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Users, Pricing and Resource Reservation: Managing Expectations. Jon Crowcroft, http://www.cs.ucl.ac.uk/staff/jon/ jon@cs.ucl.ac.uk. Reservations Call,signal,qos route, route pin, admission control, call log Hop by hop Accounting Principal objective metrics: User:Call blocking prob.

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users pricing and resource reservation managing expectations

Users, Pricing and Resource Reservation: Managing Expectations.

Jon Crowcroft,

http://www.cs.ucl.ac.uk/staff/jon/

jon@cs.ucl.ac.uk

two rival approaches
Reservations

Call,signal,qos route, route pin, admission control, call log

Hop by hop

Accounting

Principal objective metrics:

User:Call blocking prob.

Net: Erlangs

Adaption

Congestion signal (loss, ecn, delay)

Feedback control loop

TCP eqn etc etc

Principle objective metrics:

User throughput

Utilisation

Two Rival Approaches
m3i jisc approach
M3I/JISC Approach
  • End system based adaption – components:
    • Network distributes congestion data via ECN marks
    • Network Monitoring Agents distribute general load information to Tariff Agents
    • Tariff Agents distribute current spot price per region by multicasting region id (AS#?) plus price per kbps per rtt
    • End user or risk broker choose rate
    • End user may pay broker a long term price or choose to adapt to spot prince
    • Metering and Adaption models are distributed signed and sealed to end users, who send franked packets using them (otherwise packets are just BE)
partial deployment
Partial Deployment…
  • This can be partially deployed (piecewise) as it only needs congestion point to distribute infromation, and to be monitored.
  • Doesn’t require unloaded cores to do anything
  • Doesn’t usually require (most) edge routers to do anything
  • End system module can be plugin
hypothesis part 1
Hypothesis…part 1
  • Internet Traffic Matrix Is inherently unpredictable
  • How bad do users feel about unpredictable call blocking?resource reservation must FAIL (i.e. you must have a non zero call blocking probability) or it is pointless. If it fails, you have to look at user disatisfaction with call blocking
  • If the user satisfaction with call blocking is bad enough, you will lose customers to someone else who provisions. So the REAL argument is what are the relative costs of call blocking versus provisioning.
hypothesis part deux
Hypothesis …part deux
  • How bad do users feel about unpredictable pricing? In between these extremes there is dynamic pricing - you can offer users a spot price instead of a futures price - users dont like this much (we have done some experiments...) but sometimes they'll buy into it -
  • Then adapt the user’s behaviour instead of the application’s behaviour - whether you can do this enough to make the mean to peak ratio low enough without incurring the wrath of users; (unpredictable price is nearly as bad as unpredictable call success/blocking probablity) is the slightly more variable version of problem 1.
experimental plan
Experimental Plan…
  • UK Academic Community has 1 major bottleneck: UK-US link (only OC-12
  • Can meter; can place proxy servers at each end; can distribute price based on metering to proxies, and distribute applets which adpt to end users (at US and UK end)
  • Have captive groups lined up…
what next
What Next?
  • Need to look at budgets
  • Need to look at deployment
  • Need to look at protection domains too (confidence/.ring fenced qos etc)
  • Would like to do differentiation (esp. of delay bounded/EF like services( within same experiment!.
any questions
Any Questions…
  • What would you pay?
  • When?
  • How (standing order, direct debit, e-cash etc etc)
  • To whom ? (can middleware make money)?
  •  ?