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This document summarizes and compares various scheduling algorithms used in cloud computing environments. It begins with an introduction to cloud computing and the need for scheduling algorithms in cloud environments. It then describes several existing scheduling algorithms, including compromised-time-cost scheduling, particle swarm optimization-based heuristic, improved cost-based algorithm, resource-aware scheduling, innovative transaction intensive cost-constraint scheduling, scalable heterogeneous earliest-finish-time algorithm, and multiple QoS constrained scheduling strategy of multi-workflows. These algorithms aim to optimize metrics such as execution time, cost, deadline, load balancing, and quality of service. The document concludes by comparing the different scheduling strategies.
![International Journal of Engineering Inventions
ISSN: 2278-7461, www.ijeijournal.com
Volume 1, Issue 2 (September 2012) PP: 36-39
A Survey of Various Scheduling Algorithms in Cloud
Environment
Sujit Tilak 1, Prof. Dipti Patil 2,
1,2
Department of COMPUTER, PIIT, New Panvel, Maharashtra, India.
Abstract––Cloud computing environments provide scalability for applications by providing virtualized resources
dynamically. Cloud computing is built on the base of distributed computing, grid computing and virtualization. User
applications may need large data retrieval very often and the system efficiency may degrade when these applications are
scheduled taking into account only the ‘execution time’. In addition to optimizing system efficiency, the cost arising from
data transfers between resources as well as execution costs must also be taken into account while scheduling. Moving
applications to a cloud computing environment triggers the need of scheduling as it enables the utilization of various
cloud services to facilitate execution.
Keywords––Cloud computing, Scheduling, Virtualization
I. INTRODUCTION
Cloud computing is an extension of parallel computing, distributed computing and grid computing. It provides
secure, quick, convenient data storage and computing power with the help of internet. Virtualization, distribution and
dynamic extendibility are the basic characteristics of cloud computing [1]. Now days most software and hardware have
provided support to virtualization. We can virtualize many factors such as IT resource, software, hardware, operating system
and net storage, and manage them in the cloud computing platform; every environment has nothing to do with the physical
platform.
To make effective use of the tremendous capabilities of the cloud, efficient scheduling algorithms are required.
These scheduling algorithms are commonly applied by cloud resource manager to optimally dispatch tasks to the cloud
resources. There are relatively a large number of scheduling algorithms to minimize the total completion time of the tasks in
distributed systems [2]. Actually, these algorithms try to minimize the overall completion time of the tasks by finding the
most suitable resources to be allocated to the tasks. It should be noticed that minimizing the overall completion time
of the tasks does not necessarily result in the minimization of execution time of each individual task.
Fig. 1 overview of cloud computing
The objective of this paper is to be focus on various scheduling algorithms. The rest of the paper is organized as
follows. Section 2 presents the need of scheduling in cloud. Section 3 presents various existing scheduling algorithms and
section 4 concludes the paper with a summary of our contributions.
II. NEED OF SCHEDULING IN CLOUD
The primary benefit of moving to Clouds is application scalability. Unlike Grids, scalability of Cloud resources
allows real-time provisioning of resources to meet application requirements. Cloud services like compute, storage and
36](https://image.slidesharecdn.com/f0123639-121003032559-phpapp01/85/call-for-papers-research-paper-publishing-where-to-publish-research-paper-journal-publishing-how-to-publish-research-paper-Call-For-research-paper-international-journal-publishing-a-paper-IJEI-call-for-papers-2012-journal-of-science-and-technolog-1-320.jpg)
![A Survey of Various Scheduling Algorithms in Cloud Environment
bandwidth resources are available at substantially lower costs. Usually tasks are scheduled by user requirements. New
scheduling strategies need to be proposed to overcome the problems posed by network properties between user and
resources. New scheduling strategies may use some of the conventional scheduling concepts to merge them together with
some network aware strategies to provide solutions for better and more efficient job scheduling [1]. Usually tasks are
scheduled by user requirements.
