Improved Task Scheduling Strategy Using Reinforcement Learning in Cloud Environment

Sharma, Sanjeev; Pandey, Neeraj Kumar

doi:10.1109/cisct55310.2022.10046618

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The provision of shared resources by the different users as per their demand through the internet is an important facility provided by cloud computing. Cloud computing is becoming popular in IT as an adaptable, elastic, and simple approach to developing service platforms using outsourced resources [1]. Furthermore, a parallel and distributed computing hybrid allows us to use collective resources on a pay-as-you-go basis, and the only requirement is an internet connection.…”

Section: Introductionmentioning

confidence: 99%

Multi-Faceted Job Scheduling Optimization Using Q-learning With ABC In Cloud Environment

Sharma,

Kumar Pandey

2024

IJCDS

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Resource allocation is the utmost challenging and common problem, particularly in the cloud service model in Infrastructure as a Service (IaaS). The issue of load balancing is so harmful that irregular load balancing may result in a structure smash. Adopting a suitable access plan and allowing the system to spread work among all existing resources leads to utilizing Virtual Machines (VMs) appropriately. To get enhanced results from the Artificial Bee Colony Algorithm (ABC), a reinforcement learning technique Q-learning is combined using multifaceted job scheduling optimization based on ABC(QMFOABC) has been proposed. The proposed approach improves resource utilization and scheduling created on resource, cost, and makespan. The efficiency of the suggested strategy was evaluated in Datasets Synthetic Workload, Google Cloud Jobs (GoCJ), and Random by using CloudSim to the remaining scheduling strategies for load matching like Max-min, Multifaceted Cuckoo Search (MFCS), Multifaceted Particle Swarm Optimization (MFPSO), Q-learning, Heuristic job scheduling with Artificial Bee Colony approach with Largest Job First algorithm (HABC LJF), First come first serve (FCFS). According to the findings of the experiments, the algorithm that employed the QMFOABC method has better results in resource utilization, throughput, cost, and makespan. Compared to Max-Min (82.31%), MOPSO (35.62%), HABC LJF (21.65%), Q-Learning (11.72%), VTO-QABC FCFS(5.87%), and VTO-ABC LJF (5.86%) shorter time than MOCS, a considerable improvement is found.

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Section: Introductionmentioning

confidence: 99%

Multi-Faceted Job Scheduling Optimization Using Q-learning With ABC In Cloud Environment

Sharma,

Kumar Pandey

2024

IJCDS

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“…Virtualization Technology cyberinfrastructure makes cloud computing feasible. The service-oriented Infrastructure supports cloud computing on demand with high scalability, reliability, and elasticity [1], [2]. The end users and developers can use the pay-as-you-go model to use the cloud resources.…”

Section: Introductionmentioning

confidence: 99%

Efficient Virtual Machine Allocation using Human Based Optimizer (TLBO) with Artificial Neural Network Model

Sharma,

Rawat

2024

Preprint

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Cloud computing has revolutionized how resources are provisioned and utilized, offering scalability and flexibility to meet varying computational demands. Virtual Machine (VM) allocation, a critical aspect of cloud resource management, aims to optimize resource utilization while minimizing operational costs. In this paper, we propose a novel approach for VM allocation using a combination of Teaching Learning Optimization (TLO) and Artificial Neural Networks (ANN). The TLO-ANN model is designed to enhance the accuracy and efficiency of VM allocation decisions by leveraging knowledge transfer from pre-existing cloud environments. The proposed approach integrates historical allocation patterns and resource usage characteristics from multiple source clouds, allowing the model to learn from diverse scenarios. The TLO mechanism adapts the knowledge acquired from the source clouds to the target cloud's specific conditions, enabling effective allocation even in cases with limited target domain data. Furthermore, the ANN component of our approach provides a dynamic decision-making framework by continuously learning and adapting to changing workload patterns. The model considers multiple parameters to predict optimal VM allocation strategies, including CPU utilization, memory usage, and network traffic. The results demonstrate that our TLO-ANN approach outperforms traditional allocation methods regarding resource utilization, cost efficiency, and scalability. Through extensive experimentation and comparative analysis, we validate the effectiveness of the proposed TLO-ANN approach across various workload scenarios. The results showcase its ability to adapt to cloud environments, improving allocation accuracy and response times. Overall, this research contributes to advancing intelligent VM allocation techniques in the cloud computing Environment.

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Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach

Sharma,

Rawat

2024

Sustainable Operations and Computers

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Improved Task Scheduling Strategy Using Reinforcement Learning in Cloud Environment

Cited by 3 publications

References 21 publications

Multi-Faceted Job Scheduling Optimization Using Q-learning With ABC In Cloud Environment

Multi-Faceted Job Scheduling Optimization Using Q-learning With ABC In Cloud Environment

Efficient Virtual Machine Allocation using Human Based Optimizer (TLBO) with Artificial Neural Network Model

Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach

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