The document discusses optimizing VM migration in cloud workflows using ant colony optimization to reduce energy consumption and processing time. It describes components of workflow systems like the workflow engine and scheduler. The methodology section outlines parsing workflows, initially assigning tasks randomly to VMs, then optimizing VM placement through ant colony-inspired migration to minimize time and energy based on task dependencies. The goal is to decentralize failure points and improve scheduling over existing methods.

1. University Institute of Engineering
Department of CSE Chandigarh University
Guided By: Submitted By:
Er. Upma Bansal Jay Govind Chauhan
Assist. Professor(CSE) ME-CSE( SE2)
14MCS1120
Enhance the energy aware utilization of VM
migration in work flow cloud environment with ant
colony optimization

2. University Institute of Engineering
Department of CSE Chandigarh University
Introduction
With rapid growth in Information Technology, more
and more workflow systems are adopting cloud as
their execution environment. It becomes progressively
competitive on how to efficiently manage various
workflows. Workflow is one of the most challenging
problems in cloud.

3. University Institute of Engineering
Department of CSE Chandigarh University
Workflow
Workflow can be automated with software tools that
use business rules to decide when one step has been
completed successfully and the next step can begin.
Some workflow management software programs can
also coordinate dependent relationships between
individual steps, a concept known as workflow
orchestration.

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Department of CSE Chandigarh University
Components
Workflow Mapper is used to import DAG files formatted in
XML (called DAX in WorkflowSim) and other metadata
information such as file size from Workflow Generator.
Workflow Mapper creates a list of tasks and assigns these
tasks to an execution site. A task is a program/activity that a
user would like to execute.
Workflow Engine manages tasks based on their
dependencies between tasks to assure that a task may only be
released when all of its parent tasks have completed
successfully. The Workflow Engine will only release free
tasks to Clustering Engine.

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Department of CSE Chandigarh University
Clustering engine:- Merges tasks into jobs such that the
scheduling overhead is reduced WorkflowSim also perform
task reclustering in a faulty environment with transient
failures. If there are failed tasks returned from Workflow
Scheduler, they are merged again into a new job.

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Department of CSE Chandigarh University
Workflow Scheduler is used to match jobs to a worker node
based on the criteria selected by users. WorkflowSim has
introduced different layers of overheads and failures based
on our prior work, which improves the accuracy of
simulation.
Failure Generator is introduced to inject task/job failures at
each execution site during the simulation. After the execution
of each job, Failure Generator randomly generates task/job
failures based on the distribution and average failure rate that
a user has specified.

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Department of CSE Chandigarh University
Failure Monitor collects failure records (e.g., resource id,
job id, task id) to return these records to Clustering Engine to
adjust the scheduling strategies dynamically.

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Department of CSE Chandigarh University
OPERATIONALASPECT OF
WORKFLOW IN THE CLOUD
• How tasks are structured
• Who performs them
• What their relative order is
• How information flows to support the tasks
• How tasks are being tracked

9. University Institute of Engineering
Department of CSE Chandigarh University
Cloud Computing
Nowadays, Cloud computing is a growing area in
distributed computing that deliver dynamically
adaptable services on demand over the internet
through virtualization of hardware and software.
The biggest advantage of the cloud is its flexibility
to lease and release resources as per the user
requirement.

10. University Institute of Engineering
Department of CSE Chandigarh University
Workflow Scheduling In The Cloud
Scheduling of workflows require huge computation
and communication cost. It is the process of mapping
inert-dependent tasks on the available resources such
that workflow application is able to complete its
execution with user defined quality of service. This
work target random workflow requests overtime, so it
must schedule workflow execution without any
knowledge of future requests.

11. University Institute of Engineering
Department of CSE Chandigarh University
Research gap
a)In previous work, the budget was not considered
and the delay time of workflow task and this is
important for task execution cost.
b)In previous work, workflow was passed according
to levels and ignores the dependency between the
tasks.
c)Moreover, Randomly scheduled the task by FIFO
(First In First Out) and Round Robin and also
never consider the length of the task.

12. University Institute of Engineering
Department of CSE Chandigarh University
Problem Formulation
Consider the budget and deadline of work flow
task in the cloud environment and design the
algorithm fast processing ,decentralized failure
point and VM migration by underloaded virtual
machine.

13. University Institute of Engineering
Department of CSE Chandigarh University
Objectives
• To reduce processing time of Optimize VM
migration by ANT colony optimization
method
• To improve the VM migration on the basis of
energy consumption.
• To improve the failure point by decentralized
with Ant colony method.
• To compare Proposed approach by existing
method

14. University Institute of Engineering
Department of CSE Chandigarh University
Methodology
•Input the work flow in form of .dag file
format.
•Parse the work flow by Topological
sorting.
•After the parsing task input to VM
randomly.
•Optimize the VM by migration with
Antcolony optimization.
•Calculate time and energy of the VM
migration.

15. University Institute of Engineering
Department of CSE Chandigarh University
Tool Used
• NetBeans IDE 8.0.2(JAVA)

16. University Institute of Engineering
Department of CSE Chandigarh University
References
• Sakellariou, R., Zhao, H., Tsiakkouri, E., Dikaiakos,
M.D.Scheduling workflows with budget constraint. In:
Gorlatch, S., Danelutto, M. (eds.) Integrated Research in
GRID Computing, pp. 189–202. Springer (2007).
• Benyi, A., Dombi, J.D., Kertesz, A.: Energy-aware VM
Scheduling in IaaS Clouds using Pliant logic. In: Proceeding
of the 4th International Conference on Cloud Computing and
Services Science pp. 519–526 (2014)
• Verma, A., Kaushal , S.: “Deadline and budget distribution based
cost-time optimization workflow scheduling algorithm for cloud. In:
IJCA Proceeding of International Conference on Recent Advances
and Future Trends in IT”, Patiala, India, pp. 1–4 (2012)

17. University Institute of Engineering
Department of CSE Chandigarh University
• Zheng, W., Sakellariou, R.: Budget-deadline constrained
workflow planning for admission control. J. Grid
Comput.11(4), 633–651 (2013).
• Rodrigo, N.C., Ranjan, R., Anton, B., CesarA.F.D.R.,Buyya,
and R.: Cloudsim: a toolkit for modeling and simulation of
cloud computing environments and evaluation of resource
provisioning algorithms. J. Softw. Pract. Exp. (SPE) 41(1), 23–
50 (2011)