By different numbers of points used to definite integral, the functionality and effectivity of the cloud workflow engine are shown.
The waiting time of the last task is so long that it is nearly equal to the sum time of the other 9 tasks. Then, Figure 4 shows all service classes design of the workflow engine. a distinct unit of code, or a program, that can be separated and executed in a remote runtime environment Difference between Multithreaded Computing and Task computing: Multithreaded programming: mainly concerned with providing a support for parallelism within a single machine. Finally, section 6 is dedicated to the conclusion and future works. Many studies in cloud computing have addressed the expansion of local infrastructure capacity by using public cloud resources. identification on gpu,, J.Nickolls, I.Buck, M.Garland, and K.Skadron, Scalable parallel M.Garca-Borroto, and L.Altamirano-Robles, Introducing an As expected, increasing the number of workers reduces the running time of the system. For the sake of simplicity, in this paper, we only pack one record in each comparison task. Nimrod/G tool for automated modeling and execution of parameter sweep applications over global computational grids. The workflow process is composed of four steps: generations of random number, computing -axis, computing -axis, and computation of final result.. Deadline-driven provisioning of resources for scientific applications in According to the specific nature of the problem, task computing is categorized into: High-performance computing (HPC) High-throughput computing (HTC) Many-task computing (MTC). Errors Comparing with classic computing paradigms, cloud computing provides a pool of abstracted, virtualized, dynamically scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet [2]. 8. A similar work to ours is fast fingerprint identification for large databases[6] where authors proposed a distributed framework for fingerprint matching for large databases. Based on the analysis of the modeling, a cloud workflow engine is designed and implemented in Aneka cloud environment. Aneka master gathers all the similarity indexes computed by Aneka workers, finds the maximum similarity, retrieves the personal information of matched fingerprints to query fingerprints and returns them to the query makers. Message Passing Interface (MPI) is a specification for developing parallel programs that communi- cate by exchanging messages. private static AnekaApplication < AnekaTask, TaskManager > Setup (string, M. A. Vouk, Cloud computingissues, research and implementations,, I. Fast fingerprint identification for large databases,, H.H. Le, N.H. Nguyen, and T.T. Nguyen, A complete fingerprint matching December 2014 The goal of in-the-grid workflow is integration, composition, and orchestration of grid services in the grid environment, considering peer-to-peer service interaction and complicated lifecycle management of grid services [14]. [11] examine the usage cost and the performance of different public cloud resource provisioning algorithms. With detailed analysis of generalized cloud workflow systems, it is really indispensable to discriminate different parallelisms of workflow processes for adopting cloud technologies to promote its execution efficiency. A Petri net-based model called 3DWFN is given firstly, which can describe three dimensions of a workflow, that is, control flow, data flow, and resource flow. The master runs on a desktop machine residing at the University of Melbourne and workers are provisioned from the Microsoft Azure Australia Southeast region. Coupling ec2 cloud resources with hpc clusters to run large tightly coupled new AnekaApplication
[7] and Cappelli et al. Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. hybrid clouds with aneka,, M.A. Medina-Prez, O.Loyola-Gonzlez, A.E. Gutierrez-Rodrguez, The goal of our work in this paper is to design an above-the-cloud workflow engine based on Aneka cloud platform [6], considering scalability and load balance. The cloud workflow engine is in charge of running workflow instances, and the host application is in charge of defining workflow processes and monitoring workflow process executions. The second part of the system is the query makers. The operation flow is present in Figure 7. Foster, Y. Zhao, I. Raicu, and S. Lu, Cloud computing and grid computing 360-degree compared, in, X. Similarly, their framework is also flexible to any kind of fingerprint matching algorithms. According to features of services mainly delivered by the established cloud infrastructures, researchers separated the services into three levels, which are infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Figure 5 displays that the corresponding running time is nearly 25, 40, 57 and 68 seconds which is demonstrating a linear growth in time versus the number of tasks. Yuan et al. The task submission process in Aneka Task Model is as follows: firstly, to define a class UserTask, which inherits class AnekaTask in Aneka Task Model; secondly, to create an instance of UserTask for the application program; and thirdly, to package class UserTask instance to class AnekaTask and submit it to Aneka cloud by class AnekaApplication. The sequence diagram of the above process is shown in Figure 6.
