DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing

Task scheduling in cloud paradigm brought attention of all researchers as it is a challenging issue due to uncertainty, heterogeneity, and dynamic nature as they are varied in size, processing capacity and number of tasks to be scheduled. Therefore, ineffective scheduling technique may lead to incre...

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Veröffentlicht in:Multimedia tools and applications Jg. 83; H. 3; S. 8359 - 8387
Hauptverfasser: Mangalampalli, Sudheer, Karri, Ganesh Reddy, Kumar, Mohit, Khalaf, Osama Ibrahim, Romero, Carlos Andres Tavera, Sahib, GhaidaMuttashar Abdul
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.01.2024
Springer Nature B.V
Schlagworte:
Algorithms
Cloud computing
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Energy consumption
Heterogeneity
Heuristic methods
Heuristic task scheduling
Machine learning
Multimedia
Multimedia Information Systems
Scheduling
Simulation
Special Purpose and Application-Based Systems
Workload
Workloads
Energy consumption
Cloud Computing
Deep Q- Learning
Task Scheduling
SLA violation
Makespan
Machine Learning
ISSN:1380-7501, 1573-7721
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Zusammenfassung:Task scheduling in cloud paradigm brought attention of all researchers as it is a challenging issue due to uncertainty, heterogeneity, and dynamic nature as they are varied in size, processing capacity and number of tasks to be scheduled. Therefore, ineffective scheduling technique may lead to increase of energy consumption SLA violations and makespan. Many of authors proposed heuristic approaches to solve task scheduling problem in cloud paradigm but it is fall behind to achieve goal effectively and need improvement especially while scheduling multimedia tasks as they consists of more heterogeneity, processing capacity. Therefore, to handle this dynamic nature of tasks in cloud paradigm, a scheduling mechanism, which automatically takes the decision based on the upcoming tasks onto cloud console and already running tasks in the underlying virtual resources. In this paper, we have used a Deep Q-learning network model to addressed the mentioned scheduling problem that search the optimal resource for the tasks. The entire extensive simulationsare performed usingCloudsim toolkit. It was carried out in two phases. Initially random generated workload is used for simulation. After that, HPC2N and NASA workload are used to measure performance of proposed algorithm. DRLBTSA is compared over baseline algorithms such as FCFS, RR, Earliest Deadline first approaches. From simulation results it is evident that our proposed scheduler DRLBTSA minimizes makespan over RR,FCFS, EDF, RATS-HM, MOABCQ by 29.76%, 41.03%, 27.4%, 33.97%, 33.57% respectively. SLA violation percentage for DRLBTSA minimized overRR,FCFS, EDF, RATS-HM, MOABCQ by48.12%, 41.57%, 37.57%, 36.36%, 30.59% respectively and energy consumption for DRLBTSA over RR,FCFS, EDF, RATS-HM, MOABCQ by36.58%,43.2%, 38.22%, 38.52%, 33.82%existing approaches.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-023-16008-2