Tools for Smart Networks

2. Tools for Smart Networks Jean Walrand BITS (Berkeley Information Technology & Systems) U.C. Berkeley www.eecs.berkeley.edu/~wlr/mascots2000

3. Outline • What are Smart Networks? • Why Smart Networks? • Tools for Smart Networks Project • Example 1: DiffServ • Example 2: Bandwidth Allocation • Example 3: Server Allocation • Conclusions

4. What are Smart Networks? Modify Analyze Measure

5. Network Server Client Applications Applications Transport Transport IP Why Smart Networks? Before: “Simple Network”

6. Network Why Smart Networks? (continued) Now: “Complex Network” Application Servers, Content Servers Caches, Traffic Shapers, Redirection Agents, Processing

7. Why Smart Networks? (continued) • Simple Network: • IP Forwarding • Routing Table Updates • DNS • Intelligence in Hosts • Complex Network: • New Functions • New Transport Services (e.g., CoS, SLAs) • Needs Intelligence in Network

8. INTERNET TELEPHONE Applications Applications SS7, Billing, ... IP LANs, ATM, ... OC-n, DS-n, UTP Why Smart Networks? (continued) Success of Simplicity Success of Complexity

9. INTERNET Applications IP LANs, ATM, ... Why Smart Networks? (continued) Probably not very desirable!

10. INTERNET Applications Applications IP IP M/A/M LANs, ATM, ... LANs, ATM, ... Why Smart Networks? (continued) Tools for Planning, Design, Operations

11. Tools for Smart Networks Project • Joint UCB - Cisco Project • DARPA Funding + Cisco • Combines • Measurements • Analysis & Simulation • Real-time Control • Objective: Product

12. Tools for Smart Networks Project (cd) Comprehension Analysis Integrated Tools Simulations Measurements Utility

13. Tools for Smart Networks Project (cd) • Cisco: • David Jaffe (Lead Investigator) • Karl Auerbach (Lab Design and Implementation) • Anna Charny (MPLS) • TBS (DiffServ) • UCB • Anantharam, Tse, Varaiya, Walrand • Stavros Tripakis (post-doctoral scholar) • About 6 graduate students TEAM:

14. Example 1: DiffServ • Goal: • CoS without per-connection state • Solution: • No route-pinning • Planning and operations based on aggregate statistics and worst-case routing • Peer-to-peer SLAs that specify total rate but not traffic destination

15. SLA Cloud 1 Cloud 2 Example 1: DiffServ (continued) Policing Shaping

16. Bottleneck Link Example 1: DiffServ (continued) DiffServ SLA: Worst Case Admission Control Ingress 3 Ingress 3 Ingress 1 Ingress 1 Ingress 2 Ingress 2 Worst Case Typical Case Terribly wasteful!

17. New Capacity Gap Mean + 2.4s Example 1: DiffServ (continued) DiffServ SLA: Measurement-Based Admission Control Admit if peak(new) < Gap at all times

18. Example 1: DiffServ (continued) • How well does this approach work? • Simulation study: • Construct traffic model (parametric FBM) • Validate model against measurements • Simulate admission control policy • Test fraction of SLAs that see congested links and level of congestion • Experimental study (coming year) • Implement measurements and admission control • Evaluate performance Work of Linhai He and John Musacchio

19. Example 2: Bandwidth Allocation • Problems: • How to share bandwidth • How to renegotiate SLAs • Issues: • Scalability • Efficiency • Fairness, Optimality, ...

20. Number of “calls” [Voice over IP] X N Y Example 2: Bandwidth Allocation (cd) Sharing one link:

21. X N Y Feasible region N Y X N Example 2: Bandwidth Allocation (cd) Dynamic

22. X N Y Feasible region N Y N2 X N N1 Example 2: Bandwidth Allocation (cd) N1 N2 Admission policies SLAs (Committed Access Rates) Static

23. N Dynamic Y Static N2 N X N1 Example 2: Bandwidth Allocation (cd)

24. N Y N X Example 2: Bandwidth Allocation (cd) Closer Look: Assume Poisson demands, i.i.d. holding times ... For “large links”, the variance is small. => Static  Dynamic However, rates change => must adapt

25. 3 10 4 35 6 10 Example 2: Bandwidth Allocation (cd) Proposed Adaptation Scheme: Renegotiate “blocks” of permits based on thresholds 5 4 20 15 6 7 5 6 7 40

26. Example 2: Bandwidth Allocation (cd) • How well does this approach work? • Simulation study: • Birth/Death Model of Bandwidth • Study Efficiency vs. Rate of Renegotiation Work of Eric Chi and Linhai He

27. S S S S Example 3: Server Allocation Location + Load

28. S S S * * S Example 3: Server Allocation (continued) Anycast: Closest * Least Loaded Among N Closest Stats

29. * Example 3: Server Allocation (continued) Model: • Conflict between • measure lengths (by sending jobs to all queues) • send only to queue believed to be shortest

30. * Example 3: Server Allocation (continued) Algorithm: send to queue k with probability fk(T1, ..., TK) Example: fk(T1, ..., TK) = (1/Tk)/(1/T1 + ... + 1/TK) Not very sensitive to choice of function fk

31. Example 3: Server Allocation (continued) • How well does this approach work? • Simulation study: • Construct traffic model (Poisson requests, random lengths) • Simulate server allocation policy (ns) • Test response times and server utilization Work of Gaurav Agarwal and Rahul Shah

32. Research R&D Development Conclusions Common View: Stochastic Models Performance Evaluation Limit Theorems .... Prototype Hacking Tuning .... Bell Labs XEROX PARC ... Academia Industry

33. Comprehension-driven research Industry Utility-driven research Conclusions More Accurate View: Academia

34. Thank You!