1 choosing a load balancing scheme for agent-based digital libraries christos georgousopoulos and...

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Choosing a Load Balancing Scheme for Agent-Based Digital Libraries

Christos Georgousopoulos and Omer [email protected]

Cardiff University and Welsh e-Science Centre

http://www.cs.cf.ac.uk/http://www.wesc.ac.uk/

Joint Project: Giovanni Aloisio, Masimo Caffaro (University of Lecce, Italy) and Roy Williams (CACR, Caltech, US)

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Earth Observation System

Final Users

Dissemination

Acquisition

Processing

Tapes

Metadata

Archiving

NASAESA ASI

Space Agencies

DLR

3

system architecture

user

UPAURA

GISGIS

UIAUIA

LIALIA

Data Archive

LIALIA

UIA: User Interface AgentURA:User Request AgentUPA:User Presentation Agent

UIA: User Interface AgentURA:User Request AgentUPA:User Presentation Agent

LIA: Local Interface Agent LIGA: Local InteGration Agent LAA:Local Assistant Agent LRA: Local Retrieval Agent LMA:Local Management Agent LSA: Local Security Agent

LIA: Local Interface Agent LIGA: Local InteGration Agent LAA:Local Assistant Agent LRA: Local Retrieval Agent LMA:Local Management Agent LSA: Local Security Agent

LMA LRALIGALAA LSA LMA LRALIGALAA LSA

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Applications: Post-Fire Measurements

color scale:blue (rivers and lakes with no biomass) brown (non-forest areas with crown biomass of less than 4 tons/ha) light brown (areas of canopy burn with biomass of between 4-12 tons/ha) yellow (areas with a biomass of 20-35 tons/ha)green (forest with a biomass of greater than 35 ton/ha)

SIR-C/X-SAR L-band images of Yellowstone National Park, Wyoming obtained in 1994, six years following a major fire

in 1988.

map of the forest crown showing its biomass

SAR measurements of fire-affected regions can aid in monitoring forest regrowth after a fire

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Lost city of UbarArabian Peninsula

Remote desert caravan oupost2800 B.C.-300 A.D.

Applications: Archeology

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The FIPA interoperable SARA architecture

EXSA

URAS

URA

URA

AGENT ENVIRONMENT

AGENT ENVIRONMENT

LAA LRA

LMAUAA

UMA

LSA

LIGA

DB

FILEARCHIVE

COMPUTESERVER

META-DATA

URA

LAA LRA

LMA

LSA

LIGA

Web Server

Voyager platform

Voyager platform

FIPA-OS platform

FIPA-OS platform

EXSA

URA

AGENT ENVIRONMENT

UAA

UMA

Web Server

Voyager platform

FIPA-OS platform

CLIENT

EX MAS

EX MAS

CLIENT

EX MAS

Web SERVER 1

Information SERVER 1 Information SERVER 2

URAS

AGENT ENVIRONMENT

Voyager platform

FIPA-OS platform

EX MAS

Web SERVER 2

message exchange

creation of agent

Management agent’s interaction

movement

send/receive request

hidden architectural details

FIPA-compliant gateway

UIA: User Interface Agent

URA: User Request AgentUAA: User Assstant Agent

LIA: Local Interface AgentLAA:LMA:UMA:

LSA: LIGA: URAS: EXSA:

Local Assistant Agent Local Management AgentUniversal Management Agent

Local Security AgentLocal InterGration Agent

URA’s ServantExtermal Service Agent

LRA: Local Retrieval Agent

DB

FILEARCHIVE

COMPUTESERVER

META-DATA

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Web Browser

Java Applet GUI

URA

ERS-1 Data in

Italy

LSA LAA LRA

UPA

1

2

3

75

9

10

LSA LAA LRA4

8

SIR-CImaging radar in

USA 6

URA

URA

URA

URA URA

Localhost

Mobile agents for data retrieving

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Load Balancing Context

Multiple Information Servers available• Offer similar capability• Host “Management Agents”• How much intelligence in mobile vs. stationary agents?To which server should we send the mobile agent?

