Department of CSE, K L University, Vaddeswaram, Guntur, A.P, India 3.

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1 Volume 115 No , ISSN: (printed version); ISSN: (on-line version) url: AN IMPROVISED PARTITION-BASED WORKFLOW SCHEDULING ALGORITHM ijpam.eu J.Prathyusha 1, G.Sandhya 2, V.Krishna Reddy 3 1,2,3 Department of CSE, K L University, Vaddeswaram, Guntur, A.P, India 3 vkrishnareddy@kluniversity.in Abstract: Cloud computing is an evolving technology. Now a days we see most of the cloud services to complete a whole task in a workflow. Where we can execute various sub-tasks in a certain manner. Work flow Scheduling plays an important part in the cloud computing, as it calculates cost, time of execution and other functionalities based upon various attributes. By choosing the right scheduling method can cause maximum packet transmission, can control packet loss and can increase CPU utilization. Therefore we have many partition algorithms that can be used to schedule the workflows. To this paper we had proposed an improvised partition based work flow scheduling algorithm in which we tried to improvise the partition workflow algorithm which can schedule the tasks of similar type of partitions of same resource set. Through this we can serve similar kind of requests by assigning them to the same resource. Keywords: cloud computing, scheduling, partition based workflow. 1. Introduction In present scenario the cloud information centers composes a majority of the ICT services together with , finance and banking are just a few to name. The several applications in science and engineering features a wide variety of the processes or tasks forming a workflow. Each of the tasks or processes performs some part the work that is needed for the entire workflow to get completed. Now a days many organizations concentrate on parallel processing to execute their jobs in a faster way. Due to communication and synchronization there is decrease in use of CPU resources. Many algorithms are proposed regarding scheduling but very few are proposed to detect it. Therefore scheduling plays a vital role in the cloud scenario when it comes in managing the user requests in computing environment. Scheduling is of two types: one is the static scheduling and the other one is the dynamic scheduling. STATIC SCHEDULING DYNAMIC Figure1.1 Types of Scheduling In static scheduling the processes that are to be executed have fixed execution time and they do not get affected by the system. Dynamic scheduling assigns the processes or tasks as they arrive and schedule it dynamically. At the server side it receives many client requests for every second where the problem arises in scheduling these requests where we need some parameters to get the process or the task to get executed. Therefore we should have an effective schedule mechanism by the server which is optimal and fast enough to tackle the job requests and process the particular user requests in less time. In general most of the tasks in scheduling, are just limited to a single workflow application. But in most of the cases there is also a need for multiple workflow systems. Multiple workflows are managed by different users through online where different users can access and submit it at any time. Workflow scheduling comes into picture when the user generated request gets proper utilization for the request they generated with in less time. In workflow scheduling the tasks that are generated by the user may transfer from one user to the other, so that proper action could be taken by following certain rules. Workflows can have certain steps which can simplify and manage the execution of the task and also the applications. When resources are allocated during parallelism if the jobs are not assigned properly then there will be decrease in system performance. So scheduling of job is one of the important aspect of scheduling. It is nothing but delivering the tasks that are been or being computing to an resource pool among the resource users based on the set of the rules provided. The important aspect in scheduling the jobs is to attain high performance and system efficiency throughput. In general the present available scheduling algorithms cannot provide the scheduling in the environment of cloud. We have two groups of job scheduling. One is the batch mode heuristic scheduling algorithms (BMHA) and the other one is the online mode heuristic algorithms. In batch mode heuristic scheduling algorithms the jobs are placed in queue and are collected into a set when they arrived. In this time period is fixed. First come First serve, round robin, Min-Min algorithm, Max-Min algorithm comes under batch mode heuristic algorithms. In online mode the jobs are scheduled when they arrive. Online is the most appropriate one for cloud. Most first come first serve task scheduling algorithm comes under online mode. 381