Firstly, the implementation manager submits tasks to the workflow runtime engine. Secondly, the intensive computing tasks submitted to the Aneka cloud are executed in parallel by different workers. Even though the performance gain is high, the implementation of the parallel algorithms in CUDA is relatively hard and cumbersome. It is indicated that the functionality of our cloud workflow engine is normal. The top layer belongs to the fingerprint recognition application and its main functionalities. aneka,, C.P. Chen and C.-Y. According to the analysis of the existing workflow systems, it is found that cloud workflow pays more attention to data, resources, and performance than control flow and functionality researched commonly in traditional workflow. As shown in Figure 8, the screenshot of workflow log, tasks A and B represent, respectively, the two intensive computing tasks, computing X-axis and computing Y-axis. It is indicated that our cloud workflow engine is effective and efficient. Assuno et al. Processing big data is often very time-consuming while it could be decreased by increasing the computation power. Traditionally, computing grids composed of heterogeneous resources (clusters, workstations, and volunteer desktop machines) have been used to support HTC. We evaluate and analyze the performance of our system in section 5. According to our analysis, three kinds of differences or improvements can be concluded. When a task is enabled, the task executor will send its execution request to workflow engine. Definition 2. The experimental results validate the effectiveness of our approach of modeling, design, and implementation of cloud workflow. For instance, Figure 5 shows that the running time of the system for a single and two workers are almost 500 and 250 seconds, respectively, where the running time is reduced to half. Embarrassingly parallel applications Parameter sweep applications MPI applications Workflow applications with task dependencies 12. IaaS offers hardware resources and computing power, such as Amazon S3 for storage and EC2 for computing power. First, more practical workflow processes will be designed using powerful expression of 3DWFN. 212025], and Inner Mongolia Science Foundation for Distinguished Young Scholars [2012JQ03]. arXiv as responsive web pages so you Suppose represents s projection on , and or . Some popular software systems are Condor Globus Toolkit Sun Grid Engine (SGE) BOINC Nimrod/G Aneka. Section 2 discusses the related work. Cloud computing can provide an infinite amount of computing, storage, and network resources which suits big data challenges. Mean execution time is about 2.6 seconds, and mean waiting time is about 1 second except the last task.
This paper presents a Petri net-based model for cloud workflow which plays a key role in industry. arXiv Vanity renders academic papers from The detailed surveys can be found in [79]. As a result is a good practice to create a different project to keep this ITask class. SaaS refers to those software applications offered as services in cloud environments. The future direction will be to extend our system as a Software-as-a-Service (SaaS), one of the major categories of cloud computing, for the security-oriented organizations. They aim to evaluate the overhead of using public cloud resources. R. Buyya, J. Broberg, and A. M. Goscinski, Eds.. D. Hollingsworth, Workflow management coalition the workflow reference model, Tech. Then, the architecture of the Aneka cloud based workflow engine is designed. However, harnessing cloud resources for large-scale big data computation is application specific to a large extent. In this paper, we propose a system for large-scale fingerprint matching application using Aneka, a platform for developing scalable applications on the Cloud. 4. use of distributed computing facilities for solving problems that need large computing power. Figure 2 shows a layered view of our systems key components. S1 shows a sequential process and S2 shows a process including parallel tasks. The user is able to make a new record to store in the database or search a fingerprint through the database to retrieve the information of the person who is matched with the query fingerprint. 1 In this case, if the number of used points is increased in the next experiment, the total time might not be increased but decreased, because the cloud might assign more workers (resources) to execute them to save time. 7. Section 4 defines the main modules and interface of the system in detail. In brief, MTC denotes high-performance computations comprising multiple distinct activities coupled via file system operations. represents the set of users, while represents the set of resources. The requests are given to the Aneka master (main node) which is responsible to make and distribute the comparison tasks among Aneka workers. Dynamic Provision No related content is available yet for this article. 9. Then, the joint task S2.t5 could be triggered to be executed only if resources in S2.5, S2.6, S2.7, and S2.10 are all available. As shown in Figure 1 by 3DWFN, there are two parallel processes, named S1 and S2. We present the design and implementation of our proposed system and conduct experiments to evaluate its performance using resources from Microsoft Azure. As a future work, we are planning to devise a technique for dynamic resource provisioning based on the number of queries. Research of workflows building on IaaS focuses on dynamical deployment and monitoring in cloud nodes, which is used in large-scale data intensive computing [23, 24]. Finally, they return the matching similarity index to the master node to aggregate results. In this paper, the detailed hardware configuration is presented in Table 1. However, the usage of Petri net is always limited to model control flow, which is not enough for describing the above three dimensions of workflow. 