Research Article International Journals of Advanced Research in Computer Science and Software Engineering ISSN: X (Volume-7, Issue-6)

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1 International Journals of Advanced Research in Computer Science and Software Engineering Research Article June 2017 A Review Paper on Various Task Scheduling Algorithms in Cloud Computing Ramandeep Kaur, Amardeep Kaur Punjabi University Regional Centre for IT and Management Mohali, Punjab, India DOI: /ijarcsse/V7I5/0178 Abstract- Cloud Computing is a computing environment where different services are provided to users over the Web. Task Scheduling is one of the important aspects of Cloud Computing, improving the performance of the cloud system. Task Scheduling involves assignment of s to a particular task for the task to be completed within possible minimum time. Task Scheduling helps in achieving efficient utilization of s. This paper presents a review of various task s in Cloud Computing. Keywords- Task, makespan, Cloud Computing, I. INTRODUCTION U.S National institute of standards and technology proposed definition of cloud computing. Cloud Computing is a paradigm that facilitates convenient, ubiquitous, on-demand broad network access to configurable computing s(services, network, storage, applications and servers) that can be immediately provisioned and released with minimum management effort [1]. Cloud Computing is a computing environment where varying s are provided as a service to users or to number of tenants over the Web. These available s are used to execute tasks that are scheduled to cloud environment on time thus achieving efficiency, less makespan and proper utilization. Users of cloud request for computing tasks to data center. These requests are Task is piece of work that need to be executed in given span of time[5]. Proper task increases efficient utilization of s that result in reduction of task finish time. The next section discusses about Task Scheduling. II. TASK SCHEDULING Task Scheduling is one of the most important aspects of Cloud Computing. Task is a piece of work to be executed in a specified time. Task Scheduling is the process of assigning s to a particular task for the specific time for that task to be completed. The tasks are distributed over s in an appropriate manner such that necessary preferences between tasks are met and total time needed to execute all tasks is minimized. The main aim of task is to maximize utilization. It involves minimizing waiting time for Task yields less task response time so that submitted tasks execution takes place within possible minimum time. Scheduling of tasks involves finding correct sequence for task execution under time constraints. Proper sequence helps minimizing total time for task execution. Proper task improves efficiency and performance of cloud environment. In order to achieve high performance, various s for task have been proposed by researchers. The performance of cloud varies with adoption of the various. Task Scheduling s are categorised into 2 main parts: Static Scheduling and Dynamic Scheduling[2]. The two categories of task are discussed in the next section. III. TYPES OF SCHEDULING Task Scheduling is considered under two main categories as shown in Figure 1: Static Scheduling Dynamic Scheduling Fig. 1: Types of task All Rights Reserved Page 775

2 In Static Scheduling, for each and every task, communication cost and computation cost is considered beforehand. In Dynamic one, decisions are taken beforehand at run time and no cost information is available earlier. Static Scheduling further comprise Heuristic and Guided Random s. Heuristics involves 3 subcategories namely clustering[3], list [5]and duplication s[4]. In Clustering based s such as Clustering for Heterogeneous Processors(CHP)[3], clusters of tasks are assigned to appropriate processors. On the other hand, duplication s try to duplicate tasks to minimize makespan. Algorithms such as Contention aware (CA-D)[4] duplication s eliminates the communication cost by placing tasks on same processor. A large time complexity and more processor usage limit the use of duplication based s in cloud environment. List Scheduling s such as Critical Path on Processor(CPOP)[5] give minimum makespan along with efficient time complexity. The list Scheduling Algorithms are most practical. IV. REVIEW OF LITERATURE A. Static Algorithms: Minhaj Ahmad Khan[2012][2], suggested a novel approach which used concept of constrained critical paths to provide better schedule for task to be assigned to s in cloud environment. Cristina Boeres[2004][3], proposed cluster based strategy for task. The clusters