Optimal Adaptation

Presented byManuel Correa

Optimal Adaptation in Autonomic Optimal Adaptation in Autonomic Web Processes with Inter-Service Web Processes with Inter-Service DependenciesDependenciesBy Kunal Verma, Prashant Doshi, Karthik Gomadam, Amith SethBy Kunal Verma, Prashant Doshi, Karthik Gomadam, Amith Seth20062006

Web Process = WS composition = business process (BPEL)

Autonomic Web Process (AWP) = Framework to WP Self-Adaptation, Self-optimality and self-healing

Inter-service dependencies = WS dependencies within a Web process

Optimal adaptation = Adaptation to exogenous events in the web process (exogenous events = external events)

IntroductionIntroduction

Optimal Adaptation in Autonomic Web Processes Optimal Adaptation in Autonomic Web Processes with Inter-Service Dependencieswith Inter-Service Dependencies

Representation of a business process – Modele.g: Supply chain

Discovery of Web Services

Web Process composition and configuration– Process creation– Analysis of constraints. e.g.: Cost of invocation, time response

Web ProcessWeb Process

Processes must adapt to any external event in a dynamic environment

This adaptation must be optimal: Time, cost and other business constraints

Adaptation is hard to accomplish because the service inter dependencies

Problem DefinitionProblem Definition

Computer manufacturer which operates with minimal inventory The computer manufacturer order computer parts to multiple

manufactures The computer part in order to be assembly in a computer must

compatibles Example: The memory RAM must be compatible with the

motherboard

Problem Definition – ExampleProblem Definition – ExampleSupply chainSupply chain

What happens if the RAM supplier is delay? The orderRAM service must coordinate with the orderMB if a

external event happens If we change supplier for RAM, we need to change supplier fo MB

and maintain the business constraints: min cost, min time, and compatibility

Problem Definition – ExampleProblem Definition – ExampleSupply chainSupply chain

Quantitative constraints – Invocation time <10– Invocation Cost – Cost of the supplier<2000

In order to discover services with this constraints. The ILP method was applied.

ILP: Integer Linear programming

Problem Definition – ExampleProblem Definition – ExampleSupply chain – Discovery servicesSupply chain – Discovery services

Logical constraints– Computer part's

Compatibility

For each set that meet the quantitative constraints, we need to validate if the services are compatible.

Using Domain knowledge and the SWRL technique

SWRL: Semantic Web Rule Language

Self-Configuration: AWP must be able to configure itself automatically or semi-automatically

Self-healing: AWP must be able to adapt to external and internal failures

Self-Optimization: AWP must achieve their goals in a cost-effective and time-effective manner

Autonomic Web ProcessesAutonomic Web Processes

Process Manager(PM): responsible of configuring the process with help of configuration module, listening to environment variables, and working with the Service manager

Service Manager(SM): Each partner interact with the service manager rather than the process directly

Configuration Manager(CM): responsible to discover and selecting the services that satisfy the constraints

Autonomic Web ProcessesAutonomic Web Processes

Centralized Approach: M-MDP ( Multi-Agent Markov Decision process)

DecentralizedApproach: MDP-COM ( Markov decision Model – Coordination Mechanism)

Optimal AdaptationOptimal Adaptation

Mathematical Framework to model decision-making problems where the outcome is random(stochastic) and control by the decision maker

The MDP provides a policy which optimize the decision in any state

Given the process in a State s, the decision maker choose an action a from s. When moving to s' the

decision maker is optimizing given the current policy

Markov decision ProcessMarkov decision Process

MDP is a tuple where M = (S, A, T, C, H)• S= Set of states• A= set of all possible actions• T =Transition function T: SxA --> Prob(S) which specifies the probability

of the states given a current state and actions• C: SxA --> |R Cost function which specifies the cost in given state and

action• H: period the consideration where the solution is optimal, call also the

horizon 0<H<ꝏ

Markov decision Process Markov decision Process Formal definitionFormal definition

Process manager is tasked with the responsibility of controlling the service manager with the WS

SMi and Smj are services Managers

MB supplier WS and RAM Supplier WS.

The M-MDP is implemented in the Process Manager

M-MDP approach M-MDP approach

M-MDP generalize MDP by considering the joint of actions of different agents.

ModelPM=(S, PA, T, C, OC)

M-MDP ModelM-MDP Model

PM = Process manager

S = Global states of the web process

PA: A= set of joint of all the services manager's actions

T: Markovian Transition function.

C: Global cost of invoking the WS

OC: Optimal criterion. Horizon.

S= Set of states

S= Si x Sj. Factored states of the service managers

s = (si, sj);

M-MDP Model – detailsM-MDP Model – detailsPM = (S, PA, T, C, OC)PM = (S, PA, T, C, OC)

PA: S-->P(A)A =Ai x Aj Factored actions in the

services managers

P(A): Set of permitted joint actions in a State s

P(A) = PAi(Ai) x Paj(Aj)

PAi(Ai) : set of permitted action of Service Manager i

Paj(Aj): set of permitted actions of Service Manager j

C=SxA -->|R

Cost function: Cost of invocation + cost of waiting for delayed order + cost of changing supplier

OC: optimal criterion.

