Cloud Job ‎Scheduling with‎ Ions Motion Optimization Algorithm

Hassan, Mohammed-Albarra; Yousif, Adil

doi:10.48084/etasr.3408

Cited by 7 publications

(6 citation statements)

References 39 publications

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“…The selection process of genes based on natural methods revealed that simply the fittest kinds survive at the end of the process [17]. The research in [26] introduced the ions motion optimization (IMO) algorithm for IoT cloud job scheduling. The IMO mechanism includes two stages, namely the liquid and crystal phases, which work together to strike a balance between quick convergence and avoiding the local optima, thus enhancing the algorithm's performance [26].…”

Section: Related Workmentioning

confidence: 99%

“…The research in [26] introduced the ions motion optimization (IMO) algorithm for IoT cloud job scheduling. The IMO mechanism includes two stages, namely the liquid and crystal phases, which work together to strike a balance between quick convergence and avoiding the local optima, thus enhancing the algorithm's performance [26]. Job scheduling mechanisms for IoT cloud computing using optimization techniques based on shark smell optimization and chemical reaction optimization are introduced to enhance the job scheduling process [27,28].…”

Section: Related Workmentioning

confidence: 99%

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Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

Yousif,

Shohdy,

Hassan

et al. 2023

Electronics

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IoT cloud computing provides all functions of traditional computing as services through the Internet for the users. Big data processing is one of the most crucial advantages of IoT cloud computing. However, IoT cloud job scheduling is considered an NP-hard problem due to the hardness of allocating the clients’ jobs to suitable IoT cloud provider resources. Previous work on job scheduling tried to minimize the execution time of the job scheduling in the IoT cloud, but it still needs improvement. This paper proposes an enhanced job scheduling mechanism using cat swarm optimization (CSO) with modified distance to minimize the execution time. The proposed job scheduling mechanism first creates a set of jobs and resources to generate the population by randomly assigning the jobs to resources. Then, it evaluates the population using the fitness value, which represents the execution time of the jobs. In addition, we use iterations to regenerate populations based on the cat’s behaviour to produce the best job schedule that gives the minimum execution time for the jobs. We evaluated the proposed mechanism by implementing an initial simulation using Java Language and then conducted a complete simulation using the CloudSim simulator. We ran several experimentation scenarios using different numbers of jobs and resources to evaluate the proposed mechanism regarding the execution time. The proposed mechanism significantly reduces the execution time when we compare the proposed mechanism against the firefly algorithm and glowworm swarm optimization. The average execution time of the proposed cat swarm optimization was 131, while the average execution times for the firefly algorithm and glowworm optimization were 237 and 220, respectively. Hence, the experimental findings demonstrated that the proposed mechanism performs better than the firefly algorithm and glowworm swarm optimization in reducing the execution time of the jobs.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

Yousif,

Shohdy,

Hassan

et al. 2023

Electronics

View full text Add to dashboard Cite

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“…The firefly mechanism was introduced to minimize the execution times. The study in [33] proposed an Ions Motion Optimization (IMO) algorithm for scheduling cloud jobs. The IMO mechanism consists of two phases, the liquid and the crystal phases.…”

Section: Related Workmentioning

confidence: 99%

“…The IMO mechanism consists of two phases, the liquid and the crystal phases. These phases maintain a balance between rapid convergence and avoiding local optima in the algorithm's performance [33].…”

Section: Related Workmentioning

confidence: 99%

A Discrete Prey–Predator Algorithm for Cloud Task Scheduling

Abdulgader,

Yousif,

Ali

2023

Applied Sciences

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Cloud computing is considered a key Internet technology. Cloud providers offer services through the Internet, such as infrastructure, platforms, and software. The scheduling process of cloud providers’ tasks concerns allocating clients’ tasks to providers’ resources. Several mechanisms have been developed for task scheduling in cloud computing. Still, these mechanisms need to be optimized for execution time and makespan. This paper presents a new task-scheduling mechanism based on Discrete Prey–Predator to optimize the task-scheduling process in the cloud environment. The proposed Discrete Prey–Predator mechanism assigns each scheduling solution survival values. The proposed mechanism denotes the prey’s maximum surviving value and the predator’s minimum surviving value. The proposed Discrete Prey–Predator mechanism aims to minimize the execution time of tasks in cloud computing. This paper makes a significant contribution to the field of cloud task scheduling by introducing a new mechanism based on the Discrete Prey–Predator algorithm. The Discrete Prey–Predator mechanism presents distinct advantages, including optimized task execution, as the mechanism is purpose-built to optimize task execution times in cloud computing, improving overall system efficiency and resource utilization. Moreover, the proposed mechanism introduces a survival-value-based approach, as the mechanism introduces a unique approach for assigning survival values to scheduling solutions, differentiating between the prey’s maximum surviving value and the predator’s minimum surviving value. This improvement enhances decision-making precision in task allocation. To evaluate the proposed mechanism, simulations using the CloudSim simulator were conducted. The experiment phase considered different scenarios for testing the proposed mechanism in different states. The simulation results revealed that the proposed Discrete Prey–Predator mechanism has shorter execution times than the firefly algorithm. The average of the five execution times of the Discrete Prey–Predator mechanism was 270.97 s, while the average of the five execution times of the firefly algorithm was 315.10 s.

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“…For this reason, service controllers must use advanced and intelligent techniques to deploy large candidates of services. In recent years, several approaches for efficient service deployment have been proposed and are generally classified into 3 main categories [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]: (1) green IoT approaches [5][6][7][8][9], (2) optimization approaches [11][12][13][14], and (3) hybrid approaches combining mobile devices, fog, and cloud optimization techniques [15][16][17][18][19]. Authors in [8] presented a new model called Health-Fog based on deep learning for saving energy on the fog and the cloud while the data are processed and transmitted from the fog to the cloud.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Adaptive Bio-Inspired Multi-Agent System for Healthcare Task Deployment

Reffad

Alti

Almuhirat

2023

Eng. Technol. Appl. Sci. Res.

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The use of the Internet of Things (IoT) in healthcare is increasing significantly, bringing high-quality health services, but it still generates massive data with massive energy consumption. Due to the limited resources of fog servers and their impact on limiting the time needed for health data analysis tasks, the need to handle this problem in a fast way has become a necessity. To address this issue, many optimization and IoT-based approaches have been proposed. In this paper, a dynamic and adaptive healthcare service deployment controller using hybrid bio-inspired multi-agents is proposed. This method offers optimal energy costs and maintains the highest possible performance for fog cloud computing. At first, IGWO (Improved Grey Wolf Optimization) is used to initialize the deployment process using the nearest available fog servers. Then, an efficient energy-saving task deployment was achieved through Particle Swarm Optimization (PSO) to reduce energy consumption, increase rewards across multiple fog servers, and improve task deployment. Finally, to ensure continuous control of underloaded and overloaded servers, the neighborhood multi-agent coordination model is developed to manage healthcare services between the fog servers. The developed approach is implemented in the iFogSim simulator and various evaluation metrics are used to evaluate the effectiveness of the suggested approach. The simulation outcome proved that the suggested technique provides has better performance than other existing approaches.

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Cloud Job ‎Scheduling with‎ Ions Motion Optimization Algorithm

Cited by 7 publications

References 39 publications

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

A Discrete Prey–Predator Algorithm for Cloud Task Scheduling

A Dynamic Adaptive Bio-Inspired Multi-Agent System for Healthcare Task Deployment

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