of tasks were created and these clusters mapped to fixed number of available processors. Oliver Sinnen[2011][4], proposed duplication based on state of art techniques found in Task duplication. This eliminated communication cost after placing tasks upon same processor. Selvarani[2010][7], proposed an called Improved cost-based in which tasks were grouped as per processing power of s. Cost varied based on complexity of The method reduced processing cost as tasks are scheduled based on their respective cost for different s. L.F Bittencourt[2010][8], proposed Lookahead whose main feature is processor selection policy. The computed earliest finish time for child tasks on every processor. B. Dynamic Scheduling Algorithms: G.C.Sih, E.A.Lee[1993][9], proposed Dynamic Level Scheduling that computed availability of each processor and then scheduled task to current busy processor. Zhu[2003][10], proposed two novel slack sharing for set of The technique used slack time that was unused by task. Hence, there was reduction in total energy consumption. Kimura[2006][11], proposed an that calculated slack time and generated frequency that task need to be executed. The was applied only for non-critical tasks and no further communication cost was taken into consideration. C. Particle Swarm based Algorithms: Kiyarazam[2011][12], proposed based on PSO(Particle Swarm Optimization method) that minimized average utilization of s in optimal way and enhanced load balancing in multi-processor systems. Lili Xu, Kun Wang[2014][13], proposed a green cloud task (GCTA) based on improved binary particle swarm optimization(bpso). The avoided matrix operation by use of pipelined number of virtual machines. Batch Mode Scheduling Algorithm: Mao Y.ChenX[2014][14], proposed Max-Min task that maintains a task status table that estimated real time load of virtual machines and expected task completion time that allocates workload among D. Priority based s: Xiao,Jing[2012][15], proposed strategy called Priority based on basis of priorities of task. In, tasks are ranked according to profits they can bring. It maximizes benefits to service providers. Huang,Qingjia[2012][16], proposed Enhanced Energy Efficient(EEES) so that performance-based service level agreements(sla) are met. The scheduled nearby running tasks on constant frequency. Patel,Swati J[2014][17] improved priority based using iterative method. The has better makespan and consistency than other task s. Gupta,Gaurav[2014][18], proposed called Priority based Earliest deadline First that focussed on and memory utilization. The overcomes waiting time problem of tasks that have been pre-empted. For the processing of pre-empted tasks, waiting queue is introduced. E. Network based s: Kliazovich,D.Arzo[2013][19], proposed DENS (data center energy efficient network aware task ).Scheduling of task is carried out by combining network awareness and energy efficency. The improves performance of cloud and fulfils QOS requirements. Abdul Razaque[2016][20], proposed that used non-linear programming model that assigned correct number of tasks to each virtual machine. Considering network bandwidth the was designed for separable load. All Rights Reserved Page 776

3 V. COMPARISON OF VARIOUS TASK SCHEDULING ALGORITHMS A. First Come First Serve Algorithm[6] FCFS[6], an in which task that arrives first will be scheduled first of all and s are allocated to that task as it needs. Once the task is executed, the next task in queue is scheduled next. FCFS is a basic method in which tasks are queued up if s are busy. The evaluated arrival time of tasks as well the is easy to be implemented. B. Round Robin Algorithm[6] RR[6] is also earliest method in which task will be executed for a fixed time slot. The task will be put in queue at the end and will be taken again when it will reach front and remaining execution will be carried out. The calculates expected execution time as well balances the load C. Improved Cost-Based Algorithm For Task Scheduling[7] Mrs. Selvarani[7] proposed improved cost based so to make efficient mapping of tasks to s which are available. The divides all tasks into 3 different lists depending on priority of each task. The reduced processing cost as well as reduced makespan.. D. Lookahead Algorithm[8] L F Bittencourt[8], proposed Lookahead based on its processor selection policy. The iterates over all available processors to select processor for a current task t. the selected processor maximizes EFT for all child tasks of t (on every ). The worst case complexity of lookahead is O(v4.p3). The reduced makespan. E. Dynamic Level Scheduling[9] G.C.Sih[9], proposed that computed an estimate