Catnet Project Review

1
Catnet Project Review

• Project participants
• T. Eymann, M. ReinickeAlbert-Ludwigs-University,
  Freiburg (D)
• O. Ardaiz, P. Artigas, L. Díaz de Cerio, F.
  Freitag, R. Messeguer, L. Navarro, D.
  RoyoTechnical University of Catalonia, Barcelona
  (ES)
• IST-FET-Open Assessment project
• 26 mm, 100K
• Contract no. ITS-2001-34030
• 1 year March 2002 - 2003

2
Review Agenda

• 1. Motivation
• 2. ALN Background
• 3. Economic background
• 4. Overview of the Work done
• 5. Simulation overview
• 6. Simulator
• 7. Methodology
• 8. Experiments
• 9. Conclusions

3
Review objectives

• Present work we have done during this assessment
  project
• Get scientific recommendations for future work
• Get support for submitting a full proposal

4
Motivation

• CatNet Review Meeting
• Barcelona, April 25th, 2003

5
The future heads to

• Utility computing
• On-demand computing and services
• require dynamically/real-time/on-the-fly
  provisioned resources
• Demand cannot be planned in advance
• A complex and large market of services and
  resources.
• Convergence of Grid, P2P and Web Services

6
A Future Application Scenario
How to match clients and services in a utility
network?
Acrobat PDF converter service providers
(MS Word) service clients
?
7
Centralized vs. Decentralized Approaches
8
Centralized Approaches

• Employ a centralized coordinator, who matches
  clients and service providers according to some
  optimization rule
• Examples
• Globus and the Nimrod-G extension
• (simulation possible using GridSim)
• Global coordination explicitly achieved by
  coordinator

9
(Buyya 2003, p.4)
10
Cast of Characters
With centralized message flow
MSC
Client
Application
SC
SC
SC
SC
Request service
Resource
Resource
Resource
Resource
Middleware
Node
Node
Node
Node
Node
Node
Network Layer
11
Decentralized Approaches

• Clients select service provider from received
  responses according to some local optimization
  rule
• Examples
• Peer-to-Peer Networks, e.g. Gnutella
• Optimization rule applied by human user
• Catallaxy approach Local economic optimization
  leads to global coordination

12
Cast of Characters
With decentralized message flow
Client
Application
SC
SC
SC
SC
Request service
Resource
Resource
Resource
Resource
Middleware
Node
Node
Node
Node
Node
Node
Network Layer
13
Goal of the Assessment Project

• Compare performance of centralized vs.
  decentralized approach by simulation
• Using an easy simulator implementation
• Using a hypotheses-based framework
• Check feasibility of Full Project proposal
• If decentralized approach outperforms centralized
  approaches
• for creating generic Catallactic middleware for
  Application Layer Networks

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ALN background

• CatNet Review Meeting
• Barcelona, April 25th, 2003

16
ALN Background

• Aplication Layer Networks
• Programmable Infrastructures for ALN
• Resource Allocation Problem
• Related work, Resource Allocation in Grids and P2P

17
Application Layer Networks
ALN Nodes Application Server Links TCP Virtual Circuits
Web Proxy Caching Hierarchy Squid Server Parent-Child Conn.
Chat Server Network IRC server channel distribution conn.
18
ALN deployment on a Programmable Infrastructure

• Motivation
• ALN have dynamic demands -gt Need to be deployed
  to adapt to changes.
• Deployment Requirements
• Programable Infrastructure
• Nodes with BW, Storage Processing Resources.
• Deployment Mechanisms
• Resource Allocation Algorithm, .

19
Application Deployment Example

• Web Proxy Caching Hierarchy
• 6 servers each requires 1 Mbits net capacity, 200
  Mbytes Storage, Demand Regions A,B,C,D,E

Resource Allocation Algorithm

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

20
Resource Allocation Problems

• Centralized RA is computationally intensive (and
  a single point of failure).
• And it will get worse
• Very Dynamic Infrastructures (Nodes come and go
  frequently) dial up nodes, mobile nodes, ...
• High Node Density Infrastructures (Many nodes
  with little resources) pervasive computing,..
• Solution Requirement
• Decentralized autonomous RA.

