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M3I price reaction

M3I price reaction. Bob Briscoe 31 May 2000. motivation. current QoS solutions favour apps that can: predict where & what traffic they will send/rcv unpredictable apps need either over-capacity (without overbooking) or tighter app control (closer to app), leading to:

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M3I price reaction

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  1. M3I price reaction Bob Briscoe 31 May 2000

  2. motivation • current QoS solutions favour apps that can: • predict where & what traffic they will send/rcv • unpredictable apps need • either over-capacity (without overbooking) • or tighter app control (closer to app), leading to: • less overhead to adapt policy • less coupled to routing • more open to commercial innovation • BUT... feedback delay

  3. control granularity • fixed pipe per customer (diffserv) • fixed pipe per flow, but can adapt (intserv) • congestion charge reaction middleware • congestion charge reaction by app decreasing prediction requirement • what is the Internet denying by only supports predictable apps?

  4. approach • project (top down) • general solution must have extreme flexibility • consider form of general solution (architecture) • consider appropriate approximations • prototype specific solution(s) within that framework • presentation (bottom up) • start with core function: price reaction • then put in architectural context

  5. QoS control policy Iq Ip Ip Ip QoS control legend Pq I invocation of network service Ip data packet Iq QoS signalling separation of concerns

  6. QoS control policy Iq Ip Ip Ip QoS control Pq richness of QoS control policyoutput • output options: • set of QoS parameters (RSVP service class), Iq • packets, Ip, conforming to Iq • polymorphic • function depends on the type of input Iq Iq0 Iq Iq Ip Ip Ip

  7. QoS control policy Iq Ip Ip Ip QoS control Pr Pq richness of QoS control policyinput • input options (dumbest first): prescriptive • declare QoS & degrade pro rata to fit budget • Iq & budget • optimise against utility • vector of utility functions scaled by budget • vector of currently offered pricing • task oriented • dbase of assoc’s betw. QoS policies, tasks & users • continue with  declarative Iq

  8. QoS mapping • QoS conceptions • user • application • network • utility: user land • edge QoS control: application land • network QoS control: network land

  9. QoS mappinguser QoS dimensions • ???

  10. QoS mappingapplication QoS dimensions • bandwidth, x • burstiness, dx/dt • responsiveness, r = 1/latency • jitter, dr/dt • delivery probability, d = 1 - (drop prob) • availability etc: out of scope

  11. network QoS dimensionse.g. RSVP service class • guaranteed service/controlled load • flowspec • parameters for a token bucket • token rate • max token size • max burst size • max token rate • etc

  12. U/$ U/$ U1(Q1) p1Q1 U2(Q2) p2Q2 Q2 Q1 inside QoS control policy U/$ U3(Q3) p3Q3 Q2 ... Q = [Q1, Q2, Q3, Q4, Q5] U=iUi ? for any one task, may find one QoS dimension dominates

  13. setting QoS control policy add  U1(Q1) user-app-task context U/$ Q1

  14. derive demand curve price, p x* p optimal rate, x*

  15. conjectured effect of wealth on utility wealth1 $ • motivation: re-usable per-task rate control policies • conjecture: wealth affects utility more strongly than QoS sensitivity • QoS sensitivity = marginal utility wrt Q wealth2 << wealth1 U1(Q1) the arrows map between points of similar QoS sensitivity Q1

  16. approximations - summary • conjectures: • one QoS dimension dominates most applications? • use identical utility functions for related tasks? • 3 or 4 parameters can approximate a utility curve? • wealth/budget scales utility vertically?

  17. sanity checksecurity-efficiency compromise • more expressive QoS control policy has to include buying policy • customer can’t trust provider to execute buying policy • to audit seems to require complete duplication of the function • may have implications for guaranteed service provider function

  18. types of reaction sndr sndr proxy rcvr proxy rcvr preserve delay buffer buffer buffer delay mark f/b mark f/b mark re-xmt drop f/b nack f/b nack less - drop suppress nack suppress nack recode mark f/b mark f/b mark recode f/b nack f/b nack f/b nack - - f/b drop layer f/b drop layer • explicitly instruct sender what to do • sender adapts to thinner pipe through f/b • which reaction depends on relative strengths in vector • but declarative isn’t enough (need some prescription)

  19. service operation network meter rate control charge advice aggregator service creation tariff policy legend P

  20. Pq Pq Pq duration charged intserv PATH RESV data sender receiver

  21. Pq Pq Pq ‘abc’ charged intserv $=aT + bV +c PATH RESV data sender receiver

  22. Pq Pq congestion charging sender (‘s GSP) receiver (‘s GSP) congestion f/b CE data CE = congestion experienced GSP = guaranteed stream provider (Kelly’s ‘gateway’)

  23. Pq congestion charging Pq sender (‘s GSP) receiver (‘s GSP) congestion f/b CE data CE = congestion experienced GSP = guaranteed stream provider (Kelly’s ‘gateway’)

  24. budget PA PA PA Pr price reaction in context buying agent directory C ? Pr Pr C rate control application

  25. application involvement • application options: • delegate control for non-functional comms • giving context • control everything itself • buying agent shouldn’t take control of QoS unsolicited

  26. user involvement • user can: • give agent control over any stream’s QoS • giving context • user must: • monitor feedback on cost of functional comms • adapt demand accordingly, through application controls

  27. the meta-object design pattern refl_Socket Application Access to reflective objects Reflection class changeMeta( ) Access to original objects Meta calls Inherit Meta Object metaBefore( ) metaAfter( ) Some application class QoteS Java.net.Socket

  28. edge-centric use case end-customer network provider Enterprise policy agent Offer directory Price setting & reaction App or M/w Service Charging & acc’ting QoS ctrl Metering 3 1 2 Ec Ep 7 6 O O 5 8 4 AMc 16 Pc Pp 14 10 10 Sp 15 9 13 CAc CAp 11 17 Qp Qc 12 12 Mp Mc service provision

  29. edge control use case end-customer network provider Enterprise policy agent Offer directory Price setting & reaction App or M/w Service Charging & acc’ting QoS ctrl Metering 3 1 2 Ec Ep 7 6 O O 5 8 4 AMc 14 16 Pc Pp 10 Sp 13 15 9 CAp 11 Qp Qc 12 Mp service provision

  30. price reaction conclusions I • separate policy from QoS control • ultimately, QoS control must be polymorphic • QoS mapping important • QoS control policy approximations • a few parameters (can then be communicated)? • budget dependency? • many tasks to few policies? • QoS control policy implies type of reaction?

  31. price reaction conclusions II • difficult to do price reaction generically • limited application hooks • only simple tariffs allow price reaction • multi-part tariffs require charge reaction • allowing for competition is possible but complex on host • but relying on provider for charge advice inefficient

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