Resource Provisioning Based Scheduling Framework for Execution of Heterogeneous and Clustered Workloads in Clouds: from Fundamental to Autonomic Offering

  • Sukhpal Singh Gill
  • Rajkumar Buyya


Provisioning of adequate resources to cloud workloads depends on the Quality of Service (QoS) requirements of these cloud workloads. Based on workload requirements (QoS) of cloud users, discovery and allocation of best workload-resource pair is an optimization problem. Acceptable QoS can be offered only if provisioning of resources is appropriately controlled. So, there is a need for a QoS-based resource provisioning framework for the autonomic scheduling of resources to observe the behavior of the services and adjust it dynamically in order to satisfy the QoS requirements. In this paper, framework for self-management of cloud resources for execution of clustered workloads named as SCOOTER is proposed that efficiently schedules the provisioned cloud resources and maintains the Service Level Agreement (SLA) by considering properties of self-management and the maximum possible QoS parameters are required to improve cloud based services. Finally, the performance of SCOOTER has been evaluated in a cloud environment that demonstrates the optimized QoS parameters such as execution cost, energy consumption, execution time, SLA violation rate, fault detection rate, intrusion detection rate, resource utilization, resource contention, throughput and waiting time.


Cloud computing Cloud workloads Resource provisioning Resource scheduling Quality of service Autonomic computing Service level agreement Self-management Self-healing Self-configuring Self-optimizing Self-protecting Quality of service Resource management Autonomic cloud E-commerce as a cloud service 


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One of the authors, Dr. Sukhpal Singh Gill [Post Doctorate Fellow], gratefully acknowledges the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia, for awarding him the Fellowship to carry out this research work. We thank Adel Nadjaran Toosi and anonymous reviewers for their comments on improving the paper.


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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information SystemsThe University of MelbourneMelbourneAustralia

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