Proficient job scheduling in cloud computation using an optimized machine learning strategy

Neelakantan, Prasanna; Yadav, Nikhil

doi:10.1007/s41870-023-01278-8

Cited by 13 publications

(2 citation statements)

References 36 publications

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“…In this section, a detailed analysis and discussion of results which we presented earlier in Sections 5.2-5.13 are given. For the simulation of DRFTSA, we used Cloudsim [42] as the simulation platform. To evaluate our proposed DRFTSA, a real-time dataset was given as an input to the algorithm, i.e., the Google Cloud Jobs dataset from [43].…”

Section: Analysis and Discussion Of Resultsmentioning

confidence: 99%

“…This subsection discusses the configured simulation settings for DRFTSA as well as the dataset used in our research work. For our research work, Cloudsim [42] was used as a platform to simulate the cloud environment. After identifying the platform, we configured this simulation platform in our physical host.…”

Section: Simulation Settings and Dataset Used For Drftsamentioning

confidence: 99%

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A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

Shiva Rama Krishna,

Mangalampalli

2023

Applied Sciences

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Task scheduling poses a wide variety of challenges in the cloud computing paradigm, as heterogeneous tasks from a variety of resources come onto cloud platforms. The most important challenge in this paradigm is to avoid single points of failure, as tasks of various users are running at the cloud provider, and it is very important to improve fault tolerance and maintain negligible downtime in order to render services to a wide range of customers around the world. In this paper, to tackle this challenge, we precisely calculated priorities of tasks for virtual machines (VMs) based on unit electricity cost and these priorities are fed to the scheduler. This scheduler is modeled using a deep reinforcement learning technique which is known as the DQN model to make decisions and generate schedules optimally for VMs based on priorities fed to the scheduler. This research is extensively conducted on Cloudsim. In this research, a real-time dataset known as Google Cloud Jobs is used and is given as input to the algorithm. This research is carried out in two phases by categorizing the dataset as a regular or large dataset with real-time tasks with fixed and varied VMs in both datasets. Our proposed DRFTSA is compared to existing state-of-the-art approaches, i.e., PSO, ACO, and GA algorithms, and results reveal that the proposed DRFTSA minimizes makespan compared to PSO, GA, and ACO by 30.97%, 35.1%, and 37.12%, rates of failure by 39.4%, 44.13%, and 46.19%, and energy consumption by 18.81%, 23.07%, and 28.8%, respectively, for both regular and large datasets for both fixed and varied VMs.

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Section: Analysis and Discussion Of Resultsmentioning

confidence: 99%

Section: Simulation Settings and Dataset Used For Drftsamentioning

confidence: 99%

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

Shiva Rama Krishna,

Mangalampalli

2023

Applied Sciences

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Secure data transmission in cloud computing using a cyber-security trust model with multi-risk protection scheme in smart IOT application

Kamble,

Ghuge,

Jain

et al. 2024

Cluster Comput

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Virtual Machine Allocation Using Optimal Resource Management Approach

Rawat

2024

Wireless Pers Commun

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Resources are offered to customers on demand in the modern era of computing, communication, and technology. User demand of the resources depends on service provider and consumer. The optimal assignment of the cloud resources depends on fitness function and resource management technique. In this manuscript, the key focus is to proposed a model based on meta-heuristic evaluation technique. The meta-heuristic evaluation technique provides an optimal placement of the virtual machines to the user requests across the globe. The presented framework, elephant heard optimization with neural network (EHO-ANN) outperforms the existing static, dynamic and nature inspired techniques. The EHO-ANN is evaluated and analyzed against Max-Min, Genetic Approach and BB-BC cost aware approach. The evaluation and analysis include the performance metric, average execution time(ms), and cost.

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Proficient job scheduling in cloud computation using an optimized machine learning strategy

Cited by 13 publications

References 36 publications

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

Secure data transmission in cloud computing using a cyber-security trust model with multi-risk protection scheme in smart IOT application

Virtual Machine Allocation Using Optimal Resource Management Approach

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