The Journal of Supercomputing

, Volume 71, Issue 1, pp 241–292 | Cite as

QRSF: QoS-aware resource scheduling framework in cloud computing



Cloud computing harmonizes and delivers the ability of resource sharing over different geographical sites. Cloud resource scheduling is a tedious task due to the problem of finding the best match of resource-workload pair. The efficient management of dynamic nature of resource can be done with the help of cloud workloads. Till cloud workload is deliberated as a central capability, the resources cannot be utilized in an effective way. In literature, very few efficient resource scheduling policies for energy, cost and time constraint cloud workloads are reported. This paper presents an efficient cloud workload management framework in which cloud workloads have been identified, analyzed and clustered through K-means on the basis of weights assigned and their QoS requirements. Further scheduling has been done based on different scheduling policies and their corresponding algorithms. The performance of the proposed algorithms has been evaluated with existing scheduling policies through CloudSim toolkit. The experimental results show that the proposed framework gives better results in terms of energy consumption, execution cost and time of different cloud workloads as compared to existing algorithms.


Cloud workload Cloud computing Resource scheduling Energy consumption IaaS Quality of service 



One of the authors, Sukhpal Singh, gratefully acknowledges the Department of Science and Technology (DST), Government of India, for awarding him the INSPIRE (Innovation in Science Pursuit for Inspired Research) Fellowship (Registration/IVR Number: 201400000761 [DST/INSPIRE/03/2014/000359]) to carry out this research work. We would like to thank all the anonymous reviewers for their valuable comments and suggestions for improving the paper. We would like to thank Dr. Maninder Singh for helping in improving the language and expression of preliminary version of paper.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentThapar UniversityPatialaIndia

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