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Task scheduling Survey in Cloud Computing
1. TASK SCHEDULING ALGORITHM-
A SURVEY
(LOAD BALANCING IN CLOUD COMPUTING)
Guided By: Prepared By:
Miss Shilpi Saxena Swati Singh
Assistant Professor M.Tech 2nd Year (CSE)
(Deptt. Of CS/IT)
1
2. CONTENTS
What is Cloud
Introduction to Cloud Computing
Introduction to Load Balancing
Literature Survey
Terminology in Existing Algorithms
Problem Statement
References
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3. WHAT IS CLOUD ?
“The Cloud” is simply the trendy term for a network or
remote servers that can be accessed via an Internet
connection store and manage information.
Deployment Model:
Private Cloud
Public Cloud
Hybrid Cloud
Service Model:
Software as a Service (SaaS)
Platform as a Service (PaaS)
Infrastructure as a Service (IaaS)
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4. DEPLOYMENT MODEL
Private Cloud: Data center architecture owned by a single company. Eg: IBM’s
Blue Cloud, Sun Cloud, Window Azure.
Public Cloud: It is basically the internet. Service provider use internet to make
resource available to general people.
Eg: Gmail, Office 365, Dropbox.
Hybrid Cloud: For instance during peak periods individual applications, or
portion of applications can be migrate to the public cloud.
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5. SERVICE MODEL
Software as a Service (SaaS): Email, Online Games, Facebook.
Platform as a Service (PaaS): Google Compute Engine, Apache Stratos
Infrastructure as a Service (IaaS): Virtual Machine, Storage, Servers
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6. INTRODUCTION TO CLOUD COMPUTING
Cloud computing is Internet-based computing, whereby
shared resource, software, and information are provided to
computers and other devices on demand.
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7. LOAD BALANCING
It is a process of assigning the total load over each node of the
collective system to make resource utilization effective and to
improve the response time of the job.
Goals of load balancing:
1- to improve the performance substantially
2- to have a backup plan in case the system fail even partially
3- to maintain the system stability
7
T1
T2
T3 T4
R1 R2
T1 T3
T2 T4
R1 R2
8. LITERATURE SURVEY
Traditional Min-Min Heuristic Algorithm[1]
Based on the concept of Minimum Completion Time.
It works in two phase:
Expected Completion time is calculated for each task.
Task with overall minimum completion time is scheduled.
Result: No longer waiting of processor for smaller tasks.
Problem: Starvation (For larger tasks).
Unable to balance load when small task scheduled initially.
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9. LITERATURE SURVEY
Traditional Max-Min Heuristic Algorithm[1][2]
Based on the concept of Maximum Completion Time.
It works in two phase:
Expected Completion time is calculated for each task.
Task with overall minimum completion time is scheduled.
Result: No longer waiting of processor for larger tasks.
Good load balancing when smaller task are more in number
then larger task.
Problem: Starvation (For smaller tasks).
Unable to balance load when number of larger task increases.
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10. LITERATURE SURVEY
Resource Awareness Scheduling Algorithm[1][3]
Hybrid algorithm (Max-Min & Min-Min) known as RASA.
Based on the concept of Completion Time of each task.
It works in two phase:
Expected Completion time is calculated for each task.
Max-min and Min-Min is applied alternatively to schedule task.
If number of resources are odd, apply Min-Min strategy first
otherwise Max-Min strategy.
Result: No longer waiting of processor for larger or smaller tasks.
Good load balancing.
Problem: Unable to balance load when number of larger task
increases.
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11. LITERATURE SURVEY
Qos Guided Min-Min Heuristic Algorithm[4]
It adds a constraint of QoS (Quantitative QoS for
measuring bandwidth).
It works in two phase:
Task are classified under high and low bandwidth.
Task required high bandwidth are scheduled first.
Result: Task under lower and higher bandwidth are scheduled
parallel.
Good load balancing.
Problem: Unable balance load when all task satisfy low or high
bandwidth.
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12. LITERATURE SURVEY
Reliable RASA Scheduling Algorithm[7]
It added a constraint of Resource Reliability in existing
Max-Min, Min-Min & RASA Algorithm (Qualitative Qos for
improving makespan and load balance).
It works in three phase:
Resource Reliability is provided in percentage.
Resource manager sets a reliability criteria.
An appropriate resource which fulfil the criteria is selected.
Result: Execution time decreases.
Risk associated with job failure reduced.
Problem: Increase in reliability percentage, increase in execution
time.
Increment in percentage will make only few resource available.
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13. LITERATURE SURVEY
Improved Max-Min Task Scheduling Algorithm[1][5]
Based on the concept of Minimum Completion Time.
It works in two phase:
Task with maximum execution time is selected (Largest Task)
Selected task is scheduled over resource with minimum
completion time (Slowest Resource).
Result: Makespan reduced
Good load balancing.