9

Load balance

mobile static

state model

Market mechanism

Specialized agentsgather System state

information

Aim: improve the average utilization and performance of tasks on available servers

Kinds of Load Balance (LB):

Keren & Barak:mobile LB has a

30-40% improvement over

the static placement scheme

• only a price• sophisticated auction protocols• a pricing mechanism without any negotiation

• roam through the network• bid for resources

Load Balancing

Must consider: (1) Number of agents/server; (2) Number of tasks/agent

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State-based vs. Model-based Approaches

• State based• Gather system state (how much, how

frequently)• Use this to make mapping decisions(eg. Spawn, Dynast, OCEAN) – market based(montoring – eg Mats, Traveler)FLASH – a single (centralized) monitor that passes info to

nodes

• Model based• Attempt to predict system state – via a “model”• Use outcome of model as a means to make a mapping

decision(eg. Enterprise, Challenger (machine learning-based), etc)

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Gathering System State

•Roaming/Scout agents•Agents look for free resources

•Report this back to nodes

•For “N” servers, requires “N-1” serialization and migrations

•Management agents•Specialist agents capture system state

•N *(N-1) message exchanges

•Reduce who to exchange state information with–“Direct Neighbour” vs. “All Neighbour” policy

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Configuration Options

•100Mb/s network, 5 servers

•Local state info: 150 to 200 bytes–Number of active agents, utilization, resources available

•Initial agent size: 2.8KB (Voyager ORB)

•Message exchange time: 21ms to 36ms•Agent serialization time: 31ms to 47ms

•Migration time (+ time to store local state info): 564ms to 678ms

•Create reference to a proxy: 93ms to 125ms

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Comparison Results

14

1

Efficiency Trade-off

•Each migration leads to potentially (N-1) messages:•(N-1) migration * (N-1) messages

•For N=44 roaming agent approach becomes more efficient

1 2 3 4

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Our approach on LB

Provide a LB mechanism to evenly distribute agent tasks among the available servers

(i.e. equitably server the agents, there are no priorities between agents based on the time needed for their task to be accomplished)

We propose a LB mechanism based on a combination of the model-based and state-based approaches

(i.e. decisions on LB are based upon a model which adapts due to the information gathered from the state-based approach)

We demonstrate this approachfor a MAS operating on an active digital library composed of multi-spectral images of the Earth as part of the Synthetic Aperture Radar Atlas (SARA)

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The SARA LB mechanism

State-based approach

Model-based approach

(4/4) Communication between management agents

(1/4) The management agents in the SARA architecture

(3/4) Information maintained by management agents

(2/4) Distribution of information among the management agents

(1/1) LB decision model

Delegate Load Balance Decision to the ManagementAgent (2 messages exchanges between mobile and managementagent)

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Advantages of the proposed LB technique

LB decisions are supported by the management agents

Distribution of information between the management agents

More accurate LB decisions

(the variation of the global network map decentralized information distribution implies reduction of information replication)

(LB model uses the state-based information)

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E X S A

U R A S

U R A

U R A

A G E N T E NV IR O N M E N T


LA A LR A

LM AU A A

U M A

LS A

LIG A

D B

FILEA R C H IV E

C O M P U TES E RV E R

M E TA -DATA

U R A

LA A LR A

LM A

LS A

LIG A

W eb S erver

Voyage r p la tform

Voyage r p la tform

FIPA -O S p la tfo rm


E X S A

U R A


U A A

U M A

W eb S erver

Voyage r p la tform


CLIENT

EX M AS

EX M AS

CLIENT

EX M AS

W eb SERVER 1

In form ation SERVER 1 In form ation SERVER 2

U R A S


Voyage r p la tform


EX M AS

W eb SERVER 2

m essag e exchang e

creation of a gent

M anagem ent agent’s in teraction

m ovem ent

sen d/rece ive req uest

h idden arch itec tura l deta ils

F IPA -com pliant g atew ay

U IA : User In terface A gent

U R A : U ser R equ est A gentU A A : U ser A sss tant A gent

LIA : Loca l In terface A g entLA A :LM A :U M A :

LS A : L IG A: U R A S : E X S A :

Loca l A ss istant A gent Local M ana gem ent A gent

U niversa l M anage m en t A g ent

Local S ecurity A gentLocal In terG ratio n A gent

U R A’s S ervantE xterm al S e rvice A gent

LR A : Local R etrieva l A gen t

D B

FILEA R C H IV E


M E TA -DATA

EXSA

U R AS

U R A

U R A

AG EN T ENVIR O N M EN T


LAA LR A

LM AU AA

U M A

LSA

LIG A

D B

FILEAR C H IVE

C O M PU TESERVER

M ETA-DATA

U R A

LAA LR A

LM A

LSA

LIG A

W eb Server

Voyage r p la tform

Voyage r p la tform

FIPA-O S platfo rm

FIPA-O S platfo rm

EXSA

U R A


U AA

U M A

W eb Server

Voyage r p la tform

FIPA-O S platfo rm

CLIENT

EX MAS

EX MAS

CLIENT

EX MAS

Web SERVER 1

Inform ation SERVER 1 Inform ation SERVER 2

U R AS


Voyage r p la tform

FIPA-O S platfo rm

EX MAS

Web SERVER 2

m essag e exchang e

creation of a gent


m ovem ent

sen d/receive req uest

h idden architec tura l deta ils

F IPA-com pliant g atew ay

U IA : User In terface Agent

U R A: U ser R equ est AgentU AA: U ser Asss tant Agent

LIA : Loca l In terface Ag entLAA :LM A:U M A:

LSA : LIG A: U R AS: EXSA:

Local Ass istant Agent Local M ana gem ent Agent

U niversal M anage m en t Ag ent

Local Security AgentLocal In terG ratio n Agent

U R A’s ServantExterm al Se rvice Agent

LR A: Local R etrieval Agen t

D B

FILEAR C H IVE

C O M PU TESERVER

M ETA-DATA


Info. server LMA (Local Management Agent)

web server UMA (Universal Management Agent)

i) optimize mobile agents’ itinerary

ii) avoid unnecessary migrations

iii) identification & comparison of agent task

i) inform mobile agents for updates

A management agent exists for every server

Their common objective: optimize system performance

Why multiple management agents ?

i) no central point of failure

ii) over a centralized scheme: as the number of agents increase, the network load is increased

(state-based approach)(state-based approach)

LB decisions are supported through the management agents

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Minimization of information transmitted over the network

Minimization of the mobile agent’s size

System optimization

Advantages of having management agents control over LB decisionsAdvantages of having management agents control over LB decisions

(i.e. only 2 messages are exchanged between a mobile agent and a management agent: the agent’s requirements & the agent’s itinerary )

(i.e. the decision support algorithm is within the management agents. Alternatively mobile agents would have to carry it during their migration)

Information used for LB decisions may also be reused for:

i) undertaking similarity analysis between agent requests i.e. tasksii) cache techniques are possible to be applied

iii) lay the foundations for an efficient monitoring system

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(2/4) Distribution of information among the management agents(2/4) Distribution of information among the management agents

distributed scheme :information is distributed among the servers

centralized scheme :a global database is used to hold all information for each server

ii) map of the surrounding area

i) global network map

iii) neighbor map

- agent interactions

- information:

- in a case of a failure

stored in one locationnetwork overload increases

- impose agents to have a kind of intelligence

- each server has all the information: replication (for integrity)

no central point of failure

network overload decreases(provides all information for each server)

(provides information for the local server but information is reduced more and more for servers which are not in the local region)

(provides information for the local server and its neighbor servers only)


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LMA’s information

LMA’s information acquired by

Local: resources: software: status of voyager server, available analysis algorithms hardware: database server: status, processing power compute server: status, processing power, average data filtered per sec., maximum data filtered per sec.

local LAA

number of agent: active, persistent general (concerning database server): average completion task time, average server’s utilisation

LMA itself

Remote: servers’ resources:…

LMAs

servers’ bandwidths: server x with server y

sender agent

UMA’s information

UMA’s information acquired by

Local agent’s info: agent id: general: request, time of request

local UAA(upon URA’s creation)

time of request accomplished, status of the task location of results: server’s IP, physical location path, file-space acquired resources used: software: analysis algorithm (AA) used, size of custom AA hardware: database/file archives used, engagement time (from-to), server’s utilization (before-after), compute server used, engagement time (from-to)

local UAA(before URA’s death)

Remote agents’ info: server x,y: agent id, request, status of the task

UMAs

LMAs’ info: server x, y: … LMAs

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Mobile-Stationary/Management Agent Interaction

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Migration Times for Voyager-based Mobile Agents

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(state-based approach)

Management agents’ interaction

Event Interaction(sender – recipient)

Information exchange Type of mes.

on the initialization of the system LMA-LMAs/UMAs contents in row 1,2 of table 1 multicast

upon URA’s creation UAA-local UMA contents in row 1 of table 2 direct

UMA-UMAs information in bold of table 2 multicast

before URA’s death UAA-local UMA contents in row 2 of table 2 direct

UMA-UMAs information in bold, in row 2 of table 2 multicast

URA’s migration failure URA-local LMA/UMA

Voyager server is down (row 1, table 1) direct

LMA/UMA-LMAs/UMAs

multicast

database connection failure LRA-local LMA database is unavailable (row 1, table 1) direct