21
Related Work Resource Allocation in Grids

• Grids are programmable infrastructures for
  Computationally intensive apps. (demand CPU
  resources).
• Grids RA
• Condor-G, DMR-broker
• centralized heuristic based dispachers.
• Nimrod-G Broker
• centralized budget constraint sheduler.
• DataGrid OptorSim
• centralized economic based optimizer.

22
Related Work Resource Allocation in P2P systems

• P2P - Grid
• Foster et al, IPTP03 P2P are Grids with
  thousands of resource nodes, but only 1 service
  file transfer, datamining, ...
• P2P RA
• Gu et al, HPDC 2002 simulated n-hop service
  aggregation in P2P

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Economic Background the Catallaxy

• CatNet Review Meeting
• Barcelona, April 25th, 2003

25
Observations on Decentralized Networks
Application
Technology
Grid Nodes
Mobile Devices
Smart Chips
Networked Household Appliances
ResourceNetworks
Ubiquitous Computing
26
Observations on Decentralized Networks Common
Properties (1)
Application
Cooperation
Communication
Application Services
Individually owned (mobile) autonomous devices
with access to open, decentralized networks
Network Services
Physical Services
27
Observations on Decentralized Networks Common
Properties (2)
Application
Cooperation
Messaging using SOAP instead of RMI
Communication
Individually owned (mobile) autonomous devices
with access to open, decentralized networks
Application Services
Network Services
Physical Services
28
Observations on Decentralized Networks Common
Properties (3)
Application
Cooperation
Exchanging Property Rights for Utility
Maximization
Communication
Negotiation/Messaging vs. Commands/Method
Invocation
Application Services
Individually owned (mobile) autonomous devices
with access to open, decentralized networks
Network Services
Physical Services
29
Observations on Decentralized Networks Selected
Application Domains
P2P Storage Collaboration
MobileCommerce
Service Webs
Application
Cooperation
Exchanging Property Rights for Utility
Maximization
Communication
Negotiation/Messaging vs. Commands/Method
Invocation
Application Services
Individually owned (mobile) autonomous devices
with access to open, decentralized networks
Network Services
Physical Services
30
Observations on Decentralized Networks
Implementing Coordination (1)
Application
Economic Processes, e.g. Distributed Resource
Allocation, Multi-Commodity Flow Problems
?
Cooperation
Exchanging Property Rights for Utility
Maximization
Communication
Negotiation/Messaging vs. Commands/Method
Invocation
Application Services
Individually owned (mobile) autonomous devices
with access to open, decentralized networks
Network Services
Physical Services
31
Observations on Decentralized Networks
Implementing Coordination (2)
Application
Economic Processes, e.g. Distributed Resource
Allocation, Multi-Commodity Flow Problems
A mechanism to resolve interdependencies between
participants sharing resources
Coordination
Cooperation
Exchanging Property Rights for Utility
Maximization
Communication
Negotiation/Messaging vs. Commands/Method
Invocation
Application Services
Individually owned (mobile) autonomous devices
with access to open, decentralized networks
Network Services
Physical Services
32
Implementing Markets

• A market is a mechanism to resolve
  interdependencies between participants and to
  allocate resources in economic theory
• Market as the abstract point of matching supply
  and demand, as opposed to marketplace or
  market platform
• Result is market clearance, supply and demand are
  satisfied
• Evaluation Criteria
• Utility of all participants is maximized Social
  Welfare Utility
• No participant can get a better result without
  another losing utility Pareto Optimum
• But the market mechanism is not fully understood,
  so how to implement it in a technical
  environment?
• Computable General Equilibrium (top-down)
• Rooted in Neo-Classical Theory, Walras
  tâtonnement process
• Agent-Based Computational Economics (bottom-up)
• Neo-Austrian Economics, Evolutionary Economics,
  Adam Smiths invisible hand, Hayeks
  spontaneous order, Walras non-tâtonnement
  process

