1. Abhigyan, Aditya Mishra, Vikas Kumar, Arun Venkataramani University of Massachusetts Amherst 1

2.  Examples: ◦ CDNs ◦ P2P applications ◦ Mirrored websites ◦ Cloud computing 2 Location diversity: Ability to download content from multiple locations

3.  ISPs have several objectives, e.g., minimizing congestion, decisions about upgrading link capacity  ISPs optimize link utilization based metrics. e.g. maximum link utilization (MLU) 3

4. 4 Traffic engineering (ISPs) Location diversity (CDNs) Internet traffic

5.  How do TE schemes compare accounting for location diversity in the Internet? 5

6. 1. Introduction 2. Motivation 1.Location diversity and traffic engineering 2.Metric of comparison 3. Evaluation 4. Conclusion 6

7. Application adaptation to location diversity Traffic matrix New Routing 7 Traffic engineering Content demand

8. 8 100 Mbps, 0.1ms 100 Mbps, 10ms 1 2 3 10 Mb x 10 req/s = 100 Mbps 10 Mb x 5 req/s = 50 Mbps OSPF Wt = 2 OSPF Wt = 1 50 Mbps + 50 Mbps 50 Mbps Maximum link utilization ( MLU )= 1 OSPF Wt = 1

9. 9 100 Mbps, 0.1ms 100 Mbps, 10ms 1 2 3 OSPF Wt = 2 OSPF Wt = 1 50 Mbps + 50 Mbps 50 Mbps OSPF Wt = 1 25 Mbps + 25 Mbps Expected MLU = 0.5 25 Mbps +25Mbps MLU = 0.75 10 Mb x 10 req/s = 100 Mbps 10 Mb x 5 req/s = 50 Mbps OSPF Wt = 1

10. Location diversity increases capacity 100 Mbps 1 2 3 10 Mb x 10req/s = 100 Mbps 100 Mbps 10 Mb x 20req/s = 200 Mbps Increase in capacity = 200/ 100 = 2

11. 1. Motivation 1.Location diversity and traffic engineering 2.Metric of comparison 2. Evaluation 3. Conclusion 11

12.  Without location diversity ◦ Capacity = 1/MLU 12 100 Mbps 1 2 3 25 Mbps 100 Mbps MLU = 0.25 100 Mbps Capacity = 100/25 = 4 100 Mbps max supportable demand current demand Capacity =

13.  Without location diversity ◦ Capacity = 1/MLU  With location diversity ◦ Ca pacity >= 1/MLU 13 100 Mbps 1 2 3 30 Mbps 100 Mbps 25 Mbps 5 Mbps MLU = 0.25 180 Mbps 90 Mbps Capacity > 180/30 = 6 Need a new metric to quantify capacity under location diversity max supportable demand current demand Capacity =

14.  SPF = Maximum supportable surge (linearly scaled) in traffic demand 14 SPF = 200/30 = 6.66 100 Mbps 1 2 3 30 Mbps 100 Mbps 25 Mbps 5 Mbps 100 Mbps 200Mbps

15. Location diversity significantly impacts TE 1.Capacity increases 2.Capacity (SPF) not captured by 1/MLU 15

16. 1. Introduction 2. Motivation 3. Evaluation 1.TE schemes 2.Measuring SPF 3.Capacity results (SPF) 4. Conclusion 16

17. 17 TE Schemes (Almost online) optimal TE [OPT] (Offline) “optimal” TE using MPLS [MPLS] (Offline) TE using OSPF link weight optimization [OptWt] (Offline) Multi-TM optimization TE [COPE] (Oblivious) Static shortest path routing with inverse- capacity link weights [InvCap]

18. 18 Is demand satisfied ? Increase demand by Δ SPF = demand/(initial demand) Demand = initial demand YES NO

19. 19 InvCap worst case No LocDiv = 50% sub-OPT LocDiv = 30% sub-OPT 1.All TE schemes achieve near-optimal capacity with location diversity. 2.Even no TE scheme is at most 30% sub-optimal with location diversity.

20.  “How location diversity ate traffic engineering’s cake” ◦ Any TE scheme performs the same as Optimal TE. ◦ No TE scheme performs at most 30% worse. 20