Mr. Prathamesh Vijay Lahande
@sicsr.ac.in
Teaching Associate, Symbiosis Institute of Computer Studies and Research
Symbiosis International (Deemed University), Pune, India
RESEARCH INTERESTS
Reinforcement Learning, Cloud Computing
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Prathamesh Vijay Lahande and Parag Ravikant Kaveri
CRC Press
Tanishka Hemant Chopra and Prathamesh Vijay Lahande
Springer Nature Switzerland
Prathamesh Vijay Lahande and Parag Ravikant Kaveri
Springer Nature Switzerland
Prathamesh Vijay Lahande and Parag Ravikant Kaveri
Springer Nature Singapore
Prathamesh Lahande, Parag Kaveri, and Jatinderkumar Saini
MDPI AG
Cloud computing delivers robust computational services by processing tasks on its virtual machines (VMs) using resource-scheduling algorithms. The cloud’s existing algorithms provide limited results due to inappropriate resource scheduling. Additionally, these algorithms cannot process tasks generating faults while being computed. The primary reason for this is that these existing algorithms need an intelligence mechanism to enhance their abilities. To provide an intelligence mechanism to improve the resource-scheduling process and provision the fault-tolerance mechanism, an algorithm named reinforcement learning-shortest job first (RL-SJF) has been implemented by integrating the RL technique with the existing SJF algorithm. An experiment was conducted in a simulation platform to compare the working of RL-SJF with SJF, and challenging tasks were computed in multiple scenarios. The experimental results convey that the RL-SJF algorithm enhances the resource-scheduling process by improving the aggregate cost by 14.88% compared to the SJF algorithm. Additionally, the RL-SJF algorithm provided a fault-tolerance mechanism by computing 55.52% of the total tasks compared to 11.11% of the SJF algorithm. Thus, the RL-SJF algorithm improves the overall cloud performance and provides the ideal quality of service (QoS).
Prathamesh Vijay Lahande and Parag Kaveri
ACM
The cloud environment has become an essential platform due to its computing abilities and is being used in various fields and sectors all around the globe. Users from all over the globe use this computing platform to process their challenging tasks. The cloud computes these tasks on its Virtual Machines (VM) using the appropriate resource scheduling algorithms. While a particular task is being computed, there is always a chance that the cloud suffers damages due to the dynamically generated faults of the task. The cloud also needs better performance with proper resource scheduling, leading to increased costs. To focus on these problems and provide an intelligence mechanism to the cloud, an algorithm named Reinforcement Learning – First Come, First Serve (RL – FCFS) has been designed and implemented by combining the Reinforcement Learning (RL) technique with the existing resource scheduling algorithm First Come First Serve (FCFS) to handle the dynamic faults and provide better cost by improving the resource scheduling at its end. This RL – FCFS algorithm provides a fault-tolerance mechanism at the cloud's end by computing 55.5 % of tasks aggregately compared to an aggregate of 11.1 % for the FCFS. Also, it aggregately improves the cost by 18.50 % across all scenarios. With the RL – FCFS algorithm, the cloud will be in a learning phase at the beginning. With RL rewards and feedback, the cloud will adapt and begin to handle these dynamic faults over time and improve its resource scheduling process, ultimately providing the best Quality of Service (QoS).
Parag Ravikant Kaveri and Prathamesh Lahande
Springer Science and Business Media LLC
Prathamesh Vijay Lahande and Parag Ravikant Kaveri
CRC Press
Prathamesh Vijay Lahande, Parag Ravikant Kaveri, Jatinderkumar R. Saini, Ketan Kotecha, and Sultan Alfarhood
Institute of Electrical and Electronics Engineers (IEEE)
Cloud computing is a technology that enables the delivery of various computing services over the Internet. The Resource Scheduling (RS) and Load Balancing (LB) mechanisms are essential for the cloud to provide consistent results. The submitted tasks by the users are computed on the cloud platform using its Virtual Machines (VMs). The cloud ensures an ideal LB mechanism, where no VMs will be overloaded or idle. This research paper focuses on this LB mechanism by experimenting in the WorkflowSim environment and computing tasks using the Sipht task dataset. The RS algorithms First Come First Serve (FCFS), Maximum – Minimum (Max – Min), Minimum Completion Time (MCT), Minimum – Minimum (Min – Min), and Round-Robin (RR) are utilized to balance the computational load of VMs. The experiment was conducted in four phases, where the Sipht task dataset varied in task length in each phase. Each phase included sixteen scenarios, where each scenario differed from another by the number of VMs used. The final results of this experiment convey that the load balanced by the algorithms FCFS, Max – Min, MCT, Min – Min, and RR were 51.98 %, 41.71 %, 51.98 %, 59.43 %, and 52.17 %, respectively, across all four phases. Lastly, the Reinforcement Learning (RL) model is suggested to add an intelligence mechanism to LB and optimize the cloud resource utilization using these RS algorithms to provide the best Quality of Service (QoS).
Prathamesh Vijay Lahande and Parag Ravikant Kaveri
Springer Nature Singapore
Prathamesh Vijay Lahande and Parag Ravikant Kaveri
Springer Nature Switzerland
Prathamesh Vijay Lahande and Parag Ravikant Kaveri
Springer Singapore