Scheduling and Resource Allocation based on Priority and SLA in Cloud Computing

Document Type : Persian Original Article

Authors

1 Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran

2 Young Researchers Club, Ardabil Branch, Islamic Azad University of Ardabil, Ardabil, Iran.

Abstract

In recent decades, with the popularity and application of cloud computing, significant changes have taken place in communications and technologies. Most service-level service providers are offering applications and developing their own hardware infrastructure as service improvements. Since cloud computing provides different resources to these providers, of course, the cost of access, speed, and other important parameters have led to a significant response, but an important point has given the increase and volume of requests from stakeholders. It has led to challenges in providing services at the service level. Therefore, scheduling and allocating resources to requests made with low-cost horizons and completion time has become a necessity, and service providers and stakeholders seek to receive the best possible service with high efficiency, and this has led to extensive research in this area. In this research, a model for scheduling and resource allocation considering priority and SLA in cloud computing is presented. In fact, the proposed model of several different levels of access has been developed to achieve the main goal of the research, which is the optimal scheduling and allocation of resources to the requests made. In the proposed method, using the RR algorithm and the technique of weighting requests and online review of virtual machines, the best possible source for proposal allocation and scheduling has been identified. The method presented in the form of a dynamic and executable model with the help of the Cloudsim simulation tool and in terms of Makespan, cost, and speedprocess have been compared and analyzed with several similar methods. The results obtained from the simulation performed by applying different scenarios indicate average processing speed around 2.15, and average Makespan is reduced at 8.68s by new method than similarity methods. Also, the rang of cost has not big change.

Keywords

References

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Journal of Soft Computing and Information Technology
Volume 11, Issue 1 - Serial Number 35
Spring
June 2022
Pages 49-59

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