13. Task Programming Model Figure:- Task Programming Model Scenario 16. In the second experiment, the number of workers is fixed to 8 and the running time is analyzed by changing the number of working tasks to 20, 60, 100 and 140. Rep. ANL/MCS-P980-0802, Argonne National Laboratory, 2002. By creating an account, you agree to our terms and conditions. Then, the implementation and experiments are presented in Section 4. In contrary, count is not changed. There are three parts, environment, applications, and control parts, which can solve the analyzed parallelisms problems in the three levels and achieve extensibility and reusability of workflow. techniques and technologies: A survey on big data,, R.Buyya, C.S. Yeo, S.Venugopal, J.Broberg, and I.Brandic, Cloud Grid workflow can be classed into two categories, above-the-grid and in-the-grid, as the discussion of cloud workflow in the above section. Eventually, the running time reaches about 69 seconds when the number of workers is increased to 8. Next, the submission process of task is designed. Based on the above experiments, running results and workflow logs are analyzed. However, along with the development of cloud computing, corresponding issues are also arising in both theoretical and technical aspects. Task computing: What is Task? Query fingerprints are queued for finding the matched person. Second, novel cloud workflow engine will be built, which has dynamic scheduling and handling functions with complicated process. carried out researches on hierarchical scheduling strategy in commercial cloud workflows [30]. The task of computing axes is intensive computing and will be submitted to the Aneka cloud in Task Model. In the remainder of this paper, all experiments are run with Task Model. Supporting frameworks are Globus Toolkit, BOINC, and Aneka. It embraces WEB 2.0, middleware, virtualization, and other technologies and also develops upon grid computing, distributed computing, parallel computing, and utility computing, and so forth [1]. Comparing cloud computing emerging in 2007 with grid computing [2], it can be seen that their visions are the same; meanwhile, there are both similarities and differences between them, from architecture, security model, business model, and computing model to provenance and applications. Workflow management system (WFMS) is a system for defining, implementing, and managing the workflows, in which workflow engine is the most significant component for task scheduling, data movement, and exception handling. Our experimental cloud environment includes a server as a master node, some common PCs as the worker nodes, and a manager node. Firstly, cloud workflow technology research is always carried out joint with multiple technologies and computing paradigms, such as web services, P2P, and grid computing. Related work about workflow, grid workflow, and cloud workflow is given in Section 2. There is also a console showing the progress of fingerprint searching such as tasks distribution, total running time, etc. March 2015 There are many workflow studies on different levels of cloud services. After a process is started, each task will be executed following the control flow when requirements of data flow and resource flow are met. If the task is not intensive computing, then it will be executed locally. The bottom layer provides computational resources for the Aneka platform to execute its tasks. As a result, execution of a 3DWFN looks like a user acting, as some role carries some kind of data or uses some kind of resources to walk through a certain path of the net. [13] intend to utilize the temporal variation of prices public clouds to maximize profit in a hybrid cloud environment. One of the most preferred approaches to speed up big data processing is cloud computing[3]. Variable may be replaced by any element in .Then the transition rule is given. 14. December 2017 Based on the analysis of running results and workflow logs, the scalability and efficiency of the cloud workflow engine are given. Definition 3. Constitute a collection of tasks that are independent from each other and that can be executed in any order. Create your own unique website with customizable templates. A three-dimension workflow net over them is a system , where we have the following: (i)finite sets of places and of transitions: , , and , where is a set of atomic transitions, is a set of subnet transitions, and is a set of internal transitions;(ii) is a set of arcs, especially, including the set of inhibitor arcs: ;(iii) is a finite and nonempty color set including types of tokens and the default is denoted by a black dot, ;(iv) Lab: is a labeling function;(v) Exp: is an arc expression function, where denotes the set of natural numbers and is a set of expressions of numeric computation or logic computation;(vi) is the initial marking of the net, where is the multiset over .In view of defining transition rule of 3DWFN, some notations are introduced beforehand. provides a comprehensive set of tools for sharing computing power, databases, and other services. Installation Errors allows us to turn desktop machines into volunteer computing nodes that are leveraged to run jobs when such machines become inactive. A conducted performance evaluation showed that the fingerprint queries could be responded in a significantly lower timeframe using our proposed system. Compared to earlier models, MPI introduces the constraint of com- munication that involves MPI tasks that need to run at the same time. To improve scalability, the cloud workflow engine will be designed to support different parallelism levels of workflow processes. Historically, supercomputers and clusters are specifically designed to support HPC applications. Three kinds of parallelisms in cloud workflow are characterized and modeled. Some researchers use cloud workflows in community team working among multiple processes [25]. The core implementation is configured in Algorithm 2. To avoid re-extracting feature of both query and database fingerprints in the searching procedure, Aneka master extracts the feature of query fingerprints, and Aneka workers extract the features of database fingerprint records and compare them against the query fingerprint features. Task builder is the component that makes comparison tasks. Big data applications involving the analysis of large datasets becomes a critical part of many emerging paradigms such as smart cities, social networks and modern security systems. Two main components of the used framework for fingerprint recognition which are computationally heavy are 1) extracting features from fingerprint images and 2) comparing fingerprint features. Based on the analysis of workflows running in these two kinds of infrastructure, similarities and differences are also found [3, 22]. 2. Thirdly, performance of cloud workflow is paid more attention than functionalities [3133]. (i)Preconditions about a transition are denoted as for all , , and for all . Task Manager is the part of the system which tells how many database fingerprint records are needed to be packed in a comparison task. The screenshot of workflow log shown in Table 3 presents the time table of 10 tasks for their execution time, waiting time, and total time. experimental framework in c# for fingerprint recognition, in, D.Peralta, I.Triguero, R.Sanchez-Reillo, F.Herrera, and J.M. Bentez, There are three executing models in the Aneka cloud environment, Task Model, MapReduce Model, and Thread Model.
