Adaptive Deployment of Multi-Agent Systems on Cloud ...Generated deployment “nested” graph for...

Chahrazed LABBA

Dissertation Defense

Adaptive Deployment ofMulti-Agent Systems on Cloud

Environments

Mme. Narjès Bellamine Ben Saoud

Advisor

DISSERTATION DEFENSE 20 DECEMBER 2017

In collaboration with: Mme Julie Dugdale

15-03-2018Chahrazed LABBA

Introduction

State of the art

Contributions

Implementation and experimental results

Conclusion and future work

2

Outline

1

2

3

4

5

15-03-2018Chahrazed LABBA 3

Multi-agent systems

Introduction

State of the art

Contributions

Experiments and implementation

Conclusion et Future work

ContextMotivation

• Multi-Agent Systems (MAS):

A set of agents, that interact with each other, situated in acommon environment, eventually, building or participating to anorganization. [Dem2000]

• Agent:

Autonomy, Reactivity, Proactivity, Social abilities

• Model and simulate complex socio-technical systems.


Healthcare

NGO

ArmyTelco

operators

Fire-brigade

Police

An agent-based decision

support system for

emergency management

Compute

Storage

Network

Security

Compute

Storage

Network

Security

Compute

Storage

Network

Security

Availability

Compute

Storage

Network

Security

Availability

Compute

Storage

Network

Availability

Compute

Storage

Network

An Example of a Multi-Agent System

Introduction

State of the art

Contributions



ContextMotivation


Deployment of MAS on Cloud infrastructures

Introduction

State of the art

Contributions



ContextMotivation

Distributed environment

Cluster grid

Cloud

Hybrid-cloud Inter-cloud

- Not in the reach of all the organizations- Require a considerable expertise to be

installed, maintained and operated- May not fulfill all the QoS requirements

─ Scalability─ Flexiblity─ QoS requirements

Cloud federation Multi-Cloud

How to achieve a cost efficient deployment of MAS on cloud environment?


• A partitioning Problem

6

Introduction

State of the art

Contributions



ContextMotivation

Multi-Agent System partitioning on clouds

• Assign agents to low

cost cloud resources

• Maintain reduced

communication costs

• Fulfill QoS needs

• Large number of

partitioning algorithms

Among all the partitioning options, which ones are more

suitable to partition a given MAS on a Given Cloud?


Introduction

State of the art

Contributions



ContextMotivation

Complicated cloud infrastructure

Provider A

Inter-Cloud

Provider BProvider C

R1 R2

R3

R1R2

R1 R2

Availability

zones

Security

Availability

Security

Availability

Security

Availability

Dynamic agents‘ requirements Frequent load-balance issues

Multiple cloud providers Various cost models Different regions and zones Heterogeneous resources

For a given MAS(scenario), which cloud configuration settings

are suitable to achieve reduced deployment costs?


Problem Statement: Depolyment of MAS on clouds

MASDeployment

on Clouds

MAS Properties Cloud issues

Predict and optimize

(Plan)

Large-scaleQoS

requirements

Partitioning

options

communication

Complicated

infrastructure

Competitive

third parties

costs models

Conflicting

Deployment needsMultiple deployment

solutions

How to provide a cost-efficient adaptive deployment of a given

multi-agent system on a given cloud environment?

Cloud settings


Introduction

State of the art

Contributions



Partitioning ApproachesPrediction-based approches

Research Context

Partitioning approaches

• Multi-Agent Systems

• Cloud Applications

Prediction & estimation- based approach

• Multi-Agent Systems

• Cloud applications


• MAS classification

MAS_S MAS_C MAS_H

10

Introduction

State of the art

Contributions




Partitioning approaches for MAS (1/3)

