SLA-Based Advance Reservations with Flexible and Adaptive Time QoS Parameters

  • Marco A. S. Netto
  • Kris Bubendorfer
  • Rajkumar Buyya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4749)


Utility computing enables the use of computational resources and services by consumers with service obligations and expectations defined in Service Level Agreements (SLAs). Parallel applications and workflows can be executed across multiple sites to benefit from access to a wide range of resources and to respond to dynamic runtime requirements. A utility computing provider has the difficult role of ensuring that all current SLAs are provisioned, while concurrently forming new SLAs and providing multiple services to numerous consumers. Scheduling to satisfy SLAs can result in a low return from a provider’s resources due to trading off Quality of Service (QoS) guarantees against utilisation. One technique is to employ advance reservations so that an SLA aware scheduler can properly manage and schedule its resources. To improve system utilisation we exploit the principle that some consumers will be more flexible than others in relation to the starting or completion time, and that we can juggle the execution schedule right up until each execution starts. In this paper we present a QoS scheduler that uses SLAs to efficiently schedule advance reservations for computation services based on their flexibility. In our SLA model users can reduce or increase the flexibility of their QoS requirements over time according to their needs and resource provider policies. We introduce our scheduling algorithms, and show experimentally that it is possible to use flexible advance reservations to meet specified QoS while improving resource utilisation.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Marco A. S. Netto
    • 1
  • Kris Bubendorfer
    • 2
  • Rajkumar Buyya
    • 1
  1. 1.Grid Computing and Distributed Systems (GRIDS) Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia, ICT Building, 111 Barry Street, Carlton, VIC 3053 
  2. 2.School of Mathematics Statistics and Computer Science, Victoria University of Wellington, Wellington 6140New Zealand

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