flexible provisioning of service workflows sebastian stein supervisors: nick jennings terry payne...

Flexible Provisioningof Service Workflows

Sebastian Stein

Supervisors:

Nick JenningsTerry Payne

KEG Seminar, Aston University

4th March 2008

Flexible Provisioning of Service Workflows

Agenda

Background & Motivation

Flexible Service Provisioning

On-Demand Invocation Advance Agreements

Conclusions

Background & Motivation

Background

Computer systems are increasingly distributed: E-commerce

Source: National Statistics Website, http://www.statistics.gov.uk/downloads/theme_economy/ecommerce_report_2004.pdf

Use of Internet for purchases in UK businesses

Large businesses (1000+ empl.) All businesses

Background

Computer systems are increasingly distributed: E-commerce High performance computing

Service-Oriented Computing

Distributed agents offer their capabilities as computer services, which are high-level behaviours that can be procured by consumers in order to achieve their objectives. These include:

Traditional business services (e.g., ordering components, making logistic arrangements, booking a flight ticket),

Computational services (e.g., data analysis, transformation and communication),

Information services (e.g., yellow pages, weather forecast, financial data).

Workflows

Services are rarely used in isolation. Usually, they form the building blocks for more complex

applications.

Definition: A workflow is a collection of tasks and their dependencies.

Taverna Workflow (myGrid)

Source: Exploring Williams-Beuren Syndrome Using myGrid, Hannah Tipney , http://www.mygrid.org.uk/wiki/pub/Mygrid/PresentationStore/ISMB04_Glasgow.ppt

Pegasus Workflow

Source: Pegasus Teragrid Talk SC2005 Seattle Washington, http://pegasus.isi.edu/pegasus/presentations/pegasus-tg-final.ppt

Service Provisioning

Services are dynamically provisioned (selected) by consumers at run-time.

Services are provided by autonomous agents. These may be unreliable (may fail or take longer than

expected)… …and heterogeneous.

Failure!Failure!

ValueValue

DeadlineDeadline

Problem Statement

How to design a service consuming agent able to deal effectively and efficiently with unreliable and heterogeneous service providers when executing complex workflows.

Related Work

Many current approaches concentrate on functional aspects of services and assume their behaviour to be deterministic.

Some work explicitly considers service failures: Exception handling (e.g., fault handlers in WS-BPEL), Fixed redundancy (e.g., SETI@home, replicated Web services), Retry and timeout policies (Zeng 2005, Erradi 2006), Non-functional service constraints (McIlraith and Son 2002).

These require significant manual input!

Related Work (Quality-of-Service Optimisation)

Local task QoS optimisation (Zeng 2004): For each task, provision the provider that optimises some property for

that task (e.g., cost, reliability, duration).

Global workflow QoS optimisation (Zeng 2003, Yu/Lin 2005): Provision one provider for each task, so that a weighted sum of global

performance characteristics is optimised:

Adaptive variants re-provision upon failure (Canfora 2005).

But: These do not reason explicitly about failures, rely on manually specified weights and constraints, and select single provider for each task.

Flexible Provisioning

On-Demand Invocation

Central Idea

How to address uncertainty during provisioning?

Existing work mostly relies on single service for each workflow task.

We can do better by exploiting parallel and serial redundancy.

… and by taking into consideration service heterogeneity.

Service Model

We devised an abstract model to describe a service-oriented system.

Assumptions:

Assume silent “crash” failures. Providers paid on invocation. Failures and durations are

independent. Free disposal of redundant services

(but cost still incurred!)

Utility function:

• Cost: c(s1) = £100• Failure Prob.: f(s1) = 0.01• Duration:

Flexible Strategy

We want to find a provisioning allocation for each task, e.g.:

This is an optimisation problem:

Expected rewardExpected cost

Why is this difficult?

Intuitively, Combinatorial problem:

Difficult objective function (probabilistic durations).

Based on this, we can show that provisioning is inherently hard...