3 MAS types(Ngobye et all, 2010)

• NI-MAS

• SI-MAS

• CI-MAS

2 MAS types(Salem et all, 2015)

• Closed MAS

• Open MAS

2 MAS types(Glavic, 2006)

• Independent MAS

• Cooperative MAS

3 MAS types(Ferber, 1999)

• Purely communicative

• Purely situated

• Hybrid

MAS with only

Indirect interactions

MAS with only

direct interactions

MAS with Both (in)

direct interactions

- Environment + Non direct

interactions

- Different types of

interactions

- Cooperate through

interactions

- Cooperation is not

an obligation

- Interaction without

intension to do so

- Homogenous/

Heterogeneous non

Interaction

- Homogenous/

Heterogeneous interaction

- Direct interaction

- Indirect interaction

- Direct and indirect

interaction


Introduction

State of the art

Contributions





Cluster-Based

Approaches

Grid-Based

Approaches

Graph-Based

Approaches

An operational framework for MAS partitioning ×Focus on one MAS type.× Restricted number of partitioning algorithms.×Consider one single target infrastructure.× Results can not be generalized to other distributed environments and MAS types.

No overall study on the appropriateness of partitioning algorithms according to the variety of MAS types

Vigueras et al,2009

Wang et al, 2009/ 2012


× do not consider the cloud characteristics:compute costs, communication costs, resource

heterogeneity, variety of cloud providers ×Vendor lock-in problem (single cloud provider)

Introduction

State of the art

Contributions






Partitioning approaches for Cloud Applications

Introduction

State of the art

Contributions




Optimize the deployment costsSupport various QoS requirements× Dedicated for a given application type×Insufficient for some MAS types×Low granularities that induce more complexity to the partitioning


• Predict the performance and the correctness of the MAS

• Detect load-balance issues

• Evaluate partitioning mechanisms for MAS on clusters

14

Prediction approaches for MAS

Introduction

State of the art

Contributions



Partitioning ApproachesPrediction-based Approches

Prediction at the MAS level

Prediction at the infrastructure level

×No support for cloud environments


Scheduling

Provisioning

Service Selection

Estimate and optimize the cloud costs ×Cover few QoS requirements: CPU and RAM×Support a given cloud environment×Support a given application type

Introduction

State of the art

Contributions




Prediction approaches for cloud applications


1

• Recommend partitioning algorithms to develop new distributed MAS

• Deal with the different MAS types as well as cloud environments

• Analyze the existing partitioning algorithms

• Give guidlines to develop new distributed systems

2

• optimize MAS partitioning on cloud environments

• Consider cloud specifications

• Consider MAS characteristics

3

• Prediction approach

• Deal with different partitioning options

• Deal with different cloud settings

• A cost model to compare deployment solutions

16

Introduction

State of the art

Contributions



Objectives


Framework for MAS deployment on clouds

• Pre-deployment method

• Deployment process

Framework to support MAS deployment on clouds

Introduction

State of the art

Contributions



Contributions

Pre

-de

plo

ym

en

t

me

tho

dd

ep

loym

en

t

pro

ce

ss

A pre-selection Process

A prediction Process

Deployment Service

An operational framework for MAS partitioning

• Analyze MAS type

• Recommend partitioning algorithms

New extended partitioning algorithms

• New graph-based partitioning algorithms

A prediction process

• Profiling phase

• Cost deployment model


Framework to Support MAS Deployment on Clouds

Pre

-sel

ecti

on

pro

cess

Pre

dic

tio

n

Pro

cess

Pre

-dep

loy

men

t M

eth

od

Dep

loy

men

t

Pro

cess

User

specifications

Scenario Definition

Partitioning algorithms

Library

Algorithm1

Algorithm2

.

Algorithm n

Sequential simulation

Profiling phase (2)

Pre-Selection

Service (1)

Deployment Service (6)

Candidate Partitioning

Algorithm

PA_1,PA_2, ..,PA_k

Cloud Specifications

Algorithms

invocation

Service (4)Decision Making

Service (5)

Monitoring

Service (3)

PA_1 PA_2PA_k

Deployment specifications Architecture

Target Cloud

Pre-deployment

method

Deployment

Pre-selection

Process

Prediction

Process

Introduction

State of the art

Contributions



Framework for MAS deployment on cloudsAn operational Framework for MAS partitioningExtended Partitioning AlgorithmsPrediction Process: estimate and optimize deployment

