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Decentralized Resource Allocation in Application Layer Networks. T. Eymann, M. Reinicke University Freiburg, Germany O. Ardaiz , P. Artigas, F. Freitag, L. Navarro Polytecnic University Catalunya, Spain. Outline. Motivation Catallaxy Paradigm for Decentralized Resource Allocation - PowerPoint PPT Presentation
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Decentralized Resource Allocation in Application Layer Networks
T. Eymann, M. ReinickeUniversity Freiburg, Germany O. Ardaiz, P. Artigas, F. Freitag, L. NavarroPolytecnic University Catalunya, Spain
Outline
Motivation Catallaxy Paradigm for Decentralized
Resource Allocation Experiments Results Open Issues & Further Research
Application Layer Network Deployment
S
S
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S S
SS
SS
S
S
S
SS
S S
SS
S
S S
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SS
S D D
D
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Application Layer Network (Web Proxy Caching Hyrarchy): 6 servers each requires 1 Mbits net capacity, 200 Mbytes Storage, Less 2 hops from demand regions: A,B,C,D,E
Programmable Infrastructure:•30 nodes distributed throught Internet each 10 Mbit net capacity, 2 GByte Storage
Resource Allocation
Resource Allocation Problem
Centralized RA is computationally intensive (and single point of failure).
And it will get works: Very Dynamic Infrastructures (Resource nodes
come and go frequently): dial up nodes, mobile nodes, ...
High Node Density Infrastructures (Many nodes with little resources): P2P systems, pervarsive computing,..
Solution: Economic Markets
Resource Allocation works in Real World with an economic model: allocation of goods among human beings takes place in “markets”.
Markets: just distribution of utility by a central arbitrator (centralized
economy) decentralized action of utility-maximing agents using a
central auctioneer direct agreement between negotiating agents (Catalaxy)
The Catallaxy as a concept for market coordination Catallaxy is an alternative word for „market economy“ (Mises and
Von Hayek of the Neo-austrian economic school) “Fundamentally, in a system in which the knowledge of the
relevant facts is dispersed among many people, prices can act to co-ordinate the separate actions of different people in the same way as subjective values help the individual to co-ordinate the parts of his plan.” (Friedrich A. von Hayek, The Use of Knowledge in Society, 1945)
“The Market” as a technically decentralized, distributed, dynamic coordination mechanism: Adam Smith’s “invisible hand”, Hayek’s “spontaneous order”, Walras’
“non-tâtonnement process” Coordination and a stable environment are emergent features of the
market Pursuing local goals alone already stabilizes and coordinates the
system.
How to Implement Catalaxy: Agents
Environment, e.g. Market
Agent
Sensor, e.g. received offers
Effector, e.g. sent offers
(Intention: increase own utility)
Reasoning, e.g. calculation of a counter-offer using heuristics (may become arbitrarily complex, e.g. AI)
Agent-mediated digital economy
Characteristics for the agent-mediated digital economy: Software agents act selfish, because their human owners do:
Competition is the norm. Software agents keep their utility function private: If made public,
the agent can be exploited. Software agents communicate directly: Centralized control
institutions can always be bypassed.
Consequences: Cooperation is always pareto-eliciting (increases utility of all
participants) No free lunch: everyone has a utility function (business model),
even centralized institutions Information is not free or public (every participant operates on
private knowledge and subjective values)
Negotiation Protocol - Example
Buyer Seller
cfp (service access)
propose (service access, pS=$24)
propose (service access, pB=$18)
propose (service access, pS=$21)
accept-offer(service access, pB=$21)
commit (service access, pS=$21)
timetime
Client SC
Heuristic-Adaptive Reasoning:Example for a Seller (1)
propose (service access, pS=$24)
MarketPrice=offerPrice×weight+lastMarketPrice×(1-weight)
Update Market Price Valuation
propose (service access, pB=$18)
Heuristic-Adaptive Reasoning:Example for a Seller (2)
Should I leave the negotiation?
propose (service access, pS=$24)
negotiate: offerPrice > marketPrice AND () ContinuationProbability
reject: offerPrice > 2 * marketPrice
accept: offerPrice marketPrice
rnd
propose (service access, pB=$18)
Heuristic-Adaptive Reasoning:Example for a Seller (3)
Should I leave the negotiation?
Should I make a concession?
rejectYes
No
() concessionProbability: ourOfferPrice = ourLastOfferPrice
() concessionProbability: ourOfferPrice = ourLastOfferPrice - concessionAmount
rnd
rnd
propose (service access, pS=$24)
propose (service access, pB=$18)
Heuristic-Adaptive Reasoning:Example for a Seller (4)
Should I leave the negotiation?
Should I make a concession?
What amount should I concede?
reject
propose (service access, pS=$24)
Yes
No
No
Yes
concessionAmount = (hisOriginalOffer - ourOriginalOffer) × concessionAmountPercentage
propose (service access, pS=$24)
propose (service access, pB=$18)
Heuristic-Adaptive Reasoning:Example for a Seller (5)
Should I leave the negotiation?
Should I make a concession?
reject
propose (service access, pS=$24)
propose (service access, pS=$21)
propose (service access, pS=$24)
propose (service access, pB=$18)
Yes
No
No
Yes
„costs of life“ (tax) will be deducted in discrete time slots
Application
Coordination
Communication
Cooperation
Application ServicesNetwork ServicesPhysical Services
Heuristic-Adaptive Reasoning:Parameters
Negotiation Strategy:
Achieving utility maximization setting e.g. concession rate, concession amount, time pressure in relation to market (and the transaction partner).
