QRAS: efficient resource allocation for task scheduling in cloud computing

Singh, Harvinder; Bhasin, Anshu; Kaveri, Parag Ravikant

doi:10.1007/s42452-021-04489-5

Cited by 14 publications

(11 citation statements)

References 19 publications

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“…But, the energy-aware task scheduling was not designed in the real cloud environment. QoS-based Resource Allocation and Scheduling techniques were designed in [13] based on swarm-based ant colony optimization. However, resource management was not concentrated to enhance the scheduling efficiency.…”

Section: Related Workmentioning

confidence: 99%

Multivariate Piecewise Regressive African Buffalo Optimization-Based Resource-Aware Task Scheduling in Cloud

Tamilsenthil¹

2022

ijarcs

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Distributed cloud computing handles a large number of tasks and provides many dynamic virtualized resources that aim to share as a service through the internet. While handling a large volume of tasks, task execution times, throughput, and makespan are the most significant metrics in practical scenarios. So, the scheduling task is essential to achieve accuracy and correctness on task completion. A novel technique called Multivariate Piecewise Regressive African Buffalo Optimization-based Resource Aware Task Scheduling (MPRABO-RATS) is introduced for improving the task scheduling efficiency and minimizing time consumption. First, the cloud user dynamically generates numerous heterogeneous tasks in the cloud environments. After receiving the tasks, the task scheduler in the cloud server finds the resourceoptimized virtual machine using the Multivariate Piecewise Regressive African Buffalo Optimization technique. The proposed optimization technique uses the Multivariate Piecewise Regression function for analyzing the different resources availablity such as CPU Time, Memory, Bandwidth, and Energy before the task scheduling. Initially, the population of the virtual machine is defined. After that, the fitness is measured using Multivariate Piecewise Regression. Based on the fitness estimation, the resource-efficient virtual machine is determined. Finally, the task scheduler assigns the tasks to the resource-optimized virtual machine with higher efficiency. Experimental evaluation is carried out in the CloudSim simulator on the factors such as task scheduling Efficiency, Throughput, Makespan, and Memory Consumption with respect to a number of tasks. The observed results indicate that the MPRABO-RATS technique offers an efficient solution in terms of achieving higher task scheduling Efficiency, Throughput, and Minimizing the Makespan as well as Memory Consumption than the conventional scheduling techniques.

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Section: Related Workmentioning

confidence: 99%

Multivariate Piecewise Regressive African Buffalo Optimization-Based Resource-Aware Task Scheduling in Cloud

Tamilsenthil¹

2022

ijarcs

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“…The effectiveness of the approach is justified in terms of various parameters like energy consumption, load balancing, etc. The service level agreement was satisfied by Singh et al (2021) in order to upgrade the performance of task scheduling in cloud computing scenario.…”

Section: Literature Reviewmentioning

confidence: 99%

Time Effective Cloud Resource Scheduling Method for Data-Intensive Smart Systems

Duan

Liying

et al. 2022

International Journal of Information Technology and Web Engineering

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The cloud computing platforms are being deployed nowadays for resource scheduling of real time data intensive applications. Cloud computing still deals with the challenge of time oriented effective scheduling for resource allocation, while striving to provide the efficient quality of service. This article proposes a time prioritization-based ensemble resource management and Ant Colony based optimization (ERM-ACO) algorithm in order to aid effective resource allocation and scheduling mechanism which specifically deals with the task group feasibility, assessing and selecting the computing and the storage resources required to perform specific tasks. The research outcomes are obtained in terms of time-effective demand fulfillment rate, average response time as well as resource utilization time considering various grouping mechanisms based on data arrival intensity consideration. The proposed framework when compared to the present state-of-the-art methods, optimal fitness percentage of 98% is observed signifying the feasible outcomes for real-time scenarios.

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“…On the virtues of the existing challenges affecting allocation of resource in cloud computing, the need to provide more efficient resource allocation techniques is paramount. These motivated [27], to propose a QoS-based resource allocation scheme using swarm-based ant colony optimization. The effectiveness of their proposed solution was demonstrated via simulation, and results show significant performance than the benchmarked schemes.…”

Section: Introductionmentioning

confidence: 99%

Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme

Gabi

Dankolo

Muslim

et al. 2022

Neural Comput & Applic

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Achieving sustainable profit advantage, cost reduction and resource utilization are always a bottleneck for resource providers, especially when trying to meet the computing needs of resource hungry applications in mobile edge-cloud (MEC) continuum. Recent research uses metaheuristic techniques to allocate resources to large-scale applications in MECs. However, some challenges attributed to the metaheuristic techniques include entrapment at the local optima caused by premature convergence and imbalance between the local and global searches. These may affect resource allocation in MECs if continually implemented. To address these concerns and ensure efficient resource allocation in MECs, we propose a fruit fly-based simulated annealing optimization scheme (FSAOS) to serve as a potential solution. In the proposed scheme, the simulated annealing is incorporated to balance between the global and local search and to overcome its premature convergence. We also introduce a trade-off factor to allow application owners to select the best service quality that will minimize their execution cost. Implementation of the FSAOS is carried out on EdgeCloudSim Simulator tool. Simulation results show that the FSAOS can schedule resources effectively based on tasks requirement by returning minimum makespan and execution costs, and achieve better resource utilization compared to the conventional fruit fly optimization algorithm and particle swarm optimization. To further unveil how efficient the FSAOSs, a statistical analysis based on 95% confidential interval is carried out. Numerical results show that FSAOS outperforms the benchmark schemes by achieving higher confidence level. This is an indication that the proposed FSAOS can provide efficient resource allocation in MECs while meeting customers’ aspirations as well as that of the resource providers.

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QRAS: efficient resource allocation for task scheduling in cloud computing

Cited by 14 publications

References 19 publications

Multivariate Piecewise Regressive African Buffalo Optimization-Based Resource-Aware Task Scheduling in Cloud

Multivariate Piecewise Regressive African Buffalo Optimization-Based Resource-Aware Task Scheduling in Cloud

Time Effective Cloud Resource Scheduling Method for Data-Intensive Smart Systems

Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme

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