LMA-LMAs/UMAs multicast

sever will be unavailable until a specified time

LMA-LMAs/UMAs the time the server will become available (row 1, table 1)

multicast

need for further information about an agent’s task

UMA-UMA selected information of row 1,2 of table 2 based on the recipient UMA needs

direct

change on information-server’s (LMA’s) status/resources

LAA-local LMA contents in row 1 of table 1 direct

LMA-LMAs/UMAs contents in row 1,2 of table 1 multicast

change on UMA’s information (concerning URA personal details)

UMA-UMAs contents in row 3 of table 2 multicast

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LB decision modelLB decision model(model-based approach)(model-based approach)

i) agents’ tasksii) servers’ utilization (performance load)

iii) availability of resourcesiv) network efficiency

LB decisions are based on a model which accepts as:

input: an agent’s requirements & System state informationoutput: the appropriate server where an agent should migrate to

The model is a function of:

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Dynamic Approach – Track Selection by polygon

The user is allowed to select a polygonal area on a zoomable map of the world

The algorithm retrieves all the tracks intersecting the user’s polygon in at least one point

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A g e n t’s Task

N e ed f ilte r in g

N e ed f ilte r in gP a rtia lly th e sa m eE x a c tly th e sam e

D o no t ne ed f ilte r in g

D o no t ne ed f ilte r in g

C u s to m fil te r

C u s to m fil te r

S e rv e r fi lte r

S e rv e r fi lte r.. ... .

S im ila r (ca ch ed ) N o t s im ila r (no t cach e d )

(model-based approach)(model-based approach)

The model may be better expressed with reference to the agents’ task…

Load Balancing

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W ill b ec o m eav a ilab le a t T

c s

F o r u n k n o w n tim e


C o m p u te se rv e ris u n av a ilab le

C o m p u te se rv e ris a v a ilab le

S e rv e r is a v a ilab le

(iii)

(iv)

(v )

(v i)

(v ii)

(v iii)

(ix)

(ii)

(i)


s

N o

Ye s


c s





c s




BS

UUT codea

av

sav

x2

.*

case 3:Agent’s task Similar (cached) Exactlythe same Need filtering Custom filter

case 5:Agent’s task Not similar (not cached) Do not need filtering

where:Tav = the average time an agent needs to complete a task (regarding all servers) Uav = the average utilization of all serversUs = the utilization of a serverSa.code = the file-size of an agent’s code.B2 = the bandwidth between 2 information serversΤs = time needed for a server to became available

LU

*

utilization of a server

where:a = the number of agents on that serverμ = the average task time of the agentsL = the processing power of the server

examples of different agents’ tasks…

+Ts

Load Balancing (Model-based Approach)

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Details of experiments conducted: - 200 agents launched- 5 information-servers & 1 web-server (Sun-Ultra 5 workstation running on Solaris 8 with Voyager 4.5 as the agent platform)- 100Mbits/s network connection- data-repository maintained by Oracle 9 DBMS

on the execution of agents with mixed tasksmixed tasks (15% where complex task)

on the execution of agents with simple taskssimple tasks

Workload distribution – utilisation of Information-serversWorkload distribution – utilisation of Information-servers

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For other systems utilising active-archives

in which the lifetime of complex tasks cannot be estimated or tend to be

erroneous

Three different LB schemes:

adaptability algorithm

Adaptability of modelAdaptability of model

Scheme No.1Scheme No.1: represents the default LB scheme adopted in SARA MAS (lifetime of complex agent tasks is known)

Scheme No.2Scheme No.2: alternative version of No.1 (lifetime of complex agent tasks is unknown and therefore not used in calculations)

Scheme No.3Scheme No.3: alternative version of No.2 (adaptable algorithm is utilised for amending the server’s utilisation)

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Optimisation of LB scheme No.2, based on theutilisation of the adaptability algorithm

Efficiency between LB scheme No.2 & No.3

Total task time required by agentsto complete their task

optimisation in performance1.63 – 10.8 %1.63 – 10.8 %

Comparison of different LB schemesComparison of different LB schemes

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Conclusion – Future work

were specialized stationary agents are used to gather system state information and make decisions on the distribution of mobile agents among the servers,

based on a model of probabilistic estimations in relation with the information provided by the stationary agents

we demonstrated a combination of the state and model-based approaches for mobile agent load balancing

we have implement the proposed LB technique

… need to further optimize the intelligence of the management agents

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Load balancing overviewLoad balancing overview

Load balance

mobile static

state model

Market mechanism

Specialized agentsgather System state

information

Aim: improve the average utilization and performance of tasks on available servers

Kinds of Load Balance (LB):

