A SURVEY ON TRADITIONAL AND EARLIER JOB SCHEDULING IN CLOUD ENVIRONMENT

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1 Volume 120 No , ISSN: (on-line version) url: A SURVEY ON TRADITIONAL AND EARLIER JOB SCHEDULING IN CLOUD ENVIRONMENT Mrs. T.K. Lakshmi 1, Dr. N. Sudhakar Reddy 2, Mrs. Suneetha 3, Prof. R. Mariappan 4, 1 Ass. Professor, IT, 2 Principal, SVCE, 3 Ass. Professor, IT, 4 Professor, ECE, SVCE, 1 lucky.its@gmail.com, 2 sudhakar.n@svcolleges.edu.in, 3 suneetha.p@svcolleges.edu.in, 4 prof.mariappan.r@svcolleges.edu.in June 11, 2018 Abstract In the current trend, Cloud computing became a hot area of research and particularly job scheduling is one among the areas where a lot of research has been already done and still there is a need of study as there is an increase of users in cloud. As the resources are to be shared among multiple jobs, proper scheduling algorithms are necessary for efficient utilization of cloud services. Many scheduling algorithms as well as heuristic scheduling are in existence beginning from traditional algorithms like FCFS, Round robin, Priority based scheduling, optimal, Min Min, Max Min, Most fit task scheduling to advanced algorithms like Ant colony optimization, Berger model, Particle swarm optimization and many more which focuses on minimum completion time, response time, high through put, effective processor utilization and other parameters. In this paper, an attempt was made to study different

2 scheduling algorithms, how it works and what parameters it considers for scheduling and a comparison of different methods was tabularized based on the parameter considered in each algorithm in cloud environment. Keywords:job scheduling, cloud, task scheduling, heuristic scheduling. 1 INTRODUCTION Cloud computing means on demand delivery of IT resources via the internet with pay-as-you-go pricing. It provides a solution of IT infrastructure in low cost. Now a days many software companies like Google, Amazon, IBM, Microsoft, Yahoo provides many cloud based services, large number of resources through internet. A cloud users task gets executed by using these utilities provided by cloud which in turn focuses on minimum completion time, response time, maximum utilization of resources and reduced make span and many more parameters. Therefore there is a need of good, efficient, optimized scheduling approach. Cloud services are similar to usage of public utilities like gas, electricity and water. We need not to generate them; we pay for what we use to providers. Therefore it is very useful for small organizations that can not afford huge investment to obtain services from cloud. 2 SCHEDULING Scheduling is a process of allocating jobs/tasks to processors for effective utilization of resources. There are different scheduling policies: [1] [2] Static: All information about jobs and available resources are known in advance, after which job is assigned to resources. Dynamic: Jobs are allocated to resources at run time by scheduler. Online: Resources are allocated to a job the moment it arrives, on the basis of available resources at that moment. Batch: The scheduler allocates resources to batch of jobs over a period of time and then their execution starts after a specific time

3 interval.[3][15] Preemptive: A job can be interrupted during its execution and a job can be migrated to another resource other than its originally allocated resources, available at that moment. Non Preemptive: Resources are not allowed to be re allocated until the currently running jobs are finished. Scheduling can also be classified as Batch mode heuristic scheduling algorithm (BMHA) in which jobs are queued and collected in to a set when they arrive in the system in which scheduling algorithm start after some fixed period of time. Online mode heuristic scheduling algorithms (OMHSA), jobs are scheduled when they arrive in the system. Since cloud is heterogeneous, speed of each processor varies quickly. The following are few parameters used during the scheduling process: Through put: Amount of work done in a unit of time Response time: When a request is made for a service, the time it takes to reply or respond first time. Turnaround time: The sum total of waiting time & execution time. 2.1 SCHEDULING ALGORITHMS FCFS Task allocation simulated in CloudSim [3] in which first arrived job is serviced first. The different entities to implement FCFS in CloudSim are: FCFS, FCFS Broker, Data Center creater, VMs creater, cloudlet creater. FCFS: It builds the data centers, VMs, and cloudlets or tasks. Once built, FCFS Broker receives VMs and data centers FCFS BROKER: This is a datacenter broker which schedules tasks to VMs on the basis of FCFS policy. DATA CENTER CREATER: It builts Data Centers. VMs CREATER: It builts the required number of VMs asked by the user CLOUDLET CREATER: It builts the given number of tasks. That is how tasks allocation to VMs in FCFS policy is done

