Post on 13-Jan-2016
Xiao Liu
CITR - Centre for Information Technology Research
Swinburne University of Technology, Australia
xliu@swin.edu.au
Temporal Verification in Grid/ Scientific Workflows
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Grid/Scientific Workflows Temporal QOS Framework
Setting Temporal Constraints in Scientific Workflows
SwinDeW-G Grid Workflow Management System Additional Information
Research areas in Workflow Technology Program Data Mining Techniques in Workflow area Optimization Algorithms in Workflow area
Content
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Grid/Scientific Workflow Grid Workflow Management System
A type of workflow management system aiming at supporting large-scale sophisticated scientific and business processes in complex e-science and e-business applications, by facilitating the resource sharing and computing power of underlying grid infrastructure.
Scientific Workflow Management System A type of workflow management system aiming at supporting
complex scientific processes in many e-science applications such as climate modelling, astronomy data processing. It may or may not be built upon grid infrastructure. Can be cluster or P2P.
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How Are Grid Used
High-performance computing
Collaborative data-sharing
Collaborative design
Drug discovery
Financial modeling
Data center automation
High-energy physics
Life sciences
E-Business
E-ScienceNatural language processing & Data Mining
Utility computing
From www.gridbus.org
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Grid/Scientific Workflows Temporal QOS Framework
Setting Temporal Constraints in Scientific Workflows
SwinDeW-G Grid Workflow Management System Additional Information
Research areas in Workflow Technology Program Data Mining Techniques in Workflow area Optimization Algorithms in Workflow area
Where Are We
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Temporal Verification In reality, complex scientific and business processes are normally
time constrained. Hence, time constraints are often set when they are modelled as grid workflow specifications.
Temporal constraints mainly include: upper bound, lower bound and fixed-time
Upper bound constraint Lower bound constraint Fixed-time constraint
Temporal verification is used to identified any temporal violations so that we can handle them in time.
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Temporal QOS Framework Constraint Setting
Setting temporal constraints according to temporal QOS Specifications
Checkpoint Selection Selecting necessary and sufficient checkpoints to conduct temporal
verification
Temporal Verification Verifying the consistency states at selected checkpoints Temporal Consistency: SC (Strong Consistency), WC (Weak
Consistency), WI (Weak Consistency), SI (Strong Consistency)
Temporal Adjustment Handling temporal violations
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Grid/Scientific Workflows Temporal QOS Framework
Setting Temporal Constraints in Scientific Workflows
SwinDeW-G Grid Workflow Management System Additional Information
Research areas in Workflow Technology Program Data Mining Techniques in Workflow area Optimization Algorithms in Workflow area
Where Are We
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Setting Temporal Constraints Problem Statement
In scientific workflow systems, temporal consistency is critical to ensure the timely completion of workflow instances. To monitor and guarantee the correctness of temporal consistency, temporal constraints are often set and then verified. However, most current work adopts user specified temporal constraints without considering system performance, and hence may result in frequent temporal violations that deteriorate the overall workflow execution effectiveness.
Granularity of temporal constraints Coarse-grained constraints refer to those assigned to the entire
workflow or workflow segments. Fine-grained constraints refer to those assigned to individual activities.
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A Motivating Example
This workflow segment contains 12 activities which are modeled by SPN (Stochastic Petri Net) with additional graphic notations. For simplicity, we denote these activities as X1 to X12. The workflow process structures are composed with four SPN based building blocks, i.e. a choice block for data collection from two radars at different locations (activities X1 to X4), a compound block of parallelism and iteration for data updating and pre-processing (activities X6 to X10), and two sequence blocks for data transferring (activities X5 ,X11 to X12).
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Two Basic Requirements Temporal constraints should be well balanced between user
requirements and system performance. It is common that clients often suggest coarse-grained temporal
constraints based on their own interest while with limited knowledge about the actual performance of workflow systems. Therefore, user specified constraints are normally prone to cause frequent temporal violations.
Temporal constraints should facilitate both overall coarse-grained control and local fine-grained control.
Both coarse-grained temporal constraints and fine-grained temporal constraints should be supported. However, note that coarse-grained temporal constraints and fine-grained temporal constraints are not in a simple relationship of linear culmination and decomposition. Meanwhile, it is impractical to set fine-grained temporal constraints manually for a large amount of activities in scientific workflows.