2 Priority is another most important concept in scheduling. Based on the priority jobs are assigned. In cloud the process of scheduling can be generalized into three stages. The initial phase is the resource discovering and filtering, the next one is the resource selection and task submission. The scheduling framework should consists of the limitations for user inputs like deadlines, performance issues, execution cost, transmission cost, energy efficiency, Load Balancing, and Make span and so on. 2. Problem Statement We described the workflow and their respective resource capabilities. Let us take a directed acyclic graph <G> with vertices set (V) and edges (E). The vertices of the graph represent the tasks in a workflow which are to be scheduled. The edges connect the vertices or two tasks which represents either the dependency among them or precedence constraints. Also we have a resource sets R= R1, R2, R3 Rm. 3. Literature Survey In this chapter we discussed the related works that addresses either cost minimization or minimization of execution time as their main objective. While when we see at the other ones very usually becomes a constraint. In workflow scheduling we contain many works which are being performed as tasks which are been used to investigate the problem of minimization of length of execution time by taking into consideration of the budget. Greedy approach was projected by Wu et al [1]. This method is initiated by determining the cost minimization and by cost reduction iteratively and we also by reallocating the tasks by considering the budget. Similarly, Arabnejad et al presented by HBCS algorithm [2] where each iteration they try to improve the schedule which is obtained by allocating the tasks from the leftover budget. Sakellariou et al [3] has implemented two heuristics, which schedules the workflows meeting the economical constriction by amending the schedule to modify its cost and to diminish the execution time by altering or growing cost of schedule (budget), respectively. We had an IC-PCP algorithm [4] which aims at producing the schedules such that when the execution of that schedule takes place with a pre-arranged cost to a minimum extent. During its last proceedings at the exit task it completes by determining the partial critical path. The critical path is the leading path obtained from the start task to the exit task. It usually starts with the initial task to the exit or finish task, where we can determine the recent finish time of each task based upon its execution time and its successor tasks. After determining the recent finish time of every individual task, then the IC-PCP determines the resource available at low cost for its execution in appropriate time. This algorithm is repeated until all the tasks that determines the critical path are apportioned to a resource and completes the execution. Malawski et al [5] represents this model problem as a mixed integer program. He solved this problem using mathematical programming language. We have different approaches like meta heuristics that includes genetic algorithm, particle swarm optimization, bicriteria scheduling are also some of the popular approaches used in this model. In [6], the scheduling of workflow botheration was defined as a gathering beeline issues that consider the renting of unapproachable and On-demand assets from assorted IaaS suppliers as indicated by a two-level SLA. The scheduler can be kept running in either a Software as a service or Platform as a service billow supplier. And then agree to take the workflow beheading requests with dead-lines from its audience (first SLA level). However it can as well charter assets from assorted IaaS suppliers (second SLA level). Abrishami et al. [7] anticipated a Fractional Analytical Paths (PCP) algorithm. It schedules the workflow in an astern manner. The limitations are supplementary to the scheduling action. If such scheduling of jobs in a fractional analytical aisle be unsuccessful, then the algorithm will be resumed. This algorithm presents the above-mentioned characteristics as does Deadline-Markov Accommodation Action (MDP). Although it involves greater time complexity, back an almost ample amount of reorganizing can be accepted during the beheading of the algorithm. The self-adaptive all-around seek enhancement address alleged atom army enhancement (PSO) is activated to agenda workflows in the algorithm proposed in [8]. It was industrialized to plan in clouds with single-level SLAs and on-demand ability of hiring. It considers neither multi amount assets nor workflow goals, but focuses alone on budgetary amount minimization. Khaled Almi ani [9] proposed a partition based workflow scheduling algorithm used in cost minimization by taking the budget constraint. We have many workflow scheduling algorithms which partitions the workflows into different partitions. Here in the partition based workflow scheduling algorithm the input is taken as a directed acyclic graph. Directed acyclic graph has no cycles, it contains a directed path but not back to itself. In a directed acyclic graph the output of one task is considered as an input of the other task. In this manner the graph gets executed if the tasks have dependencies between them. If the tasks are independent to each other the graph precedes by opting a different approach. The graph with cycles cannot be ordered topologically. The directed acyclic graph with no cycles can be ordered topologically. Mostly all the directed acyclic graphs can be topologically ordered. A 382