Horizon= Expected cost over finite steps.

M-MDP Model – detailsM-MDP Model – detailsPM = (S, PA, T, C, OC)PM = (S, PA, T, C, OC)

T = SxAxS --> [0,1]. Markovian transition function

Captures the global effect of jointly invoking WS by SM

T(s'|s,a) = T[(s'i, s'j) | (si, sj),(ai, aj)]

...

T(s'|s,a)= Ti[s'i | (si, ai)] * Tj[s'j, (sj,aj)]

Ti and Tj individual transition functions

This is possible because each service manager's next state is influenced only by its own action and its current state.

M-MDP Model – detailsM-MDP Model – detailsExample: Supply chainExample: Supply chain Global state Process:

This means the Service Manager SMi has placed an order but not yet received it nor has any indication of delay. SMi has not changed supplier

SMj has placed an order that has been delayed. SMj has not change supplier

O: Place orderD: DelayedCS: Change SupplierR: Received

Possible actions

Ai= Aj = {Order(O), Wait(W), ChangeSupplier(CS) }

M-MDP Model – detailsM-MDP Model – detailsExample: Supply chainExample: Supply chain

Table; Partial cost in PM

M-MDP Model – detailsM-MDP Model – detailsExample: Supply chainExample: Supply chain

Graph with extended function. Modeling the external events

T=Si x Ai x Ei x Si. Ei = {delayed, received, None)

M-MDP ModelM-MDP ModelGlobal Policy computationGlobal Policy computation Global policy: optimal action that must performed by each service

manager given a global state in Process Manager (PM)

In order to compute the global policy, each global state is associated with a value that represents the long term expected cost in that state

An optimal global policy is guarantee.

M-MDP ModelM-MDP ModelPolicy computationPolicy computation

(a) Represents the worst case scenario where all the SM have to coordinate with each other

(b) Represents a realistic case scenario

A MDP for each Service Manager

Each SM makes its own decision

Coordination mechanism ensure the inter-dependencies coordination

Each SM observes its own state but not other states.

MDP-COM approach MDP-COM approach

ModelSMi =(Si, PAi, Ti, Ci, OCi)

MDP-COM ModelMDP-COM Model

SMi = Service Manager ith

Si = Local states of the SMi

Pai: Si -->P(Ai). Local permissible actions by state

Ti: SixAixSi -->[0,1} Markovian Transition function.

C: Si x Ai -->R Local cost of invoking the WS

OC: Optimal criterion. Horizon.

MDP-COM Model – detailsMDP-COM Model – detailsExample: Supply chainExample: Supply chain Local state Process:

O: Place orderD: DelayedCS: Change SupplierR: Received

Possible actions

Ai= Aj = {Order(O), Wait(W), ChangeSupplier(CS) }

MDP-COM Model – detailsMDP-COM Model – detailsCoordination processCoordination process Since the optimal decision to respond to exogenous events is compute by

each SM. A coordination mechanism must be implement to preserve the compatibility constraints

A SMi must communicate to the others Service Manager its intent to perform an action to respond to an event. ( Game Theory)

The coordination mechanism is a Finite State machine. Two states: Uncoordinate and Coordinate.

Supply chain: when a SM for RAM change Supplier, the orderMB must follow this action and change supplier

MDP-COM ModelMDP-COM ModelLocal Policy computationLocal Policy computation Each state is associated with coordination states.

Each Service Manager does not take into account the other states, actions and costs of others Service Manager

The model MDP-COM calculate policies locally and then coordinate with other Service Managers to make the decision in the process

Supply chain: If orderRAM change supplier then it coordinates with orderMB to change supplier as well. Even though this is not the most optimal solution

Empirical ResultsEmpirical Results

This two methods were tested in METEOR-S framework. BPEL4WS was used to implemented the Web process with WSDL-S

First experiment M-MDP was tested with different horizons

Second Experiment: Probability of events to occur. Such as delay in order. And comparing the model with random choice of actions

Third experiment: Number of service manager and time respond

The test included a Hybrid model. With a MDP-COM giving the process manager the ability to take some actions over the Service Manager.

e.g. if orderMB change supplier then the process Manager decides if the process is better off changing supplier or taking another action.

Empirical ResultsEmpirical Results

ConclusionsConclusions

Web processes are increasing and the study of optimal adaptability with service inter dependencies is very important

The M-MDP method does no t scale well with multiple Service Manager

M-MDP computes optimal solutions because it has the whole picture of t he process

MDP-COM scales well. But it does not offer an optimal solution all the times

Future work: An hybrid model that takes advantages of both models

Questions?

Optimal Adaptation in Autonomic Optimal Adaptation in Autonomic Web Web ProcessesProcesses with Inter-Service with Inter-Service DependenciesDependencies