Initially, scheduling algorithms were being implemented in grids [2] [8]. Due to the reduced performance faced in
grids, now there is a need to implement scheduling in cloud. The primary benefit of moving to Clouds is application
scalability. Unlike Grids, scalability of Cloud resources allows real-time provisioning of resources to meet application
requirements. This enables workflow management systems to readily meet Quality of- Service (QoS) requirements of
applications [7], as opposed to the traditional approach that required advance reservation of resources in global multi-user
Grid environments. Cloud services like compute, storage and bandwidth resources are available at substantially lower costs.
Cloud applications often require very complex execution environments .These environments are difficult to create on grid
resources [2]. In addition, each grid site has a different configuration, which results in extra effort each time an application
needs to be ported to a new site. Virtual machines allow the application developer to create a fully customized, portable
execution environment configured specifically for their application.
Traditional way for scheduling in cloud computing tended to use the direct tasks of users as the overhead
application base. The problem is that there may be no relationship between the overhead application base and the way that
different tasks cause overhead costs of resources in cloud systems [1]. For large number of simple tasks this increases the
cost and the cost is decreased if we have small number of complex tasks.
III. EXISTING SCHEDULING ALGORITHMS
The Following scheduling algorithms are currently prevalent in clouds.
3.1 A Compromised-Time-Cost Scheduling Algorithm:Ke Liu, Hai Jin, Jinjun Chen, Xiao Liu, Dong Yuan, Yun Yang [2]
presented a novel compromised-time-cost scheduling algorithm which considers the characteristics of cloud computing to
accommodate instance-intensive cost-constrained workflows by compromising execution time and cost with user input
enabled on the fly. The simulation has demonstrated that CTC (compromised-time-cost) algorithm can achieve lower cost
than others while meeting the user-designated deadline or reduce the mean execution time than others within the user-
designated execution cost. The tool used for simulation is SwinDeW-C (Swinburne Decentralised Workflow for Cloud).
3.2 A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications: Suraj Pandey, LinlinWu,
Siddeswara Mayura Guru, Rajkumar Buyya [3] presented a particle swarm optimization (PSO) based heuristic to schedule
applications to cloud resources that takes into account both computation cost and data transmission cost. It is used for
workflow application by varying its computation and communication costs. The experimental result shows that PSO can
achieve cost savings and good distribution of workload onto resources.
3.3 Improved Cost-Based Algorithm for Task Scheduling: Mrs.S.Selvarani, Dr.G.Sudha Sadhasivam [1] proposed an
improved cost-based scheduling algorithm for making efficient mapping of tasks to available resources in cloud. The
improvisation of traditional activity based costing is proposed by new task scheduling strategy for cloud environment where
there may be no relation between the overhead application base and the way that different tasks cause overhead cost of
resources in cloud. This scheduling algorithm divides all user tasks depending on priority of each task into three different
lists. This scheduling algorithm measures both resource cost and computation performance, it also Improves the
computation/communication ratio.
3.4 Resource-Aware-Scheduling algorithm (RASA): Saeed Parsa and Reza Entezari-Maleki [2] proposed a new task
scheduling algorithm RASA. It is composed of two traditional scheduling algorithms; Max-min and Min-min. RASA uses
the advantages of Max-min and Min-min algorithms and covers their disadvantages. Though the deadline of each task,
arriving rate of the tasks, cost of the task execution on each of the resource, cost of the communication are not considered.
The experimental results show that RASA is outperforms the existing scheduling algorithms in large scale distributed
systems.
3.5 Innovative transaction intensive cost-constraint scheduling algorithm: Yun Yang, Ke Liu, Jinjun Chen [5] proposed a
scheduling algorithm which takes cost and time. The simulation has demonstrated that this algorithm can achieve lower cost
than others while meeting the user designated deadline.
3.6 Scalable Heterogeneous Earliest-Finish-Time Algorithm (SHEFT): Cui Lin, Shiyong Lu [6] proposed an SHEFT
workflow scheduling algorithm to schedule a workflow elastically on a Cloud computing environment. The experimental
results show that SHEFT not only outperforms several representative workflow scheduling algorithms in optimizing
workflow execution time, but also enables resources to scale elastically at runtime.