of whether processor is available or not. The thus allowed a task to be scheduled to the current busy processor. The has a much higher time complexity. F. PSO- Based Algorithm For Task Scheduling & Load Balancing[12] PSO method provided an optimal way to minimize average utilisation of s. The enhanced load balancing in multi-processor systems. Each particle in is represented by 1 or 0. The reduces consumption and takes less execution time. G. Improved Priority Based Task Scheduling Algorithm[17] Patel[17], proposed in which for, priority is considered. The criteria in this is multiple criteria decision making model. The considered priority of task before it to the processor. The helped in reducing the length and has a less finish time. H. Earliest Deadline First Scheduling Algorithm[18] Gupta, Gaurav[18], proposed an in which task having shortest deadline is scheduled. When a a scheduled task is released after its execution, then queue is searched for next task close to its deadline. The helped in reducing the time complexity. I. Dens Algorithm[19] Kliazovich[19], proposed DENS. In this, network awareness and energy efficiency is combined for performing of Network awareness is obtained from main network switches by using feedback channels. The reduces number of computing servers and it improves performance of J. Efficient Task Scheduling Algorithm[20] Abdul Razaque[20], proposed that used non-liner programming model for assigning correct number of task to each virtual machine.the reduces total execution time and reduces consumption, Thus enabled efficient task as s are utilised efficiently. The Comparison table Table 1 describes comparison between various task s in next section. Table 1: Comparison Algorithms Description Findings Limitations Scheduling Parameters First Come First Serve [6] First of all the tasks, the task that arrived first will be scheduled. Once the task is executed, the next task Evaluates arrival time of Algorithm easy to implement. No other criteria used for. Arrival time Future work To find another criteria for Tool Simulation All Rights Reserved Page 777

4 waiting in queue will be scheduled next. Task to be Minimizes Pre-emption is Arrival time, To Simulation executed for expected required. time slot investigate fixed time slot. delay. online online Remaining task to be put in end to in queue & schedule premptive when comes to front then remaining execution to be carried out. Generalised Round Robin [6] Improved costbased for task [7] Lookahead [8] Dynamic level [9] Particle Swarm Optimization[12] Improved Priority based task [17] Makes efficient mapping of available s to Divides task into three different list depending on priority of each task. Based on its processor selection policy. The selected processor maximizes EFT(Earliest Finish Time) for all child tasks of current task. Algorithm computes estimate of processor being available or not. The task is scheduled to current busy processor. Algorithm, an optimal way for minimizing average utilisation of s. Enhances load balance in multiprocessor systems. Priority is considered for Measures both computation performance and cost of cost of processing.. makespan. The is fast. Time complexity of is O(v3.p) consumption. Takes less execution time. makespan.. Doesn t improve utilization. Doesn t reduce energy consumption. More Time complexity. EST(Earliest Start Time) doesn t guarantee minimum completion time for task. The suffers from partial optimism. Doesn t work out for scattering. Consistency and complexity of needs to be Cost of processing, activity based cost Scheduling Length ratio, Computation to communication ratio. Scheduling progression, speedup. Number of iterations, number of particles in swarm. Priority, Expected completion time Algorithm needs to be improved to work in a dynamic environment. Improve makespan by taking additional levels of forcasting in the. Another approach rather than EST. Plan to endow the with fitness sharing. Needs to be improved to achieve less finish time. Green Cloud All Rights Reserved Page 778