This scenario shows the parallelism in application level. Thus, 3DWFN is suitable for modeling of cloud workflow processes. 6. aims to bridge the gap between HPC and HTC. The Globus Toolkit collection of technologies that enable grid computing. With the successful cases of the worlds leading companies, for example, Amazon and Google, cloud computing has become a hot topic in both industrial and academic areas. The data could also be stored entirely in a local infrastructure and only transferred to public infrastructure for more computation power while the trade-off between data transfer and computation power need to be considered. Each comparison task consists all query fingerprints and a single fingerprint record in the database. From the mid-1990s to around 2000, business process management and workflow technology were developed rapidly, and many valuable results and products were gained. Task computing: provides distribution by harnessing the computer power of several computing nodes Now clouds have emerged as an attractive solution. The Aneka.Tasks.ITask should be create in a project of type Class Library, because this dependency have to be pass to the Aneka Worker. Effectiveness and correctness of the novel workflow engine are shown by analysis results. The main aim of the application is to find personal information attached to the matched fingerprint. Considering that the number of records in the database and the number of queries can be huge, the importance of parallel searching in the database is obvious. Details are presented in Figure 2. An advanced model founded on the basic Petri net has been developed in our previous paper [35], called 3DWFN. Both experiments are run on a master and a set of worker machines. One of the most prominent problems is how to minimize running costs and maximize revenues on the premise of maintaining or even improving the quality of service (QoS) [3]. We showed that how Aneka provides the required platform for scheduling and parallel execution of tasks on public cloud resources, e.g., Azure. Any distributed computing framework that provides support for embarrassingly parallel applica- tions can also support the execution of parameter sweep applications, since the tasks composing the application can be executed independently of each other. Our example is to compute definite integral by a probabilistic method. In the next ten years or so, with the emergence of new computer technologies and computing paradigms, for example, web services, P2P, and grid computing, workflow can be developed in two aspects. It is concerned with recognition and execution of workflow tasks in cloud environment [26, 27]. ITask Interface Namespace Aneka.Tasks { //Codes public interface ITask { //Codes public void Execute(); } } 18. In the future, the research can be improved in following directions. 21. Then, the implementation of workflow tasks is presented in Figure 6. Three-Dimension WorkFlow Net, 3DWFN.Let , , and be finite alphabet sets of activity names, data names, and resource names, respectively. Simple Application Event based notification of tasks completion or failure. R. N. Calheiros, C. Vecchiola, D. Karunamoorthy, and R. Buyya, The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid clouds,, W. M. P. Van Der Aalst, A. H. M. Ter Hofstede, and M. Weske, Business process management: a survey, in, R. Lu and S. Sadiq, A survey of comparative business process modeling approaches, in, E. M. Bahsi, E. Ceyhan, and T. Kosar, Conditional workflow management: a survey and analysis,, H. Schonenberg, R. Mans, N. Russell, N. Mulyar, and W. Van Der Aalst, Process flexibility: a survey of contemporary approaches, in, S. Smanchat, S. Ling, and M. Indrawan, A survey on context-aware workflow adaptations, in, S. Rinderle, M. Reichert, and P. Dadam, Correctness criteria for dynamic changes in workflow systemsa survey,, F. Casati, S. Ceri, S. Paraboschi, and G. Pozzi, Specification and implementation of exceptions in workflow management systems,. It accords with the mathematical regular rule. and are colored sets. The middle layer belongs to the Aneka cloud which performs as a middleware providing computational resources to the fingerprint matching application. It uses Aneka ITask interface to prepare the comparison task for the master. The remainder of the paper is organized as follows. Task Submission Static Submission Creation of all tasks to be executed in one one loop and submission as single bag Dynamic Submission Submission as a result of event-based notification mechanism 22. multiscale applications,, H.Yuan, J.Bi, W.Tan, and B.H. Li, Temporal task scheduling with Then, we match and combine values of to a .
