Chapter 15: Network Applications Helmut Simonis Cork Constraint - PowerPoint PPT Presentation

Traffic Placement Capacity Management Other Problems Chapter 15: Network Applications Helmut Simonis Cork Constraint Computation Centre Computer Science Department University College Cork Ireland ECLiPSe ELearning Overview Helmut Simonis Network Applications 1

Traffic Placement Capacity Management Other Problems Licence This work is licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License. To view a copy of this license, visit http: //creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. Helmut Simonis Network Applications 2

Traffic Placement Capacity Management Other Problems Outline Traffic Placement 1 Capacity Management 2 Other Problems 3 Helmut Simonis Network Applications 3

Traffic Placement Capacity Management Other Problems Common Theme How can we get better performance out of a given network? Make network transparent Users should not need to know about details Service maintained even if failures occur Restricted by accepted techniques available in hardware Interoperability between multi-vendor equipment Very conversative deployment strategies Helmut Simonis Network Applications 4

Traffic Placement Capacity Management Other Problems Reminder: IP Networks Packet forwarding Connection-less Destination based routing Distributed routing algorithm based on shortest path algorithm Routing metric determines preferred path Best effort Packets are dropped when there is too much traffic on interface Guaranteed delivery handled at other layers (TCP/applications) Helmut Simonis Network Applications 5

Traffic Placement Capacity Management Other Problems Disclaimer Flexible border between CP and OR CP is ... what CP people do. what is published in CP conferences. what uses CP languages. Does not mean that other approaches are less valid! Helmut Simonis Network Applications 6

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Outline Traffic Placement 1 Link Based Model Path-Based Model Node-Based Model Commercial Solution Multiple Paths Capacity Management 2 Other Problems 3 Helmut Simonis Network Applications 7

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Example Network (Uniform metric 1, Capacity 100) 1 1 A R1 R2 C 1 1 1 1 1 1 E 1 1 1 B R3 R4 D 1 1 1 Helmut Simonis Network Applications 8

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Example Traffic Matrix Only partially filled in for example A B C D E A 0 0 10 20 20 B 0 0 10 20 20 C 0 0 0 0 0 D 0 0 0 0 0 E 0 0 0 0 0 Helmut Simonis Network Applications 9

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Using Routing Demand AC 10 10 A R1 R2 C 0 10 1 E B R3 R4 D Helmut Simonis Network Applications 10

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Using Routing Demand AD 20 20 20 A R1 R2 C 20 E B R3 R4 D Helmut Simonis Network Applications 11

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Using Routing Demand BC 10 A R1 R2 C 5 5 5 E 5 B R3 R4 D 5 5 Helmut Simonis Network Applications 12

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Using Routing Demand BD 20 10 A R1 R2 C 10 10 E B R3 R4 D 10 10 10 Helmut Simonis Network Applications 13

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Using Routing Demand AE 20 20 A R1 R2 C 20 E B R3 R4 D Helmut Simonis Network Applications 14

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Using Routing Demand BE 20 A R1 R2 C 20 20 E B R3 R4 D Helmut Simonis Network Applications 15

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Resulting Network Load 50 30 A R1 R2 C 5 55 1 15 35 E 5 R3 B R4 D 5 15 15 Helmut Simonis Network Applications 16

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Considering failure of R1-E 35 35 A R1 R2 C 30 15 10 10 E 5 20 40 R3 B R4 D 55 65 10 Helmut Simonis Network Applications 17

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Can we do better? Choose single, explicit path for each demand Requires hardware support in routers (MPLS-TE) Baseline: CSPF, greedy heuristic Helmut Simonis Network Applications 18

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Why not just use Multi-Commodity Flow Problem Solution? Can not use arbitrary, fractional flows in hardware MILP does not scale too well Helmut Simonis Network Applications 19

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Modelling Alternatives Link based Model Path based Model Node based Model Helmut Simonis Network Applications 20

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Variants Demand Acceptance Choose which demands to select fitting into available capacity Traffic Placement All demands must be placed Helmut Simonis Network Applications 21

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Link-Based Model: Intuition Decide if demand d is run over link e Select which demands run over link e (Knapsack) Demand d must run from source to sink (Path) Sum of delay on path should be limited (QoS) Helmut Simonis Network Applications 22

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Link Based Model 1 � � bw ( d ) X de bw ( d ) X de { X de } max min or min cap ( e ) e ∈ E { X de } d ∈ D e ∈ E , d ∈ D st.  − 1 n = dest ( d )   � � ∀ d ∈ D , ∀ n ∈ N : X de − X de = 1 n = orig ( d ) e ∈ OUT ( n ) e ∈ IN ( n )  0 otherwise  � ∀ e ∈ E : bw ( d ) X de ≤ cap ( e ) d ∈ D � ∀ d ∈ D : del ( e ) X de ≤ req ( d ) e ∈ E X de ∈ { 0 , 1 } Helmut Simonis Network Applications 23

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Solution Methods Lagrangian Relaxation Path decomposition Knapsack decomposition Probe Backtracking Helmut Simonis Network Applications 24

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Lagrangian Relaxation - Path decomposition [Ouaja&Richards2003] Dualize capacity constraints Starting with CSPF initial solution Finite domain solver for path constraints Added capacity constraints from st-cuts At each step solve shortest path problems Helmut Simonis Network Applications 25

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Lagrangian Relaxation - Knapsack decomposition [Ouaja&Richards2005] Dualize path constraints At each step solve knapsack problems Reduced cost based filtering Helmut Simonis Network Applications 26

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Probe Backtracking [Liatsos et al 2003] Start with (infeasible) CSPF heuristic Consider capacity violation Resolve by forcing one demand off/on link Find new path respecting path and added constraints with ILP Repeat until no more violations, feasible solution Optimality proof when exhausted search space Search space often very small Helmut Simonis Network Applications 27

Link Based Model Traffic Placement Path-Based Model Capacity Management Node-Based Model Other Problems Commercial Solution Multiple Paths Path-Based Model: Intuition Choose one of the possible paths for demand d This paths competes with paths of other demands for bandwidth Usually too many paths to generate a priori, but most are useless Helmut Simonis Network Applications 28