optimization-seeking ability of ALO. HALO is used to optimize the scheduling scheme of fog computing tasks. The simulation experiments are conducted under different data task volumes, compared with several other task scheduling algorithms such as the original algorithm of ALO, Genetic Algorithm (GA), Whale Optimizer Algorithm (WOA) and Salp Swarm Algorithm (SSA). HALO has good initial population quality, fast convergence speed, and high optimization-seeking accuracy. The scheduling scheme obtained by the proposed method in this paper can effectively reduce the latency of the system and reduce the energy consumption of the system.

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“…It is a kind of specific energy efficiency analysis, which is a more reasonable evaluation index for specific energy efficiency analysis. A reasonable definition of the conversion efficiency based on CC is conducive to comparing the technical development level of various technologies, which is more reasonable than the traditional thermal efficiency analysis method, and can accurately analyze the weak links of certain types of equipment [17][18].…”

Section: Ece Analysis Theory Based On CC Environmentmentioning

confidence: 99%

Energy Conversion Efficiency in Cloud Computing Environment

2021

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With the requirements of large-scale computing and green concepts, cloud computing(CC) has become a hot topic. As one of the key problems in CC, energy conversion efficiency(ECE) has also received more and more optimized solutions from all angles under the attention of many scholars. This paper studies the ECE in CC environment, introduces the basic theory and related knowledge of CC, as well as the whole concept and framework of CC, discusses the ECE analysis theory based on CC environment, and analyzes the algorithm of energy conversion under CC technology. On this basis, the conversion efficiency is analyzed and studied, and good results are achieved.

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GA-IRACE: Genetic Algorithm-Based Improved Resource Aware Cost-Efficient Scheduler for Cloud Fog Computing Environment

Cited by 14 publications

References 48 publications

A Mobility Prediction Based Adaptive Task Migration in Mobile Edge Computing

A Mobility Prediction Based Adaptive Task Migration in Mobile Edge Computing

Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer

Energy Conversion Efficiency in Cloud Computing Environment

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