1. Michael Schapira School of Computer Science and Engineering Hebrew University of Jerusalem Some Open Questions on the Borderline of Distributed Computing and Networking

2. This Talk 1.New questions in Internet protocol design 2.Self-stabilizing Internet protocols 3.Incentive-compatible network protocols … illustrated via Internet routing examples

3. The Internet Tremendous success – from research experiment to global infrastructure Enables innovation in applications – Web, P2P, VoIP, social networks, virtual worlds But, the Internet infrastructure fairly stagnant for decades…

4. Why Can’t We Innovate? “Closed” equipment – software bundled with hardware – vendor-specific interfaces Slow protocol standardization Few people can innovate – equipment vendors write the code – long delays to introduce new features

5. Traditional Computer Networks data plane: packet streaming Handle packets in “real time”: forward, filter, buffer, mark, rate-limit, measure, …

6. slower time scale: track topology changes, compute routes, install forwarding rules, … control plane: distributed algorithms Traditional Computer Networks

7. Software Defined Networking (SDN): a New Paradigm API to the data plane (e.g., OpenFlow) Controller: logically-centralized control, smart, slow, implemented in software, … Switch: dumb, fast, implemented in hardware

8. 8 Network OS Controller Application events from switches topology changes, traffic statistics, arriving packets, … commands to switches (un)install rules, query statistics, … Software Defined Networking (SDN): a New Paradigm

9. So… Change is finally on the horizon But many challenges remain… – Realizing SDN (e.g., distribute the controller?) – What are the “right” protocols (for routing, traffic engineering, etc.)? Distributed computing theory can play an important role here

10. Distributed Controller? 10 Network OS Controller Application Network OS Controller Application for scalability and reliability partition and replicate state Elect a leader? Distribute the computation? How to ensure consistency (across controllers / switches)? Where to place the controller(s)? Elect a leader? Distribute the computation? How to ensure consistency (across controllers / switches)? Where to place the controller(s)?

11. Rethinking (Routing) Protocols Routing is a control plane operation – slow (milliseconds – seconds) Packet forwarding is a data plane operation – fast (microseconds) Today’s (intradomain) routing – establishes connectivity – optimizes routes (= shortest paths) failure ⇒ re-convergence ⇒ dropped packets!

12. Pushing Connectivity (Only!) to the Data Plane … while retaining scalability – implemented in hardware – low overhead (end-to-end backup paths too costly…) – static forwarding tables (no changes in packet rates) – no change to packet header When packet to a node d arrives at node i, i’s outgoing link is a function only of i i d d incoming link set of “live” outgoing edges f id : E i x P(E i ) -> E i

13. Resilient Forwarding A “forwarding pattern” {f id } i is t-resilient if for any (at most) t-edge-failures the existence of a path between a node i and the destination d implies loop-free forwarding from i to d. Perfect resilience ≣ t → ∞ i i d d j j x

14. Theoretical Perspective Thm [Feigenbaum-Godfrey-Panda-S-Shenker-Singla] : 1-resilient forwarding pattern always exists Thm [Feigenbaum-Godfrey-Panda-S-Shenker-Singla] : Perfect resilience is not achievable Big gap! – does a 2-resilient forwarding pattern always exist? – specific families of graphs? – relax restrictions (randomness, dynamic forwarding tables, …)?

15. Practical Perspective A perfectly-resilient mechanism for achieving connectivity in the data plane – [“Data Driven Connectivity”, Liu-Panda- Singla-Godfrey-S-Shenker, NSDI 2013] – utilizes existing mechanisms – small (few bits) changes to forwarding tables at packet rate

16. How to distribute the controller? Data-plane/control-plane perspective on other networking tasks (e.g., traffic engineering) Connectivity in the data plane Directions for Future Research

17. (Self-)Stabilizing Internet Routing

18. Border Gateway Protocol Google Verizon Comcast AT&T The Border Gateway Protocol (BGP) establishes routes between the (over 42,000) networks that make up the Internet

19. BGP ≠ Shortest-Path Routing! Google Verizon Comcast AT&T I want to avoid routes through Comcast if possible I won’t carry traffic between AT&T and Verizon I want a cheap route I want short routes

20. Illustration: BGP Dynamics 1 2 d 2, I’m available 1, my route is 2d 1, I’m available Prefer routes through 2 Prefer routes through 1 A stable state is reached

21. 1 2 d BGP might oscillate indefinitely between 1d, 2d and 12d, 21d 1, 2, I’m the destination 1, my route is 2d 2, my route is 1d Illustration: BGP Oscillation Prefer routes through 2 Prefer routes through 1 Conjecture [Griffin-Wilfong, SIGCOMM 99] : 2+ stable states → BGP can oscillate

