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1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 07 | July -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 3129 Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Computing H. Rifaya Baswan1, Dr. A. Nagarajan2 1M.phil scholar, department of computer applications, Alagappa university, Karaikudi, Tamilnadu, India. 2Assistant professor, department of computer applications, Alagappa university, Karaikudi, Tamilnadu, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In emerging trends in cloud computing to build and maintain the cloud infrastructure with flexible by virtualization technology. Virtualization context, it offers the resource allocation, virtualization flexible platform, and reliability. It provides the resource allocation to requesting user needs flexibly. In this resource allocation, considering satisfy the most of the requesting users. It is the resources based on scheduling process done by service level agreement. In this process priority based allocation, it contains allocating resource for priority based. Its occurs the starvation to the lowest priority process it is leave the low priority task. We propose the allocating resource based PERT technology scheduling algorithm and multiple SLA parameters such as to allocate memory space, it locating distributed load to the equal nodes. The capacity of physical machine to satisfy the all needs of all virtual machine. Inthisexperimentalresults the process of where the resources obtained in our proposed algorithm to provide efficiency in scheduling allocating process for all tasks. Key Words: virtualization technique, PERT based scheduling algorithm, resource allocation. 1. INTRODUCTION In this cloud computing in advanced information and technology, computing Paradigm largely connected to the public and private network, it is dynamically provided information to be resources and storage files. With this technology in this evaluate cost, computation cost, hosting application, content storage and performance of delivery time is reduced. Cloud computingexperimentallyproved the cost and efficient, where the data shared by software and information send over the network. Cloud computing provided information via the internet, which are getting from web browser, while the performance software and information are stored on servers at remote location. Scheduling process to be highly efficient manner and proper virtual machine as per the SLA performance for each process and at the same time performance is high, resource management is the important concept in the cloud computing it consists of factors are cost management, performance, efficiency they are affected by resource management. Resource management mainly concentrates with allocating resource and scheduling task. When a job requesting to the cloud, it is performed different tasks. In parallel processing in this task 1) the job requesting how the resources to be allocating 2)cloudwhat process to execute in cloud. Resource management model it is executed task to be interconnected with shared resources and task in this process to be workflow application model it represented by the directed acyclic graph (DAG) it is the process of nodes and edges denoted from the task, in this paper we propose dynamic resource allocation to overcome the workload in application using PERT technology to plan, schedule and large task to control. The process of lower priority and higher priority advanced task to perform workload from the waiting queue. In this PERT know about the input details from thecloudbut cannot know certainly the cloud how to allocate resource in cloud. DAG this approach uses the relation to compare network it identify the queries related to the cloud resources. Priority scheduling process to allocate the completion of the task and decide by total completion of the task and earliest completion of the task. In this PERT algorithm, higher priority from the critical and non-critical task it will be executed. And comparison of the priority and our proposed to equalize the lower priority to take time completion. 2. RELATED WORK Resource allocation, resource scheduling andjobscheduling algorithms are proposed in the cloud computing. [1] In proposed performance of the optimization model in workflow application, it is presented and performance is better for completion time and allocating resource. [2] Scheduling and scheduler to get resources in cloud, optimal cloud computing in preceding the allocating do the job best machines and minimize the lower priority and execution cost. [3] In this allocating resources and scheduling workflow process in data center, it also considers execution time and cost expenditure are both. In this paper model, infrastructure as a service architecture to be performed and presentation of the priority based algorithm it to be allocated resources to be priority wise. Itisprocessofhigher priority to finish first and its starvation of the lower priority that is the priority to satisfy them. [4] Proposed algorithm and then efficient memory allocation using MPS (memory processor storage) it is a matrix model. Priority based algorithm it is a minimum wastage and maximum profit. [5]
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 07 | July -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 3130 Workflow scheduling algorithmisbasedon qualityofservice and user defined to know the critical path method and minimize the cost in workflow execution. And a cost based resource allocation scheduling algorithm it is used the market theory to resource schedule to refer user’s requirement. [6] In a priority based scheduling algorithm it is performed by the dynamic allocation of the jobs to be processed multiple SLA in cloud jobs. 3. PROPOSED SCHEDULING ALGORITHM The scheduling modelling algorithmisdirectedacyclicgraph (DAG) G (T, E), it T represents the set of n tasks and E represents the set of all directed edges m. A DAG it is calleda task graph, it nodes represent a task of the workflow application and edges represent the edges the relationship between these tasks. Each edge is tij=(ti,tj) ∈E it between these tasks.it ti and tj represents inter tasks communication and precedence. And task ti must complete execution before task tj. In this task graph, it must completed from the before task and follows successor of the completion of the given task. Each successor of a task if it is completion of the giventask,a task graph without parent it is called an entry task and task without child it is called an exit task. This is an algorithm for a single entry task and a single exit task, and two dummy tasks tentry and texit is added from the beginningand endof the task graph. These are the dummy task have a zero execution time and they connected with the zero weighted edges to the entry and exit tasks. Sequentially connected to the task nodes in