Framework for Task scheduling in Cloud using Machine Learning Techniques

Shetty, C. M.; Sarojadevi, H.

doi:10.1109/icisc47916.2020.9171141

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…Supervised machine learning techniques can be used here. The outcome of the proposed work leads to the selection of the best task scheduling algorithm for the input task(request [2]. Attiya, I., Abd Elaziz, M., & Xiong, S fiscussed in their paper that in recent years, cloud computing technology has attracted extensive attention from both academia and industry.…”

Section: Literature Reviewmentioning

confidence: 99%

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

2024

IJSREM

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With the rapid development of cloud computing, more and more enterprises would like to upload and store their data in the public cloud. When the parts of the business of an enterprise are purchased by another enterprise, the corresponding data will be transferred to the acquiring enterprise. For the usual case, how to outsource the computation cost of data transfer to the cloud? How to ensure the remote purchased data integrity? Thus, it is important to study provable data possession with outsourced data transfer (DT-PDP). For the first time, we propose the novel concept: DT-PDP. By taking use of DT- PDP, the following three security requirements can be satisfied: (1) the other un-purchased data security of acquired enterprise can be ensured; (2) the purchased data integrity and privacy can be ensured; (3) the data transferability’s computation can be outsourced to the public cloud servers. For the security concept of DT-PDP, we give its motivation, system model and security model. Then, we design a concrete DT-PDP scheme based on the bilinear pairings. At last, we analyze the security, efficiency and flexibility of the concrete DT- PDP scheme. It shows that our scheme is provably secure and efficient.

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Section: Literature Reviewmentioning

confidence: 99%

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

2024

IJSREM

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“…The paper [32] proposes a new heuristic method termed Efficient Resource Allocation with Score(ERAS) for task scheduling in a cloud computing network. A supervised machine learning technique has been used in [33] to select the best scheduling algorithm for effectively allocating tasks to VMs. The method proposed in [34] uses the Bumble Bee Mating Optimization (BBMO) algorithm to optimize the makespan of the tasks.…”

Section: Related Workmentioning

confidence: 99%

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

et al. 2023

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In this paper, a task scheduling problem for a cloud computing environment is formulated by using the M/M/n queuing model. A priority assignment algorithm is designed to employ a new data structure named the waiting time matrix to assign priority to individual tasks upon arrival. In addition to this, the waiting queue implements a unique concept based on the principle of the Fibonacci heap for extracting the task with the highest priority. This work introduces a parallel algorithm for task scheduling in which the priority assignment to task and building of heap is executed in parallel with respect to the non-preemptive and preemptive nature of tasks. The proposed work is illustrated in a step-by-step manner with an appropriate number of tasks. The performance of the proposed model is compared in terms of overall waiting time and CPU time against some existing techniques like BATS, IDEA, and BATS+BAR to determine the efficacy of our proposed algorithms. Additionally, three distinct scenarios have been considered to demonstrate the competency of the task scheduling method in handling tasks with different priorities. Furthermore, the task scheduling algorithm is also applied in a dynamic cloud computing environment.

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“…To select a proper task scheduling algorithm for better performance in cloud computing is a critical task. Author in [9] suggested a Framework for the above problem. Author suggested that the decision of which task scheduling algorithm is suitable for a particular task should be taken by machine learning algorithm.…”

Section: Literature Review On Task Scheduling In Fog-cloud Environmentmentioning

confidence: 99%

An Adaptive Task Scheduling in Fog Computing

Harkut¹,

Thakar²,

Mutneja³

2023

Search Algorithm - Essence of Optimization

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Internet applications generate massive amount of data. For processing the data, it is transmitted to cloud. Time-sensitive applications require faster access. However, the limitation with the cloud is the connectivity with the end devices. Fog was developed by Cisco to overcome this limitation. Fog has better connectivity with the end devices, with some limitations. Fog works as intermediate layer between the end devices and the cloud. When providing the quality of service to end users, scheduling plays an important role. Scheduling a task based on the end users requirement is a tedious thing. In this paper, we proposed a cloud-fog task scheduling model, which provides quality of service to end devices with proper security.

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Framework for Task scheduling in Cloud using Machine Learning Techniques

Cited by 9 publications

References 11 publications

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

An Adaptive Task Scheduling in Fog Computing

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