33
How to implement a market? (1)
The market as a decentralized, dynamic
coordination mechanism
Economic Concept
just distribution of utility by a central
arbitrator
direct agreement between negotiating agents
decentralized action of utility-maximing agents
using a central auctioneer
WALRASian Auctioneer
Technical Implementation
Multiagent systems
34
How to implement a market? (2)
The market as a decentralized, dynamic
coordination mechanism
Economic Concept
direct agreement between negotiating agents
decentralized action of utility-maximing agents
using a central auctioneer
WALRASian Auctioneer
MISES/HAYEKs Catallaxy
Market-Oriented Programming
Technical Implementation
Nimrod-G
Multiagent systems
35
How to implement a market? (3)
The market as a decentralized, dynamic
coordination mechanism
Economic Concept
WALRASian Auctioneer
MISES/HAYEKs Catallaxy
Market-Oriented Programming
Catallactic Information Systems
Technical Implementation
Multiagent systems
36
Catallaxy

• Catallaxy is an alternative word for market
  economy (coined by 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)
• An economic metaphor for complex adaptive systems
  (CAS)
• Coordination and a stable environment are
  emergent features of the market
• Pursuing local goals alone already stabilizes and
  coordinates the system
• Economist Research Agent-Based Computational
  Economics

37
Catallaxy Characteristics

• 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.
• 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 private values)
• Utility is the difference between transaction
  price and private value

38
Example CatNet

• Clients negotiate with Service Copies (SC)
• Goal of Client is to buy service access for the
  lowest price
• Goal of SC is to sell service access for the
  highest price

Client
Application
SC
SC
SC
SC
Request service
Resource
Resource
Resource
Resource
Middleware
Node
Node
Node
Node
Node
Node
Network Layer
39
The Negotiation Protocol and the Goal Function
Application
Goal Function maximize the spread between input
and output prices
Coordination
Negotiation Protocol Monotonic Concession
Protocol, based on Alternating Offers between
Buyer and Seller
Cooperation
Communication
Application Services
Network Services
Physical Services
40
Principles of Software 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
41
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
42
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
43
Heuristic-Adaptive ReasoningExample for a
Seller (1)
propose (service access, pS24)
propose (service access, pB18)
Update Market Price Valuation
44
Heuristic-Adaptive ReasoningExample for a
Seller (2)
propose (service access, pS24)
propose (service access, pB18)
Should I leave the negotiation?
45
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?
46
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?
47
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
48
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)
49
Preliminary Results

• Catallaxy works in a small scale for a multiagent
  system simulation (B2B-OS)
• (Demonstration possible if time permits)
• Coordination can be achieved emergently without a
  central coordinator (auctioneer) if the software
  agents
• reason in an economical sense about alternative
  actions
• implement feedback learning and adapt reasoning
  and price-setting strategies
• Further research from here
• Evaluate approach in different application
  domains and network technologies (? CatNet)
• Construct Institutions to control and secure open
  multiagent system environments (? reputation
  tracking, emergent norms)
• Formalize approach (? Agent-Based Computational
  Economics)
• Optimize heuristics and learning mechanisms

50
Hypotheses on using Catallaxy

• Catallaxy should
• Be more scalable than a centralized coordinator
• Because all computation is decentralized
• Be more flexible
• Because all actions are based on local knowledge
  only
• Because agents are able to adapt strategies
• Use more bandwidth
• Because negotiations need more communication
• Grow better results over time
• Because agents begin with inferior results and
  then adapt
• Optimal results may only be reachable in static
  scenarios

