2009 kalman.graffi emanics_aspects_ofautonomiccomputing_20090617

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  • Roter Faden: Evtl. Unklare Details:
  • Roter Faden: Was ist P2P? Welche QoS Anforderungen bisher gestellt? Evtl. Unklare Details:
  • Roter Faden: Viele Qualitätsaspekte wichtig für P2P Systeme Evtl. Unklare Details:
  • Planung?? Was kann man bei planung schreiben? Vielleicht management by alternatives?? Keine der Techniken bietet – für sich alleine angewandt – ein geschlossenes Führungskonzept. Die Techniken ergänzen sich gegenseitig.
  • | | November 19, 2007
  • 2009 kalman.graffi emanics_aspects_ofautonomiccomputing_20090617

    1. 1. Aspects of Autonomic Computing in P2P Systems EMANICS Workshop on Management in P2P, 27.April 2009
    2. 2. The Peer-to-Peer Paradigm <ul><li>Peer-to-Peer Systems: </li></ul><ul><ul><li>Users of a system provide the infrastructure </li></ul></ul><ul><ul><li>Service is provided from users/peers to users/peers </li></ul></ul><ul><ul><li>Peer-to-Peer overlays: </li></ul></ul><ul><ul><ul><li>virtual networks, providing new functionality </li></ul></ul></ul><ul><ul><ul><li>E.g. Distributed Hash Tables, Keyword-based Search </li></ul></ul></ul><ul><li>Evolution of applications </li></ul><ul><ul><li>File sharing: </li></ul></ul><ul><ul><ul><li>No QoS requirements </li></ul></ul></ul><ul><ul><li>Voice over IP </li></ul></ul><ul><ul><ul><li>Real-time requirements </li></ul></ul></ul><ul><ul><li>Video-on-demand </li></ul></ul><ul><ul><ul><li>Real-time and bandwidth requirements </li></ul></ul></ul><ul><ul><li>Online community platforms </li></ul></ul><ul><ul><ul><li>Potential for high user interaction </li></ul></ul></ul>
    3. 3. Quality of Service is Key Success Factor <ul><li>Quality aspects gain importance </li></ul><ul><ul><li>Reliability: expected professionalism </li></ul></ul><ul><li>Client/Server vs. P2P </li></ul><ul><ul><li>Same functionality (video streaming, file distribution) </li></ul></ul><ul><ul><li>P2P: No maintenance and administration costs </li></ul></ul><ul><ul><li>Client / Server: Guaranteed Quality of Service </li></ul></ul><ul><li>Goal of P2P-System-Management: </li></ul><ul><li>Reach and keep predefined quality levels </li></ul>Costs Security Quality of P2P Systems Retrievability Coherence Consistency Correctness Performance Scalability Flexibility Stability Dependability Service Provisioning Overlay Operations Individual Node Complete System IP Infrastructure Availability Reliability Robustness / Fault tolerance Integrity Confidentiality Authentication Non - repudiation Trust Validity Efficiency Adaptability Costs Security Quality of P2P Systems Retrievability Coherence Consistency Correctness Performance Scalability Flexibility Stability Dependability Service Provisioning Overlay Operations Individual Node IP Infrastructure Availability Reliability Robustness / Fault tolerance Integrity Non - Trust Validity Efficiency Adaptability
    4. 4. Preset Quality Intervals <ul><li>Goal of Management: reach and keep preset quality intervals </li></ul> See: K. Graffi, D.Stingl, J.Rückert, A.Kovacevic and R.Steinmetz “Monitoring and Management of Structured Peer-to-Peer Systems”, 9th International Conference on Peer-to-Peer Computing (IEEE P2P '09)
    5. 5. Management & Leadership Strategies Management by Techniques Management by Objectives Management by Exception Management by Delegation Management by Decision Rules Management by Direction & Control Management by Results Management by Motivation Management by Participation Integrated Management Models St. Galler Management Model Zürcher Approach MAM Model Hoshin Management Other Management Concepts Total Quality Management Lean Management Change Management Kaizen 7-S-Model
    6. 6. Management by Techniques in the Management Process Formulating of Objectives Management by Objectives Planning Management by Alternatives Decision Making Management by Decision Rules Management by Exception Implement Management by Delegation Communicating Management by Participation Management by Motivation Controlling Management by Results Management by Systems
