1. . Post-Internet QoS Research Jorg Liebeherr Department of Computer Science University of Virginia IWQoS 2004 Panel

2. . IWQoS 94 Montreal QoS Venues: First there was one … IWQoS 95 Brisbane IWQoS 96 Paris IWQoS 97 New York IWQoS 98 Napa IWQoS 99 London IWQoS 00 Pittsburgh IWQoS 01 Karlsruhe IWQoS 04 Montreal IWQoS 94 Aachen IWQoS 03 Monterey IWQoS 02 Miami Beach QofIS’01 Berlin QofIS’04 Barcelona QofIS’02 Coibmra QofIS’03 Stockholm COQODS 04 Singapore QoS-IP 2001 Rome QoS-IP 2005 Catania QoS-IP 2003 Milan QShine 2004 Dallas.. now there are several

3. . While QoS research has thrived … Many fundamental advances: –Much improved understanding of packet scheduling –A new theory: network calculus –Numerous proof-of-concept implementations of even the most difficult systems problems

4. . … QoS deployment has failed No QoS approach seems to be able to take hold: –Connection-oriented (ATM) –Flow-based reservation (IntServ) –Class-based differentiation (DiffServ) –Overlay approach (Service overlays)

5. . Possible Reasons for Lack of QoS Deployment No applications/business cases –VoD used to be the driver Botched standardization efforts –Ignored analytical aspects of QoS service –Problems in control path addressed late (e.g, policy) Naive implementations –Software-only realization of QoS scheduling in core routers No need for QoS –E.g., in backbone networks, most applications are elastic and enough capacity is available

6. . Lack of deployed QoS infrastructure in the Internet does not make QoS research less important However, QoS research needs to take into account that there is no deployed infrastructure QoS deployment ≠ QoS research

7. . Frontiers of QoS Research after the Internet Apply QoS principles to different contexts: QoS in wireless LANs QoS in sensor networks QoS in wireless ad-hoc QoS in P2P networks QoS in access networks QoS in VoIP QoS in MPLS QoS in QoS Fundamentals: 1.Provide new insights into fundamentals of packet networking 2.Develop system design principles for QoS systems 3.Develop new analytical tools

8. . Fundamentals: Statistical Multiplexing QoS research knows a lot about complex scheduling However, often it takes a worst-case view of the network and ignores statistical multiplexing Opportunity: QoS research that considers statistical characteristics of traffic can provide insights into fundamental properties of packet networks (Note: Statmux is the reason d’être of packet networks) Example problems: –What is the impact of scheduling compared to statmux? –How does this vary with type of traffic? (e.g., self- similar traffic)

9. . Worst-case Expected case Probable worst- case

10. . Impact of Statistical Multiplexing vs. Scheduling Statistical multiplexing makes a big difference Scheduling has small impact Example: MPEG videos with delay constraints at C= 622 Mbps Deterministic service vs. statistical service (  = 10 -6) Thick lines: EDF Scheduling Dashed lines: SP scheduling Data from: IEEE SAC. 18(12):2651–2664, Dec. 2000

11. . Impact of traffic type on statistical multiplexing Comparisons of statistical service guarantees for different schedulers and traffic types Schedulers: SP- Static Priority EDF – Earliest Deadline First GPS – Generalized Processor Sharing Traffic: Regulated – leaky bucket On-Off – On-off source FBM – Fractional Brownian Motion C= 100 Mbps,  = 10 -6 Data from: Technical Report, Univ. of Virginia, CS Dept., No. CS-20-2003, 2003

12. . Design principles: QoS systems Systems with QoS have separate design components: –Admission control –Traffic conditioning –Scheduling –Signaling –Policy and Accounting Numerous trade-offs: –Edge vs. endsystem vs. core implementations –Soft state vs. hard-state signaling –Centralized, distributed, user-based, or no admission control Opportunity: Exploit available know-how to develop guidelines for choices in QoS design space for any given networking context

13. . Search for “Toy Models” Learn from physics: –Wide use of toy models that capture key characteristics of studied system (without being an exact characterization) –Look for models that permit back-of-the-envelope calculations –Toy models are usable by non-theorists Early days of networking used toy models: M/M/1 Queue Kleinrock’s PhD Dissertation (cited as laying the foundation for packet networks) heavily uses M/M/1 type models Today: ns-2 culture M/M/1 has lost appeal as toy model, and was replaced with ns-2 Simulations are good to evaluate incremental changes to existing systems, but not to evaluate radically different designs ns-2 may be partially responsible for  incremental thinking in networking

14. . My proposal: Develop network calculus into new “Toy Model” Today, fundamental progress in networking is hampered by the lack of methods to evaluate how radically new designs will perform. Opportunity: Simple (`toy') models that permit fast (`back-of-the-envelope') evaluations can become an enabling factor for breakthrough changes in networking research Approach: Probabilistic version of min-plus network calculus (stochastic network calculus) is a candidate for a new class of toy models for networking

15. . Network Calculus (Cruz, Chang, LeBoudec) Sender Receiver S3S3 S1S1 S2S2 Network Calculus: Arrivals are described by envelopes and service by “service curves”. If S 1, S 2 and S 3 are service curves that describe the service to a flow then S net = S 1 * S 2 * S 3 Many similarly elegant results S net

16. . Stochastic Network Calculus State of the art: –Effective bandwidth theory is integrated –Envelope derived for numerous traffic models –Various S net formulations exist (some wrong) What is open: –A lot of technical issues (but problems are difficult) –No simple computational algorithms exist –Relationship to other theories (queueing theory, control theory) not clear –How to reduce learning curve and complexity, to make it attractive for non-theorists? –Suitability of model to real problems (ie., “non toy problems”) is untested

17. . QoS is like World Peace It is a worthy goal, It is difficult to achieve, And progress is made in small steps