Task Scheduling Algorithms with Multiple Factor in Cloud Computing Environment

Bansal, Nidhi; Awasthi, Amit; Bansal, Shruti

doi:10.1007/978-81-322-2755-7_64

Cited by 22 publications

(11 citation statements)

References 8 publications

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“…The result proves that the suggested algorithm outperformed DSOS to achieve better convergence ratio and quality of results. Bansal, et al [ 16 ] consider the parameters for cost and load balancing by Virtual Machine Tree (VMT) enhanced task scheduling algorithm and verified that the parameter for cost is not so effective with proposed algorithm. Razaque, et al [ 17 ] put forward an efficient task scheduling algorithm that offers divisible task scheduling in view of network bandwidth and automatically implements the tasks when tasks are scheduled for the execution.…”

Section: Related Workmentioning

confidence: 99%

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment

et al. 2017

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Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.

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Section: Related Workmentioning

confidence: 99%

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment

et al. 2017

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“…Keeping in mind the recent and valuable research, related work has been written in the paper and is shown in Table 1. With the help of nature, technology is created [30], [31].…”

Section: Related Workmentioning

confidence: 99%

Effective task scheduling algorithm in cloud computing with quality of service alert bees and grey wolf optimization

Bansal

Singh

2022

IJEECS

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Quality-based services are an indicative factor in providing a meaningful measure. These measures allow labeling for upcoming targets with a numerical comparison with a valid mathematical proof of reading and publications. By obtaining valid designs, organizations put this measure into the flow of technology development operations to generate higher profits. Since the conditions were met from the inception of cloud computing technology, the market was captured heavily by providing support through cloud computing. With the increase in the use of cloud computing, the complexity of data has also increased greatly. Applying natural theory to cloud technology makes it a fruit cream. Natural methods often come true, because survival depends on the live events and happenings, so using it in real life as well as any communication within technology will always be reliable. The numerical results are also showing a better value by comparing the optimization method. Finally, the paper introduces an adaptation theory with effective cloudsim coding of honey bees and grey wolf in conjunction with their natural life cycle for solving task scheduling problems. Using adapted bees improved the results by 50% compared with the original bees and secondly by honeybees and grey wolf improved 60%.

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“…Nidhi Bansal and et.al (2015) analysed the performance of cloud computing 3.0 in terms of installation and operation costs. The author also designed a QoS driven task scheduling process [11] using NetBeans IDE 8.0 and had compared with the performance of the existing algorithms and the results proves that the proposed model consumes less operational costs.…”

Section: Recent Research Activitiesmentioning

confidence: 99%

An Efficient Hybrid Block Chain -vibrant Neighborhood Partile Swarm Optimization (B- Vnpso) Algorithm for Enhancing Qos in Cloud Computing Services

Tamilarasu

Singaravel²

2022

Preprint

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As the digital era has progressed, so has the volume of digital data, making secure storage of this vast amount of data an increasingly important concern for data administrators. Cloud computing and the ease of storing and accessing data attracted a large number of IT professionals and administrators to migrate from physical infrastructure to cloud infrastructure. Cloud computing provides a wide range of services, but its primary functionality is the efficient distribution of resources among the many people who need to use the cloud. Effective resource allocation, decreased response time, and increased resource availability are all benefits of optimising cloud computing Quality of Service (QoS), which also helps to keep user data safe in the cloud infrastructure. Even though many studies have sought a high quality of service (QoS), no success has been found in combining efficient resource sharing with privacy protection. This paper uses Block chain technology and the Vibrant Neighborhood Particle Swarm Optimization (VNPSO) algorithm to overcome the aforementioned challenge of achieving better QoS with high levels of data security. While the Block chain has a reduced parameter, it is highly efficient in protecting cloud network storage data, while the Vibrant Neighborhood Particle Swarm Optimization (VNPSO) algorithm is an improvised version of Particle Swarm Optimization (PSO) and is efficient in resource allocation without any restrictions or boundaries in the neighbourhood available resources. Experimentation shows that the proposed meta-heuristic hybrid algorithm outperforms conventional algorithms in terms of Quality of Service.

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Task Scheduling Algorithms with Multiple Factor in Cloud Computing Environment

Cited by 22 publications

References 8 publications

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment

Effective task scheduling algorithm in cloud computing with quality of service alert bees and grey wolf optimization

An Efficient Hybrid Block Chain -vibrant Neighborhood Partile Swarm Optimization (B- Vnpso) Algorithm for Enhancing Qos in Cloud Computing Services

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