QoS and Contention-Aware Multi-Resource Reservation

  • Authors:

  • Dongyan Xu;Klara Nahrstedt;Duangdao Wichadakul

  • Affiliations:

  • Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

  • Venue:
  • Cluster Computing
  • Year:
  • 2001

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Abstract

To provide Quality of Service (QoS) guarantee in distributed services, it is necessary to reserve multiple computing and communication resources for each service session. Meanwhile, techniques have been available for the reservation and enforcement of various types of resources. Therefore, there is a need to create an integrated framework for coordinated multi-resource reservation. One challenge in creating such a framework is the complex relation between the end-to-end application-level QoS and the corresponding end-to-end resource requirement. Furthermore, the goals of (1) providing the best end-to-end QoS for each distributed service session and (2) increasing the overall reservation success rate of all service sessions are in conflict with each other. In this paper, we present a QoS and contention-aware framework of end-to-end multi-resource reservation for distributed services. The framework assumes a reservation-enabled environment, where each type of resource can be reserved. The framework consists of (1) a component-based QoS-Resource Model, (2) a runtime system architecture for coordinated reservation, and (3) a runtime algorithm for the computation of end-to-end multi-resource reservation plans. The algorithm provides a solution to alleviating the conflict between the QoS of an individual service session and the success rate of all service sessions. More specifically, for each service session, the algorithm computes an end-to-end reservation plan, such that it guarantees the highest possible end-to-end QoS level under the current end-to-end resource availability, and requires the lowest percentage of bottleneck resource(s) among all feasible reservation plans. Our simulation results show excellent performance of this algorithm.