Overview of the Framework


Introduction

State of the art

Contributions




An operational Framework for MAS

Partitioning

Enter MAS

Specifications

Specify Deployment

Infrastructure

Analyze MAS

Specifications

Determine MAS

Type

Recommend a set

Of candidate partitioning

algorithms

Pre-selection process

- Criteria

- Reference combinations

- Matching grid


Introduction

State of the art

Contributions




Analyze MAS types

Situated MAS

Communicative

MAS

Hybrid MAS


Introduction

State of the art

Contributions




A matching Grid for recommending partitioning algorithms


Extended Partitioning Algorithms

Introduction

State of the art

Contributions





Introduction

State of the art

Contributions




Graph partitioning on cloud Infrastructure

Objective function to minimize the inter-cloud

Communication costs

Agents

Interactions

Agents requirements

Amount of communicated data

CPU requirements

RAM requirements

Security

Availability

Bandwidth


Introduction

State of the art

Contributions




Graph partitioning algorithm for cloud Infrastructures

Assign each agent to a cloud provider and a resource

Apply Graph partitioning algorithm

Resource allocation

Initial deployment

SolutionAdaptive deployment


Introduction

State of the art

Contributions




Generation of a deployment Graph

Cloud1Cloud2

VM2

VM1

VM1

VM2

VM3

0,5 Go1 Go

Generated deployment “nested” graph for inter-clouds

On-premisePublic Clouds

VM1

VM2

VM2

VM1Private cloudCloud1

Cloud20,2 Go

0,5 Go0,9 Go

0,5 Go

0,9 Go

0,5 Go0,5 Go

0,3 Go

Generated deployment “nested” graph for Hybrid-clouds


Introduction

State of the art

Contributions




Extended FM algorithms (1/3)

Takes into consideration the agents QoS requirement while searching for minimal costs.Minimizes the communication costs instead of their numbers. No random initial partitioning

Compute the gain

Move the agent with

Maximum gain (feasible move)

Update the gain


• Cloud Environment E-FM (C2E-FM)

• Inter-Cloud E-FM (ICE-FM)

• Hybrid-Cloud E-FM (HCE-FM)

27

Introduction

State of the art

Contributions




Extended FM algorithms (2/3)


Introduction

State of the art

Contributions




PMFM and Extended PMFM algorithms

• Cloud Environment E-PMFM (C2E-PMFM) : Calls C2E-FM

• Inter-Cloud E-PMFM (IC2E-PMFM): Calls ICE-FM

Divide the graph into k bloks

Creates pairs of blocks

Call FM algorithm


• The new algorithms

29

Introduction

State of the art

Contributions




Proposed Partitioning algorithms (1/2)


Introduction

State of the art

Contributions




Proposed Partitioning algorithms (2/2)


Prediction Process: Estimate and optimize

Deployment costs

Introduction

State of the art

Contributions



Framework for MAS deployment on cloudsAn operational Framework for MAS partitioningExtended Partitioning AlgorithmsPrediction Process: estimate and optimize deployment costs


Introduction

State of the art

Contributions



Framework for MAS deployment on cloudsAn operational Framework for MAS partitioningExtended Partitioning AlgorithmsPrediction Process: estimate and optimize deployment costs

Description of the full prediction Process


Introduction

State of the art

Contributions




Cost model for comparing deployment solutions

Communication

cost +

Migration

cost +

Execution time

cost∑ cost of all the inter-messages


• Migration costs

• Execution Time costs

34

Introduction

State of the art

Contributions




Cost model for comparing deployment solutions

∑ cost of all the migrations



Introduction

State of the art

Contributions



ImplementationCase study1: Emergency managementCase study 2: Dynamic business processes


• Agent Technology (JADE)

Pre-selection process

Prediction process

Emergency management simulator

Dynamic business process

• Programming language:

Partitioning algorithms

36

Introduction

State of the art

Contributions



ImplementationCase study 1: Emergency managementCase study 2: Dynamic business processes

Implementation


Introduction

State of the art

Contributions




Experimental settings

Experiment 1: compare HCPAdata, HCPAcost

And HCPAedge

• Inter-communication costs

• Overall deployment costs

Experiment 2: Impact of cloud configurations

on the overall deployment costs

• Hybrid cloud settings

• Cloud providers

• Availability zones

• Bandwidth


Introduction

State of the art

Contributions




Experiment 1

- HCPAcost algorithm outperforms HCPAedge

and HCPAdata in terms of both communication

and overall deployment costs.

