Utility Driven Service Routingover Large Scale InfrastructuresPablo ChacinPolytechnic University of Catalonia(UPC), Spain
Authors• Pablo Chacin, Polytechnic University ofCatalonia, Spain (UPC)• Leandro Navarro, UPC• Pedro Garcia López, Rovira i...
Key Points   • UDON is an Utility Driven Overlay Network for routing     service requests to service instances that match ...
Outline   •   Defining the problem context   •   Design principles   •   Experimental evaluation   •   Conclusions13-15 De...
Internet of Services        Source: Schroth, C., Janner, T.: Web 2.0 and soa: Converging concepts enabling        the inte...
Service Deployment13-15 December 2010   ServiceWave 2010
Challenges   • Non dedicated Servers      – The QoS a server can offer is hard to predict   • Fluctuations in the demand  ...
Guiding principles   • Decentralized decisions using local information      – No global view; no single point of failure; ...
System Model13-15 December 2010    ServiceWave 2010
Utility Function   • In economics, utility is a     measure of relative     satisfaction   • Summarizes multiple     attri...
Epidemic Overlay   • Simple maintenance algorithm      – Each node has a local view of        the state of a set of neighb...
Randomized Greedy Utility              Routing   • Multi-hop routing using local     information      – On each hop, ranks...
Evaluation13-15 December 2010   ServiceWave 2010
Simulation Model   • Network topology is abstracted      – One single cluster, 1000s of servers.      – Constant, negligib...
The Simulation of the Utility             Function13-15 December 2010   ServiceWave 2010
Metrics   • Overlay (information dissemination)      – Age: how old is the information in the        local view (average) ...
Overlay                                         Maintains “fresh”                                         information     ...
Performance Tolerance: maximum allowed difference between required QoS and nodes utility:       Allocates requests with hi...
Performance looking for            scarce resources                                         Allocates requests            ...
Churn                                         Performance                                         “gracefully” degrades   ...
Variation in Utility                      Allocates requests even under                      highly fluctuating conditions...
Sensitivity to Operational              Parameters           Optimal setup demands low           communication overhead13-...
Discussion13-15 December 2010   ServiceWave 2010
Conclusions   • Simple, principled solution for routing requests     over large-scale cluster-based web services on     sh...
(Near) Future work   • Apply UDON to A concrete scenario      – Simulated cluster based web services      – Use concrete u...
Questions? . . . Thanks.                      pchacin@ac.upc.edu13-15 December 2010      ServiceWave 2010
ICSOC-ServiceWave 2009
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Pablo Chacin (Polytechnic University of Catalonia, Spain): Utility Driven Service Routing over Large Scale Infrastructures

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Pablo Chacin (Polytechnic University of Catalonia, Spain): Utility Driven Service Routing over Large Scale Infrastructures

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Transcript of "Pablo Chacin (Polytechnic University of Catalonia, Spain): Utility Driven Service Routing over Large Scale Infrastructures "