Problem: Task with largest execution time (greater then the sum of
smaller task) results in the increment of makespan.
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14. LITERATURE SURVEY
Enhanced Max-min Task Scheduling Algorithm[6]
Based on the concept of Minimum Completion Time.
It works in two phase:
Expected Completion time is calculated for each task.
Average or nearest greater then average task is selected.
Selected task is scheduled over resource with minimum
completion time (Slowest Resource).
Result: Makespan reduced.
Better load balancing.
Problem: Unable to balance load when number of larger task
increases.
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15. PROBLEM STATEMENT
An optimized Task Scheduling Algorithm is need to
design which makes:
Proper resource utilization.
Reduces makespan.
Reduces overhead from resource.
Balance proper load over resource.
Increase throughput and system performance.
Better CPU utilization.
Minimize turnaround, waiting and response time.
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17. TOOL USED FOR SIMULATION
CloudSim: It is a simulation tool such as SimJava &
GridSim.
It is used to calculate performance of the environment.
It is the only simulator which support virtualization of
Cloud Computing.
To guarantee Qos in CloudSim, we need to propose an
optimized Task Scheduling Algorithm.
NetBeans: It is a Software Development platform written in
Java.
It allows applications to be developed from a set of
modular software components called modules. 17
19. REFERENCES
[1] S. Devipriya & C. Ramesh, "Improved Max-Min Heuristic Model for Task Scheduling
in Cloud," International Conference on Green Computing, Communication and
Conservation of Energy (ICGCE), 978-1-4673-6126-2/13 2013 IEEE.
[2] Etminani .K, and Naghibzadeh. M, "A Min-min Max-min Selective Algorithm for
Grid Task Scheduling,“ Third IEEE/IFIP International Conference on Internet,
Uzbekistan, 2007
[3] SaeedParsa and Reza Entezari-Maleki, "RASA: A New Grid Task Scheduling
Algorithm," International Journal of Digital Content Technology and its
Applications,Vol. 3, pp. 91-99, 2009.
[4] He. X, X-He Sun, and Laszewski. G.V, "QoS Guided Min-min Heuristic for Grid
Task Scheduling," Journal of Computer Science and Technology, Vol. 18, pp. 442-
451, 2003
[5] O. M. Elzeki, M. Z. Reshad, M. A. Elsoud, "Improved Max-Min Algorithm in Cloud
Computing," International Journal of Computer Applications (0975 – 8887), Volume
50 – No.12, July 2012.
[6] Upendra Bhoi1, Purvi N. Ramanuj, “Enhanced Max-min Task Scheduling Algorithm
in Cloud Computing,” International Journal of Application or Innovation in
Engineering & Management on Volume 2, 2013. 19
20. REFERENCES
[7] Sunilkumar Nakum, C. Ramakrishna, Amit Lathigara, “Reliable RASA Scheduling
Algorithm for Grid Environment,” 2014 IEEE International Conference on Computer
Communication and Systems(ICCCS '14), Feb 20-21, 2014.
[8] Sung-Min Jung, Nam-Uk Kim, Tai-Myoung Chung, “Applying Scheduling
Algorithms with QoS in the Cloud Computing,” 978-1-4799-0604-8/13 2013 IEEE.
[9] Yash P. Dave, Avani S. Shelat, Dhara S. Patel, Rutvij H. Jhaveri, “Various Job
Scheduling Algorithms in Cloud Computing: A Survey,” ICICES2014 - S. A.
Engineering College, Chennai, ISBN No.978-1-4799-3834-6/14 2014 IEEE
20
Deployment Model:
Private Cloud: IBM’s Blue Cloud, Sun Cloud, Window Azure.
Public Cloud: Gmail, Office 365, Dropbox.
Hybrid Cloud: Private + Public
Service Model:
Software as a Service (SaaS): Email, Online Games, Virtual Desktop, Google App, Twitter, Facebook.
Platform as a Service (PaaS): Elastic Beanstalk, Window Azure, Google Compute Engine, Apache Stratos.
Infrastructure as a Service (IaaS): Virtual Machine, Server, Storage, Load Balancer, Network, Firewall, IP addresses, Processing Power.
Services provided by a public cloud are offered over the Internet and are owned and operated by a cloud provider. Some examples include services aimed at the general public, such as online photo storage services, e-mail services, or social networking sites. However, services for enterprises can also be offered in a public cloud.
In a private cloud, the cloud infrastructure is operated solely for a specific organization, and is managed by the organization or a third party.
In a community cloud, the service is shared by several organizations and made available only to those groups. The infrastructure may be owned and operated by the organizations or by a cloud service provider.
A hybrid cloud is a combination of different methods of resource pooling (for example, combining public and community clouds).
There are two types of QoS: Quantitative QoS and Qualitative QoS. Quantitative QoS is having standard measurements for example bandwidth can be
measured in Mbps & Qualitative in terms of makespan, execution time etc.