Peter Key Cambridge UK joint work with Richard Gibbens, Statistical Laboratory, Cambridge Uni. UK The.

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Presentation transcript:

Peter Key Cambridge UK joint work with Richard Gibbens, Statistical Laboratory, Cambridge Uni. UK The Use of Games for assessing user strategies for differential QoS in the Internet

Outline Background Congestion Pricing A File Transfer Game

Access to / Control of Scarce Resources Users and the Network have different objectives … So why not use the right signals to encourage cooperation? Signals reflect congestion costs Send a signal to users when traffic that should not be carried enters (moveable threshold) The ECN bit could be used to carry the information

Theory & Background Work of Kelly et al, has shown users’ optimum converges to System Optimum (maximum welfare) with right marking scheme. Related work by Low etc Gibbens & Kelly put in a Congestion pricing framework Architecture & experiments in Key et al.

Network vs Users “My work is a game, a very serious game” Escher Users Signals Data/Info Network

Distributed Multi-player Game Internet MSR Cambridge Game server Players

Example Game Transfer a given amount of data F at minimum cost in time T, maximum rate P eg F=1000pkts, T=100s, P=20pps Background load of 100 WTP users alternating on and off periods (10 & 30 s) Willing to pay different amounts 600 pps bottleneck link (eg 5Mb/s) shadow queue marking (threshold 9, cap 540 pps) Repeated runs

WTP background users ‘Willing-to-pay’ an amount w per unit time Elastic users — adjust rate of sending to keep marking rate close to w Defines a packet-send strategy

Background Load

Marking periods Shadow queueReal queue

A baseline Strategies CBR : send at constant rate If in stationary regime, this is an optimal strategy if price function “convex” in region wrt load (lightly loaded) and prices iid, or a Martingale

A last packet strategy (like tit- for-tat) Use feedback to dynamically adjust rate If (last packet not marked /dropped) {send at high rate = peak rate } else {send at low rate} A variant is …

A last-2 packet strategy Attempts to determine non-marking periods If (last 2 packets not marked /dropped) {send at high rate = peak rate } else {send at low rate}

Estimation Algorithms Use a Statistical procedure to estimate trends Eg attempt to estimate p(mark) eg use a Bayesian update based on last n packet history send rate related to More complex algorithms attempt to estimate marking/non-marking periods

Sending rates example

Results for high load, T=100 Raw FTP sends in 5 seconds, cost 410 Start seed Marks

Results for high load, T=100 Raw FTP sends in 5 seconds, cost 410 time to complete Marks

Results for high load, T=10 Raw FTP sends in 5 seconds, cost 410 time to complete Marks

Conclusions Experiments suggest simple strategies are powerful (cf Axelrod’s work) Simulation environment with ‘game playing’ enables strategies to be compared and developed Future work will look at different and mixed objectives The Internet is a non-cooperative game, but the right signals can encourage effective cooperation