the task graph from the entry node to exit task, is called path. The path of the length is measured by the sum of weights on the task edges on the path graph. The longest path is called critical path and nodes of the corresponding task is called critical task nodes andthatthey must be completed as scheduled to meet by the scheduled time. If the entire process is completed it is a ready task. Transmission of data from the task is represented by the communication time CT (ti,tj) is the time and it is represented by the matrix form CT [ti,tj]n*n, n represents the number of tasks the edge of the weight, wij it represent the communication time CT (ti,tj) between two tasks of ti and tj. A logical unit of work of a task ti is executed by a resource. The estimated execution time tocompletetask tibyresource rj gives from the execution time can be represented by a form of matrix form, ET [ti,rj]n*m itnrepresentsthenumber of task and m represents the number of resources. The task is to wait for until allocated resource completes the execution of the current task. In case, waiting time, WT [ti,tj] of task ti, and resource rj is zero, if the resource is process means does not executing another task. In this paper, represented by problem of the task duration where low priority leave the process and it the waiting time is long, it cannot be the execution certainly and that the schedulingprocessthroughnormal probabilitydistributions. 4. SCHEDULING PROCESS 4.1 Problem Definition In cloud computing for priority based scheduling algorithm for resources allocates from cloud computing resources in effective, efficient, and optimized task. This algorithm contains that a set of resources is certainly distributed and heterogeneous in nature. Priority assign by the task of completion of time and earliest finished time of task. 4.2 Problems Identifying the Scheduling Task In this approach, tasks duration is not known certainly. The task duration is performed to have normal distribution, this is the task is distribution task duration. EX: randn(1,20000) this means two thousand number in random on normal distribution 4.3 Directed Acyclic Graph Scheduling Algorithm 1) First give the input n task and m resources 2) N task in the DAG 3) Its communication is randomly 4) ET [ti,rj]n*m. 5) Its resources in randomly available time. 6) Its task i=1 to n 7) Find the execution process is minimum priority for all task is optimized. 5. RESULTS In this paper, priority based scheduling model its performance is evaluated. A comparison of the algorithm first is priority based algorithm, second is PERT based scheduling technique, and third is priority and PERT based scheduling algorithm. Mainly priority based algorithm to be executed task and then secondlyPERTmodel firstlyexecutes all critical tasks it have a lowest completion time and then and then non critical tasks with lower completion time and then execute the higher priority to the critical task and then execute non critical task to the higher priority and considering the various tasks in completion time. 5.1 Comparative analysis Analyses Task 1 Firstly performed by the small workflow network for DAG, below the figure as shown in 1: that have an eight numbers
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 07 | July -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 3131 of task (nodes) and nine numbers of activities among these task. Fig -1: Example for small workflow for DAG Analyses Task 2 Secondly performed by the large workflownetwork forDAG, below the figure as shown in 1: that have an eleven numbers of task (nodes) and twenty numbers of activities among these task. Fig -2: Example for large workflow for DAG Analyses Task 3 Third analysis is performed by the number of tasks performed by the workflow network for DAG, below the figure as shown in 3 and 4: that have a large workflow has a small makespan and small completion time as compared to small workflow network. Chart -1: Makespan Chart -2: Completion Time 6. CONCLUSIONS Resource management technique it is one of the important issues to be solved in cloud computing.Inthispaper,priority based algorithm is the proposed model. It is compared with other scheduling model for lower priority task to optimize the completion time. Results of the proposed algorithm it is efficiency and effectiveness of the proposed model.Itreduce the completion time, in future work critically in priority model will be to make more effective and efficiencymodel in resource management in cloud computing. ACKNOWLEDGEMENT I would like to thank Dr.A.Nagarajan for his guidance and support for preparing this paper.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 07 | July -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 3132 REFERENCES [1] M. Guzek, P. Bouvry, E.-G. Talbi, "A survey of evolutionary computation for resource management of processing in cloud computing [review article]", Computational Intelligence Magazine IEEE, vol.10,no.2, pp. 53-67, 2015.S. [2] S. Manvi, G. K. Shyam, "Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey", Journal of Network and Computer Applications, vol. 41, pp. 424-440, 2014. [3] S. K. Garg, R. Buyya, and H. J. Siegel, “Time and cost trade off management for scheduling parallel applications on utility grids,” Future Generation. Computer System, 26(8):1344–1355, 2010. [4] M. Salehi and R. Buyya, “Adapting market-oriented scheduling policies for cloud computing,” In Algorithms and Architectures for Parallel Processing, volume 6081 of Lecture Notes in Computer Science, pages 351–362. Springer Berlin / Heidelberg, 2010. [5] J. M. Wilson, “An algorithm for the generalized assignment problem with special ordered sets,” Journal of Heuristics, 11(4):337–350, 2005. [6] M. Qiu and E. Sha, “Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems,” ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 14, no. 2, pp. 1–30, 2009. [7] M. Qiu, M. Guo, M. Liu, C. J. Xue, and E. H.-M. S. L. T. Yang, “Loop scheduling and bank type assignment for heterogeneous multibank memory,” Journal of Parallel and Distributed Computing(JPDC), vol. 69, no. 6, pp. 546–558, 2009. [8] A. Dogan and F. Ozguner, “Matching and scheduling algorithms for minimizing execution time and failure probability of applications in Heterogeneous computing,” IEEE Transactions on Parallel and Distributed Systems, pp. 308–323, 2002. [9] T. Hagras and J. Janecek, “A high performance, low complexity algorithm for compile-time task scheduling in heterogeneous systems,” Parallel Computing, vol. 31, no. 7, pp. 653–670, 2005. [10] J. M. Wilson, “An algorithm for the generalized assignment problem with special ordered sets,” Journal of Heuristics, 11(4):337–350, 2005. BIOGRAPHIES Rifaya Baswan .H is a M.Phil Scholar from the department of computer applications, Alagappa University, Karaikudi, Tamil Nadu, India. 1’st A uthor Photo
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