Decentralized Resource Allocation in Application Layer Networks

1
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

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Outline

• Motivation
• Catallaxy Paradigm for Decentralized Resource
  Allocation
• Experiments
• Results
• Open Issues Further Research

3
Application Layer Network Deployment
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
S
S
S
S
S
Resource Allocation
D
D
S
S
S
S
S
D
S
D
S
S
D
S
S
S

• Programmable Infrastructure
• 30 nodes distributed throught Internet each 10
  Mbit net capacity, 2 GByte Storage

S
S
S
S
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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,..

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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)

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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 Smiths invisible hand, Hayeks
  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.

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How to Implement Catalaxy Agents
Reasoning, e.g. calculation of a counter-offer
using heuristics (may become arbitrarily complex,
e.g. AI)
Agent
Effector, e.g. sent offers (Intention increase
own utility)
Sensor, e.g. received offers
Environment, e.g. Market
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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)

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Negotiation Protocol - Example
Client
SC
Buyer
Seller
cfp (service access)
propose (service access, pS24)
propose (service access, pB18)
propose (service access, pS21)
accept-offer(service access, pB21)
commit (service access, pS21)
time
time
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Heuristic-Adaptive ReasoningExample for a
Seller (1)
propose (service access, pS24)
propose (service access, pB18)
Update Market Price Valuation
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Heuristic-Adaptive ReasoningExample for a
Seller (2)
propose (service access, pS24)
propose (service access, pB18)
Should I leave the negotiation?
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Heuristic-Adaptive ReasoningExample for a
Seller (3)
propose (service access, pS24)
propose (service access, pB18)
Should I leave the negotiation?
Yes
reject
No
Should I make a concession?
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Heuristic-Adaptive ReasoningExample for a
Seller (4)
propose (service access, pS24)
propose (service access, pB18)
Should I leave the negotiation?
Yes
reject
No
Should I make a concession?
No
propose (service access, pS24)
Yes
What amount should I concede?
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Heuristic-Adaptive ReasoningExample for a
Seller (5)
propose (service access, pS24)
propose (service access, pB18)
Should I leave the negotiation?
Yes
reject
No
Should I make a concession?
No
propose (service access, pS24)
Yes
propose (service access, pS21)
costs of life (tax) will be deducted in
discrete time slots
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Heuristic-Adaptive ReasoningParameters
Concession Probability
Application
Concession Amount
Mark-up
Continuation Probability
Market Price Learning Weight
Coordination
Negotiation Strategy Achieving utility
maximization setting e.g. concession rate,
concession amount, time pressure in relation to
market (and the transaction partner).
Cooperation
Communication
Application Services
Network Services
Physical Services
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Heuristic-Adaptive Reasoning adaptation by
evolutionary learning
?
Send plumage (?profitx, Genotypex)
?profit1 Genotype1
?profit2 Genotype2
?profit3 Genotype3
?profit4 Genotype4
?
select Genotype (?profitx)
?
Create agent (Genotype ? Genotype1)
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Experiments

• Simulated Scenarios
• Evaluated Dimensions

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Simulated Application Scenario
How to match a network of clients and services?
1 2
3
Acrobat Service Copy of Document
MyCompanyPortfolio.pdf (6 Mbytes)
Web Server with limited Resource (4 60 Mbits)
Clients (ADSL 1 Mbit)
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Catallactic Message Flow
Client request_Service (MyComPortfolio.pdf)

BW Negotiation
Service Negotation
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Baseline Message Flow
Client request_Service (MyComPortfolio.pdf)

Master Service Copy as Centralized Auctioner for
BW and SC
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Evaluation Dimensions
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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)
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Simulator ScenariosDynamic Values
Very dynamic Nodes up down with 40
probability every 200 ms.
Dynamic Nodes up down with 20 probability
every 200 ms.
Quasi-static Nodes always up.
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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)

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Simulator Choice

• The Catnet simulator is build over JavaSim Univ.
  Ohio JavaSim is a network simulator based in
  autonomous components.

• Javasim implemented in javagt Ease of
  development, and efficient .
• Javasim models every aspect of a real network
  latency, bandwith, lost packets, routing,gt We
  take into account resource locality (vs. MAS
  simulators)
• Application module implement interfaces of common
  Inet protocols TCP, UDP, Mcast gt our
  components can be modified to work in real world
  without modification.

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Preliminary Results

• Evaluation Criteria.
• Preliminary Results
• Comparison by Scenarios,
• Adaptability Evaluation.

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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
  requesttime between cfp and accept
• CC (Communication Costs)
• How much communication is needed until the
  result messages hops.

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Results by criterion RAE ()
RAE better _at_ very dynamic Scenario
Topology Dependency _at_ middle density
Catallactic
Baseline
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Results by criterion REST(ms)
REST is higher for catalactic but not as much as
expected.GOOD
Catallactic
Baseline
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Results by criterion CC ( messages hops)
CC is similar. But it was expected to be higher
because of more negatiations messages GOOD.
Catallactic
Baseline
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Results by Scenario
Communicationcost
ResourceAllocationEfficiency
Reactiontime
System

• Quasi-static
• High node density
• Very dynamic / low ND
• Very dynamic / high ND
• Green confirmed, Red rejected

b
b
b

b
b
b
c
b
b
b
b
c
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Adaptation Baseline Simulation
In baseline system prices keep constant gt no
adaptation
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Adaptation Catallactic Simulation
In catalactic system prices adapt over time
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Open Issues Further Research

• Oscillations, Caotic behaviour.
• Tragedy of commons.
• Malevolous agents.

• Scalability, dynamics.
• Theoretical Modelling.

Colaboration with agent researchers
Colaboration with Complex Adaptive System
researchers.

• Implementation in grids P2P scenarios.

Colaboration with Grid / P2P projects
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Thank you, Questions?

• More info
• http//research.ac.upc.es/catnet/