Provisioning

Provisioning Problem

Knapsack(NP-

complete)

PERT CDF(#P-

complete)

PROVCDF ptPROVKS p

Provisioning is NP-hard Provisioning is #P-hard

Big problem as we wanted efficient methods for realistic workflows!

Flexible Strategy

Approximate the expected utility of an allocation using a heuristic utility function:

Optimise this with local search.

Estimated utility

Success probabilityEstimated workflow duration pdf

Estimated costReward function

Local Task Calculations

We start by calculating a number of performance parameters for each task in the workflow:

Success Probability: 95.00%Expected Cost: £30.00Expected Duration: 60.50 minVariance: 361.19 min2Success Probability: 96.83%Expected Cost: £7.23Expected Duration: 75.59 minVariance: 4030.84 min2

Cost: £1 £30

Success:

25% 95%

Duration:

Exp(80)

Gamma(10,6)

2 Service populations:

Success Probability: 99.99%Expected Cost: £26.15Expected Duration: 56.23 minVariance: 1501.57 min2

Global Workflow Calculations

These task parameters are then combined to estimate the overall expected profit:

Global Parameters:Success Probability: 68%Estimated Cost: £98.40Estimated Duration: 132 minVariance: 912 min2

£3 £42

164 292

98.40 0.68 383.64

= 162.48

Empirical Evaluation

To test the strategy, we compare it to a number of benchmarks:

Naïve: Provisions a single provider for each task. Models current approaches that do not consider service

unreliability.

Global QoS: Optimises weighted QoS measures over entire workflow (set all wi=1/3, use maximum utility and zero reward time as budget/time constraints).

Adaptive Global QoS: As above, but also uses timeouts and re-provisions dynamically.

Local QoS: Optimises weighted QoS measure for each task.

Further Results

We can compare our performance to an optimal strategy for very small workflows (3 tasks!). Achieves around 98% of optimal utility.

Results indicate that our strategy is robust to inaccurate information (with errors up to 10-15%). Beyond that, generally degrades gracefully, but problems when expected utility very low.

Trends hold on larger workflows (tested up to 1000 tasks).

So far…

We have proposed a flexible provisioning strategy that deals with uncertain service providers: By provisioning multiple providers redundantly for critical tasks. By re-provisioning services that seem to have failed. By exploiting the heterogeneity of providers.

Our strategy outperforms the state of the art in flexible service provisioning.

But so far, our strategy: Assumes that service populations are static throughout

execution. Assumes that services are always invoked on demand. Does not adapt to new information during execution.

Flexible Provisioning

Advance Agreements

Advance Provisioning

Increasingly, services will be offered in the context of pre-negotiated agreements (this is already emerging in computational Grids).

The agreements form a contract about when and how a service will be provided in the future.

I need service X in 2 hours.I need service X in 2 hours.

Reservation Cost: £20Invocation Cost: £10Start time: 2:00Completion time: 2:30

Advance Provisioning

Performance characteristics might vary depending on time of provisioning (e.g., airline pricing policies):

Contract Term On-Demand

1h Advance

12h Advance

Cost £10 £5 £15

Duration 45min 20min 10min

Failure Probability

10% 2% 0.1%

Modified System Model

Providers

0 100 200 300

Model a dynamic market:

Each time step: Providers post offers, according to some

stochastic process. Consumer provisions offers. Offers disappear (acquired by other

consumers or withdrawn).

Consumer

Service Type: T1

Start Time: 200

End Time: 220

Reservation Cost: £1Execution Cost: £5Penalty: £20Failure Probability: 10%Defection Probability:

Challenges

Future availability of offers uncertain.

Fixing advance agreements may mean that reservations costs are lost if preceding services fail.

Need to balance benefits of advance provisioning with risk!

Our Approach

Gradual Provisioning:

First make high-level provisioning decisions (how and when to provision tasks).

Follow these at run-time. Adapt strategies when failures occur.