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A Probabilistic Strategy Probability based temporal consistency
A novel probability based temporal consistency which utilise the weighted joint distribution of workflow acitivity durations is proposed to facilitate setting temporal constraints.
Two assumptions on activity durations Assumption 1: The distribution of activity durations can be obtained
from workflow system logs. Without losing generality, we assume all the activity durations follow the normal distribution model, which can be denoted as N(µ,σ2) .
Assumption 2: The activity durations are independent to each other. Exception handling of assumptions : Using normal transformation and
correlation analysis, or moreover, ignoring first when calculating joint distribution and then added up afterwards.
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Weighted Joint Normal Distribution Joint normal distribution
If there are n independent variables of Xi~N (µi,σi2) and n real numbers θi,
where n is a limited natural number, then the joint distribution of these variables can be obtained with the following formula:
Weighted joint normal distribution For a scientific workflow process SW which consists of n activities, we
denote the activity duration distribution of activity ai as N (µi,σi2) with
(1≤i≤n). Then the weighted joint distribution is defined as:
where wi stands for the weight of activity ai that denotes the choice probability or iteration times associated with the workflow path where ai belongs to.
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Probabilistic Specification of Activity Durations
Maximum Duration, Mean Duration, Minimum Duration The 3σ rule depicts that for any sample comes from
normal distribution model, it has a probability of 99.73% to fall into the range [µ-3 σ, µ+3 σ] of which is a systematic interval of 3 standard deviation around the mean. According to this, in our strategy, we have the following specification of activity durations:Maximum Duration D(ai)= µ+3 σ
Mean Duration M(ai)= µ
Minimum Duration d(ai)= µ-3 σ
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Probability based Temporal Consistency
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Setting Strategy
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Stpe1: Weighted Joint Normal Distribution
Here, to illustrate and facilitate the calculation of the weighted joint distribution, we analyse basic SPN based building blocks, i.e. sequence, iteration, parallelism and choice. These four building blocks consist of basic control flow patterns and are widely used in workflow modelling and structure analysis. Most workflow process models can be easily built by their compositions, and similarly for the weighted joint distribution of most workflow processes.
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Step2: Setting Coarse-grained Constraints
I Want the process be
completed in 48 hours
Let me check the probability
The negotiation process
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Step2: Setting Coarse-grained Constraints
That’s not good, how
about 52 hours
Sir, its 70%, do you agree?
Adjust the constraint
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Step2: Setting Coarse-grained Constraints
Err… how long will it take if I want to have
90%
Then, it increases to
85%
Adjust the probability
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Step2: Setting Coarse-grained Constraints
Ok, that’s the deal! Let’s do
it!
It will take us 54 hours
Negotiation result
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Step2: Setting Coarse-grained Constraints
Ok! But, sir, I need to remind you that this is only a guarantee from statistic sense. If we cannot make it, please
blame the stupid guy who invents the strategy!
Sorry, statistically, no predictions can be 100% sure!
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Step3: Setting Fine-grained Constrains
Setting fine-grained constraints for individual activities Assume the probability gained from the last step is θ% that is
with a normal percentile of λ. Then the fine-grained constraints for individual activities are (µi +λσi).
For example, if the coarse-grained temporal constraints are of 90% consistency, that is a normal percentile of 1.28, then the fine-grained constraint for activity ai with a distribution of N(µI, σi) is (µi +1.28σi).
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Evaluation--Specification
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Setting Results: Coarse-grained Constraint
Negotiation for coarse-grained constraint
6300s
6360s
6390s
6400s
66%
75%
79%
81%
WS~N(6210,2182)
U(WS)=6400, λ=0.87
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Setting Results: Fine-grained Constraint
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Grid/Scientific Workflows Temporal QOS Framework
Setting Temporal Constraints in Scientific Workflows
SwinDeW-G Grid Workflow Management System Additional Information
Research areas in CITR Workflow Technology Program Data Mining Techniques in Workflow area Optimization Algorithms in Workflow area
Where Are We
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SwinDeW-G Grid Workflow System SwinDeW-G stands for Swinburne Decentralised
Workflow for Grid. SwinDeW-G is a peer-to-peer based scientific grid workflow
system running on the SwinGrid (Swinburne service Grid) platform. Swinburne CITR (Centre for Information Technology Research) Node, Swinburne ESR (Enterprise Systems Research laboratory) Node, Swinburne Astrophysics Supercomputer Node, and Beihang CROWN (China R&D environment Over Wide-area Network) Node in China. They are running Linux, GT4 (Globus Toolkit) or CROWN grid toolkit 2.5 where CROWN is an extension of GT4 with more middleware, hence compatible with GT4.
