1. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University1 The Struggle for Network Control: How Can Distributed and Centralized Controls Effectively Collaborate? T. S. Eugene Ng Department of Computer Science Rice University Joint work with Alan L. Cox, Zheng Cai, Florin Dinu, Jie Zheng

2. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University2 Beyond Best Effort Datagram Service in Present and Future Networks Controller Best Effort Datagram Autonomous Network Routin g Protoc ol Routin g Protoc ol Routin g Protoc ol Routin g Protoc ol Routin g Protoc ol Virtual Private Network VPN Provisionin g Auto Load Balance IGP Link Weight Optimization Reachability Policy Packet Filter Configuratio n DDoS Mitigation Content Distribution Elastic Cloud Computing Big Data Computing

3. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University3 Fundamental Need for Control Component Collaboration (SLA Compliance Example) Routing Load balancing DDoS filtering DDoS

4. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University4 Control Component Collaboration is Tricky Pair-wise collaboration does not scale Routing Protocol Content Distribution Optimization Packet Filter Configuration IGP Link Weight Optimization Lack of state consistency

5. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University5 Maestro Unified Network State Management …….. Logic 1Logic 2Logic 3Logic N Virtual Network States Underlying Network States Environmental State Computed State Performance State

6. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University6 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

7. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University7 Maestro Architecture Overview Physical Network Driver State Dissemination Global Environment Driver BSG Local Environment Snapshot CLG Logic CLG Logic Transactional Update Local Environment Snapshot

8. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University8 Application to SLA Compliance DPC Coordination Protocol –Regulates forwarding table changes –Ensures routers adopt consistent forwarding tables Maestro DPC Driver Logic

9. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University9 CLG 1: Evaluates Acceptability of Routing State on New Observed Topology OSPF Routing Prediction Access Control Configuration SLA Compliance Analysis From local env Connectivity To temp env PredictedIntraDomainRoutingTable From local env TrafficDemandMatrix Connectivity ApprovedIntraDomainRoutingTable From temp env PredictedIntraDomainRoutingTable To temp env Null From local env Connectivity From temp env PredictedIntraDomainRoutingTable PredictedAccessControlConfiguration Terminal To global env ApprovableConnectivity ApprovableIntraDomainRoutingTable ApprovableAccessControlConfiguration From local env Connectivity AccessControlPolicy ApprovedAccessControlConfiguration From temp env PredictedIntraDomainRoutingTable To temp env PredictedAccessControlConfiguration Activation Connectivity

10. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University10 CLG 2: Computes IGP Link Weights for Load Balance Compute or Select Precomputed OSPF Link Weights for Improved SLA Compliance From local env Connectivity TrafficDemandMatrix To temp env OSPFLinkWeights From temp env OSPFLinkWeights Terminal To global env OSPFLinkWeights Activation Connectivity

11. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University11 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

12. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University12 Number of Flows Affected by Packet Loss

13. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University13 Reduction in SLA Violations

14. T. S. Eugene Ngeugeneng at cs.rice.edu Rice University14 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