High-level decision

Provisioned

Completed

Failure

High-Level Decisions

Assume we have a set of atomic provisioning strategies for each service type:

Performance statistics of strategies are learnt offline by observing the market.

Service Types

Strategies

……

Strategy w:• Advance time• Number of offers• Selection strategy

Expected performance:• Reservation cost• Execution cost• Failure probability• Duration (if successful)• Duration (if failed)• Variances of above

Contingency Planning

Atomic strategies represent single attempt at completing a task.

We can build simple plans from several such strategies to deal with failures:

Try strategy x(e.g., cheapest offer, 10 min in advance)

Success? Doneyes

Try strategy y(e.g., 2 most reliable offers, immediately)

Success?no Doneyes

Expected task performance:• Success probability• Reservation cost• Execution cost• Duration• Variance

Overlapping Provisioning

Finally, associate a late probability pl with each task plan. This indicates when services should be provisioned.

Higher pl results in less delays when provisioning in advance, but also increases probability that provisioned offers are lost when preceding tasks overrun.

Use heuristic based on critical path to estimate delays and to determine during which task to provision.

tx-2tx-2 tx-1

tx-1 txtx

pl = 0.0

provision after tx-1

tx-2tx-2 tx-1

tx-1 txtx

pl = 0.05

provision during tx-1

tx-2tx-2 tx-1

tx-1 txtx

pl = 0.1

provision after tx-2

Strategy Summary

Given a high-level plan and late probability for each task, estimate utility in a similar manner as for on demand invocation, but include delays and reservation costs.

Optimise this using simulated annealing.

At run-time, follow task strategies, then incorporate information about provisioned offers and adapt strategy accordingly.

Create initial strategy S

Start of time step:Update S with outcomes

Optimise S

Realise high-level decisions

Was S altered in previous step?

ExpectedUtility of S > 0?

Abandon workflow

Provision chosen servicesyes

Invoke due services

Small 8-task workflow with 5 service types.

Offer characteristics drawn from uniform distributions.

Comparison with three benchmark strategies: Global QoS Adaptive Global QoS Local QoS

Also assume services always provide refunds for failures.

Conclusions

We proposed a novel algorithm that uses redundancy and dynamic re-provisioning to deal with uncertain service providers.

It does this in a flexible way by reasoning about service behaviours in the context of a decision-theoretic framework.

We first showed how it applies to scenarios where services are invoked on-demand, then extended it to environments with advance agreements.

In most scenarios considered, our strategy outperforms the state of the art in service provisioning.

Future Work

Improved prediction of workflow durations.

More expressive workflow models with branches and loops.

Consider more dynamic environments.

Incorporate meta-reasoning about time spent on optimisation.

Bibliography

Presented work from: Stein, Jennings, Payne (2007). Provisioning Heterogeneous and Unreliable Providers

for Service Workflows. In: AAAI-07. pp. 1452-1458. Stein, Jennings, Payne (2008). Flexible Service Provisioning with Advance

Agreements. In: AAMAS-08. (in press).

Related work on homogeneous providers: Stein, Payne, Jennings (2008). Flexible Provisioning of Web Service Workflows. In:

ACM Toit 8(4). (in press).

Other related work on QoS-based optimisation: Zeng et al (2003). Quality driven web services composition. In: WWW-03. pp. 411-

421. Zeng et al (2004). QoS-Aware Middleware for Web Services Composition. In IEEE

Soft. Eng. pp. 311-327. Yu and Lin (2005). Service Selection Algorithms for Composing Complex Services

with Multiple QoS Constraints. In: ICSOC-05. Canfora et al (2005). QoS-Aware Replanning of Composite Web Services. In: ICWS-

05. pp. 121-129.

Thank you!

Any Questions?

This work was sponsored by: :

flexible provisioning of service workflows sebastian stein supervisors: nick jennings terry payne...

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computer services

computational services

traditional business

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