SYNERGISTIC NETWORK OPERATIONS

Saqib RazaUniversity of California, Davis

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A SNAPSHOT OF NETWORK OPERATIONS

Maintenance

Forensics

Scheduling

Inter-domain TE

Forwarding

FirewallsIntra-domain TE

Diagnostics

Accounting

Overlay RoutingPower Manageme

nt

Traffic Policin

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Overlay Routing Intra-domain TE

EXAMPLE: INTER-OPERATION DYNAMICS

ISP A

A

D

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C

xy

Initially, traffic between overlay nodes A and D does not traverse ISP-A

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

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THE HIPPOCRATIC OATH FOR NETWORK OPERATIONS

Do No HarmOperations should be cognizant of any disruptive effects to other operations.

Strive to do GoodOperations should seek to enhance the efficacy of other operations.

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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

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GRACEFUL NETWORK MIGRATIONminimizing performance disruption

during planned network maintenance …

Maintenance

Intra-domain TE

Do No Harm

Joint work with:

Yuanbo Zhu & Chen-Nee Chuah (UC Davis)

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MOTIVATION

Inadvertente.g. fiber-

cuts, router crashes

Premeditated

e.g. firmware upgrades

Network Events

Performance

Disruption

Premeditated network tasks can be judiciously scheduled to minimize performance disruption

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GRACEFUL STATE MIGRATION (GSM) GSM represent a class of problems

characterized by two essential characteristics:

Network needs to transition from an initial state to a final state

Sequence of atomic network operations (e.g.

deactivating/activating a router or link)

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SAMPLE APPLICATION Link Maintenance Scheduling

(LMS)Maintenance activities account for more than 20% of failures in backbone ISPs [Markopoulou ‘04].

Weekly maintenance windows: multiple links need to be maintained in each window.

Each link needs to be deactivated and then reactivated .

Link failures can disrupt intra-domain TE.

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LMS: ILLUSTRATIVE EXAMPLE

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I need to repair links (a,c) and (c,f)

Careful! Watch out for the Maximum Link Utilization (MLU)

Link WeightsFlow Size = ½ C

Max Link Util = 50%

Link Capacity = C

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(a,c) ↓

(a,c) ↑

(c,f) ↓

(c,f) ↑ MLU = 100%

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100%

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↓(c,f)

↓(c,f)

↑(a,c)

↑ MLU = 50%

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LMS: ILLUSTRATIVE EXAMPLE

(a,c) ↓ (a,c) ↑ (c,f) ↓ (c,f) ↑ MLU = 100%

(a,c) ↓ (c,f) ↓ (c,f) ↑ (a,c) ↑ MLU = 50%

Schedule 1

Schedule 2

The schedule with multiple links simultaneously deactivated causes less

disruption

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THE GENERAL GSM PROBLEMs0 s1 s3 sn

min C(s0,s1, …sn-1,sn)

(si,si+1) ∈ A

(s0,sn) = (sinitial,sfinal) n ≤ B

Specify (sinitial,sfinal), A, B, & C to define a concrete GSM problem, e.g., LMS

nrdn

nrrn

, Anrdn

rrdd

, Arepaired deactivated not repaired

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A GENERAL GSM SOLUTION FRAMEWORK

c2k(sx,sz)=miny(ck(sx,sy) + ck(sy,sz))

• The minimum cost of going from sx to sz in 2k steps is equal to the minimum cost of going from sx to sy in k steps plus the cost of going from sy to sz in k steps.

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COMPUTATIONAL COMPLEXITY

000

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010

001

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011

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020

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Solution space of LMS

has 2n!/2n solutions

GSM is a combinatoria

l optimization

problem

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ANTS COLONY OPTIMIZATION

n n n

f f f Swarm intelligence

meta-heuristic

Near optimal solutions for the Traveling

Salesman Problem

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PERFORMANCE EVALUATION

> 20 node/80 link topology> 100 experiments per data point> Report Cost Reduction (MLU) over Single-Failure Heuristic

Single-Failure Heuristic works well generally

What about the worst case?

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GST: APPLICATIONS

•Link Weight Assignment Scheduling•Network Evolution & Upgrade•MPLS Reroute Sequencing

Link Weight Reassignment Scheduling

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OUTLINE Graceful Network State Migration [Infocom `09] MeasuRouting: A Framework for Routing Assisted Traffic

Monitoring [Infocom `10] Future Directions

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MEASUROUTINGa framework for routing assisted

network measurements…

Measurements

Intra-domain TE

Strive to do Good

Joint work with: Guanyao Huang & Chen-Nee Chuah (UC Davis)Srini Seetharaman & Jatinder Singh (DT Labs)

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THE MONITOR PLACEMENT PROBLEM

?