4 2.1.2 Round Robin algorithm (RR) In this algorithm, processes are dispatched in FIFO manner and a limited amount of CPU time called time slice is provided. If the process is not completed in the assigned time then the CPU is allocated to job in the waiting queue and the cycle goes on. Min- Min Algorithm:In this algorithm, small jobs are run first, which results delay the longer jobs for longer period of time. Max-Min Algorithm :Here larger jobs are run first resulting delay of smaller jobs for longer period of time Most fit task scheduling Algorithm In this algorithm, job which is best fit in the queue is executed first SJF Small jobs are executed first which leads to starvation for longer jobs Scheduling and workflow management based on priority This category discusses two set of algorithms, first one follows prioritize by organizing, streamlining, economizing and contributing in which decision making is done based on priority and importance. This algorithm mainly focuses on two parameters, urgency and importance which has a score on the scale of 10 given by cluster member of cloud and resource manager respectively based on level of severity. Depending upon this score the task gets serviced as follows: Urgency Score Importance Score Result High High alert is sent immediately High Low alert sent on high priority basis when resources are free. Low High sent after emptying. Low Low When resources are free, job queue is empty, is sent on lower priority basis The second algorithm of task scheduling follows Pareto analysis in which 80% of tasks consume 20% of disposable time and remaining 20% of tasks takes 80% of time. This principle is used to sort tasks in two parts and tasks fall in first part has higher importance. The algorithm has 2 steps to be done, first is

5 task ordering in which priority/urgency score is obtained and secondly, resource allocation in which time t is calculated where t= (time for executing job / time estimated) % if the value of t is 100, then it can be concluded that there is a proper usage of resources high score importance is given. If t is less than 80, the importance score is considered as low. If t is less than 100 and t is greater than 80, the cloud managers resources are said to be overloaded. According to the principle of Paletos 80:20, a high importance score is generated. If t is greater than 100, the cloud manager resources are said to be under utilization, for which a high importance score is given by the cloud. Again based on score obtained, cloud service is provided Improved task scheduling in cloud Sumit Arora, Sami Anand proposed an algorithm according to which all the tasks are arranged in the increasing order of their lengths in cloud. Groups are created according to the available resources for sorted tasks. The Mid value, Mean and standard deviation(sd) is calculated.. If SD is greater than the value at mid of the total numbers of tasks then tasks in groups with the maximum length will be allotted to resources in descending MIPS else the tasks with the minimum length will be allotted to the resources with the resource in increased order of MIPS Customer facilitated cost based scheduling (CFCBS) When make span is taken in to consideration, there are many list scheduling algorithms of which HEFT is popular for heterogeneous systems. According to CFCBS method, the speed of CPU is responsible for calculating the cost of using the virtual and the price for executing task is calculated as P (i, j) = (W (i, j) * cost of VirtualMachine j)/minute. From which monetary cost is obtained RASA (Resource aware scheduling algorithm) It works based on the scalability characteristics of grid resources and distribution.[12] The benefits of Min-min, Max-min are taken

6 in to consideration and attempted to cover disadvantages in them. As in diagram given below, the method computes the time of completion of tasks by considering all the available resources. Later the Max Min and Min Min are applied one by one, for smaller tasks Min-min is used and for larger tasks it uses Max-min to avoid the large tasks execution time delays [13]. This method results in a matrix M. Mij is the time of finishing task with Ti task and Rj resources Verma etal [14] suggested an algorithm focusing on Deadline and Budget distribution-based Cost-Time Optimization (DBD-CTO). It delivers the result at appropriate time and reduces the computation cost. Instance intensive cost-constrained workflows can be properly implemented in cloud computing on the basis of execution time and cost. It works according to the deadline given by the user with the minimum execution time. The Improvised cost-based scheduling algorithm by Selvarani etal 16 gives importance to both cost and performance of computations. For a particular cloud resource, the user tasks are grouped and the allocates resources to tasks thereby recducing computation/communication ratio. 3 SUMMARY OF ALGORITHMS The above discussed scheduling algorithms are briefed in tabular form as follows: Challenges: The algorithms discussed so far are the earlier, traditional algorithms which focuses on parameters like Arrival time, Time slicing, Urgency score, Importance Score, Make span, Priority and scheduling of tasks requires pre defined and calculated values. The traditional algorithms lacks optimized solutions which should be addressed in future algorithms