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Grid/Scientific Workflows Temporal QOS Framework
Setting Temporal Constraints in Scientific Workflows
SwinDeW-G Grid Workflow Management System Additional Information
Research areas in CITR Workflow Technology Program Data Mining Techniques in Workflow area Optimization Algorithms in Workflow area
Where Are We
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Research Areas in WT http://www.swin.edu.au/ict/research/citr/wt/research.php Peer-to-peer based, service oriented and grid workflows
SwinDeW-A: SwinDeW with agent enhanced negotiation SwinDeW-B: SwinDeW incorporating BPLE4WS (past) SwinDeW-G: peer-to-peer based service grid workflow system SwinDeW-S: SwinDeW incorporating Web services (past) SwinDeW-V: temporal constraint verification in grid workflows SwinDeW: peer-to-peer based decentralised workflow system (past)
Service-oriented computing SwinGrid - a Swinburne Service Grid Platform which connects Swinburne
CITR nodes and Swinburne Supercomputer with external nodes nationally and internationally, forming a Grid computing environment.
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Recent Publications in WT http://www.ict.swin.edu.au/personal/yyang/Publications.html X. Liu, J. Chen and Y. Yang, A Probabilistic Strategy for Setting Temporal Constraints in Scientific
Workflows, Proc. 6th International Conference on Business Process Management (BPM2008), Sept. 2008 Milan, Italy.
K. Ren, X. Liu, J. Chen, N. Xiao, J. Song, W. Zhang, A QSQL-based efficient Planning Algorithm for fully-automated Service Composition in Dynamic Service Environments, Proc. of IEEE International Conference on Services Computing (SCC2008), Honolulu, Hawaii, USA, July 2008.
J. Chen and Y. Yang, A Taxonomy of Grid Workflow Verification and Validation. Concurrency and Computation: Practice and Experience, Wiley, 20(4):347-360, 2008.
J. Chen and Y. Yang, Adaptive Selection of Necessary and Sufficient Checkpoints for Dynamic Verification of Temporal Constraints in Grid Workflow Systems. ACM Transactions on Autonomous and Adaptive Systems, 2(2):Article6, June 2007.
Q. He, J. Yan, R. Kowalczyk, H. Jin, Y. Yang, Lifetime Service Level Agreement Management with Autonomous Agents for Services Provision. Information Sciences, Elsevier, to appear.
K. Liu, J. Chen, Y. Yang and H. Jin, A Throughput Maximisation Strategy for Scheduling Transaction Intensive Workflows on SwinDeW-G. Concurrency and Computation: Practice and Experience, Wiley, to appear.
J. Yan, Y. Yang and G. K. Raikundalia. SwinDeW - A Peer-to-peer based Decentralized Workflow Management System. IEEE Transactions on Systems, Man and Cybernetics, Part A, 36(5):922-935, 2006.
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Grid/Scientific Workflows Temporal QOS Framework
Setting Temporal Constraints in Scientific Workflows
SwinDeW-G Grid Workflow Management System Additional Information
Research areas in CITR Workflow Technology Program Data Mining Techniques in Workflow area Optimization Algorithms in Workflow area
Where Are We
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Data Mining Techniques in Workflow area
Process Mining Overview
1) basic performance metrics
2) process modelStart
Register order
Prepareshipment
Ship goods
(Re)send bill
Receive paymentContact
customer
Archive order
End
3) organizational model 4) social network
5) performance characteristics
If …then …
6) auditing/security
From www.processmining.org
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Process Mining
Registerorder
Prepareshipment
Shipgoods
Receivepayment
(Re)sendbill
Contactcustomer
Archiveorder
Materialis released
TO itemconfirmed
withoutdifferences
Warehouse/Stores
Transferorderitem
is confirmed
Paymentmust
be effected
PurchaseRequisition
Requirementfor materialhas arisen
Requisitionreleased
for schedulingagreement
schedule/SA release
InvoiceVerification
Purchaserequisitionreleased
for purchaseorder
Inbounddeliveryentered
Goodsreceived
Goodsreceiptposted
GoodsReceipt
Purchaseorder
created
Purchasing
Invoicereceived
Decide To Buy Computer
Choose Model
Save Money
Read Test Reviews
Check Bank Account
[reviews ok]
[bad reviews]
[enough]
Order Machine
Order Screen
Receive Machine
[desktop]
Receive Screen
Set Up And Connect
Plug In And Power On
[laptop]
Open Lid
Choose Operating System
Order Windows
Receive Windows
[windows]
Download Linux
[linux]
Install Operating System
Work Hard
[not enough]
[laptop]
[desktop]
From www.processmining.org
1. Process Discovery2. Conformance testing3. Log based verification
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Other Workflow Mining Topics Successful Termination Prediction.