Oops!

?1. Measurement objectives change

3. Traffic placement changes2. New Traffic gets introduced

important very important

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• Configure intra-domain routing to route important traffic sub-populations across paths where they could best be monitored, while avoiding disruption to default traffic engineering.

PROBLEM STATEMENT

Measurements

Intra-domain TE

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TE POLICY VIOLATION

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COMPLIANT REROUTINGTE policy is defined

for aggregated flows

Sub-populations of aggregated flows, indistinguishable from a TE perspective, can be distinguishable from a measurement perspective

Monitor

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OTHER ENABLING FACTORS

• Aggregate traffic placement may be altered without violating TE 0bjectives: e.g., links with utilization below maximum utilization have free capacity

Aggregate TE Objectives

• TE objectives may be violated to maximize global network utility.

TE-Measurement Tradeoff

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TE Flowset (macro-flowset)

Measurement Flowsets (micro-flowsets)

1. Aggregated TE Flows e.g. OD pair traffic

2. Traffic placement given:

Γ(i,j)E

1. TE flowset de-composes into k measurement flowsets

2. A measurement flowset has:

a) Sizeb) Importance

3. Decision variable:

(i,j)E

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MEASUROUTING OBJECTIVE

pijijbyiypij

ij by

iy

Flowset Routing

Flowset Size

Flowset Importance

Link Sampling

Rate Points gained for sampling flowset y on link

(i,j)

Network Flow Conservation Constraints

Ensure that TE performance remains within some value of the default TE performance

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THE LOOPING PROBLEM

Measurement-flowset can only traverse links in a Directed Acyclic Graph (DAG)

RSR: use DAG for the associated OD pair

NRL: add additional links to the original DAG

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SYNTHETIC EXPERIMENTSSelect 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

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AS122144 nodesAS1239

52 nodes

NETWORK SIZE

K : 10Importance : Pareto (=2)

Performance sensitive to number of multiple paths

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AS122144 nodes

DEGREES OF FREEDOM

: 0.1Importance : Pareto (=2)

Diminishing marginal returns of increasing k

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Trace capture infrastructure selectively deployed

Increase representation of interesting traffic in traces

A REAL APPLICATIONTrace Capture for

Deep Packet Inspection (DPI)

Q(i)

P(i)

ln(1-|P(i)-Q(i)|) Field of Interest: Destination Port

Long Term History: 3 months Short Term History: 2 days

Abilene9

nodes

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REAL WORLD MEASUROUTING

• Configurable Routing: MPLS, OpenFlow

• IP Routing: Equal Cost Multipath

Underlying Routing Substrates

• Heterogeneous Sampling Algorithms• Distributed Firewalls

Applications

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OUTLINE Graceful Network State Migration [Infocom `09] MeasuRouting: A Framework for Routing Assisted Traffic

Monitoring [Infocom `10] Future Directions

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OPTIMAL STATES OF BEING

GSM

Policy Decisions

Discrete Intervals

Atomic Transitions

•Data Center Job Scheduling•Data Center Load Distribution

Graceful Network State Migration

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DATA CENTER JOB SCHEDULING

Power Management Scheduling

Power conserved by switching off data center components, dynamic voltage scaling etc.

Jobs scheduled on different servers to optimize performance (MapReduce, Dyrad).

Jointly optimize job scheduling and power management decisions.

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DATA CENTER LOAD DISTRIBUTION

Power Management Inter-domain TE

Data center operation costs vary geographically due to energy market price fluctuations [Qureshi `09] 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.

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A CALCULUS FOR SYNERGISTIC OPERATIONS

Common

Resource Pool

Global Utility

CPU CyclesBandwidth

Power

Revenue Contribution

Each marginal unit of a resource ought to be allocated to the operation that derives the highest marginal utility from consuming it.

Network-wide Security

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Questions

wwwcsif.cs.ucdavis.edu/~razawww.ece.ucdavis.edu/rubinet

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AS122144 nodes

MEASUREMENT UTILITY DIVERSITY

k=10; M=3000Importance: Pareto (=2)

Performance improves with variance in importance

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LMS IN A SMALL NETWORK (ABILENE)

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MEASUROUTING PATH INFLATION