Task scheduling in cloud computing using hybrid optimization algorithm

Khan, Mohd Sha Alam; Santhosh, R.

doi:10.1007/s00500-021-06488-5

Cited by 38 publications

(20 citation statements)

References 27 publications

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“…Khan and Santhosh [33] have presented a work scheduling method based on a hybrid optimization algorithm that has improved parameters such as waiting time, total production time, execution time, efciency, and utilization. In terms of performance, this scheduling method is superior to the scheduling algorithms based on the optimization of PSO and ACO.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Gravitational Emulation Local Search‐Based Algorithm for Task Scheduling in Cloud Computing

Praveen¹,

Ghasempoor

Shahabi

et al. 2023

Mathematical Problems in Engineering

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The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Gravitational Emulation Local Search‐Based Algorithm for Task Scheduling in Cloud Computing

Praveen¹,

Ghasempoor

Shahabi

et al. 2023

Mathematical Problems in Engineering

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“…A hybrid optimization algorithm for resource selection is presented by Khan et al (2021) [17]. The average waiting time is compared with existing approaches to show the model's effectiveness.…”

Section: Lin Et Al (2014)mentioning

confidence: 99%

“…Fig.3shows the AWT of RR, SRDQ,Khan et al (2021), proposed algorithm vs. various data sets. The simulation result shows that the proposed algorithm decreased up to 39%, 34% and 30% of the AWT over RR, SRDQ, Khan et al (2021) scheduling algorithms respectively.…”

mentioning

confidence: 99%

Interpretive Structural Modeling Based Assessment and Optimization of Cloud with Internet of Things (CloudIoT) Issues Through Effective Scheduling

Shukla¹,

Khan²,

Kumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Integrated CloudIoT is an emerging field of study that integrates the Cloud and the Internet of Things (IoT) to make machines smarter and deal with real-world objects in a distributed manner. It collects data from various devices and analyses it to increase efficiency and productivity. Because Cloud and IoT are complementary technologies with distinct areas of application, integrating them is difficult. This paper identifies various CloudIoT issues and analyzes them to make a relational model. The Interpretive Structural Modeling (ISM) approach establishes the interrelationship among the problems identified. The issues are categorised based on driving and dependent power, and a hierarchical model is presented. The ISM analysis shows that scheduling is an important aspect and has both (driving and dependence) power to improve the performance of the CloudIoT model. Therefore, existing CloudIoT job scheduling algorithms are analysed, and a cloud-centric scheduling mechanism is proposed to execute IoT jobs on a suitable cloud. The cloud implementation using an open-source framework to simulate Cloud Computing (CloudSim), based on the job's workload, is presented. Simulation results of the proposed scheduling model indicate better performance in terms of Average Waiting Time (AWT) and makespan than existing cloud-based scheduling approaches.

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“…In every parameter, the proposed hybrid optimization-based work scheduling outperforms previous approaches. is work cannot explore better QoS with VM allocation [24].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

et al. 2022

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Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real-time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology-enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES-ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC-PSO, and PSO-COGENT algorithms, the created HES-ACO algorithm was simulated at CloudSim and found to optimize all parameters.

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Task scheduling in cloud computing using hybrid optimization algorithm

Cited by 38 publications

References 27 publications

A Hybrid Gravitational Emulation Local Search‐Based Algorithm for Task Scheduling in Cloud Computing

A Hybrid Gravitational Emulation Local Search‐Based Algorithm for Task Scheduling in Cloud Computing

Interpretive Structural Modeling Based Assessment and Optimization of Cloud with Internet of Things (CloudIoT) Issues Through Effective Scheduling

Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

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