Keren & Barak:mobile LB has a

30-40% improvement over

the static placement scheme

• only a price• sophistiated auction protocols• a pricing mechanism without any negotiation

• roam through the network• bid for resources

Load Balancing

34

Our approach on LBOur approach on LB

Provide a LB mechanism to evenly distribute agent tasks among the available servers

(i.e. equitably serve the agents, there are no priorities between agents based on the time needed for their task to be accomplished)

We propose a LB mechanism based on a combination of the model-based and state-based approaches

(i.e. decisions on LB are based upon a model which adapts due to the information gathered from the state-based approach)

We demonstrate this approachfor a MAS operating on an active digital library composed of multi-spectral images of the Earth as part of the Synthetic Aperture Radar Atlas (SARA)

Load Balancing

35

The SARA LB mechanismThe SARA LB mechanism

State-based approach

Model-based approach




(2/4) Distribution of information among the management agents

(1/1) LB decision model

Load Balancing

36

The SARA architectureThe SARA architecture

E X S A

U R A S

U R A

U R A



LA A LR A

LM AU A A

U M A

LS A

LIG A

D B

FILEA R C H IV E


M E TA -DATA

U R A

LA A LR A

LM A

LS A

LIG A

W eb S erver

Voyage r p la tform

Voyage r p la tform



E X S A

U R A


U A A

U M A

W eb S erver

Voyage r p la tform


CLIENT

EX M AS

EX M AS

CLIENT

EX M AS

W eb SERVER 1

In form ation SERVER 1 In form ation SERVER 2

U R A S


Voyage r p la tform


EX M AS

W eb SERVER 2

m essag e exchang e

creation of a gent


m ovem ent

sen d/rece ive req uest

h idden arch itec tura l deta ils

F IPA -com pliant g atew ay

U IA : User In terface A gent

U R A : U ser R equ est A gentU A A : U ser A sss tant A gent

LIA : Loca l In terface A g entLA A :LM A :U M A :

LS A : L IG A: U R A S : E X S A :

Loca l A ss istant A gent Local M ana gem ent A gent

U niversa l M anage m en t A g ent

Local S ecurity A gentLocal In terG ratio n A gent

U R A’s S ervantE xterm al S e rvice A gent

LR A : Local R etrieva l A gen t

D B

FILEA R C H IV E


M E TA -DATA

Load Balancing

37

EXSA

U R AS

U R A

U R A



LAA LR A

LM AU AA

U M A

LSA

LIG A

D B

FILEAR C H IVE

C O M PU TESERVER

M ETA-DATA

U R A

LAA LR A

LM A

LSA

LIG A

W eb Server

Voyage r p la tform

Voyage r p la tform

FIPA-O S platfo rm

FIPA-O S platfo rm

EXSA

U R A


U AA

U M A

W eb Server

Voyage r p la tform

FIPA-O S platfo rm

CLIENT

EX MAS

EX MAS

CLIENT

EX MAS

Web SERVER 1

Inform ation SERVER 1 Inform ation SERVER 2

U R AS


Voyage r p la tform

FIPA-O S platfo rm

EX MAS

Web SERVER 2

m essag e exchang e

creation of a gent


m ovem ent

sen d/receive req uest

h idden architec tura l deta ils

F IPA-com pliant g atew ay

U IA : User In terface Agent

U R A: U ser R equ est AgentU AA: U ser Asss tant Agent

LIA : Loca l In terface Ag entLAA :LM A:U M A:

LSA : LIG A: U R AS: EXSA:

Local Ass istant Agent Local M ana gem ent Agent

U niversal M anage m en t Ag ent

Local Security AgentLocal In terG ratio n Agent

U R A’s ServantExterm al Se rvice Agent

LR A: Local R etrieval Agen t

D B

FILEAR C H IVE

C O M PU TESERVER

M ETA-DATA

(1/4) The management agents in the SARA architecture (1/4) The management agents in the SARA architecture

Info. server LMA (Local Management Agent)

web server UMA (Universal Management Agent)

i) optimize mobile agents’ itinerary

ii) avoid unnecessary migrations

iii) identification & comparison of agent task

i) inform mobile agents for updates

A management agent exists for every server

Their common objective: optimize system performance

Why multiple management agents ?

i) no central point of failure

ii) over a centralized scheme: as the number of agents increase, the network load is increased


LB decisions are supported through the management agents

Load Balancing

1 choosing a load balancing scheme for agent-based digital libraries christos georgousopoulos and...

Documents

local security agent

local management agent

local interface agent

local assistant agent

local retrieval agent

local integration agent

user interface agent

user presentation agent