decentralized vs. centralized economic coordination of resource allocation in grids t. eymann, m....
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Decentralized vs. Centralized Economic Coordination of Resource Allocation in Grids
T. Eymann, M. ReinickeAlbert-Ludwigs-University, Freiburg (DE)O. Ardaiz, P. Artigas, L. Díaz de Cerio, F. Freitag, R. Messeguer, L. Navarro, D. RoyoUniversitat Politècnica de Catalunya, Barcelona (ES)
CATNET project – Open Research, Evaluation(3/2002-3/2003)
Problem and objective
Problem: Provisioning services Requiring (huge amount of) resources From large number of computers CDN, Grid and P2P
Objective: evaluation of decentralized mechanism for resource allocation, based on economic paradigm: Catallaxy. (compare against a centralized mechanism using an
arbitrator object)
Methodology: simulation Network simulator (javasim)
+ application network (catnet)
Resource infrastructuresContent distribution networks, GridPeer-to-Peer
Application networks on top, run in multiple resource locationsExample: word-processor requiring service for creation of PDF files Client: Look for nearest/cheapest svc. Instance Network: always provide svc, optimize
provisioning costs and network communication
Service control, resource allocationService control, resource allocation
Service control+resource allocation
Decentralized economic coordinationPrice generation and negotiationTrading resources and servicesRegulation of supply and demand in large and complex systemsCatallaxy
Catallaxy Basics (1)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”
How does Catallaxy avoid chaos and achieve order?
Spontaneous order of the participants
„Unplanned result of individuals' planful actions“ (Hayek)
Constitutive Elements of the Catallaxy Access to a Market
Knowledge about scarceness of resources is transported through price information
Constitutional Ignorance Self-interest and autonomy
of participants Ability to choose between
alternative actions
Institutions and Evolution "Institutions are frictions
which, like frictions in mechanical systems, by restricting movement may make controlled movement possible.” (Loasby 2000, p. 299).
Implementation of Norms, Rules, Objects
Learning Dynamic Co-Evolution Income expectations and
price relations stabilize development
Catallactic Information Systems – Internal model
Self-Interest Individual goals of the agent can be formalized (e.g. profit maximization) Agent attempts to prognose future world state Actions effect environmental state in order to achieve goals
Choice Agent can choose between diverse alternatives Agent can rank alternatives according to prognosed goal approximation Environment is worth-oriented domain (cf. Rosenschein/Zlotkin)
Constitutional Ignorance No agent can exactly prognose a future market state („future is blind“) No agent can exactly prognose a „best strategy“ (always historically
bound) You never step twice in the same river (Heraklit)
Strategy is sophisticated trial and error procedure at best Requires adaptive and learning strategy Learning procedures are based on subjective past experiences
Consequences for Application Development
Application must be a Worth-Oriented Domain Application Domain needs common
value denominator (money) Requires “money vs. Goods“ exchange However: if the application domain
already uses money, it can be directly modelled
Consequences for Application Development
Agent-based solution is always inferior to analytical optimizationCatallaxy is inverse scalable Works better, the larger the network isInformation The more information is available, the more
accurate are the choices The more agents, the more information existsComputation Computation is fully parallel (no central bottleneck) Solution always exists in the system (no non-
allocated resource)
What we could expect?Catallaxis good for certain situations: Load balancing Large systems: inherent cost of global/up-to-date
state information for resource allocation where autonomous and decentralized algorithms work well
Adaptive to changes: in demand, topology, location and number of resources evolutionary learningself-organisation (specially for non-uniform systems with “hot spots”) Centralized/de-centralized systems may have
oscillatory behaviour “constitutional ignorance” Centralized: tragedy of state info overload with scale; Decentralized: tragedy of commons
Catallactic Information systems
The Catnet network simulator
Client: computer program at host, requests serviceService Copy: instance of service, hosted in a resource computerResource: host computer with limited storage and bandwidth
We are measuring...Social Welfare: the sum of all utilities over all participants in a given timespan. Utility = Benefit - Cost, basic utility
function per participant.
Resource Allocation Efficiency (RAE): [Marketing] "fill rate", the ratio of matched
transactions divided by the number of all proposals. (#"accepts“/#"proposals“)
Comm.Cost= #messages * #hopsResponse time
Our goal: compare baseline/Catallactic
Quasi-staticVery dynamicLow node densityHigh node density
Dynamics: change: %node disconnection time (SC?)Node density: many small nodes / few large nodes
SWF
Reso
urce
Allo
catio
nEf
ficie
ncy
BW
utiliz
atio
n
Com
mun
icatio
nco
st
Reac
tion
time
~
C
B B B B
C CC C
C C CB C
~ B B B B
Syst
em
/
Message Flows (Baseline)
Message Flows (Catallactic)
Scenarios
Appropiate scale: 10th or 100th or 1000th nodes … Change (dynamics): Movement / failure, creation (R) Change of demand (C)
Location of demand (which clients) Characteristics (many, including temporal
distribution)
Density: Fragmentation of resource capabilities
Same global amount of resources: P2Pmany small PC, CDNfew large servers /
Demand
From several clientsAt the same time, at different timesRequests with different price/priorityRate: #requests/second distribution in time, space.Deterministic, random
Dynamics
Node density (1/concentration)P2P
GridCDN
Very-dynamic (Many disconn.)
Quasi-static (few disconn.)
low high
Exp. 1
Exp. 2Fixed
netw
ork
sMob
ile, ad
-hoc,
overl
oad
ed
netw
ork
s
E1.2 E1.3
E2.3
E2.2
Dynamics
Node density (1/concentration)P2P
GridCDN
Very-dynamic (Many disconn.)
Quasi-static (few disconn.)
low high
Exp. 1
Exp. 2Fixed
netw
ork
sMob
ile, ad
-hoc,
overl
oad
ed
netw
ork
s
E1.2 E1.3
E2.3
E2.2
Catallactic better than Centralized
Ongoing work
0
10
20
30
40
50
60
70
80
90
100
10 s50 s75 s100 s125 s150 s
RAE (%)
C/B C/B C/B C/BC/B
C/B C/B C/B C/BDynamics
Density/#SC: 0/5 1/25 2/75
Network of 105 nodes, 75 Clients, 105 ResourcesResource Density/#SC: 0/5, 1/25, 2/75
500 Client requests for service, during 10, 50, 75, 100, 125, 150 seconds
Conclusions
Initial simulation results prove that a decentralized, economic model works better in certain situations. “Better” is a combination of factors (SWF)
Promising: Large scale Dynamic Saturation Resource allocation