51
Baseline shows stable results
52
Catallaxy shows development over time
53
What can it be used for?

• Application areas where
• a centralized marketmaker is impractical or not
  feasible
• trade items are commodities, interactions are
  numerous
• Examples are
• Distributed resource allocation problems like
  e.g. markets for communication bandwidth,
  computational resources, natural resource
  exchanges, low-value procurement markets
• Multi-commodity workflow, e.g. agile/holonic
  manufacturing, telecommunications network
  routing, supply network coordination
• Systems implementing this concept could be called
  Catallactic Information Systems (cf. Hayeks
  Catallaxy)

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Overview of the Work done

• CatNet Review Meeting
• Barcelona, April 25th, 2003

56
Objective of the project

• Objective Evaluation of a decentralized
  mechanism for resource allocation, based on
  economic paradigm Catallaxy.
• (compare against a centralized mechanism using an
  arbitrator object)
• Methodology Simulation

57
Persons and Institutions

• Institutions
• Albert-Ludwigs-Universität Freiburg, Institute
  for Computer Science and Social Studies (UF-IIG).
• Technical University of Catalonia (UPC), Computer
  Architecture Department (DAC).
• Persons involved in the project
• UF-IIG Torsten Eymann, Michael Reinicke.
• UPC-DAC Oscar Ardaiz, Pau Artigas, Luis Diaz,
  Felix Freitag, Roc Messeguer, Leandro Navarro,
  Dolors Royo.

58
Project Coordination Management

• Collaborative Work with BSCW
• Code development using CVS
• Project meetings and research stays in Barcelona
  and Freiburg.

59
Project Development

• Simulator (WP1)
• Coordination mechanism (WP1)
• Application scenarios measurement simulations
  (WP2)
• Performance evaluation (WP3)
• Catallaxy evaluation (WP3)

60
The Catnet simulator (WP1)

• Agents
• Client computer program at host, requests
  service
• Service Copy instance of service, hosted in a
  resource computer
• Resource host computer with limited storage and
  bandwidth.

61
Coordination mechanisms (WP1)

• Messaging Protocol
• Catallactic coordination
• Baseline coordination
• Agent Strategy

request(..,...,...)
Strategy
decision
62
Application scenarios, measurement simulations
(WP2)

• Application scenarios
• Mapping applications into a
• two-dimensional design space.
• Measurement
• Criteria Social Welfare, Resource Allocatio
• Efficiency, Response Time, Communication Cost
• Simulations
• Base experiment and sensitvity experiments.

parameter
dyn.
dens.
63
Performance and Catallaxy Evaluation (WP3)

• Experimental results
• Catallactic Baseline model
• Per scenario (indiv., design space)
• Per criterion (SWF, RAE, REST, CC, ...)
• Sensitivity experiments

64
Project dissemination

• Participation in Workshops and Conferences
• communities MAS, Grids, P2P, Complex Systems
• Publication of papers
• CatNet Catallactic Mechanisms for Service
  Control and Resource Allocation in Large Scale
  Application-Layer Networks. Workshop on Global
  and Peer-to-Peer Computing on Large Scale
  Distributed Systems, 2nd IEEE/ACM International
  Symposium on Cluster Computing and the Grid, May
  2002, Berlin, Germany.
• Decentralized vs. Centralized Economic
  Coordination of Resource Allocation in Grids.
  1st European Across Grids Conference, February
  2003, Santiage de Compostela, Spain.
• Exploring decentralized resource allocation in
  application layer networks. Agent-based
  Simulation 4, April 2003, Montpellier, France.
• Decentralized resource allocation in application
  layer networks. Workshop on Agent based Cluster
  and Grid Computing. 3rd IEEE/ACM International
  Symposium on Cluster Computing and the Grid, May
  2003, Tokyo, Japan.
• 2 more under review
• Project Webpage http//research.ac.upc.es/catnet

65
Project findings (summary)

• Catallactic coordination
• SWF, RAE
• - CC, REST
• smooth performance
• can solve complex system

abstraction level
Math. Simulation SW
CATNET
Real Application
66
Conclusions

• Development of the experimental simulator with
  Catallactic and Baseline mechanism.
• Simulation of Catallactic and Baseline
  coordination in different scenarios.
• Evaluation of the Catallactic coordination
  mechanism.