3 directed acyclic graph can be represented as shown in the below figure: The research done till date in scheduling the workflows by using partition based workflow procedure schedules the tasks into different partitions which are independent. In this partition based workflow scheduling algorithm we have three steps described. Step 1: The first step is subjecting the input graph into partitions. The partitions of the tasks are made by calculating the critical path of the vertices that are nothing but the vertices of the graph considered. Step 2: The second step is the partition adjustment step. In this step we check whether the partitions done in the first step are correct or not. If the partition obtained is not correct then we re-arrange the partitions. Step 3: The final step is the resource allocation step. In this an algorithm is applied to the partitions made from the input graph and each task in the partition is subjected to the respective resource scheduler to schedule the task. The tasks which are subjected to partition are of different kind. 4. Proposed system The proposed system consists of an improvised partition based workflow scheduling algorithm which helps us to reduce the wait time. In this we consider different jobs of the form a Directed acyclic graph. We get many tasks that are to be scheduled. In general a partition based algorithm receives the jobs/tasks and form them into a direct acyclic graph where we apply two algorithms namely the partition algorithm and a resource allocation algorithm. In this we by applying the partition algorithm we divide the tasks in the graph into certain partitions by considering certain attributes such as partitions can be made by considering the critical path. Next we can verify the partitions made above are correct or not by the partition adjustment step. After applying the partition step we can allocate the tasks to the particular resource were it can get executed. In this paper the above mentioned partition algorithm is modified where it can check for the tasks sharing the common set of resources that belong to the same partition through our improved partition algorithm. In this paper the partition algorithm checks for the tasks that share a common resource and group them as a single partition by considering the certain attributes such as the type of the task obtained and if the task does not belong to the same partition then the partition is done based upon the critical path calculation. After the partitioning step we verify whether the partitions made are correct or not and then subject them to the resource allocation step where we get the tasks get scheduled. A. Partitioning Step In this step we the input graph is subjected to different partitions. The partitions are done based upon their critical path length. We calculate the critical path of the tasks and then they are subjected to partitions. And here we also check whether the tasks obtained are of same type that is whether they share the common resources or not. If the tasks share a common set of resources then we group them into a single partition. B. Partitioning Adjustment Step In this step we check whether the above made partitions are correct or not. Here we verify the partitions based on certain attributes. We cross check the partitions made depending upon the tasks and then if all the partitions made above are correct then we subject the same group of partitions to the resources and if the partitions made are different then we rearrange the partitions and subject them to the resource allocator C. Resource Allocation Step In this step we allocate the partitions to the resource sets available. For example if we have three partitions which are to be assigned to a particular resource set, for its completion. Let us consider that one of partition among the three partitions is the printer task. We may have many printers, let s consider we have four printers then if we get tasks that are to be allocated. We check the load availability and depending on that availability we try to complete the task and then get it completed. If no printer is available and all are busy or have high load then we try to adjust the load and then execute the task. The partition of the tasks can be done based on the following code which is described below: Bool issubsetsum(int N,int arr[],int ASUM) { if(asum == 0) RETURN TRUE; if(n== 0 && ASUM! = 0) RETURN FALSE; if(arr[n-1]>asum) return issubsetsum(arr, N); return issubsetsum(arr, N-1); } Bool FindPartition (int arr[], int N) { int ASUM=0; for(i=0 ; i < N ; i++) { ASUM+= arr[i]; if(asum % 2!= 0) RETURN FALSE; RETURN issubsetsum(arr,n,asum) } } In the issubsetsum function takes the input of how number of tasks. It calculates the sum of all the tasks and if the sum is equals to zero then it returns true and 383

4 if it the number of tasks taken as a input is zero and the sum of the tasks is not equal to zero then it returns false. If the last element of the array that is the last but one task is greater than the sum of the tasks then we return the function issubset(arr, n). Then we execute the function findpartition where we divide the tasks into several partitions. 5. Conclusion In this paper we are applying an improvised partitioning based workflow scheduling algorithm so that the complexity and scalability increases for the practical applications of cloud. As the scheduling is applied the cost and execution time gets reduced. It provides better significance whencompared to other existing scheduling algorithms in our evaluation. It solves the problem faced by partition sharing the common resources. It can be used to solve various complex tasks that share common set of resources in the coming future. We further try to work on the dynamic resource allocation of the tasks. References [1] Wu, Chase Qishi, et al, "End-to-End Delay Minimization for Scientific Workflows in clouds under budget constraint", IEEE Transactions on Cloud Computing 3.2 (2015): [7] Abrishami, Saeid, Mahmoud Naghibzadeh, and Dick HJ Epema, "Cost-driven scheduling of grid workflows using partial critical paths", IEEE Transactions on Parallel and Distributed Systems 23.8 (2012): [8] Pandey, Suraj, et al, "A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments", Advanced information networking and applications (AINA), th IEEE international conference on. IEEE, [9] Almi'Ani, Khaled, and Young Choon Lee, "Partitioning-Based Workflow Scheduling in Clouds", Advanced Information Networking and Applications (AINA), 2016 IEEE 30th International Conference on. IEEE, 2016 [2] Arabnejad, Hamid, and Jorge G. Barbosa, "A budget constrained scheduling algorithm for workflow applications", Journal of grid computing 12.4 (2014): [3] Sakellariou, R., Zhao, H., Tsiakkouri, E., & Dikaiakos, M. D. (2007), Scheduling workflows with budget constraints, In Integrated research in GRID computing (pp ). Springer US. [4] Abrishami, S., Naghibzadeh, M., & Epema, D. H. (2013), Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds, Future Generation Computer Systems, 29(1), [5] Malawski, M., Figiela, K., Bubak, M., Deelman, E., & Nabrzyski, J, (2013, September), Cost optimization of execution of multi-level deadline-constrained scientific workflows on clouds, In International Conference on Parallel Processing and Applied Mathematics (pp ). Springer Berlin Heidelberg. [6] Genez, Thiago AL, Luiz F. Bittencourt, and Edmundo RM Madeira, "Workflow scheduling for SaaS/PaaS cloud providers considering two SLA levels", Network Operations and Management Symposium (NOMS), 2012 IEEE. IEEE,

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