3.7 Multiple QoS Constrained Scheduling Strategy of Multi-Workflows (MQMW): Meng Xu, Lizhen Cui, Haiyang Wang,
Yanbing Bi [7] worked on multiple workflows and multiple QoS.They has a strategy implemented for multiple workflow
management system with multiple QoS. The scheduling access rate is increased by using this strategy. This strategy
minimizes the make span and cost of workflows for cloud computing platform.
The following table summarizes above scheduling strategies on scheduling method, parameters, other factors, the
environment of application of strategy and tool used for experimental purpose.
37](https://image.slidesharecdn.com/f0123639-121003032559-phpapp01/85/call-for-papers-research-paper-publishing-where-to-publish-research-paper-journal-publishing-how-to-publish-research-paper-Call-For-research-paper-international-journal-publishing-a-paper-IJEI-call-for-papers-2012-journal-of-science-and-technolog-2-320.jpg)
![A Survey of Various Scheduling Algorithms in Cloud Environment
Comparison between Existing Scheduling Algorithms
Scheduling Schedulin Scheduling Scheduling Finding Environme Tool
Algorithm g Method Parameters factors s nt s
A
compromised Batch Cost and An array 1. It is used to reduce Cloud SwinDeW-C
-Time-Cost mode time of cost and cost Environment
Scheduling workflow
Algorithm [3] instances
Dependency Resource Group of 1. it is used for three Cloud Amazon
A Particle mode utilization, tasks times cost savings as Environment EC2
Swarm time compared to BRS
Optimization- 2.It is used for good
based distribution of workload
Heuristic for onto resources
Scheduling [4]
Improved Batch Cost, Unscheduled 1.Measures both Cloud Cloud
cost-based Mode performan task groups resource cost and Environment Sim
algorithm for ce computation
task performance
scheduling [1] 2. Improves the
computation/communica
tion ratio
RASA Batch make Grouped 1.It is used to reduce Grid GridSi
Workflow mode span tasks make span Environment m
scheduling [2]
Innovative Batch Execution Workflow 1.To minimize the cost Cloud SwinDeW-C
transaction Mode cost and with large under certain user- Environment
intensive cost- time number of designated
constraint instances Deadlines.
scheduling 2. Enables the
algorithm [5] compromises of
execution cost and time.
SHEFT Dependency Execution Group of 1. It is used for Cloud CloudSim
workflow Mode time, tasks optimizing workflow Environment
scheduling scalability execution time.
algorithm [6] 2. It also enables
resources to scale
elastically during
workflow execution.
Multiple QoS Batch/de Scheduling Multiple 1. It is used to schedule Cloud CloudSim
Constrained pendenc success Workflow the workflow Environment
Scheduling y mode rate,cost,time, s dynamically.
Strategy of make span 2. It is used to
Multi- minimize the execution
Workflows [7] time and cost
IV. CONCLUSION
Scheduling is one of the key issues in the management of application execution in cloud environment. In this
paper, we have surveyed the various existing scheduling algorithms in cloud computing and tabulated their various
parameters along with tools and so on. we also noticed that disk space management is critical in virtual environments. When
a virtual image is created, the size of the disk is fixed. Having a too small initial virtual disk size can adversely affect the
execution of the application. Existing scheduling algorithms does not consider reliability and availability. Therefore there is
a need to implement a scheduling algorithm that can improve the availability and reliability in cloud environment.