67
Future Work
Design and system characteristics of catallactic
networks Interface computer networks economics
Catallactic prototype implementation in
middleware service layer Economic reasoning MAS

• Catallactic coordination works and has robust
  performance in dynamic environments

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Simulation OverviewWP2

• CatNet Review Meeting
• Barcelona, April 25th, 2003

70
Simulation of ALN
ALN
71
Configuration of simulator

• Dynamics
• Connection and disconnection of SC
• Density
• Number of Resources and ServiceCopies
• Total capacity of Resources is constant
• Same amount of R / resources
• Input demand trace
• Initial conditions
• Initial budget, prices
• Control mechanism, parameters

72
Parameters to measure

• Social Welfare (SWF)
• Global figure sum of utilities over all
  participants.
• Response Time (REST)
• time observed by the client to get a service.
• Resource allocation efficiency (RAE)
• ratio of service demands, for which the network
  provides a service to all sent service demands.
• Communication cost (CC)
• number of hops used by the control messages.
• Price
• evolution of prices in the network during the
  simulation.
• Client-Resource assignment
• number of hops between a Client and Resource once
  a successful service provision is achieved.

73
Configuration of simulator

• Tcl scripts to set-up
• physical network topology
• application layer network
• service demand
• Nucleus of the simulator in Java code
• Client, Resource, ServiceCopy, MasterSC,
  strategy, Message,
• Simulation infrastructure is JavaSim
• Node, packet, link,

74
Experiments

• Template of
• Input
• Topology
• Parameters
• Results
• Graphics
• Observations

• Log files (monitor class)
• Network level
• Nam movies
• CatNet level
• Time series
• Aggregated values Parameters

Comparison of Several scenarios
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Simulator

• CatNet Review Meeting
• Barcelona, April 25th, 2003

77
Simulator - Javasim

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

• Javasim models every aspect of a real network
  latency, bandwith, lost packets, routing,
• It has some of the more common internet protocols
  like DV, TCP, UDP,
• So our components can be easily modified to work
  in the real world changing the middleware to real
  sockets.

78
Simulator - Nodes
A network in javasim

• A network is composed of
• Nodes they can be routers or end nodes and you
  can decide the component composition of each
  node.
• Links you can decide the latency and the
  bandwith.

Our nodes

• Independent on each other at javasim level
• Running as programs with a socket on a computer
• Configuration made at startup script

UDP
IP
79
Simulator - Components

Generic behaviour on messages
Using generic functions - Bargain/RecommendedAct
ion - Price management So changing strategies is
easy
Particular behaviour on some messages
80
Simulator - Messages

• InformResourceHost
• Status
• RequestService
• Cfp
• Reject
• Proposal
• Accept
• Confirm
• MoneyTransfer
• ProvideService
• Plumage

81
Simulator Message Flow Baseline

82
Simulator Message FlowCatallactic

83
Simulations - Broadcast
It uses a broadcast mechanism with a ttl (time
to live) to know the neighbour ServiceCopies.It
is called also flooding.
Example with a ttl of 3.
84
Simulator - Topologies
This is the topology used for the experiments.
There is a Resource in each node, but with
different bandwith capacity. There is also a
Client in every leave. The ServiceCopies are
placed in different positions depending on the
density parameter. The MSC is in the center.

• We used this topology
• Easy to configure
• Easy to verify

85
Simulator Alternate Topologies
We tested the simulator against other topologies,
and also we modified the link latencies.
More connectivity
Diameter is bigger
86
Simulator - Parameters

• Available parameters
• AllocationTime the time the MSC waits to decide
  winner.
• HopFactor influence of distance on bandwith
  price.
• MigrationOn migration of ServiceCopies.
• BaselineOn Baseline/Catallactic.
• MSCUpdateTime time the ServiceCopies wait to
  send status.
• DynamicParameters initial off, change
  prob., change time.
• LearningOff learning between agents.
• StockMarketOn stock exchange market model.
• HopLimit maximum broadcast hop count (ttl).