    7. 7. Autonomic Computing Cycle
    8. 8. Autonomic Computing Cycle in P2P Systems
    9. 9. Applied for P2P Systems Hop Count = 20 Hop Count to high Increase routing table size Set routing table size to 80 Monitoring Analyze Plan Execute
    10. 10. Monitoring in Structured P2P Systems <ul><li>Goals: </li></ul><ul><ul><li>Statistical representation of system state </li></ul></ul><ul><ul><li>Overlay indepedency  user Key Based Routing Interface </li></ul></ul><ul><ul><li>Lightweight, robust, efficient, fresh, precise </li></ul></ul>
    11. 11. SkyEye.KOM <ul><li>SkyEye.KOM </li></ul><ul><ul><li>Is a monitoring mechanism for structured P2P systems </li></ul></ul><ul><ul><li>Enables gathering of statistics on P2P systems </li></ul></ul><ul><ul><li>Enables capacity-based peer search </li></ul></ul><ul><li>Properties </li></ul><ul><ul><li>Scalable and self-organizing due to use of underlying DHT </li></ul></ul><ul><ul><li>Overlay independent </li></ul></ul><ul><ul><ul><li>Operation on new ID space </li></ul></ul></ul><ul><ul><ul><li>Reusing DHT functionality </li></ul></ul></ul><ul><ul><li>Peers form a tree topology for aggregating system statistics / capacities </li></ul></ul>
    12. 12. SkyEye.KOM – Design <ul><li>Utilizes unified ID-Space within the Interval [0;1] </li></ul><ul><ul><li>Chord ID space [0, 2^128] </li></ul></ul><ul><ul><li>Kademlia ID space [0, 2^160] </li></ul></ul><ul><li>Applicable on any DHT </li></ul><ul><li>Assumes a certain functionality of the DHT </li></ul><ul><ul><li>void route(key, msg, nextHop) </li></ul></ul><ul><ul><li>boolean resp(key) </li></ul></ul>0 1 1 10 50 20 30 40 45 15 0,09 0,2 0,3 0,4 0,51 0,6 0,75 0,9 SkyEye on a Chord Overlay
    13. 13. The Monitoring Tree
    14. 14. SkyEye.KOM – Information Flow <ul><li>Construction of a tree topology for gathering and aggregating data </li></ul><ul><li>Assumes a certain functionality of the DHT </li></ul><ul><ul><li>void route(key, msg, nextHop) </li></ul></ul><ul><ul><li>boolean resp(key) </li></ul></ul>0 0,25 0,75 0,125 0,375 0,625 0,875 0,1875 0,0625 0,3125 0,4375 0,5625 0,6875 0,8125 0,9375 0,5 1
    15. 15. Simulation Setup (Monitoring) <ul><li>Evaluated in PeerfactSim.KOM </li></ul><ul><li>Already existing: </li></ul><ul><ul><li>Chord </li></ul></ul><ul><ul><li>Global Network Positioning delay model </li></ul></ul><ul><ul><li>Churn model based on KAD measurements of Steiner </li></ul></ul><ul><li>Simulation Setup </li></ul><ul><ul><li>IdealDHT: Dispatches messages to responsible peer </li></ul></ul><ul><ul><li>5000 Nodes </li></ul></ul>Application Transport Overlay User Simulation Engine Network PeerfactSim.KOM Service
    16. 16. <ul><li>Number of peers during the simulation (KAD churn) </li></ul><ul><li>Structure of the monitoring tree during the simulation </li></ul>Node Count and Churn <ul><li>All Peers join during the first 300s </li></ul><ul><li>Initiation of churn after 4000s </li></ul><ul><li>Tree adapts to node count </li></ul><ul><li>Logarithmic height  information age </li></ul>Online and offline peers
    17. 17. Smoothing: Eliminate Outliners <ul><li>First initial monitoring reveals several outliners </li></ul><ul><li>We use smoothing techniques: </li></ul><ul><ul><li>Median based </li></ul></ul><ul><ul><li>Exponential Smoothing based </li></ul></ul>
    18. 18. Relative Error <ul><li>Median based smoothing </li></ul><ul><ul><li>Loss of information freshness </li></ul></ul><ul><li>Exponential smoothing </li></ul><ul><ul><li>Weighted average over H values, weights for value i: a * (1-a)^i </li></ul></ul><ul><ul><li>Good precision, few outliners </li></ul></ul>
    19. 19. Branching Factor and Average Freshness <ul><li>Information freshness </li></ul><ul><ul><li>Higher node degree (BF)  lower tree heigth  fresher information at root </li></ul></ul><ul><ul><li>Small update interval (UI)  more frequent updates  fresher information </li></ul></ul><ul><li>Costs – out bandwidth consumption </li></ul><ul><ul><ul><li>Solely depends on update interval </li></ul></ul></ul>