38](https://image.slidesharecdn.com/f0123639-121003032559-phpapp01/85/call-for-papers-research-paper-publishing-where-to-publish-research-paper-journal-publishing-how-to-publish-research-paper-Call-For-research-paper-international-journal-publishing-a-paper-IJEI-call-for-papers-2012-journal-of-science-and-technolog-3-320.jpg)
call for papers, research paper publishing, where to publish research paper, journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJEI, call for papers 2012,journal of science and technolog
- 1. International Journal of Engineering Inventions ISSN: 2278-7461, www.ijeijournal.com Volume 1, Issue 2 (September 2012) PP: 36-39 A Survey of Various Scheduling Algorithms in Cloud Environment Sujit Tilak 1, Prof. Dipti Patil 2, 1,2 Department of COMPUTER, PIIT, New Panvel, Maharashtra, India. Abstract––Cloud computing environments provide scalability for applications by providing virtualized resources dynamically. Cloud computing is built on the base of distributed computing, grid computing and virtualization. User applications may need large data retrieval very often and the system efficiency may degrade when these applications are scheduled taking into account only the ‘execution time’. In addition to optimizing system efficiency, the cost arising from data transfers between resources as well as execution costs must also be taken into account while scheduling. Moving applications to a cloud computing environment triggers the need of scheduling as it enables the utilization of various cloud services to facilitate execution. Keywords––Cloud computing, Scheduling, Virtualization I. INTRODUCTION Cloud computing is an extension of parallel computing, distributed computing and grid computing. It provides secure, quick, convenient data storage and computing power with the help of internet. Virtualization, distribution and dynamic extendibility are the basic characteristics of cloud computing [1]. Now days most software and hardware have provided support to virtualization. We can virtualize many factors such as IT resource, software, hardware, operating system and net storage, and manage them in the cloud computing platform; every environment has nothing to do with the physical platform. To make effective use of the tremendous capabilities of the cloud, efficient scheduling algorithms are required. These scheduling algorithms are commonly applied by cloud resource manager to optimally dispatch tasks to the cloud resources. There are relatively a large number of scheduling algorithms to minimize the total completion time of the tasks in distributed systems [2]. Actually, these algorithms try to minimize the overall completion time of the tasks by finding the most suitable resources to be allocated to the tasks. It should be noticed that minimizing the overall completion time of the tasks does not necessarily result in the minimization of execution time of each individual task. Fig. 1 overview of cloud computing The objective of this paper is to be focus on various scheduling algorithms. The rest of the paper is organized as follows. Section 2 presents the need of scheduling in cloud. Section 3 presents various existing scheduling algorithms and section 4 concludes the paper with a summary of our contributions. II. NEED OF SCHEDULING IN CLOUD The primary benefit of moving to Clouds is application scalability. Unlike Grids, scalability of Cloud resources allows real-time provisioning of resources to meet application requirements. Cloud services like compute, storage and 36
- 2. A Survey of Various Scheduling Algorithms in Cloud Environment bandwidth resources are available at substantially lower costs. Usually tasks are scheduled by user requirements. New scheduling strategies need to be proposed to overcome the problems posed by network properties between user and resources. New scheduling strategies may use some of the conventional scheduling concepts to merge them together with some network aware strategies to provide solutions for better and more efficient job scheduling [1]. Usually tasks are scheduled by user requirements. Initially, scheduling algorithms were being implemented in grids [2] [8]. Due to the reduced performance faced in grids, now there is a need to implement scheduling in cloud. The primary benefit of moving to Clouds is application scalability. Unlike Grids, scalability of Cloud resources allows real-time provisioning of resources to meet application requirements. This enables workflow management systems to readily meet Quality of- Service (QoS) requirements of applications [7], as opposed to the traditional approach that required advance reservation of resources in global multi-user