A multi-objective fog computing task scheduling strategy based on ant colony algorithm

Gu, Jingjun; Mo, Jiadi; Li, Ben; Zhang, Yueqing; Wang, Wei

doi:10.1109/iciscae52414.2021.9590674

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…In their publication [18], the researchers propose and the writers detail a technique for arranging tasks with multiple goals in fog computing, which utilizes an upgraded ant colony algorithm. This approach is customized to the features of fog nodes and accounts for expenses related to computing resources, power consumption, and network usage.…”

Section: Related Workmentioning

confidence: 99%

“…Methodology Key Parameters [11] Incorporates SSA into AEO IoT concerns, processing time, latency, memory, cost [12] Enhanced firework algorithms Explosion radius detection, cloud-fog system, load balancing [13] GWO and RIL for serverless computing Task parallelization, task allocation, runtime, energy [6] ACO algorithm for fog-cloud IoT scheduling Makespan, task deadline satisfaction [14] Multi-objective GWO for fog computing Energy consumption, task execution time, container migration [15] Modified GWO for cloud task scheduling Makespan, cost, job scheduling efficiency [16] Bumble Bee Mating Optimization for cloud Makespan, VMs, comparison with HBMO and GA [17] ACO for load balancing in fog computing Latency, quality of service, response time [18] Upgraded ant colony algorithm for fog Computing resource costs, power consumption, network usage [19] PSO-GWO hybrid for cloud task scheduling Execution cost, time, performance [20] Dynamic and Fault-Tolerant Scheduling Algorithm Task priorities, unit electricity cost, deep reinforcement learning [21] SCEHO and IPSO for cloud task scheduling Load balancing, resource allocation…”

Section: Referencementioning

confidence: 99%

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Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm

Alsamarai,

Uçan

2024

Applied Sciences

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Today, the IoT has become a vital part of our lives because it has entered into the precise details of human life, like smart homes, healthcare, eldercare, vehicles, augmented reality, and industrial robotics. Cloud computing and fog computing give us services to process IoT tasks, and we are seeing a growth in the number of IoT devices every day. This massive increase needs huge amounts of resources to process it, and these vast resources need a lot of power to work because the fog and cloud are based on the term pay-per-use. We make to improve the performance and cost (PC) algorithm to give priority to the high-profit cost and to reduce energy consumption and Makespan; in this paper, we propose the performance and cost–gray wolf optimization (PC-GWO) algorithm, which is the combination of the PCA and GWO algorithms. The results of the trial reveal that the PC-GWO algorithm reduces the average overall energy usage by 12.17%, 11.57%, and 7.19%, and reduces the Makespan by 16.72%, 16.38%, and 14.107%, with the best average resource utilization enhanced by 13.2%, 12.05%, and 10.9% compared with the gray wolf optimization (GWO) algorithm, performance and cost algorithm (PCA), and Particle Swarm Optimization (PSO) algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm

Alsamarai,

Uçan

2024

Applied Sciences

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“…Kruekaew et al [15] proposed using a multi-objective task scheduling optimization based on the artificial bee colony algorithm with a Q-learning algorithm, which adopting Qlearning algorithm to strengthen the learning ability of artificial bee colony algorithm. Gu et al [16] proposed a multiobjective fog computing task scheduling algorithm based on improved ACO algorithm, which considers the cost of nodes from time and cost comprehensively. Meanwhile, the algorithm introduced key factors of task allocation to improve the convergence.…”

Section: Resource Scheduling Algorithmmentioning

confidence: 99%

An optimized resource scheduling algorithm based on GA and ACO algorithm

Yin

Peng

et al. 2023

Preprint

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With the development of Internet of things (IoT) technology, fog computing has gradually come into people’s vision because of the characteristics of real-time and low delay. Providing computing and storage services at the edge of the network, fog computing can meet the needs of users in delay-sensitive applications as a highly virtualized platform. Resource scheduling plays an important role in fog computing, because it can meet the requirements of customers and improve the quality of service. However, if largescale service requests can not solve the resource scheduling problem effectively, it will reduce resource utilization and user satisfaction. Focus on the above problem, we establish a resource scheduling model named normalization processing, which can achieve the goal of the lowest total cost by finding the optimal pheromone. The optimal resource scheduling results can be obtained by changing the pheromone concentration of ants during the simulated foraging process. At the same time, we propose a new resource scheduling algorithm named new genetic ant colony optimization (NGACO) algorithm, which can update pheromone generation by roulette algorithm. The experimental results show that the resource scheduling performance of NGACO algorithm is 14.7%, 25%, 12.8% lower than that of ACO algorithm in makespan, economic cost, total cost respectively, and load balancing is 34.7% higher than ACO algorithm.

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“…In this paper [20], they propose a multi-objective fog computing task scheduling algorithm based on an improved Ant-colony algorithm, which optimizes the Ant-colony algorithm to make it more suitable for the characteristics of the fog node, uses time and cost (TAC) to comprehensively consider the cost of the node, and introduce the critical factor in task allocation to improve the convergence speed of the algorithm. Different simulation experiments show that the efficiency of the improved Ant-colony algorithm is enhanced in processing time, cost, and load balance.…”

Section: An Ant-colony Based Model For Load Balancing In Fog Environm...mentioning

confidence: 99%

An Ant-colony Based Model for Load Balancing in Fog Environments

2023

JSFI

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Delay-sensitive applications are becoming more and more in demand as a result of the development of information systems and the expansion of communication in cloud computing technologies. Some of these requests will be overlooked in cloud environments due to the communication delay between the processing center and the client's request. The 'fog-based computing paradigm', a novel processing model, can be added to cloud computing to help with the aforementioned issues. The performance of computing systems is always influenced by latency. In this paper, we focus on balancing the load on the fog nodes to lower the latency. It is also a crucial component of fog computing devices. It necessitates the use of load-balancing algorithms to select the optimal hosts, resulting in an even distribution of the load on the available resources. We provide a load-balancing approach based on the Ant-colony optimization algorithm's latency rate for responding to tasks. A random data set evaluation of this model reveals shorter response times than those of earlier strategies suggested in this field.

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A multi-objective fog computing task scheduling strategy based on ant colony algorithm

Cited by 9 publications

References 13 publications

Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm

Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm

An optimized resource scheduling algorithm based on GA and ACO algorithm

An Ant-colony Based Model for Load Balancing in Fog Environments

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