Level Based Task Prioritization Scheduling for Small Workflows in Cloud Environment

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1 Indian Journal of Science and Technology, Vol 8(33), DOI: /ijst/2015/v8i33/71741, December 2015 ISSN (Print) : ISSN (Online) : Level Based Task Prioritization Scheduling for Small Workflows in Cloud Environment D. I. George Amalarethinam 1 and T. Lucia Agnes Beena 2 * 1 Department of Computer Science, Jamal Mohamed College, Trichy , Tamil Nadu, India; di_george@ymail.com 2 Deptartment of Information Technology, St. Joseph s College, Trichy , Tamil Nadu, India; jerbeena@gmail.com Abstract Cloud computing is aimed at delivering computing services as a utility. One of the Cloud computing services is Workflow as a Service (WFaaS). Workflow scheduling is a vital area in WFaaS. The objective of this paper is to propose a scheduling algorithm that meets the Quality of Service constraints such as Makespan and Cost. The proposed algorithm Level Based Task Prioritization (LBTP) follows the list scheduling strategy. The LBTP algorithm alters task prioritization phase and the resource selection phase, where three different algorithms like Earliest Finish Time, Parent Resource Allocation and Round Robin are applied. The LBTP algorithm is tested for small workflows. The performance analysis is done by varying the Communication to Computation Ratio and number of tasks. The scheduling metrics, Makespan and Cost of the proposed algorithm are compared with the Customer Facilitated Cost based Scheduling (CFCSC) algorithm. The experimental results show that Earliest Finish Time resource selection procedure outperforms CFCSC algorithm with respect to Makespan for communication intensive graphs. For computation intensive graphs, the parent resource allocation procedure produces better Makespan. With respect to Cost parameter, irrespective of total number of tasks in the Direct Acyclic Graph, all the resource selection procedures have incurred minimum cost. The proposed algorithm helps the Cloud Provider to select the scheduling algorithm in accordance with the Quality of Service parameters. Further, the LBTP algorithm can be tested with Regular Scientific workflows like Montage and Cybershake. Keywords: Cloud Computing, Cost, Makespan, Resource Allocation, Tasks Scheduling, Workflow Scheduling 1. Introduction The quality of Cloud services must be improved and the computational expenses must be reduced to retain the Cloud users 1. The service driven aspect of Cloud workflow system has been classified into three perspectives, namely, Service consumer perspective, Utility provider perspective and Market-oriented perspective 2. The workflow scheduling is a key area of concern in Market-oriented perspective. The Quality of Service (QoS) challenges have a major effect on workflow scheduling in Cloud computing of Market-oriented perspective. The parameters that provide QoS are quality of results, execution time (Makespan), throughput, reliability, monetary cost 3, deadline, trust, budget, etc. The inferences of Cloud workflow scheduling research 1 are: Promoting the user s QoS request of gratification. Attracting the users to use cloud services, thus help to achieve maximum profit. Improving the resource utilization of Cloud services provided by Cloud service providers. Promoting the development and application of Cloud computing and workflow technology, especially in the areas of biomedicine, chemistry, gene expression data analysis, astrophysics and the instance-intensive applications such as e-commerce, etc. *Author for correspondence

2 Level Based Task Prioritization Scheduling for Small Workflows in Cloud Environment This paper concentrates on two vital QoS parameters, Makespan and cost. Delay in execution of any one of the tasks in the workflow will affect the Makespan of the application. Thus, Cloud-based workflow scheduling focus on effective assignment of tasks to the resources so that the precedence constraints are retained and the Makespan is reduced. In allocation of tasks to the resources, efficient and careful resources must be chosen to reduce the execution cost so that the user s requirements are met. Workflows are used to represent a variety of scientific and engineering applications which involve high processing and storage. To satisfy these applications, the Cloud Computing Environment has emerged as a new paradigm. Scheduling problems represented by Directed Acyclic Graph (DAG) are NP-Complete 4. Numerous works have been done in the area of workflow scheduling algorithms. The workflow scheduling algorithms can be heuristic or meta-heuristic in nature. The heuristic algorithms are priority based and mainly problem centric. The developer can use his own