87
Simulator - Values
Change propability 40
Change propability 20
Change propability 0
Service Copy
88
Simulator - Demand

• Creating demands
• Random
• of demands
• of serviceIDs
• Clients
• Average time betwen demands
• Moving (random parameters )
• Movement time
• Movement radius
• Movement percent

89
Simulation - Experiments

• Experiments done
• Normal the base experiment used for the
  comparison.
• Demand time changing the demand rate.
• MSC Update Time changing the time that a SC
  waits to send the status message to the MSC.
• MSC Allocation Time changing the time the MSC
  waits till it decides the used ServiceCopy from
  the received offers.
• Movement testing a moving demand.
• Migration testing the SC migration feature.
• Movement Migration testing the SC migration
  feature against a moving demand.
• Hop Factor changing how the distance affects
  the bandwith price.

90
Simulation - Configuration

• We use TCl to set-up the experiments
• Topology
• Node configuration wich components (C/R/SC/MSC)
  should be on each node.
• Application Layer Network initialitzation
• Agent parameters bandwith, price ranges, money
  balance, genotype,
• Current experiments parameters

91
Simulations - Running

• Each time 18 simulations x number of
  configurations of the experiment
• We used a cluster, using 9 dual PIII 1Ghz
• Scripts for
• Sending current data to nodes
• Viewing status
• Getting results
• Mixing results in a spreadsheet-aware file

92
Simulations Data

• The simulations writes data to a log file and to
  a DB. DB is for debugging, and the results are
  recovered from the log. We also generated NAM
  traces.

Final info
Computed
For each demand
SC that served Response Time Price Distance between C and R RAE Average distance Average response time RAE Average distance Average response time SWF Communication Cost
SWF Communication Cost RAE Average distance Average response time SWF Communication Cost
For each experiment
93
Simulator - Graphics

94
Simulator - Graphics

95
Simulator - Graphics

One for each parameter commented before.
So we can check the behaviour of any parameter
based on the changes on the diferent series.
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Methodology

• CatNet Review Meeting
• Barcelona, April 25th, 2003

98
Methodology

1. Select Scenarios
2. Create Hypotheses
3. Select Performance Criteria
4. Evaluate Hypotheses according to Criteria in
   Scenarios

99
Scenarios and Experiments
Catallactic Baseline
18 Scenarios in total
100
(_22)
(_20)
(_21)
High (40 changes)
Dynamics
(_10)
(_11)
(_12)
Medium (20 changes)
(_00)
(_01)
(_02)
Low(0 changes)
Low (5 SC)
High (75 SC)
Medium (25 SC)
Node density
101
Evaluation Criteria

• Social Welfare
• Sum of all utilities over all participants, in a
  given timespan
• Clients subjectively value SC access
• Prices change due to supply and demand
• Individual utility transaction price market
  value
• Clients utility
• Service Copies utility
• Resources utility

102
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.

103
Hypotheses
Communicationcost
ResourceAllocationEfficiency
Responsetime
System
SWF

• Quasi-static
• Very dynamic
• Low node density
• High node density

B
B
B
C
C
C
C
B
B
B

C
C
C
B
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105
Experiments

• CatNet Review Meeting
• Barcelona, April 25th, 2003

106
Experimental Results Quasi-static scenario

• (H1) In a quasi-static scenario using the
  Catallactic model
• (H1.1) the SWF is nearly equal to the results of
  the Baseline model,
• (H1.2) the RAE is slightly less than in the
  Baseline system,
• (H1.3) the REST is slightly longer than in the
  Baseline system,
• (H1.4) the bandwidth utilization is slightly
  higher than in the Baseline model.

