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Utility Computing

Utility Computing. Amin Vahdat Internet Systems and Storage Group http://issg.cs.duke.edu vahdat@cs.duke.edu. OPUS: An Overlay Utility Service. Dynamically allocate resources to competing services Based on changing application and network characteristics, SLAs

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Utility Computing

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  1. Utility Computing Amin Vahdat Internet Systems and Storage Group http://issg.cs.duke.edu vahdat@cs.duke.edu

  2. OPUS: An Overlay Utility Service • Dynamically allocate resources to competing services • Based on changing application and network characteristics, SLAs • Create topology based app requirements • Bandiwdth, latency, loss rate, cost ($) sensitivity Peering Overlay node App demand

  3. Current Questions • What are the proper SLA’s for specifying utility for competing applications • Allocate resources among competing applications based on changing network/application conditions • Overlays that dynamically trade “performance” for “cost” • Two-metric network optimization (NP-complete with centralized information) • Scale to 10,000 nodes • Service overlays versus application overlays

  4. ModelNet • Step 1: specify target wide-area topology • Graph with edges augmented to include BW, latency, loss rates, etc. • Step 2: map topology to router core • Graph partitioning • Accuracy? Congestion? • Step 3: run real code on edge nodes • Subjected to specified network char’s EdgeNodes 100MbSwitch RouterCore GbSwitch

  5. Open Questions and Status • How to introduce noise into emulation environment? • Scalability versus accuracy • How far can system scale? • Target: 10 node core (gigabit interconnect), 100 edge nodes • Current status • Reproduced CFS results from SOSP 2001 paper • Using RON network connectivity information • Figures 6, 7, 8 • 4000-node gnutella network

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