    20. 20. Smooth Monitoring View
    21. 21. Autonomic Computing: Next Steps
    22. 22. Rule based Planing <ul><li>Analysis </li></ul><ul><ul><li>Compare preset quality intervals with monitored status </li></ul></ul><ul><ul><li>No deviance detected: nothing to do </li></ul></ul><ul><ul><li>Deviance detected (hop count): </li></ul></ul><ul><ul><ul><li>Wait until current changes take effect </li></ul></ul></ul><ul><li>Plan </li></ul><ul><ul><li>Metric: Hop Count </li></ul></ul><ul><ul><li>Parameter: Routing table size </li></ul></ul><ul><ul><li>Adapt number of fingers </li></ul></ul><ul><ul><ul><li>+100% if too small </li></ul></ul></ul><ul><ul><ul><li>-10% if too large </li></ul></ul></ul><ul><li>Execute </li></ul><ul><ul><li>Inform all peers </li></ul></ul><ul><ul><li>Adapt changes </li></ul></ul>
    23. 23. Stepwise Adaptation <ul><li>Introduce execution time </li></ul><ul><ul><li>Give time for changes to take effect </li></ul></ul><ul><ul><li>Analyze slope of value history, act only if small </li></ul></ul>
    24. 24. Execution: Spreading of new Configuration <ul><li>Reminder: </li></ul><ul><ul><li>SkyEye.KOM aggregates system statistics up the tree </li></ul></ul><ul><ul><li>Every update message is replied an ACK: </li></ul></ul><ul><ul><ul><li>Global view from above </li></ul></ul></ul><ul><ul><ul><li>Policy of new actions to implement </li></ul></ul></ul><ul><li>Root has global view </li></ul><ul><li>and can reach all leafs </li></ul>[µ,σ,σ²,Σ, min, max] [µ,σ,σ²,Σ, min, max] [µ,σ,σ²,Σ, min, max]
    25. 25. Analysis and Planing <ul><li>Our Approach: </li></ul><ul><ul><li>Root analyzes monitored data, detects missed quality intervals </li></ul></ul><ul><ul><li>Root decides which correction task to initiate </li></ul></ul><ul><ul><li>Spreading the information to all leafs using SkyEye.KOM </li></ul></ul><ul><ul><li>Peer adopt locally the new rules </li></ul></ul>[µ,σ,σ²,Σ, min, max] + Decision
    26. 26. Simulation Setup <ul><li>Simulator PeerfactSim.KOM </li></ul><ul><li>Key question: </li></ul><ul><ul><li>Does the self-configuration work? </li></ul></ul><ul><ul><li>Are preset quality intervals reached and hold? </li></ul></ul>Application Transport Overlay User Simulation Engine Network PeerfactSim.KOM Service
    27. 27. Starting with Low Hop Count <ul><li>Quick convergence towards preset quality zone </li></ul><ul><li>Analysis: </li></ul><ul><ul><li>To small hop count is detected </li></ul></ul><ul><ul><li> Routing table size – 10% </li></ul></ul><ul><ul><li>Quick adaptation </li></ul></ul>
    28. 28. Starting with High Hop Count <ul><li>Quick convergence towards preset quality zone </li></ul><ul><li>Analysis: </li></ul><ul><ul><li>To large hop count is detected </li></ul></ul><ul><ul><li> Routing table size + 100% </li></ul></ul><ul><ul><li>Quick adaptation </li></ul></ul>
    29. 29. Simulation with 10,000 nodes
    30. 30. Conclusion <ul><li>Management of p2p systems: </li></ul><ul><ul><li>Reach and hold preset quality intervals </li></ul></ul><ul><ul><li>Through Autonomic Computing cycle </li></ul></ul><ul><li>Monitoring: SkyEye.KOM provides </li></ul><ul><ul><li>Global view on statistics of running system: avg./std./min./max on all metrics </li></ul></ul><ul><li>Analysis / Plan / Execute in Chord </li></ul><ul><ul><li>Hop count  Routing table size </li></ul></ul><ul><li>Evaluation shows </li></ul><ul><ul><li>Precise and very cost effective monitoring </li></ul></ul><ul><ul><li>Preset quality intervals are reached and hold </li></ul></ul><ul><li>Part of the Skynet Project: </li></ul><ul><ul><li>Building a self-optimizing, self-aware autonomous P2P system </li></ul></ul><ul><ul><li>Have a look at www.skynet-project.com </li></ul></ul>? Can I keep the quality of my p2p system? System quality ? SkyEye.KOM Information Management Over-Overlay
    31. 31. Questions? KOM Have a look at: www.lifesocial.org www.skynet-project.com www.kom.tu-darmstadt.de

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