_
_
_
_
_
_
p acq
del change
del jumpG
p sat
w mem
p rep
Concession Probability
Continuation Probability
Concession Amount
Market Price Learning Weight
Mark-up
Heuristic-Adaptive Reasoning: adaptation by evolutionary learning
Send „plumage“ (profitx, Genotypex)
profit1 Genotype1
profit2 Genotype2
profit3 Genotype3
profit4 Genotype4
Create agent (Genotype Genotype1)
select Genotype (profitx)
Experiments
Simulated Scenarios Evaluated Dimensions
Simulated Application Scenario
How to match a network of clients and services?
Clients
(ADSL 1 Mbit)
Acrobat Service Copy of Document
MyCompanyPortfolio.pdf
(6 Mbytes)
Web Server with limited Resource
(4 – 60 Mbits)
1
2 3
Catallactic Message Flow
Client request_Service (MyComPortfolio.pdf)
BW Negotiation
Service Negotation
Baseline Message Flow
Master Service Copy as Centralized Auctioner for BW and SC
Client request_Service (MyComPortfolio.pdf)
Evaluation Dimensions
CDN P2P
GRIDA few, powerful
A lot, modest
Fix
ed
netw
orks
Mob
ile, a
d-ho
c,ov
erlo
ade
dne
twor
ks
Stable
Changing
node density
node dynamics
low medium high
medium
high
CDN
P2P
GRID
It is required an “abstract” simulator
Simulator Scenarios: Resource Density Variations
Low Density:Few nodes (5)
Lots Resources per Node (60 Mbits)
Middle Density:More nodes (25)
Less Resources per Node (12 Mbits)
High Density:More nodes (75)
Less Resources per Node (4 Mbits)
Simulator Scenarios:Dynamic Values
Dynamic: Nodes up & down with 20 % probability every 200 ms.
Quasi-static: Nodes always up.
Very dynamic: Nodes up & down with 40 % probability every 200 ms.
Simulator - Demand
Clients located in every edge node. Client request_Service (1 Mbit Server Net
Bandwidth, 50 sec). Random values:
# of demands (among clients) # of serviceIDs (among 50 diferent videos) time betwen demands (average 2000 ms)
Moving clients: Movement time (How often demand moves) Movement radius (How far demand moves) Movement percent (How much demand moves)
Simulator Choice
The Catnet simulator is build over JavaSim [Univ. Ohio]: JavaSim is a network simulator based in autonomous components.
• Javasim implemented in java=> Ease of development, and efficient [].
• Javasim models every aspect of a real network: latency, bandwith, lost packets, routing,=> We take into account resource locality (vs. MAS simulators)
• Application module implement interfaces of common Inet protocols: TCP, UDP, Mcast => our components can be modified to work in real world without modification.
Preliminary Results
Evaluation Criteria. Preliminary Results:
Comparison by Scenarios, Adaptability Evaluation.
Evaluation Criteria
RAE (Resource Allocation Efficiency) The ratio of matched transactions divided by the number of
all proposals: # "accepts“/ #"proposals“
REST (Response Time (Service Access Time)) How long does it take on average to fill a request:
time between “cfp” and “accept”
CC (Communication Costs) How much communication is needed until the result:
# messages * # hops.
Results by criterion – RAE (%)
0
1
2
0
1
2
0
10
20
30
40
50
60
70
80
90
100
RAE (%)
dynamics
density
0
1
2
0
1
2
0
10
20
30
40
50
60
70
80
90
100
RAE (%)
dynamics
densi ty
Catallactic Baseline
Topology Dependency @ middle density
RAE better @ very dynamic Scenario
RAE at quasi-static, slow scenarios
Results by criterion – REST(ms)
Catallactic Baseline
0
1
2
01
2
0,00
50,00
100,00
150,00
200,00
250,00
300,00
REST (ms)
dynamics
density
0
1
2
0
12
0,00
50,00
100,00
150,00
200,00
250,00
300,00
REST (ms)
dynamics
density
REST is higher for catalactic: but not as much as expected.GOOD
Results by criterion: CC (# messages * #hops)
0
2
01
20
20000
40000
60000
80000
100000
120000
CC (#hops)
dynamics
density
0
2
01
2
0
20000
40000
60000
80000
100000
120000
CC (#hops)
dynamics
density
Catallactic Baseline
CC is similar. But it was expected to be higher because of more negatiations messages: GOOD.
CC increases with density, since higher density means more nodes to send to.
Results by Scenario
Quasi-static High node density Very dynamic / low ND Very dynamic / high ND
Green: confirmed, Red: rejectedRe
sour
ceAl
loca
tion
Effic
ienc
yCo
mm
unica
tion
cost
Reac
tion
time
b b b
b bb
b bc
c b
Syst
em
b
Adaptation: Baseline Simulation
In baseline system prices keep constant => no adaptation
Adaptation: Catallactic Simulation
In catalactic system prices adapt over time
Open Issues & Further Research Oscillations, Caotic behaviour. Tragedy of commons. Malevolous agents.
Colaboration with agent researchers
Colaboration with Complex Adaptive System researchers.
Colaboration with Grid / P2P projects
Scalability, dynamics. Theoretical Modelling.
Implementation in grids & P2P scenarios.
Thank you, Questions?
More info: http://research.ac.upc.es/catnet/