experience to assign priority to workflow applications and Cloud resources. Meta-heuristic scheduling algorithms do not need human interaction and provide general solution to workflow applications. These algorithms are applicable to wider range of workflow applications but the heuristic scheduling algorithms are fit for only specific applications 5. The heuristic algorithms are categorized into List Scheduling Algorithm, Task Duplication-based Scheduling Algorithm, Clustering Algorithm and Guided Random Search Algorithms 6. This paper falls into the list scheduling category. Some of the popular list scheduling algorithms are Highest Level First with Estimate Time (HLFET) algorithm, Modified Critical Path (MCP) algorithm, Earliest Time First (ETF) algorithm and Dynamic Level Scheduling (DLS) algorithm. The list scheduling algorithms have two phases, namely, task prioritization and resource selection 7. Most algorithms designed by the researchers follow any one of the techniques like B-level, T-level, static B-level and static level or combination of these for task prioritization. Few algorithms follow the breadth first search method. Rajak Ranjit 8 presented a queue based scheduling algorithm called TSB to schedule tasks on homogeneous parallel multiprocessor system. It performs better than other list scheduling algorithms with respect to Makespan and efficiency. Amal et al. 9 proposed a new static scheduling algorithm called Leveled DAG Prioritized Task (LDPT). In LDPT, the task prioritization phase arranged the tasks with respect to the descending order of the computation cost of the tasks. The LDPT outperforms B-level in terms of Makespan, speedup and efficiency. From the literature, it was found that these breadth first algorithms are defined for homogeneous systems. The Cloud being a heterogeneous environment, the new algorithm Level Based Task Prioritization (LBTP) scheduling is proposed, which prioritizes the task based on the computation cost and communication cost. The resource selection phase applies three different procedures, namely, Earliest Finish Time (EFI), Parent Resource Allocation (PRA) and Round Robin (RR) to find out the best possible allocation of resource to the task to minimize the Makespan and cost of small workflows. 2. Problem Definition The mathematical model of representing the Cloud user application is Directed Acyclic Graph (DAG). DAG is an acyclic graph with nodes and directed edges. Nodes in DAG represent tasks in the application and directed edges represent precedence (data dependency) relation between two tasks. A task without any predecessors is called entry node and the task without any successors is called exit node. The j th predecessor and k th successor of node i is denoted as pre (i, j) and suc (i, k), respectively. A DAG normally has only one entry node and exit node. If an application has multiple entry nodes, a node with zero computation time and transmission cost is added to the beginning of DAG as a dummy entry node. In the case of multiple exit nodes, a dummy exit node is appended in a similar manner. Formally, a DAG is defined 10 as a tuple G = (M,E,C,D), where M is the set of nodes; E is the set edges e and e i,j represents the directed edge from node i to node j; C is the set of computation time and C (i) denotes the computation time required for executing task i; Dis the set of transmission time and D (i, j) represents the transmission time associated with the edge e i,j. When node i and node j are scheduled on the same resource, D (i, j) = 0. Figure 1 is an illustration of Cloud user application in DAG and the task priority for the DAG is given by Table The Proposed Algorithm The proposed algorithm Level Based Task Prioritization (LBTP) is also a List Scheduling algorithm. The LBTP algorithm makes certain assumptions such as, a set of 2 Vol 8 (33) December Indian Journal of Science and Technology

3 D. I. George Amalarethinam and T. Lucia Agnes Beena Every List Scheduling algorithm has two phases, namely, Task Prioritization phase (TP) and Resource Selection phase (RS). The TP phase has two steps; the first step calculates the priority for all the tasks involved in the DAG and the second step creates the execution order of the tasks based on the priorities calculated in the first step. In the RS phase, based on the execution order, the tasks are assigned to the best resource which minimizes its completion time. Figure 1. Table 1. Task Sample DAG. Level based task priority Computation Cost Communication Cost heterogeneous Virtual Machines (VMs) denoted by V are considered for creating Cloud environment, the communication network is always connected, tasks are executed without any failures and they are non-preemptive. The objectives of