To choose an activity from a given set of potential activities which is the choice performed in the past that had more frequently led to a desired final configuration.
Identification of Critical Activities. To discover those activities that can be considered critical in the sense that they
are scheduled by the system in every successful execution. Failure/Success Characterization.
By analysing the past experience, a workflow administrator may be interested in knowing which discriminate factors characterize the failure or the success in the executions.
Workflow Optimization. The information collected into the logs of the system can be profitably used to
reason on the “optimality” of workflow executions. Workflow Performance Related Analysis and Prediction
Time series mining used in the prediction of activity durations, setting temporal constraints and dynamic temporal verification
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References on Workflow Mining G. Greco, A. Guzzo, G. Manco and D. Sacca, Mining and Reasoning on
Workflows, IEEE Trans. on Knowledge and Data Engineering, Vol. 17, No. 4, pp.519-534, APRIL 2005.
W.M.P. van der Aalst, B.F. van Dongen, J. Herbst, L. Maruster, G. Schimm, and A.J.M.M. Weijters, Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering, Vol. 47, No. 2, pp.237-267, 2003.
A.K.A. de Medeiros, W.M.P. van der Aalst, and A.J.M.M. Weijters, Workflow Mining: Current Status and Future Directions, CoopIS 2003, volume 2888 of Lecture Notes in Computer Science, pages 389-406. Springer-Verlag, Berlin, 2003.
W.M.P. van der Aalst, H.T. de Beer, and B.F. van Dongen, Process Mining and Verification of Properties: An Approach based on Temporal Logic, CoopIS 2005, volume 3760 of Lecture Notes in Computer Science, pages 130-147. Springer-Verlag, Berlin, 2005.
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Grid/Scientific Workflows Temporal QOS Framework
Setting Temporal Constraints in Scientific Workflows
SwinDeW-G Grid Workflow Management System Additional Information
Research areas in CITR Workflow Technology Program Data Mining Techniques in Workflow area Optimization Algorithms in Workflow area
Where Are We
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Grid Resource Management System
ResourceBroker
Grid Resource Manager
Grid Resource Manager
Grid Resource Manager
Information Services
MonitoringServices
SecurityServices
Core Grid Infrastructure Services
Grid Middlewar
e
PBS LSF …
Resource Resource Resource
Local Resource
Management
Higher-Level Services
User/Application
From http://www.coregrid.net
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Grid Workflow Scheduling
Scheduler
Schedule
tim
e
Job-Queue
Machine 1
Scheduler
Schedule
tim
e
Job-Queue
Machine 2
Scheduler
Schedule
tim
e
Job-Queue
Machine 3
Grid-SchedulerGrid User
From http://www.coregrid.net
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A taxonomy of Grid workflow scheduling algorithms
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GA based Scheduling
Fundamentals for GA based Scheduling1. Encoding/Decoding2. Genetic Operators: Crossover, Mutationand Selection.3. Fitness Evaluation Function
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Others Simulated Annealing Ant Colony
Workflow Rescheduling When any QOS constraints are violated, how to handle those
violations by rescheduling current task list to compensate, e.g. time or budget deficits.
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Summary Grid/Scientific Workflows Temporal Verification and Temporal Adjustment to
Support Temporal QOS Framework Workflow Mining (More than process mining ) Optimization Algorithms for Workflow Scheduling and
Rescheduling
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Useful Links www.swinflow.org
Our work on temporal verification in scientific/grid workflows
http://is.tm.tue.nl/staff/wvdaalst/ Home page of Pro. Wil van der Aalst, Workflow Research
http://www.buyya.com/ Home page of Dr. Rajkumar Buyya, Grid Research
http://www.cs.ucr.edu/~eamonn/ Home page of Eamonn Keogh, Time Series Mining http://www.cs.ucr.edu/~eamonn/time_series_data/ , UCR
Time Series Database
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The End Any questions or comments?