- HCP Aedge results in increased inter

communication and deployment costs.


Introduction

State of the art

Contributions




Experiment 2

- HCPAcost algorithm

- Availability zone R1

- 100 Mbit/s of bandwidth (on/off premises)

- 300 Mbit/s of bandwidth (inter-cloud)

- Setting 1, setting 2, setting 3

Setting 1 provides better results in

terms of overall deployment costs




- Hybrid setting1

- P1(vm1), P2(vm2), P3(vm3)

The use of P2 provides better results

in terms of overall deployment costs



- Hybrid setting1

- P2

- Availability zone R1, R2, R3

The use of R1 provides better results



- Hybrid setting1

- P2


- Bandwidth 300Mbit/s, 100Mbit/s, 500Mbit/s

The use of B1 provides better results



Introduction

State of the art

Contributions




Input for first fit algorithm


• ICPAcost Vs ICPAdata

41

Introduction

State of the art

Contributions




Experiment 1: Towards an enhanced initial partitioning (1/2)

• Regardless the type of the infrastructure as well as the initial partitions

ICPAcost outperforms ICPAdata


Introduction

State of the art

Contributions




Experiment 2: Towards an enhanced initial partitioning (2/2)

• The initial partitioning is

impacted by different criteria:

Type of infrastructure

Number of allowed

instances

The order of the agents,

cloud and VM lists


Introduction

State of the art

Contributions




Experiment 2: ICPAcost Vs a Naive approach

• The use of our algorithm provides better results in terms of deployment costs

regardless the used initial partitions.


Introduction

State of the art

Contributions



ConclusionFuture work

Conclusion and Future Work


• Framework to support MAS deployment on cloudenvironments

o Pre-Deployment method

o Deployment Process

• An operational framework for MAS partitioning

o Analyze the MAS type through defined criteria

o Determine the MAS type

o Recommend a set of candidate partitioning algorithms

• New graph-based partitioning algorithms

• A prediction process

o Profiling phase

o Deployment costs models

45

Introduction

State of the art

Contributions





• Real deployment

• New partitioning algorithms

• Cloud Resource description

• Impact of agent development framework

• Support more distributed systems

46

Introduction

State of the art

Contributions





International journal papers:1. Chahrazed Labba, Narjes Bellamine Ben Saoud, Julie Dugdale, A predictive ap- proachfor the efficient distribution of agent-based systems on a hybrid-cloud, In FutureGeneration Computer Systems, 2017, ISSN 0167-739X, (impact factor: 3.997)

International conference papers1. Chahrazed Labba, Nour, Assy, Narjes Bellamine Ben Saoud, Walid Gaaloul,: “Adap- tiveDeployment of Service-based processes into cloud federations”, The 18th Inter-national Conference on Web Information Systems Engineering, WISE 2017, accepted paper.(Rank A)

2. Chahrazed Labba, Noura ben Saleh, Narjes Bellamine Ben Saoud: “An Agent-based Meta-Model for Response Organization Structures”, The 4 th International Confer- ence onInformation Systems for Crisis Response and Management in Mediterranean CountriesISCRAM-med 2017.

3. Chahrazed Labba, Narjes Bellamine Ben Saoud, Julie Dugdale: “Towards a con- ceptualframework to support adaptative agent-based systems partitioning”. SNPD 2015: 692-696(Rank C).

4. Chahrazed Labba and Narjes Bellamine Ben Saoud: “Cost-Based Assesment Of PartitioningAlgorithms Of Agent-Based Systems On Hybrid Cloud Environments”, EMSS 2015, Italy(Rank B).

5. Chahrazed Labba, Narjes Bellamine Ben Saoud, Karim Chine: “Towards Large-Scale CloudBased Emergency Management Simulation : SimGenis Revisited". ISCRAM- med 2014: 13-20.

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Publications List


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