7 4 CONCLUSION & FUTURE ENHANCEMENT Proper scheduling always plays vital role on performance issues in cloud computing. There were many scheduling algorithms in cloud computing which focuses on arrival time, length, make span, cost, performance, processing time, priority, quality of service, load balance, execution time, finishing time, response time, efficient resource utilization, optimization, reliability, availability and many more parameters to be considered for efficient and effective utilization of resources. A good scheduling algorithm must consider the requirements of users, satisfying their needs provided in SLA and at the same time beneficial to the cloud providers. This paper covers few scheduling algorithms and many algorithms still exists and much work could be done in this area. As we can see each algorithm ends with certain drawbacks like in FCFS priority to first arrived job, last jobs with smaller burst time has to wait, in priority method, preference to high priority like this every method has one or the other drawback so an efficient method is required to overcome above aspects, the

8 benefits of various algorithms are to be considered and performance enhancement can be obtained in future. Figure 1: Task allocation Figure 2: RASA algorithm References [1] Pinal salot. A survey of various scheduling algorithm in cloud environment. IJRET. Vol 2 issue 2, Feb [2] Teena Mathew, K. Chandra Sekaran etal. Study and Analysis of Various Task Scheduling Algorithms in the Cloud Computing Environment. IEEE, [3]

9 [4] Harshith vashisth, Kamal Prakash. The Art of Scheduling in cloud computing International journal of advance research, ideas and innovations in Technology. Vol 2, Issue 4, [5] Deepika saxena, Dr. R.K. chauhan. Shortest job first with fair priority and energy awareness scheduling in green cloud computing. International journal of trend in research and development. volume 3 issue 6, [6] Navjot kaur, Taranjit singh etal. Comparision of workflow scheduling algorithms in cloud computing. International journal of advanced computer science and applications. vol 2, 10, [7] Sumit Arora, Sami Anand. Improved task scheduling algorithm in cloud environment. International journal of computer Applications. vol 96, No 3, June [8] D.I.George Amalarethinama, T. Lucia Agnes Beena. Customer Facilitated cost based scheduling in cloud. International Conference on Information and Communication Technologies [9] Pinal salot. A survey of various scheduling algorithm in cloud environment. IJRET, Vol 2 issue 2, Feb [10] Teena Mathew, K. Chandra Sekaran etal. Study and Analysis of Various Task Scheduling Algorithms in the Cloud Computing Environment. IEEE, [11] Sirisha potluri, Katta subba Rao. Quality of Service based Task Scheduling Algorithms in Cloud Computing. International journal of electrical and computer Engineering. Vol 7, No 2, April [12] S. Parsa, et al. RASA: A New Task Scheduling Algorithm in Grid Environment, World Applied Sciences Journal. vol. 7, pp , [13] S. Parsa, et al. RASA: A New Grid Task Scheduling Algorithm. International Journal of Digital Content Technology and its Applications. vol/issue: 3(4),

10 [14] AmandeepVerma, SakshiKaushal. Deadline and Budget Distribution based Cost- Time Optimization Workflow scheduling Algorithm for Cloud. International Conference on Recent Advances and Future Trends in IT (IJCA), [15] Ke Liu, Hai Jin, Jinjun Chen, Xiao Liu, DongYuan,Yun Yang. A Compromised- Time-Cost Scheduling Algorithm in SwinDeW-C for Instance-Intensive Cost-Constrained Workflows on a Cloud Computing Platform. The International Journal of High Performance Computing Applications [16] Selvarani, S., and G. SudhaSadhasivam. Improved costbased algorithm for task scheduling in cloud computing. In Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference. 2010; p [17] Gang Liu, Jing Li, and Jianchao Xu. An Improved Min Min algorithm in cloud computing. proceedings of International Conference of MCSA [18] H. Izakian, A. Abraham. Comparison of Heuristics for Scheduling Independent Tasks on Heterogeneous Distributed Environments. in Proc. of the International Joint Conference on Computational Sciences and Optimization, IEEE. vol. 1, 2009, pp

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