22. Why are Oscillations Bad? Make the network unpredictable and hard to debug. Might lead to the flooding on the network with BGP update messages. Deteriorate performance! –almost 50% of VoIP disruptions are due to BGP route fluctuations

23. Internet Protocols, Markets, and Beyond Often, in computational and economic environments 1.the prescribed behavior for each “node” (human, machine) is simple and natural 2.nodes’ interaction is not synchronized How can we reason about such environments? – Internet protocols (BGP routing, TCP congestion control) – large-scale markets – social networks – …

24. Dynamics: Game Theory vs. Distributed Computing Game theory: – establishes convergence to equilibrium for “natural dynamics” (best-/better-response, fictitious play, no- regret, …) – … but typically assumes synchronization. Distributed computing theory: – analyzes system behavior in asynchronous environments – … but no general notions of natural behavior.

25. n nodes 1,…,n Node i has action space A i – A=A 1 … A n – A -i =A 1 … A i-1 A i+1 … A n Node i has reaction function f i :A -i → A i – f=(f 1,…,f n ) – f i can capture node i’s “best-responses” Simple Model

26. Infinite sequence of discrete time steps t=1,… A schedule  :{1,…} → 2 [n] maps each time step to the subset of nodes “activated” at that time step – a fair schedule activates each node infinitely often An initial action-profile and schedule naturally induce a dynamics. Simple Model (Cont.)

27. Defn: An action-profile a*=(a 1,…,a n ) is a stable state if f i (a*)=a i for all i. – that is, a* is a fixed point of f – abusing notation… Defn: A system is convergent if for every choice of initial action-profile and fair schedule the induced dynamics converge to a stable state. Simple Model (Cont.)

28. Thm [Jaggard-S-Wright] : If there exist multiple stable states, then the system is not convergent. – valency argument! – no failures, just dumb nodes! So, a unique stable state is a necessary condition for guaranteed convergence. Can be generalized to bounded-recall, non- stationary reaction functions. Towards a Characterization of Convergent Systems

29. Application: Internet Routing BGP establishes routes between the smaller networks that make up the Internet Question [Griffin-Shepherd-Wilfong, 2001] : Do multiple stable routing configurations imply the possibility of persistent route oscillations? Answer [Sami-S-Zohar, 2009] : Yes! AT&T Qwest Comcast Sprint

30. Other Applications Our “two people in a corridor” example… Models of congestion control on the Internet Load balancing Diffusion of technologies in social networks Asynchronous circuits …

31. Meanwhile, back in the corridor…

32. Defn: An r-fair schedule activates each node at least once in every r consecutive time steps Defn: A system is r-convergent if for all choices of initial action-profile and r-fair schedule the induced dynamics converges to a stable state. – convergent  r-convergent – not r-convergence  not convergent Thm [Erdmann-S] : If there exist multiple stable states, then the system is not (n-1)-convergent. – tight! – much more delicate valency argument Strengthening the Result: Convergence vs. Synchronism

33. Thm [Jaggard-S-Wright] : Determining if a system with n nodes is convergent requires exponential communication (in n). Thm [Engelberg-Fabrikant-S-Wajc] : Determining if a succinctly described system is convergent is PSPACE-complete. Both results extend also to “stochastic convergence”. Complexity of Convergent Systems

34. Other protocols! Identify specific classes of (stochastically) convergent games and measure convergence rate (e.g., in terms of asynchronous rounds). Characterize guaranteed convergence, and design algorithms for determining such convergence for  other game dynamics (e.g., fictitious play, no-regret dynamics)  other notions of equilibrium (e.g., mixed Nash, correlated)  other notions of asynchrony Directions for Future Research

35. Incentive-Compatible Network Protocols

36. queue routerlink TCP Congestion Control is NOT Incentive Compatible AIMD = Additive Increase Multiplicative Decrease

37. What About BGP? BGP was designed to guarantee connectivity between largely trusted and obedient parties. In today’s commercial Internet ASes are owned by self-interested, often competing, entities – might not follow the “prescribed behaviour” Simple examples show that BGP is, in fact, not incentive compatible – a node can obtain a better route by “lying”

38. How Can We Fix This? Economic Mechanism Design: “the reverse-engineering approach to game- theory”. Goal: Incentivize players to follow the prescribed behaviour – if others run the protocol so should I! – without money! Thm [Levin-S-Zohar] : Secure variants of BGP are incentive compatible.

39. An exciting time to be in networking Internet protocols motivate new research directions Distributed computing theory has much to contribute Conclusion

40. Thank You