Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing

Mapetu, Jean Pepe Buanga; Chen, Zhen; Kong, Lingfu

doi:10.1007/s10489-019-01448-x

Cited by 89 publications

(31 citation statements)

References 46 publications

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“…Authors in References 51‐53 optimized the makespan of the workflow application. Makespan and Cost minimization of the workflow execution have been considered by many authors in References 54‐62 without any budget or deadline constraints. Authors in References 63‐69, considered the minimization of makespan and cost under the budget and deadline constraint; authors have not considered the simulator of cloud computing environment.…”

Section: Application Of Gtlbo In Workflow Scheduling Problem On Cloudmentioning

confidence: 99%

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Ram

Srivastava

Mishra

2021

Concurrency and Computation

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Teaching learning‐based optimization (TLBO) was proposed by Rao to solve optimization problems. It is based on the theory of teaching‐learning mechanism. Although it performs well in unimodal problems yet its performance is not good in multimodal problems. To further improve this algorithm's performance and make it suitable for both unimodal problems and multimodal problems, we made some major changes in the theory and the algorithm's operators. The proposed algorithm is able to capture diverse optimal solutions in less number of iterations and is very good for solving multimodal problems. This newly created variant of TLBO is named generalized TLBO (GTLBO). The performance of GTLBO is tested on CEC−06, 2019 benchmark functions and other 15 classical benchmark functions, and it is found that the proposed algorithm is performing better comparatively. Then it is simulated for solving the workflow scheduling problem in CloudSim. Standard scientific workflow applications as Montage, Epigenomics, Sipht, and a sample workflow are used as dataset to test algorithms' performance in cloud environments. Our proposed approach, GTLBO, provides the proper distribution of workloads and offers minimal execution‐cost for the workflow applications. Results reflect the supremacy of the proposed algorithm GTLBO comparatively.

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Section: Application Of Gtlbo In Workflow Scheduling Problem On Cloudmentioning

confidence: 99%

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Ram

Srivastava

Mishra

2021

Concurrency and Computation

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“…However, there are still disadvantages for improvement in terms of load and resource utilization. Based on the particle swarm optimization algorithm, Mapetu et al [31] designed a load balancing strategy for updating particle location, which has better performance in task scheduling and load balancing. Chen and Long [32] combine particle swarm algorithm and ant colony algorithm by adjusting the learning factors to optimize the task scheduling on fitness, cost, and operation cycle.…”

Section: Related Workmentioning

confidence: 99%

Many-Objective Cloud Task Scheduling

Geng

Wang

et al. 2020

IEEE Access

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Task scheduling problem refers to how to reasonably arrange many tasks provided by users in virtual machines, which is very important in the cloud computing. And the quality of the scheduling performance directly affects the customer satisfaction and the provider benefits. In order to describe the task scheduling problem of cloud computing more precisely and improve the scheduling performance. This paper establishes many-objective cloud model, including four objectives: minimizing time, minimizing costs, maximizing resource utilization, and balancing load. At the same time, a many-objective optimization algorithm based on hybrid angles (MaOEA-HA) is proposed to solve this model. Hybrid angle strategy is designed to optimize the algorithm better, which combines two angle strategies: individual-to-individual angle and individual-to-reference point angle. One by one elimination strategy was introduced to remain individuals with better performance. By comparing with five other advanced many-objective optimization algorithms, MaOEA-HA shows the best performance on the DTLZ test suite. Moreover, different algorithms are applied to solve the cloud task scheduling problem, and MaOEA-HA algorithm achieves best results.INDEX TERMS Cloud computing, hybrid-angle strategy, many-objective algorithm, task scheduling.

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“…J. P. B. Mapetu, et al, [21] scheduled and balanced the tasks by developing the binary version of PSO as BPSO with low cost and low time complexity. The execution cost was reduced and avoided the under-loaded VM as well as over-loaded VM by improving the updating method for particle position.…”

Section: Literature Workmentioning

confidence: 99%

An efficient Task Scheduling and Load Balancing in Cloud Computing using KD-Tree Algorithm

S P*,

D’Mello

2019

IJITEE

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Cloud Computing provides the sharing ability and access for available cloud host and various distributed environments, namely Load Balancing (LB), virtualization technologies and scheduling techniques. The satisfaction of both users and cloud providers are the major issues for effective LB and task scheduling algorithms in cloud resource management, where the requirements namely high resource utilization, low monetary costs and minimum makespan. Many researchers tried to develop various heuristic and meta-heuristic algorithms to attain the aforementioned user requirements. But, when the number of tasks grows exponentially, these algorithms failed to achieve LB, lower running time, and it faces the high time complexity. In this research work, a KD-Tree algorithm is developed to address the issues of heuristic algorithms and provide efficient LB by partitioning the environments into several tasks. According to the deadline of task execution, the remaining tasks are adjusted dynamically by the proposed KD-tree algorithm in the virtual environment. The experiments are conducted to evaluate the efficiency of KD-Tree algorithm with existing heuristic techniques by using makespan, energy consumption and task migrations. When the number of tasks is 20, the proposed KD-Tree algorithm achieved 71.33% makespan and 5% task migrations.

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Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing

Cited by 89 publications

References 46 publications

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Many-Objective Cloud Task Scheduling

An efficient Task Scheduling and Load Balancing in Cloud Computing using KD-Tree Algorithm

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