107
Experimental Results Highly dynamic scenario

• (H2) In a highly dynamic scenario using the
  Catallactic model
• (H2.1) the SWF is higher than the results of the
  Baseline model,
• (H2.2) the RAE is higher than in the Baseline
  system,
• (H2.3) the REST is lower than in the Baseline
  system,
• (H2.4) the communication costs are less.

108
Experimental Results Low node density scenario

• (H3) In a low node density scenario using the
  Catallactic model
• (H3.1) the SWF is slightly lower than the results
  of the Baseline model,
• (H3.2) the RAE is less than in the Baseline
  system,
• (H3.3) the REST is longer than in the Baseline
  system,
• (H3.4) the CC are slightly higher.

109
Experimental Results High node density scenario

• (H4) In a high node density scenario using the
  Catallactic model
• (H4.1) the SWF is equal or better than the
  results of the Baseline model,
• (H4.2) the RAE is slightly less than in the
  Baseline system,
• (H4.3) the REST is higher than in the Baseline
  system,
• (H4.4) the CC are lower.

110
Results
Communicationcost
ResourceAllocationEfficiency
Responsetime
System
SWF

• Quasi-static
• Very dynamic
• Low node density
• High node density

b
b
B
c
b
B
C
C
b
B

b
b
B
b
111
Comparing Results to Hypotheses
Communicationcost
ResourceAllocationEfficiency
Responsetime
System
SWF

• Quasi-static
• Very dynamic
• Low node density
• High node density
• Green confirmed, Red rejected

B
B
B
C
C
C
C
B
B
B

C
C
C
B
112
Results by criterion SWF
113
Results by criterion - SWF
Catallactic
Baseline
114
Results by criterion RAE
115
Results by criterion - RAE
Catallactic
Baseline
116
Results by criterion REST
117
Results by criterion - REST
Catallactic
Baseline
118
Results by criterion CC
119
Results by criterion - CC
Catallactic
Baseline
120
Summary

• A mixed bag of results
• Outcomes different than expected, but concise and
  repeatable
• By scenario
• Significant differences between scenarios
• Middle density scenarios show particular results
  for Baseline
• By criterion
• Density has a greater effect than Dynamics
• REST result shows underestimation of Catallactic
  communication and reasoning complexity

121
Sensitivity ExperimentsDemand Frequency on SWF
122
Sensitivity ExperimentsDemand Frequency on RAE
123
Sensitivity ExperimentsDemand Frequency on REST
124
Sensitivity ExperimentsDemand Frequency on Hop
Count
125
Intermediate Conclusions

• Sensitivity results are explainable
• (REST for Baseline shows repeatable experiment
  results)
• Networks behave as expected
• Low density and slow demand allow good
  computation in both mechanisms
• High density and high frequent demand pose a
  scalability problem
• Check other parameters
• MSC Allocation Time for Baseline
• Migration of Service Copies for Catallaxy

126
Sensitivity Experiments MSC Allocation Time on
SWF
127
Sensitivity Experiments MSC Allocation Time on
RAE
128
Sensitivity Experiments MSC Allocation Time on
REST
129
Sensitivity Experiments MSC Allocation Time on
avg. Hops
130
Sensitivity Experiments Migration of Supply and
Demand

• Two migration cases add even more dynamics to
  system
• Moving SCs between resources (left)
• Moving SCs oscillating demand queue (right)
• Caveat
• Different parameter settings than in basic
  experiments
• Migration only for Catallactic experiments (left
  bars)

131
Sensitivity Experiments Migration

• RAE results are similar to SWF
• Allowing migration is better for Catallactic,
  even more in the moving demand case

132
Sensitivity Summary

• Other parameters affect either Baseline or
  Catallaxy
• May change relations on inter-mechanism
  comparison
• E.g. MSC Allocation Time even reverses statements
  on Baseline and Catallactic comparison
• Correct settings of these parameters should be
• Practically, like in real applications
• Theoretically, such that they have no effect on
  the outcome
• Requires further research and experimentation
• either by building a practical application
• Or by creating a more abstract simulation