the LBTP algorithm are: To minimize the Makespan. To minimize the total monetary cost. Level Based (LB)- value T T T T T T T T T T T T Task Prioritization Phase The proposed LBTP algorithm, divides the DAG into different levels based on the task dependency with the entry node. At each level, the tasks are prearranged according to their computation cost in descending order. When more than one task have the same computation cost, the task with higher communication cost is given higher priority. In case of the same communication cost, the tasks are arranged in the topological order. The Task Prioritization steps are listed in Table Resource Selection Phase In this phase, the task in the execution order list is assigned to the best resource which minimizes the total completion time. The proposed LBTP algorithm, applies three different procedures namely, Earliest Finish Time (EFI), Parent Resource Allocation (PRA) and Round Robin (RR) to find out the best possible allocation of resource to the task. 2.4 Earliest Finish Time (EFI) In EFI, the virtual machine which minimizes the execution time of the given task is assigned with that machine by calculating the earliest execution start time EST (T i, ) Table 2. Task Prioritization Steps Input: DAG (including the number of tasks, with computation cost and communication cost) Output: List of tasks in execution order. For each task T i in DAG G do If task Ti is a root node then the level value L i = 1 Otherwise the level value L i = 1 + L i of its parent End for. For each Level Li do Sort the tasks by descending order of their computation cost, then by communication cost. Add the tasks to the Execution order task list. End for. Vol 8 (33) December Indian Journal of Science and Technology 3

4 Level Based Task Prioritization Scheduling for Small Workflows in Cloud Environment and the earliest execution finish time EFT (T i, ) of Task T i on resource, respectively. They are defined by: EST(T i, ) = max{t_available[j], max Tm pred(tk) ((EFT(Tm, Tk) +c(m, i))}} (1) EFT(T i, ) = w i,j + EST(T i, ) (2) Where pred (T k ) is the set of immediate predecessors of task T i and the task execution start time is represented as T_ Available [j]. Also the execution of the task is initiated only after the necessary data are sent by the V i has reached at the host which is denoted by the inner max block of equation 1. This procedure is listed in Table Parent Resource Allocation (PRA) In PRA, after sorting the virtual machines in the ascending order of their cost, each task in the execution order list is selected for virtual machine assignment. For the entry task, any Virtual Machine (VM) that minimizes the computation cost is selected for processing. For the subsequent tasks in the DAG, at each level, based on the count of the tasks assignment of VM vary. If the level has only one task, the parent task s VM is assigned for execution. When there is more than one task in the same level either the parent task s VM or the VM which minimizes the computation cost is selected for that particular task. This procedure is briefed in Table Round Robin (RR) In RR, the VM selection for the task is based on its position in the particular level. For the entry task, the VM that minimizes the computation cost is selected. The tasks other than the entry task, based on its position in that level, the unallocated VM that minimizes the computation cost is assigned for the task. Table 5 explains this procedure. Table 3. Earliest Finish Time Input: List of tasks in execution order, the number of tasks, number of Virtual machines and List of Virtual Machines sorted in ascending order by their price. Output: List of (Task, Resource) pairs Repeat The first task T i in the execution order list is removed. Find the EFT value of task T i for all virtual machines. Find the which has minimum EFT value for task T i and assign it to. Until all the tasks in the execution order list are scheduled Table 4. Table 5. Parent Resource Allocation Input: List of tasks in execution order, the number of tasks, number of Virtual machines and List of Virtual Machines sorted in ascending order by their price. Output: List of (Task, Resource) pairs Repeat Find the entry task; assign the VM that minimizes the computation cost. // for tasks other than the entry task For each level do Calculate the number of tasks. If there is only one task at the levelthen // check for parent If there is only one parentthenassign the parent s VM Else assign the VM of the parent task that completes last. Else If that is the first task at the level then assign the parent s VM Else select the VM that minimizes the computation cost for that task. Until all the tasks in the execution order list are scheduled Round Robin Input : List of