  1. 1. Utility Driven Service Routingover Large Scale InfrastructuresPablo ChacinPolytechnic University of Catalonia(UPC), Spain
  2. 2. Authors• Pablo Chacin, Polytechnic University ofCatalonia, Spain (UPC)• Leandro Navarro, UPC• Pedro Garcia López, Rovira i VirgiliUniversity, Spain
  3. 3. Key Points • UDON is an Utility Driven Overlay Network for routing service requests to service instances that match some QoS requirements • It is aimed for highly dynamic large-scale shared infrastructures. • Combines an application provided utility function to express QoS with an epidemic protocol to disseminate the information that supports the routing • Experimental analysis shows that UDON allocates requests meeting QoS with a high probability and low overhead; it is scalable, robust and adapts well to a wide range of conditions.13-15 December 2010 ServiceWave 2010
  4. 4. Outline • Defining the problem context • Design principles • Experimental evaluation • Conclusions13-15 December 2010 ServiceWave 2010
  5. 5. Internet of Services Source: Schroth, C., Janner, T.: Web 2.0 and soa: Converging concepts enabling the internet of services. IT Professional 9(3), 36–41 (May/June 2007)13-15 December 2010 ServiceWave 2010
  6. 6. Service Deployment13-15 December 2010 ServiceWave 2010
  7. 7. Challenges • Non dedicated Servers – The QoS a server can offer is hard to predict • Fluctuations in the demand • Different QoS requirements for different users – e.g. free/paid; bronze/silver/gold • Large scale • Number of instances may vary – Activations/deactivations due to fluctuations on the demand – Failures13-15 December 2010 ServiceWave 2010
  8. 8. Guiding principles • Decentralized decisions using local information – No global view; no single point of failure; more scalable and adaptable • Representation of QoS as an Utility Function – Compact representation – Facilitate comparisons despite heterogeneity • Model-less adaptation – No need to elicit or learn a performance model for the systems – If information is not exact, rationality may not help.13-15 December 2010 ServiceWave 2010
  9. 9. System Model13-15 December 2010 ServiceWave 2010
  10. 10. Utility Function • In economics, utility is a measure of relative satisfaction • Summarizes multiple attributes into a single scalar value – F(a1,..an) → [0,1] • Facilitates comparison, allow private evaluations Cobb-Douglas utility function U(t,c) = t(ac(1-a) t = execution time c = cost13-15 December 2010 ServiceWave 2010
  11. 11. Epidemic Overlay • Simple maintenance algorithm – Each node has a local view of the state of a set of neighbors – Periodically choses some neighbors and sends its local view + own state – Each node merges its local view with the received views keeping the most recently updated entries • Disseminates information with low overhead • Highly scalable and resilient13-15 December 2010 ServiceWave 2010
  12. 12. Randomized Greedy Utility Routing • Multi-hop routing using local information – On each hop, ranks neighbors based on its (potentially outdated) utility – Forward to the node with a probability based on ranking • Simple concept. Allows multiple heuristics for Image source: physics.org ranking (evaluation is an Greedy Routing Enables Network Navigation Without a Map ongoing work) http://www.physorg.com/news154093231.html13-15 December 2010 ServiceWave 2010
  13. 13. Evaluation13-15 December 2010 ServiceWave 2010
  14. 14. Simulation Model • Network topology is abstracted – One single cluster, 1000s of servers. – Constant, negligible delays • Utility Function simulated as a Random Process – Make evaluation more general, not tied to a particular utility definition – Evaluate the effect of different parameters • Compared with other overlays of the same family – Random: no organization (baseline) – Gradient: keep instances with similar QoS close13-15 December 2010 ServiceWave 2010
  15. 15. The Simulation of the Utility Function13-15 December 2010 ServiceWave 2010
  16. 16. Metrics • Overlay (information dissemination) – Age: how old is the information in the local view (average) – Staleness: how accurate is the local view with respect of real current information • Routing – Satisfied demand: how effective and reliable is the allocation (% of success) – Hops: how efficient13-15 December 2010 ServiceWave 2010
  17. 17. Overlay Maintains “fresh” information Minimizes staleness13-15 December 2010 ServiceWave 2010
  18. 18. Performance Tolerance: maximum allowed difference between required QoS and nodes utility: Allocates requests with high ~ 1.0 any node with a higher utility matches probability, and low number or ~ 0.0 only node with the exact demanded utility matches hops, even under very demanding search criteria (low tolerance)13-15 December 2010 ServiceWave 2010
  19. 19. Performance looking for scarce resources Allocates requests even when target nodes are scarce.13-15 December 2010 ServiceWave 2010
  20. 20. Churn Performance “gracefully” degrades under high churn13-15 December 2010 ServiceWave 2010
  21. 21. Variation in Utility Allocates requests even under highly fluctuating conditions.13-15 December 2010 ServiceWave 2010
  22. 22. Sensitivity to Operational Parameters Optimal setup demands low communication overhead13-15 December 2010 ServiceWave 2010
  23. 23. Discussion13-15 December 2010 ServiceWave 2010
  24. 24. Conclusions • Simple, principled solution for routing requests over large-scale cluster-based web services on shared infrastructures • UDON meets requirements on scenarios of interest and shows desirable properties – Effective – Low overhead – Scalable – Very adaptable – Robust13-15 December 2010 ServiceWave 2010
  25. 25. (Near) Future work • Apply UDON to A concrete scenario – Simulated cluster based web services – Use concrete utility functions • Evaluate alternative routing heuristics • Propagate information based on usefulness: see which QoS are more demanded and propagate information of nodes that offer it with higher probability • Consider locality when selecting neighbors to adapt to wide area distributed clusters (multi- site)13-15 December 2010 ServiceWave 2010
  26. 26. Questions? . . . Thanks. pchacin@ac.upc.edu13-15 December 2010 ServiceWave 2010
  27. 27. ICSOC-ServiceWave 2009

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