133
Soundness of Criteria

• Interdepencies
• SWF and RAE are dependent
• Every transaction adds to SWF
• More transactions add to RAE
• SWF and CC are dependent
• Higher CC lowers SWF
• SWF and REST are dependent
• Higher REST means more transactions
• More transactions add to RAE and SWF
• SWF captures all costs and revenues
• Dependencies are an emergent feature of the
  system
• No direct links have been implemented economic
  reasoning works bottom-up in an ACE sense

134
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135
Conclusions

• CatNet Review Meeting
• Barcelona, April 25th, 2003

136
Achievements of the project

• Developed an experimental simulator to compare
  different allocation mechanisms in ALN (Grid,
  P2P)
• Defined a comparison framework consisting of two
  dimensions Dynamics and Density
• Conducted an experimental evaluation study of two
  allocation mechanisms

137
Experimental Simulator

• Abstracts from a concrete application and
  implementation.
• Allows plug-in of different middleware
  resource allocation mechanisms,
• Allows easy changes of
• decentralized agent strategies or
• centralized allocation mechanisms in the MSC.
• Ability to create Real Applications with little
  additional work.

138
Comparison Framework

• Dynamics and Density explain most changes in the
  experimental results.
• It is possible to categorize Grids, P2P, in
  that framework.
• Existing ALN can principally be categorized and
  evaluated against each other.
• Allows evaluation of non-technical criteria.

139
Evaluation Study

• Initial simulation results show that a
  decentralized, economic model has some advantages
  in certain situations.
• Improvement is measured as combination of factors
  (SWF)
• Other parameters (REST, CC) can also be studied
  in detail, but are mostly includable in SWF
• Practicability depends on concrete network and
  application
• Characteristics of promising applications
• Large scale network, large number of participants
• Highly dynamic supply and demand
• No statement possible for real existing ALN

140
Potential Limitations for practical use

• Currently, no micropayment solution exists.
• Current ALN do not control problems of tragedy of
  commons, free riding, oscillatory behaviour.
• No security models for malicious clients, service
  providers, malicious resource brokers.
• Assumption other research will address these
  issues, so future work can head either in a

141
Future Research Directions for CatNet

• Practical Direction
• Build middleware in real Grid
• With realistic setting of constant parameters,
  real topology
• In comparison to the real centralized resource
  broker
• Increases practicability of statements
• ? FET Global Computing
• Theoretical Direction
• Build more abstract simulation
• Increase theoretical understanding by formal
  approach
• Get rid of implementation and topology
  restrictions
• ? FET Complex Systems

142
Future Work
Design and system characteristics of catallactic
networks Interface computer network theory
Economics
Catallactic prototype implementation in
middleware service layer Economic reasoning in
Grids

• Catallactic coordination works and has robust
  performance in dynamic environments

143
Theoretical Catnet

• Course of Work
• Create more formal description of both allocation
  mechanisms
• Restrict simulator functionality and parameters
• Make assumptions on missing parameters
• Investigate shortcuts to potential limitations
  for Catallaxy
• Monopolies, Tragedy of commons, Free riding
• Pro
• Generalizable statements for (AL) networks may be
  achievable
• Contra
• Practicability of results for real networks
  doubtful
• Propose full project to FET Complex Systems

144
Practical Catnet

• Course of Work
• Create Catallactic resource allocation
  middleware
• Include in Globus or DataGrid
• Measure performance of original and Cat.
  Allocation
• Examples
• Catallactic Resource Allocation in Grids
• Implement catalaxy between Globus or DataGrid
  resource providers and service clients.
• Catallactic Resource Allocation in P2P
• Peers configure their resources for maximum
  revenue, best service,
• Pro
• Take parameters, utility functions, topology from
  real application
• Contra
• Complex questions yield complex answers
• Maybe no generalizable statement
• Propose full project to successor of FET Global
  Computing

145
Thanks!