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S.R.MUGUNTHAN SUBMITTED BYASSISTANT PROFESSOR(SG)&HOD/CSE B.POORNIMA

MECSE II YEAR

Meeting Deadlines of Scientific Workflows in

Public Clouds with Tasks Replication

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Outline

• Objective

• General architecture of workflow system

• Issues in workflow

• Literature survey

• Proposed work

• System model

• Existing work

• References

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Objective

• To reduce the impact of performance variation of public cloud

resources in the deadlines of workflow

• Deadline constrained workflow –Its delivers the result before

the deadline meets.

• To minimize the workflow execution time by ignoring deadline

and budgets.

• To use idle time of provisioned resources and budgets surplus to

replicate task.

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General Architecture of Cloud Workflow Systems

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Issues in workflow

• Evaluate the performance of their implementations.

• Extremely valuable for the development and comparison of

workflow management systems.

• Characterizations of five scientific workflows:

Montage: astronomy

CyberShake: earthquake science

Epigenomics: biology

LIGO Inspiral Analysis Workflow: gravitational physics

SIPHT: biology

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Literature Support

• Deadline-constrained workflow scheduling algorithms

for Infrastructure as a Service Clouds(2013)

• Multiple QoS Constrained Scheduling Strategy of

Multiple Workflows for Cloud Computing(2009)

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Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds(2013)

• In this paper use PCP algorithm for the Cloud environment

and propose two workflow scheduling algorithms.

• Which aims to minimize the cost of workflow execution while

meeting a user defined deadline.

• One-phase algorithm which is called IaaS Cloud Partial

Critical Paths (IC-PCP)

• Two-phase algorithm which is called IaaS Cloud Partial

Critical Paths with Deadline Distribution (IC-PCPD)

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The IC-PCP Scheduling Algorithm

1: procedure ScheduleWorkflow(G(T , E), D)

2: determine available computation services

3: add tentry, texit and their corresponding dependencies to G

4: compute EST (ti), EFT (ti) and LFT (ti) for each task in G

5: AST(tentry) ← 0, AST(texit ) ←D

6: mark tentry and texit as assigned

7: call AssignParents(texit )

8: end procedure

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Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds(2013)

Advantage

• The new algorithms consider the main features of the current

commercial Clouds such as on-demand resource provisioning,

homogeneous networks, and the pay-as-you-go pricing model.

Disadvantage

• In accuracy of the estimated execution and transmission

times.

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Multiple QoS Constrained Scheduling Strategy of Multiple Workflows for Cloud Computing(2009)

• In this paper introduce a Multiple QoS Constrained Scheduling

Strategy of Multi-Workflows (MQMW) to address the problem.

• The strategy started at any time and QoS requirements are taken into

account .

• First, cloud provides services for multi-users. So the scheduling

strategy must provide different QoS requirements to different users.

• Second, there will be many workflow instances on the cloud

platform at the same time.

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Advantage

• Used to develop multiple workflow with different QoS

requirements.

• Increase the effect of total makespan and cost of workflow

greatly.

Disadvantage

• QoS constrained not include the parameters of reliability and

availability to the workflow.

Multiple QoS Constrained Scheduling Strategy of Multiple Workflows for Cloud Computing(2009)

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Proposed Work

• To increase the performances variation of the resources on the

softdeadline of workflow application, here use an algorithm

that uses idle time of provisioned resources.

• Its meet and reduces the total execution time of applications as

the budget available for replication increases.

• The workflow model is extensively applied in diverse areas

such as astronomy, bioinformatics, and physics.

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Proposed Work(Cont..)

• Scientific workflows are described as direct acyclic graphs

(DAGs)whose nodes represent tasks and vertices represent

dependencies among tasks.

• To being able to schedule the workflow in such a way that it

completes before its deadline.

• The workflow scheduler needs an estimation and run time of

the applications.

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System Model• A scientific workflow application is modeled as a Direct Acyclic Graph (DAG) G=(T,ET).

• Dependencies are denoted in the form of  Ei,j=(ti,tj),ti,tj€ T.

• Task ti is a parent task of tj and tj is a child task of ti.

• Each workflow G has a soft deadline dl(G) associated to it.

• The problem addressed in this paper consists in the execution of a workflow G in the cloud on or

before dl(G).

• For this problem to be solved, two subproblems have to be solved , namely provisioning and

scheduling.

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Existing work

• Existing research in execution of scientific workflows in

Clouds either try to minimize the workflow execution time

ignoring deadlines and budgets.

• Also focus on the minimization of cost while trying to meet

the application deadline.

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References

• M. Xu, L. Cui, H. Wang, and Y. Bi, ‘‘AMultiple QoS Constrained Scheduling Strategy of Multiple Workflows for Cloud Computing,’’ in Proc. Int’l Symp. ISPA, 2009, pp. 629-634.

• Saeid ,Mahmoud , Dick H.J,” Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds” in proc. Journal In Future Generation Computer System 29(2013) 158-169.

• G. Juve, A. Chervenak, E. Deelman, S. Bharathi, G. Mehta, and K. Vahi, ‘‘Characterizing and Profiling Scientific Workflows,’’ Future Gener. Comput. Syst., vol. 29, no. 3, pp. 682-692, Mar. 2013

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• J. Yu, R. Buyya, and K. Ramamohanarao, ‘‘Workflow Scheduling Algorithms for Grid Computing,’’ in Metaheuristics for Scheduling in Distributed Computing Environments, F. Xhafa and A.Abraham, Eds. New York, NY, USA: Springer-Verlag, 2008

• Y.-K. Kwok and I. Ahmad, ‘‘Static Scheduling Algorithms

for Allocating Directed Task Graphs to Multiprocessors,’’ ACM Comput. Surveys, vol. 31, no. 4, pp. 406-471, Dec. 1999.

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Thank You