5 improved. Task having time Doesn t Deadline of The shortest complexity improve QOS. tasks deadline will Doesn t needs to be be scheduled reduce improved for first. processing use in real The next task cost. time systems close to deadline is searched in queue for next execution. Earliest deadline first [18] Data-center energy-efficient network aware [19] Efficient task in cloud computing[20] Scheduling of tasks is performed by combination of network awareness and energy efficiency. Use of nonlinear programming model so that virtual machine can be assigned correct number of number of computing s. Optimises gap between task consolidation and traffic pattern distribution. consumption. Bandwidth friendly. total execution time. Has less computational and memory overhead. Time consuming Efficiency, congestion, Traffic load Network bandwidth Needs to be improved to save more power. To focus on dependency between Green cloud Eclipse VI. CONCLUSION Task Scheduling plays significant role in improving the performance of Cloud Computing. This paper reviewed existing task s used in Cloud Computing. The existing review paper on task discussed task s based on cost, priority, energy consumption. This review paper discussed static and dynamic task s and their further categories. REFERENCES [1] [2] Khan, Minhaj Ahmad. "Scheduling for heterogeneous systems using constrained critical paths." Parallel Computing 38.4 (2012): [3] C. Boeres, J.V. Filho and V.E.F. Rebello, A Cluster-based Strategy for Scheduling Task on Heterogeneous Processors 16th Symposium on Computer Architecture and High Performance Computing, pp , [4] Sinnen, Oliver, Andrea To, and Manpreet Kaur. "Contention-aware with task duplication." Journal of Parallel and Distributed Computing 71, no. 1 (2011): [5] Topcuoglu, H., S. Hariri, and W. Min-You, Performance-effective and low-complexity task for heterogeneous computing. Parallel and Distributed Systems, IEEE Transactions on, (3): p [6] Arian,Yair, and Yonatan Levy. Algorithms for generalised round robin routing. Operations Research Letters 12.5(1992): [7] Selvarani, S., and G. Sudha Sadhasivam. "Improved Cost-Based Algorithm For Task Scheduling In Cloud Computing." Computational Intelligence And Computing Research (ICCIC), 2010 IEEE International Conference on. IEEE, [8] L.F. Bittencourt, R. Sakellariou and E.R.M. Madeira, DAG Scheduling Using a Lookahead Variant of the Heterogeneous Earliest Finish Time Algorithm, 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 10), pp , [9] G.C. Sih and E.A. Lee, A Compile-Time Scheduling Heuristic for Interconnection-Constrained Heterogeneous Processor Architecture, IEEE Transactions on Parallel and Distributed Systems, vol. 4, no. 2, pp , [10] Zhu, Dakai, Rami Melhem, and Bruce R. Childers. "Scheduling with dynamic voltage/speed adjustment using slack reclamation in multiprocessor real-time systems." Parallel and Distributed Systems, IEEE Transactions on 14.7 (2003): All Rights Reserved Page 779

6 [11] H. Kimura, M. Sato, Y. Hotta, T. Boku, D. Takahashi, Emprical study on reducing energy of parallel programs using slack reclamation by dvfs in a power- scalable high performance cluster, in: IEEE International Conference on Cluster Computing, IEEE, 2006, pp [12] Kiyarazm O Moeinzade h M Sharifian-R S. A new method for load balancing in multi-processor systems based on PSO Proceedings of Second International Conference on Intelligent Systems Modelling and Simulation Kuala Lumpur and Phnom Penh Cambodia 2011 pp [13] Xu, Lili, et al. "An improved binary PSO-based task in green cloud computing." Communications and Networking in China (CHINACOM), th International Conference on. IEEE, [14] Mao, Yingchi, Xi Chen, and Xiaofang Li. "Max min task for load balance in cloud computing." Proceedings of International Conference on Computer Science and Information Technology. Springer India, [15] Xiao, Jing, and Zhiyuan Wang. "A Priority Based Scheduling Strategy for Virtual Machine Allocations in Cloud Computing Environment." Cloud and Service Computing (CSC), 2012 International Conference on. IEEE, [16] Huang, Qingjia, et al. "Enhanced energy-efficient for parallel applications in cloud." Proceedings of the th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012). IEEE Computer Society, [17] Patel, Swati J., and Upendra R. Bhoi. "Improved Priority Based Job Scheduling Algorithm in Cloud Computing Using Iterative Method." Advances in Computing and Communications (ICACC), 2014 Fourth International Conference on. IEEE, [18] Gupta, Gaurav, et al. "A simulation of priority based earliest deadline first for cloud computing system." Networks & Soft Computing (ICNSC), 2014 First International Conference on. IEEE,2014. [19] Kliazovich, D., Arzo, S. T., Granelli, F., Bouvry, P., & Khan, S. U. (2013, August). e-stab: energy-efficient for cloud computing applications with traffic load balancing. In Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (ithings/cpscom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing(pp. 7-13). IEEE. [20] Razaque, Abdul, Nikhileshwara Reddy Vennapusa, Nisargkumar Soni, and Guna Sree Janapati. "Task in Cloud computing." In Long Island Systems, Applications and Technology Conference (LISAT), 2016 IEEE, pp IEEE, All Rights Reserved Page 780