Towards a Transparent and Proactively-Managed Internet Ehab Al-Shaer School of Computer Science DePaul University Yan Chen EECS Department Northwestern University

Motivations The Internet has evolved to become a un- cooperative ossificated network of networks –Network has to be treated as a blackbox »Performance of even neighboring networks are opaque »Inter-domain routing based on policies but not performance »Have to resort to overlay networks which are suboptimal –Diagnosis and fault location extremely hard Network config management reactive and expensive –Reactive configurations: tune after deployment –Vulnerable: manually handled and subject to conflicts –Imperative & fragmented: need to access several specific devices in order to implement a service goal

Proposed Solution I: Transparent Internet Every network shares its measurement and management information with other networks when necessary (glass box) –Link-level performance: delay, loss rate, available bandwidth, etc. –Management info »Configuration: QoS setting, traffic policing »Middle box settings: firewalls, etc. The information sharing –As part of the inter-domain protocols: Transparent Gateway Protocols (TGP) –Other applications: leverage DHT

Analogy to the Airline Alliance When airlines compose multi-lag flights, they need more than just route info. –Type of aircraft, # of vacancies, probability of punctuation, etc. Such open model is mutual beneficial –Provide the best flight composition for clients –Similarly, open network model can provide best communications for applications

Proposed Solution II: Proactive Configuration Management Proactive verification: configuration verified and translated to different vendor specific devices Proactive validation: Test the configuration changes on the real archived network traffic without interrupting the main operation network Autonomic configuration: configurations are auto-tuned dynamically to achieve the “objectives definingVerifying Deploying Evaluating Optimizing Validation Dynamic Validation: auto-tuning

Objectives Provides a completely transparent view of the Internet to networks and applications Diagnosis & trouble shooting becomes extremely easy –No more Internet tomography needed Flexible inter-domain routing –Not just based on policy or # of AS/hops –Flexible metrics based on bandwidth, latency, etc. Global traffic engineering –Each AS performs its own local traffic engineering –Provide AS path-level routing guide Unified framework that applications query (push/pull) info as needed –Streaming media, content distribution –Anomaly/security applications

Flexible Inter-domain Routing Multiple routing paths with TGP –Incorporate measurement info into AS paths –Bandwidth-intensive and latency-intensive applications can take different AS paths. Challenge: inter-domain routing based on bandwidth without making reservation Solution: Discretize the bandwidth for better stability –Though stability is a classical problem, not unique to TGP

Global Traffic Engineering For the current Internet, only local optimum is achieved in each AS –Allowing the network to handle all traffic patterns possible, within the networks ingress-egress capacity constraints (e.g. two phase routing) With global information, we can potentially achieve global optimum (or Nash equilibrium) –Each AS is a selfish individual –A center (or each AS) infers the Nash equilibrium –Each AS can try the Nash equilibrium, or attempt to benefit itself based on the inferred Nash equilibrium

Example of Benefit of Global TE AS 1 AS 2 AS 3 AS 5 AS 4 1G 2G 1G 2G 1G traffic to AS 1

Without Global TE Example of Benefit of Global TE AS 1 AS 2 AS 3 AS 5 AS 4 1G 2G 1G 2G 1G traffic to AS 1 1G 0.5G 1.5G 0.5G

With Global TE Example of Benefit of Global TE AS 1 AS 2 AS 3 AS 5 AS 4 1G 2G 1G 2G 1G traffic to AS 1 1G

Unified Transparency Framework for Various Functionality Sharing of anomaly/security-related measurement –Various characteristics of traffic: heavy hitter, heavy changes, histogram, etc. –Self-diagnosis to survivability Adaptations –Routing adaptations at router level or application level

Practical Issues and Solutions Incentives for information sharing –Mandatory for next-generation Internet ? –Alliance model for incremental growth Security/cheating: Trust but verify –Trust most of the info shared but periodically verify »Much easier than the current Internet tomography unless many ASes collude –Verification part of the protocol »Some fields in the packet headers designed for that purpose

Backup Materials

Measurement Info to Share Basic metrics –Delay, loss rate, capacity, available bandwidth –Demand (or traffic volume) and application types Intra-AS Measurement Info –Link-level info »Queried only when necessary –Aggregated Info »OD flow level info »Path segment b/t entry and exit points in each AS Inter-AS Measurement Info –General AS relationship –AS-level topology –Inter-AS link metrics

Combined w/ routing info and export to neighboring ASes through TGP protocol Provide global retrievable Management Information Base (MIB) with DHT Network link-level monitoring Transparent Internet Architecture

Methodology Network topology Web workload Network end-to-end latency measurement Analytical evaluation Algorithm design Realistic simulation iterate PlanetLab tests

TGP MIB Dissemination Architecture Leverage Distributed Hash Table - Tapestry for –Distributed, scalable location with guaranteed success –Search with locality data plane network plane data source Web server SCAN server client replica always update cache DHT mesh Replica Location Dynamic Replication/Update and Replica Management adaptive coherence Overlay Network Monitoring

X UC Berkeley UC San Diego Stanford HP Labs Adaptive Overlay Streaming Media Implemented with Winamp client and SHOUTcast server Congestion introduced with a Packet Shaper Skip-free playback: server buffering and rewinding Total adaptation time < 4 seconds

Summary A tomography-based overlay network monitoring system –Selectively monitor a basis set of O(n logn) paths to infer the loss rates of O(n 2 ) paths –Works in real-time, adaptive to topology changes, has good load balancing and tolerates topology errors Both simulation and real Internet experiments promising Built adaptive overlay streaming media system on top of TOM –Bypass congestion/failures for smooth playback within seconds

Tie Back to SCAN Provision: Dynamic Replication + Update Multicast Tree Building Replica Management: (Incremental) Content Clustering Network End-to-End Distance Monitoring Internet Iso-bar: latency TOM: loss rate Network DoS Resilient Replica Location: Tapestry

Contribution of My Thesis Replica location –Proposed the first simulation-based network DoS resilience benchmark and quantify three types of directory services Dynamically place close to optimal # of replicas –Self-organize replicas into a scalable app-level multicast tree for disseminating updates Cluster objects to significantly reduce the management overhead with little performance sacrifice –Online incremental clustering and replication to adapt to users’ access pattern changes Scalable overlay network monitoring

Existing CDNs Fail to Address these Challenges Non-cooperative replication inefficient No coherence for dynamic content Unscalable network monitoring - O(M × N) M: # of client groups, N: # of server farms X

Problem Formulation Subject to certain total replication cost (e.g., # of URL replicas) Find a scalable, adaptive replication strategy to reduce avg access cost

CDN Applications (e.g. streaming media) SCAN: Scalable Content Access Network Provision: Cooperative Clustering-based Replication User Behavior/ Workload Monitoring Coherence: Update Multicast Tree Construction Network Performance Monitoring Network Distance/ Congestion/ Failure Estimation red: my work, black: out of scope

Comparison of Content Delivery Systems (cont’d) Properties Web caching (client initiated) Web caching (server initiated) Pull-based CDNs (Akamai) Push- based CDNs SCAN Distributed load balancing NoYes NoYes Dynamic replica placement Yes NoYes Network- awareness No Yes, unscalable monitoring system NoYes, scalable monitoring system No global network topology assumption Yes NoYes