article

QoS and Contention-Aware Multi-Resource Reservation

Authors:

Klara Nahrstedt,

Duangdao WichadakulAuthors Info & Claims

Cluster Computing, Volume 4, Issue 2

Pages 95 - 107

https://doi.org/10.1023/A:1011408729750

Published: 01 April 2001 Publication History

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.

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Tunc CMachovec DKumbhare NAkoglu AHariri SKhemka BSiegel H(2017)Value of service based resource management for large-scale computing systemsCluster Computing10.1007/s10586-017-0901-920:3(2013-2030)Online publication date: 1-Sep-2017
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cover image Cluster Computing

Cluster Computing Volume 4, Issue 2

April 2001

75 pages

ISSN:1386-7857

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Copyright © Copyright © 2001 Kluwer Academic Publishers.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2001

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Cited By

Surya KRajam V(2021)Prediction of resource contention in cloud using second order Markov modelComputing10.1007/s00607-021-00967-1103:10(2339-2360)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s00607-021-00967-1
Tunc CMachovec DKumbhare NAkoglu AHariri SKhemka BSiegel H(2017)Value of service based resource management for large-scale computing systemsCluster Computing10.1007/s10586-017-0901-920:3(2013-2030)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s10586-017-0901-9
Machovec DTunc CKumbhare NKhemka BAkoglu AHariri SSiegel HChard KCostan ANicolae BZhao D(2016)Value-Based Resource Management in High-Performance Computing SystemsProceedings of the ACM 7th Workshop on Scientific Cloud Computing10.1145/2913712.2913716(19-26)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1145/2913712.2913716
Oxley MPasricha SMaciejewski ASiegel HApodaca JYoung DBriceno LSmith JBahirat SKhemka BRamirez AZou Y(2015)Makespan and Energy Robust Stochastic Static Resource Allocation of a Bag-of-Tasks to a Heterogeneous Computing SystemIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2014.236292126:10(2791-2805)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1109/TPDS.2014.2362921
Khemka BFriese RBriceno LMaciejewski AKoenig GOkonski GHilton MRambharos RPoole SGroer C(2015)Utility Functions and Resource Management in an Oversubscribed Heterogeneous Computing EnvironmentIEEE Transactions on Computers10.1109/TC.2014.236051364:8(2394-2407)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1109/TC.2014.2360513
Al-Qawasmeh APasricha SMaciejewski ASiegel H(2015)Power and Thermal-Aware Workload Allocation in Heterogeneous Data CentersIEEE Transactions on Computers10.1109/TC.2013.11664:2(477-491)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1109/TC.2013.116
Young BPasricha SMaciejewski ASiegel HSmith JRodero IParashar M(2013)Heterogeneous makespan and energy-constrained DAG schedulingProceedings of the 2013 workshop on Energy efficient high performance parallel and distributed computing10.1145/2480347.2480348(3-12)Online publication date: 17-Jun-2013
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Briceño LSiegel HMaciejewski AOltikar M(2012)Characterization of the iterative application of makespan heuristics on non-makespan machines in a heterogeneous parallel and distributed environmentThe Journal of Supercomputing10.1007/s11227-011-0729-762:1(461-485)Online publication date: 1-Oct-2012
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Garcia-Valls MBasanta-Val PEstévez-Ayres IGiese HCheng B(2011)Supporting service composition and real-time execution throught characterization of QoS propertiesProceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1145/1988008.1988024(110-117)Online publication date: 23-May-2011
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Al-Qawasmeh AMaciejewski AWang HSmith JSiegel HPotter J(2011)Statistical measures for quantifying task and machine heterogeneitiesThe Journal of Supercomputing10.1007/s11227-011-0572-x57:1(34-50)Online publication date: 1-Jul-2011
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