Grid environments. Cloud services like compute, storage and bandwidth resources are available at substantially lower costs. Cloud applications often require very complex execution environments .These environments are difficult to create on grid resources [2]. In addition, each grid site has a different configuration, which results in extra effort each time an application needs to be ported to a new site. Virtual machines allow the application developer to create a fully customized, portable execution environment configured specifically for their application. Traditional way for scheduling in cloud computing tended to use the direct tasks of users as the overhead application base. The problem is that there may be no relationship between the overhead application base and the way that different tasks cause overhead costs of resources in cloud systems [1]. For large number of simple tasks this increases the cost and the cost is decreased if we have small number of complex tasks. III. EXISTING SCHEDULING ALGORITHMS The Following scheduling algorithms are currently prevalent in clouds. 3.1 A Compromised-Time-Cost Scheduling Algorithm:Ke Liu, Hai Jin, Jinjun Chen, Xiao Liu, Dong Yuan, Yun Yang [2] presented a novel compromised-time-cost scheduling algorithm which considers the characteristics of cloud computing to accommodate instance-intensive cost-constrained workflows by compromising execution time and cost with user input enabled on the fly. The simulation has demonstrated that CTC (compromised-time-cost) algorithm can achieve lower cost than others while meeting the user-designated deadline or reduce the mean execution time than others within the user- designated execution cost. The tool used for simulation is SwinDeW-C (Swinburne Decentralised Workflow for Cloud). 3.2 A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications: Suraj Pandey, LinlinWu, Siddeswara Mayura Guru, Rajkumar Buyya [3] presented a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. It is used for workflow application by varying its computation and communication costs. The experimental result shows that PSO can achieve cost savings and good distribution of workload onto resources. 3.3 Improved Cost-Based Algorithm for Task Scheduling: Mrs.S.Selvarani, Dr.G.Sudha Sadhasivam [1] proposed an improved cost-based scheduling algorithm for making efficient mapping of tasks to available resources in cloud. The improvisation of traditional activity based costing is proposed by new task scheduling strategy for cloud environment where there may be no relation between the overhead application base and the way that different tasks cause overhead cost of resources in cloud. This scheduling algorithm divides all user tasks depending on priority of each task into three different lists. This scheduling algorithm measures both resource cost and computation performance, it also Improves the computation/communication ratio. 3.4 Resource-Aware-Scheduling algorithm (RASA): Saeed Parsa and Reza Entezari-Maleki [2] proposed a new task scheduling algorithm RASA. It is composed of two traditional scheduling algorithms; Max-min and Min-min. RASA uses the advantages of Max-min and Min-min algorithms and covers their disadvantages. Though the deadline of each task, arriving rate of the tasks, cost of the task execution on each of the resource, cost of the communication are not considered. The experimental results show that RASA is outperforms the existing scheduling algorithms in large scale distributed systems. 3.5 Innovative transaction intensive cost-constraint scheduling algorithm: Yun Yang, Ke Liu, Jinjun Chen [5] proposed a scheduling algorithm which takes cost and time. The simulation has demonstrated that this algorithm can achieve lower cost than others while meeting the user designated deadline. 3.6 Scalable Heterogeneous Earliest-Finish-Time Algorithm (SHEFT): Cui Lin, Shiyong Lu [6] proposed an SHEFT workflow scheduling algorithm to schedule a workflow elastically on a Cloud computing environment. The experimental results show that SHEFT not only outperforms several representative workflow scheduling algorithms in optimizing workflow execution time, but also enables resources to scale elastically at runtime. 3.7 Multiple QoS Constrained Scheduling Strategy of Multi-Workflows (MQMW): Meng Xu, Lizhen Cui, Haiyang Wang, Yanbing Bi [7] worked on multiple workflows and multiple QoS.They has a strategy implemented for multiple workflow management system with multiple QoS. The scheduling access rate is increased by using this strategy. This strategy minimizes the make span and cost of workflows for cloud computing platform. The following table summarizes above scheduling strategies on scheduling method, parameters, other factors, the environment of application of strategy and tool used for experimental purpose. 37
