Measurement-aware Monitor Placement and Routing

Transcription

1 Measurement-aware Monitor Placement and Routing A Joint Optimization Approach for Network-Wide Measurements Guanyao Huang 1 Chia-Wei Chang Chen-Nee Chuah 1 Bill Lin 1 University of California at Davis, CA USA University of California at San Diego, CA USA 010

2 task: measure network traffic with limited resources and QoS constraints in mind

3 question: which monitors should be activated, how to route traffic over them to maximize measurement gain, given limited resources

4 current state of the art

5 pick monitor locations without changing routing decison

6 might miss important traffic

7 decide on routing based on static monitors (MeasuRouting)

8 might violate QoS constraints

9 MMPR Motivational Example

10 find monitor locations first, then use MeasuRouting

11 maximize measurement resolution function β β = y θ (i,j) E I y p (i,j) u (i,j) Γ x y Υ x G(V, E) - network graph V - set of nodes E - set of edges (i, j) - tuple denoting edge in E θ - mutually exclusive flowsets x - an OD pair Υ x - set of flowsets belonging to OD pair x y - flowset, part of an Υ x I y - measurement utility of flowset y u (i,j) - boolean monitor placement for link (i, j) p i,j - sampling rate of link (i, j) Γ x y Υ x - original routing γ y θ (i,j) E - traffic demand flowset y places on link (i, j) ψ y - traffic demand of flowset y

12 search for best γ y (i,j) and u (i,j) assignments in network with M nodes, minimizing β with limiting amount of monitors to K

13 K-Best algorithm 1. start with All-On configuration, calculate maximum β and optimal traffic assignment γ y i,j. rank monitors according to a metric least utility y p (i,j)γ y (i,j) I y least traffic y γy (i,j) ψ y least importance y γy (i,j) I y least rate p (i,j) least neighbours 3. remove the top M-K monitors

14 Successive Selection

15 Greedy Algorithm

16 Quasi-Greedy Algorithm

17 Experimental Evaluation I y = f v y i f b f Abilene public academic network in the US 11 nodes 8 10Gbps links AS6461 RocketFuel (Topology Mapping Engine) topology 19 nodes 68 links GEANT European research/education network 3 nodes 74 (155Mbps - 10Gbps) links

18 All-On, Placement-only, MR-only, β Abiliene 10 x All On Placement only MR only β AS x All On 1 Placement only MR only β GEANT 4 x All On 1 Placement only MR only

19 MMPR performance using K-Best β Abiliene 10 x KB/utility KB/traffic 4 KB/importance KB/rate KB/neighbor β AS x KB/utility KB/traffic KB/importance 1 KB/rate KB/neighbor β GEANT 4 x KB/utility KB/traffic KB/importance 1 KB/rate KB/neighbor KB/utility 0.11 KB/traffic KB/importance KB/rate KB/neighbor CPU Time AS KB/utility KB/traffic 1.1 KB/importance KB/rate KB/neighbor CPU Time GEANT KB/utility KB/traffic 1.6 KB/importance KB/rate KB/neighbor

20 MMPR performance using Successive Selection β Abiliene 10 x SS/utility SS/traffic SS/importance β AS x SS/utility SS/traffic SS/importance β GEANT 3.5 x SS/utility SS/traffic SS/importance

21 MMPR performance using Quasi-Greedy β AS x QG/λ=0.05 QG/λ=0.1 QG/λ=0. CPU Time AS QG/λ=0.05 QG/λ=0.1 QG/λ=

22 Compare different heuristics 3 x x x 106 β Abiliene KB/utility SS/utility QG/λ=0.15 β AS KB/utility SS/utility QG/λ=0.15 β GEANT Network 3.5 KB/utility SS/utility QG/λ= CPU Time Abiliene 3 1 KB/utility SS/utility QG/λ= CPU Time AS KB/utility SS/utility QG/λ= CPU Time GEANT KB/utility SS/utility QG/λ=

23 best choice: K-Best using least utility reduces computation time by 3X, 46X and 33X for Abilene, AS6461 and GEANT respectively produces near optimal solution

24 opportunities sampling rates as another degree of freedom future implementation in OpenFlow (programmable routing platform) issues in practice how to select traffic importance? what routing protocol? how to estimate flow importance dynamically? how to configure routing tables dynamically?

25 questions?