T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

1

The Struggle for Network Control: How Can Distributed and Centralized Controls Effectively

Collaborate?

T. S. Eugene NgDepartment of Computer Science

Rice University

Joint work withAlan L. Cox, Zheng Cai, Florin Dinu, Jie Zheng

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

2

Beyond Best Effort Datagram Servicein Present and Future Networks

ControllerBest Effort Datagram

Autonomous Network

Routing

Protocol

Routing

Protocol

Routing

Protocol

Routing

Protocol

Routing

ProtocolVirtual Private Network

VPNProvisionin

g

Auto Load Balance

IGP Link Weight

Optimization

Reachability Policy

Packet FilterConfiguratio

n

DDoS Mitigation

Content Distribution

Elastic Cloud Computing

Big Data Computing

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

3

Fundamental Need for Control Component Collaboration (SLA Compliance Example)

• Routing• Load balancing• DDoS filtering

DDoS

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

4

Control Component Collaboration is Tricky

• Pair-wise collaboration does not scale

RoutingProtocol

Content Distribution

Optimization

Packet FilterConfiguration

IGP Link Weight

Optimization

• Lack of state consistency

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

5

MaestroUnified Network State Management

……..Logic 1 Logic 2 Logic 3 Logic N

Virtual Network States

Underlying Network States

Environmental State

Computed State

Performance State

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

6

Requirements

• Synchronized access to state– Granularity of locking

• Consistency of input state of collaborating controls– Even when underlying network state changes

• Maintaining a history of state– For trend analysis and incremental computations

• Extensible network state– Support new state associated with new network functions

• Extensible control logic– Programmatic, reusable, reconfigurable logic

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

7

Maestro Architecture Overview

Physical Network

Driver

State Dissemination

Global Environment

Driver Driver

BSG BSG BSGBSG

Local Environment

Snapshot

CLG

Logic Logic Logic

CLG

Logic

Logic

Logic

Transactional Update

Local Environment

Snapshot

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

8

Application to SLA Compliance

• DPC Coordination Protocol– Regulates forwarding table changes– Ensures routers adopt consistent

forwarding tables

Maestro

DPC Driver

LogicLogic

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

9

CLG 1: Evaluates Acceptability ofRouting State on New Observed Topology

OSPFRouting

Prediction

Access ControlConfiguration

SLA ComplianceAnalysis

From local envConnectivity

To temp envPredictedIntraDomainRoutingTable

From local envTrafficDemandMatrix

ConnectivityApprovedIntraDomainRoutingTable

From temp envPredictedIntraDomainRoutingTable

To temp envNull

From local envConnectivity

From temp envPredictedIntraDomainRoutingTable

PredictedAccessControlConfiguration

To global envApprovableConnectivity

ApprovableIntraDomainRoutingTableApprovableAccessControlConfiguration

From local envConnectivity

AccessControlPolicyApprovedAccessControlConfiguration

From temp envPredictedIntraDomainRoutingTable

To temp envPredictedAccessControlConfiguration

ActivationConnectivity

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

10

CLG 2: Computes IGP Link Weights for Load Balance

Compute or SelectPrecomputed

OSPF Link Weightsfor Improved SLA

Compliance

From local envConnectivity

TrafficDemandMatrix

To temp envOSPFLinkWeights

From temp envOSPFLinkWeights

Terminal

To global envOSPFLinkWeights

ActivationConnectivity

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

11

Experimental Results

• NS-2 simulator interfaced with Java implementation of Maestro

• 79-node, 147-link Rocketfuel topology• 100 Poisson traffic flows, random source-destination

– Average rates follow Zipf distribution

• 5 “malicious” flows that need to be blocked• Conduct random link failure experiments, observe

impact to traffic flows

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

12

Number of Flows Affected by Packet Loss

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

13

Reduction in SLA Violations

T. S. Eugene Ng

eugeneng at cs.rice.edu

Rice University

14

Summary

• Future networks will no doubt be rich in services• Control components (distributed or centralized) need

to collaborate• Maestro proposes an “hourglass” architecture for

control component collaboration– Provides consistent access to network state– Programmable, extensible– Measurable benefits (e.g. SLA compliance)

• Target to release the software by the end of summer

• Work supported by NSF FIND and Microsoft Research