- 3. A Survey of Various Scheduling Algorithms in Cloud Environment Comparison between Existing Scheduling Algorithms Scheduling Schedulin Scheduling Scheduling Finding Environme Tool Algorithm g Method Parameters factors s nt s A compromised Batch Cost and An array 1. It is used to reduce Cloud SwinDeW-C -Time-Cost mode time of cost and cost Environment Scheduling workflow Algorithm [3] instances Dependency Resource Group of 1. it is used for three Cloud Amazon A Particle mode utilization, tasks times cost savings as Environment EC2 Swarm time compared to BRS Optimization- 2.It is used for good based distribution of workload Heuristic for onto resources Scheduling [4] Improved Batch Cost, Unscheduled 1.Measures both Cloud Cloud cost-based Mode performan task groups resource cost and Environment Sim algorithm for ce computation task performance scheduling [1] 2. Improves the computation/communica tion ratio RASA Batch make Grouped 1.It is used to reduce Grid GridSi Workflow mode span tasks make span Environment m scheduling [2] Innovative Batch Execution Workflow 1.To minimize the cost Cloud SwinDeW-C transaction Mode cost and with large under certain user- Environment intensive cost- time number of designated constraint instances Deadlines. scheduling 2. Enables the algorithm [5] compromises of execution cost and time. SHEFT Dependency Execution Group of 1. It is used for Cloud CloudSim workflow Mode time, tasks optimizing workflow Environment scheduling scalability execution time. algorithm [6] 2. It also enables resources to scale elastically during workflow execution. Multiple QoS Batch/de Scheduling Multiple 1. It is used to schedule Cloud CloudSim Constrained pendenc success Workflow the workflow Environment Scheduling y mode rate,cost,time, s dynamically. Strategy of make span 2. It is used to Multi- minimize the execution Workflows [7] time and cost IV. CONCLUSION Scheduling is one of the key issues in the management of application execution in cloud environment. In this paper, we have surveyed the various existing scheduling algorithms in cloud computing and tabulated their various parameters along with tools and so on. we also noticed that disk space management is critical in virtual environments. When a virtual image is created, the size of the disk is fixed. Having a too small initial virtual disk size can adversely affect the execution of the application. Existing scheduling algorithms does not consider reliability and availability. Therefore there is a need to implement a scheduling algorithm that can improve the availability and reliability in cloud environment. 38
- 4. A Survey of Various Scheduling Algorithms in Cloud Environment REFERENCES 1. Mrs.S.Selvarani1; Dr.G.Sudha Sadhasivam, improved cost-based algorithm for task scheduling in Cloud computing ,IEEE 2010. 2. Saeed Parsa and Reza Entezari-Maleki,” RASA: A New Task Scheduling Algorithm in Grid Environment” in World Applied Sciences Journal 7 (Special Issue of Computer & IT): 152-160, 2009.Berry M. W., Dumais S. T., O’Brien G. W. Using linear algebra for intelligent information retrieval, SIAM Review, 1995, 37, pp. 573-595. 3. K. Liu; Y. Yang; J. Chen, X. Liu; D. Yuan; H. Jin, A Compromised-Time- Cost Scheduling Algorithm in SwinDeW-C for Instance-intensive Cost-Constrained Workflows on Cloud Computing Platform, International Journal of High Performance Computing Applications, vol.24 no.4 445-456,May,2010. 4. Suraj Pandey1; LinlinWu1; Siddeswara Mayura Guru; Rajkumar Buyya, A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments. 5. Y. Yang, K. Liu, J. Chen, X. Liu, D. Yuan and H. Jin, An Algorithm in SwinDeW-C for Scheduling Transaction- Intensive Cost-Constrained Cloud Workflows, Proc. of 4th IEEE International Conference on e-Science, 374-375, Indianapolis, USA, December 2008. 6. Cui Lin, Shiyong Lu,” Scheduling ScientificWorkflows Elastically for Cloud Computing” in IEEE 4th International Conference on Cloud Computing, 2011. 7. Meng Xu, Lizhen Cui, Haiyang Wang, Yanbing Bi, “A Multiple QoS Constrained Scheduling Strategy of Multiple Workflows for Cloud Computing”, in 2009 IEEE International Symposium on Parallel and Distributed Processing. 8. Nithiapidary Muthuvelu, Junyang Liu, Nay Lin Soe, Srikumar Venugopal, Anthony Sulistio and Rajkumar Buyya. “A Dynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids”,in Australasian Workshop on Grid Computing and e-Research (AusGrid2005), Newcastle, Australia. Conferences in Research and Practice in Information Technology, Vol. 44. 39