tasks in execution order, the number of tasks, number of Virtual machines and List of Virtual Machines sorted in ascending order by their price. Output : List of (Task, Resource) pairs Repeat Find the entry task, assign the VM_no that minimizes the computation cost. // for tasks other than the entry task For each level do Count the number of tasks at each level. Number the tasks at that level sequentially. If there is only one task at the level then Assign the VM that minimizes the computation cost. Else If TaskCount > VMCount then VM_no= TaskCount modulus VMCount. Else if TaskCount = VMCount VM_no = TaskCount VMCount. End for Until all the tasks in the execution order list are scheduled 3. Results and Discussion The performance of the LBTP algorithm is compared with the CFCSC algorithm 11. The CFCSC algorithm outperforms the popular list scheduling algorithm HEFT. 4 Vol 8 (33) December Indian Journal of Science and Technology

5 D. I. George Amalarethinam and T. Lucia Agnes Beena The arbitrary task graphs needed for the experiments are produced using the DAGEN tool 12. The proposed algorithm LBTP is coded using Netbeans 7.1. The number of tasks are varied from 25 to 200 and correspondingly, the number of resources are also varied from 3 to 14. The VMs needed for the given number of tasks are calculated using the relation: R = N (3) Where R is the number of VMs needed to execute N number of tasks. Equation 3 is formulated after conducting different trials by varying the number of VMs. Experimental results showed that increase in R beyond floor ( N) limit, does not have any effect on the scheduling parameters. Thus it is effective to use Equation 3 for better performance of the scheduling algorithms. The communication to Computation Cost Ratio (CCR) is defined as the average communication cost divided by the average computation cost of the application DAG. For this work, the CCR value is kept below and above one to check the performances of the algorithms. 3.1 An Illustration Feeding DAG as an input, the proposed algorithm LBTP, divides the nodes into different levels. The task priority list for the sample DAG is listed in Table 1. There are ten tasks in the sample DAG, named T0 to T9. The entry task T0 is assigned one as the level value as mentioned in the LB-value column of Table 1. T1 and T3 form level two. The third level consists of T2 and T4. T5 and T8 forms the fourth level. The fifth level consists of tasks T6 and T7. T9 being the exit task forms the final level. In each level the nodes are arranged in descending order of their computation cost. For example, in level two T3 is given higher priority than T1 because the computation cost of T3 is greater than the computation cost of T1. In this way, the execution order list is created. The number of resources for the sample DAG is decided by the Equation 3. For this example, the number of tasks N is ten; hence the number of resources R gets the value 3. Therefore, three virtual machines with varying capacities are allotted randomly from the list 1x, 1.25x, 1.5x, 1.75x. The computation cost per minute of the virtual machine is set as $0.01, $0.03, $0.05 and $0.06 respectively. The computation cost for the machines are decided based on the Amazon Web Services 13 and Google AppEngine 14. In the second phase, any one of the resource selection procedures is selected and the corresponding results are tabulated in Table 6 and Table 7. The simulation is carried out by applying all the three resource selection procedures of LBTP algorithm, namely, EFI, PRA and RR. The results are compared with the CSFSC algorithm. The scheduling metrics taken for comparison are Makespan and Cost. 3.2 Performance Analysis Makespan The Makespan is an important performance criterion of scheduling heuristics. It is defined as the maximum completion time of application tasks executed on Cloud resources. Formally, it is computed by using Equation 4. Makespan = Max {FT i i T} (4) Where FT i is the finishing time of task i belonging to the list T. Table 6. Coarse grained DAGs (Computationintensive) MAKESPAN (CCR > 1) LBTP Tasks Resources CFCSC EFI PRA RR Table 7. Fine grained DAGs (Communicationintensive) MAKESPAN (CCR < 1) LBTP Tasks Resources CFCSC EFI PRA RR Vol 8 (33) December Indian Journal of Science and Technology 5

