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S y n e r g i s t i c Network Operations

S y n e r g i s t i c Network Operations. Saqib Raza University of California, Davis. A Snapshot Of Network Operations. Scheduling. Accounting. Maintenance. Firewalls. Forensics. Inter-domain TE. Power Management. Traffic Policing. Diagnostics. Intra-domain TE. Forwarding.

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S y n e r g i s t i c Network Operations

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  1. SynergisticNetwork Operations Saqib Raza University of California, Davis

  2. A Snapshot Of Network Operations Scheduling Accounting Maintenance Firewalls Forensics Inter-domain TE Power Management Traffic Policing Diagnostics Intra-domain TE Forwarding Overlay Routing

  3. Example: Inter-Operation Dynamics B A Initially, traffic between overlay nodes A and D does not traverse ISP-A D ISP A x y C ISP-A alters link weights to direct away from link (x,y). Sensing reduced delay through ISP-A the routing overlay starts sending traffic from A to D through ISP-A Intra-domain TE Overlay Routing

  4. The Hippocratic Oath For Network Operations • Do No Harm • Operations should be cognizant of any disruptive effects to other operations. Strive to do Good Operations should seek to enhance the efficacy of other operations.

  5. Summary/Outline • Interface-Split Forwarding for Finer-Grained Traffic Engineering [Performance `07, Eval `07] • Cooperative Peer-to-Peer Repair of 3G Broadcast Losses [Broadnets `08, ICC `08, ICME `07] • Network-level footprints of Online Social Network Applications [IMC `09, IMC `08] • Graceful Network State Migration [Infocom `09] • MeasuRouting: A Framework for Routing Assisted Traffic Monitoring [Infocom `10] • Future Directions

  6. Maintenance Graceful Network Migration minimizing performance disruption during planned network maintenance … Joint work with: Yuanbo Zhu & Chen-Nee Chuah (UC Davis) Intra-domain TE

  7. Motivation • Premeditated network tasks can be judiciously scheduledto minimize performance disruption

  8. Graceful State Migration (GSM)

  9. Sample Application

  10. LMS: Illustrative Example Link Capacity = C 1 Link Weights Flow Size = ½ C 2 1 1 1 1 Max Link Util = 50% 3 1 I need to repair links (a,c) and (c,f) g c a e f b d Careful! Watch out for the Maximum Link Utilization (MLU)

  11. 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 100% 3 3 3 1 1 1 1 g g g g c c c c a a a a e e e e f f f f b b b b d d d d 3

  12. 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 3 3 3 1 1 1 1 1 g g g g c c c c a a a a e e e e f f f f b b b b d d d d 3

  13. LMS: Illustrative Example • The schedule with multiple links simultaneously deactivated causes less disruption

  14. Specify (sinitial,sfinal), A, B, & C to define a concrete GSM problem, e.g., LMS The General GSM Problem • sn , , n r d n n r d n n r r n r r d d A A repaired  deactivated  not repaired 

  15. A General GSM Solution Framework

  16. Computational Complexity GSM is a combinatorial optimization problem 002 011 001 122 101 Solution space of LMS has 2n!/2nsolutions 000 010 212 222 020 100 220 110 200

  17. Ants Colony Optimization Swarm intelligence meta-heuristic f f f Near optimal solutions for the Traveling Salesman Problem n n n

  18. Performance Evaluation Single-Failure Heuristic works well generally What about the worst case? > 20 node/80 link topology > 100 experiments per data point > Report Cost Reduction (MLU) over Single-Failure Heuristic

  19. GST: Applications Link Weight Reassignment Scheduling

  20. Outline • Graceful Network State Migration [Infocom `09] • MeasuRouting: A Framework for Routing Assisted Traffic Monitoring [Infocom `10] • Future Directions

  21. Measurements MeasuRouting a framework for routing assisted network measurements… Joint work with: Guanyao Huang & Chen-Nee Chuah (UC Davis) Srini Seetharaman & Jatinder Singh (DT Labs) Intra-domain TE

  22. An evolving universe The Monitor Placement Problem Oops! important very important ? ? 1. Measurement objectives change 2. New Traffic gets introduced 3. Traffic placement changes

  23. Measurements Intra-domain TE

  24. Congestion TE Policy Violation

  25. Compliant Rerouting Monitor TE policy is defined for aggregated flows Sub-populations of aggregated flows, indistinguishable from a TE perspective, can be distinguishable from a measurement perspective

  26. Other Enabling Factors

  27. 1. Aggregated TE Flows e.g. OD pair traffic 2. Traffic placement given: Γ(i,j)E TE Flowset (macro-flowset) • 1. TE flowset de-composes into kmeasurement flowsets • 2. A measurement flowsethas: • a) Size • b)Importance • 3. Decision variable: • (i,j)E Measurement Flowsets (micro-flowsets) 27

  28. Maximize score across allmeasurement flowsets across alllinks MeasuRouting Objective Flowset Size Flowset Routing Network Flow Conservation Constraints Ensure that TE performance remains within some value of the default TE performance 1 2 Link Sampling Rate Flowset Importance Points gained for sampling flowset y on link (i,j)

  29. The Looping Problem

  30. Report improvement in Measurement Score over default routing Synthetic Experiments • Select the number of Measurement Flowsets per OD pair (K) • Divide all flows between an OD pair into the K measurement flowsets • Assign size and importance of the measurement flowsets • Choose the permissible TE violation parameter 

  31. Network Size • Performance sensitive to number of multiple paths AS1221 44 nodes AS1239 52 nodes

  32. Degrees Of Freedom AS1221 44 nodes • Diminishing marginal returns of increasing k

  33. A Real Application • Trace Capture for Deep Packet Inspection (DPI) • Trace capture infrastructure selectively deployed • Increase representation of interesting traffic in traces Abilene 9 nodes ln(1-|P(i)-Q(i)|)

  34. Real World MeasuRouting

  35. Outline • Graceful Network State Migration [Infocom `09] • MeasuRouting: A Framework for Routing Assisted Traffic Monitoring [Infocom `10] • Future Directions

  36. Optimal States Of Being Graceful Network State Migration

  37. Data Center Job Scheduling Scheduling Power conserved by switching off data center components, dynamic voltage scaling etc. Power Management Jobs scheduled on different servers to optimize performance (MapReduce, Dyrad). Jointly optimize job scheduling and power management decisions.

  38. Data Center Load Distribution Inter-domain TE Data center operation costs vary geographically due to energy market price fluctuations [Qureshi `09] Power Management Makes sense to operate data centers in diverse energy markets. Data center load can not be instantaneously shifted from one location to another. Chalk out optimal state trajectory of BGP route advertisements.

  39. A Calculus For Synergistic Operations Revenue Contribution CPU Cycles Bandwidth Network-wide Security Power • Each marginal unitof a resource ought to be allocated to the operation that derives the highest marginal utility from consuming it.

  40. Questions wwwcsif.cs.ucdavis.edu/~raza www.ece.ucdavis.edu/rubinet

  41. Measurement Utility Diversity AS1221 44 nodes • Performance improves with variance in importance

  42. LMS In A Small Network (Abilene)

  43. MeasuRouting Path Inflation

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