6 Level Based Task Prioritization Scheduling for Small Workflows in Cloud Environment Cost In Cloud computing, one of the vital parameters for evaluating the performance of the algorithm is the cost of renting the virtual machine. In LBTP, the Cost C is calculated using the definition: c(i,j)=w(i,j) * (cost of /minute) (5) where c (i,j) is the cost of executing task T i in the virtual machine and W (i,j) is the processing time of task T i on virtual machine.the total monetary cost is given by: C = j selected v c(i,j) (6) It is observed that for Computation intensive graphs, PRA gives the better Makespan compared to other algorithms. For Communication intensive graphs, EFI procedure out performs CFCSC algorithm. The performance of the algorithms are tabulated in Table 6 and Table 7. From the tabulated values, it is found that all the three resource selection procedure produces minimum Makespan for DAGs with maximum of 100 tasks. With respect to the Cost parameter, irrespective of the total number of tasks in the DAG, all the resource selection procedures incurred minimum cost. In particular, PRA has produced minimum cost when the CCR is greater than one and RR produced minimum cost when the CCR is less than one. Figure 2 and Figure 3 are the graphical representation which explains the Cost analysis between the CFCSC and LBTP algorithms. Figure 2. Graphical representation of total monetary cost of CFCSC and LBTP algorithms for Coarse-Grained DAGs. Figure 3. Graphical representation of total monetary cost of CFCSC and LBTP algorithms for Fine-Grained DAGs. Graphical representation of Total monetary cost of CFCSC and LBTP algorithms. In summary, it is viable to state that each algorithm discussed here has better performance based on a set of scheduling parameters taken into consideration. 4. Conclusion The Cloud Computing imposes new challenge like multiobjective criteria optimization in scheduling the workflow applications. This paper is one of the attempts to achieve the multi-objective optimization. The proposed LBTP algorithm tries to achieve minimum Makespan at minimum cost. The LBTP algorithm divides the DAG into levels according to the precedence relations. In each level, tasks are sorted in descending order of their computation cost, communication cost to form the execution order list. This execution order list is subjected to three different resource selection algorithms, namely, EFI, PRA and RR. According to the results, it is found that the PRA resource selection procedure outperforms CFCSC algorithm with respect to Makespan and cost for Coarse grained DAGs. In case of Fine grained DAGs, EFI yields better Makespan and RR reduces the cost when compared to CFCSC algorithm. From the experiments, it can be predicted that it is the duty of the scheduler to select the intelligent scheduling algorithm based on the input and QoS parameters. As a future work, the proposed LBTP algorithm can be implemented in Cloudsim, to analyze its performance. 6 Vol 8 (33) December Indian Journal of Science and Technology

7 D. I. George Amalarethinam and T. Lucia Agnes Beena 5. References 1. Chen C, Liu J, Wen Y, Chen J. Research on workflow scheduling algorithms in the Cloud. Springer-Verlag Berlin Heidelberg. 2015; 495: Alkhanak EN, Lee SP, Khan SR. Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities. Future Generation Computer Systems. Elsevier publication Sep; 50: Shyamala K, Sunitha RT, An analysis on efficient resource allocation mechanisms in Cloud Computing. Indian Journal of Science and Technology May; 8(9): Ullman J. NP-complete scheduling problems. Journal of Computer and System Sciences Jun; 10(3): Wang J, Korambath P, Altintas I, Davis J, Crawl D. Workflow as a service in the Cloud: Architecture and Scheduling Algorithms. Procedia Computer Science. Elsevier Publications. 2014; 29: Su S, Li J, Huang Q, Huang X, Shuang K, Wang J. Costefficient task scheduling for executing large programs in the Cloud. Journal of Parallel Computing. Elsevier publication Apr-May; 39(4): Yu J, Buyya RK, Ramamohanarao K. Workflow scheduling algorithm for grid computing. meta-heuristics for scheduling in distributed computing environment. Springer Berlin Heidelberg. 2008; 146: Rajak R. A novel approach for task scheduling in multiprocessor system. International Journal of Computer Applications (IJCA) Apr; 44(11): El Amal N, Nirmeen A, Bahnasawy EL, El Ayman S. A new task scheduling algorithm for maximizing the distributed systems efficiency. International Journal of Computer Applications Jan; 110(9): Kwok YK, Ahmad I. Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Computing Surveys Dec; 31(4): George Aamalarethinam DI, Lucia Agnes Beena T. Customer Facilitated Cost-based Scheduling algorithm (CFCSC) in Cloud. Procedia Computer Science. Elsevier Publications April; 46: George Aamalarethinam DI, Joyce Mary GJ. DAGEN A tool to generate arbitrary directed acyclic graphs used for multiprocessor scheduling. International Journal of Research and Reviews in Computer Science (IJRRCS) Jun; 2(3): AmazonWebServices. Available from: com/ec2/ Google AppEngine. Available from: com/appengine/ Vol 8 (33) December Indian Journal of Science and Technology 7