The layered system design contains three layers. When the workflow system enter state S2.2, S2.3, and S2.4 after transition S2.t1 fired, three tasks that are represented by S2.t2, S2.t3, and S2.t4 are enabled to be carried out in parallel at different computing node in cloud environment controlled by a single user or multiple users. And workflow schema models business processes; it is characterized by the decomposition into subflows and atomic activities, the control flow between activities (subflows), data flow and data, and the assignment of resources (including human resources and equipment resources) to each activity [5]. The cloud-based fingerprint searching is controlled by Aneka cloud platform. Workflow technology can be regarded as one of the solutions [24]. Application state and task state monitoring 3. Three analysis conclusions are given as follows. It shows that the degree of accuracy increases along with the increase of tasks number. Read the winning articles. For instance, we suppose that a police department is responsible for finding the information of a person whose fingerprint has been found in a crime scene rapidly in a massive database of records. Then, the task is packaged and submitted to the cloud by the above mentioned class AnekaApplication. Otherwise, many workflow studies in PaaS pay attention to the integration of cloud and workflow. [8] benefit from graphics processing unit (GPU) computing implementation of a fingerprint matching algorithm to speed up their system performance on large databases. Worker machines are single core Azure Instances (Standard DS1) with a 2.4GHz processor and 3.5GB main memory running Windows Server 2012 as the operating system. However, there are two challenges against this goal. [10] propose a High-Performance Computing (HPC) infrastructure architecture to execute scientific applications. Jiantao Zhou, Chaoxin Sun, Weina Fu, Jing Liu, Lei Jia, Hongyan Tan, "Modeling, Design, and Implementation of a Cloud Workflow Engine Based on Aneka", Journal of Applied Mathematics, vol. meets grid and cloud computing,, M.D. deAssuno, A.diCostanzo, and R.Buyya, A cost-benefit We're working on it and we'll get it fixed as soon possible. As mentioned above, Task Model is chosen. Upon the receipt of a request for fingerprint matching, the features of the query fingerprint are extracted. This kind of cloud workflow is regarded as above-the-cloud, such as Microsoft Biztalk workflow service and IBM Lotuslive. 61262082 and 61261019], Key Project of Chinese Ministry of Education [no. This scenario illustrates the parallelism of task level execution in the cloud workflow system. There is still much timely and worthwhile work to do in the field of cloud workflow, for example, scalability and load balance of above-the-cloud workflow, optimization and integration of in-the-cloud workflow, and so on. This section provides a general overview of how the entire system works. In this processing, there are three correlating tasks, which are generation of random numbers, computation of -axis and computation of -axis . We use the task generation of random numbers to compute real and imaginary axes. Firstly, as shown in Table 2, the results of definite integral workflow are presented with different number of points. Berkeley Open Infrastructure for Network Computing (BOINC) framework for volunteer and grid computing. Petri net is a simple, graphical, yet rigorous, mathematical formalism, which has been used to model workflow processes [34]. Install FTP server on the master node and worker nodes. Wu et al. Copyright 2014 Jiantao Zhou et al. The tasks might be of the same type or of different types, and they do not need to communicate among themselves. Finally, if a single task is intensive computing, such as the task execution between S1.2 and S1.3, it could be divided into more fine-grained subtasks and carried out in parallel at different computing node in cloud environment controlled by a single user. Finally, in Section 5, the main results of the paper are summarized. On the other hand, adapting to new features of technologies and paradigms, workflow technology should make a progress. Cloud workflow, also called cloud-based workflow [1] or cloud computing-oriented workflow [3], is a new application mode of workflow management system in the cloud environment, whose goal is to optimize the system performance, guarantee the QoS, and reduce running cost. The database record issuers is the part of the system which provide the input data for the system. In most of the cases, the data requires being processed and structured for further procedures[2]. The workflow runtime environment is shown in Figure 3, which is composed of three parts, host application, workflow instances, and runtime engine. ITask Interface Implementaion Using System; Using Aneka.Tasks; namespace GaussSample { [Serializable] public class GaussTask:ITask { private double x; public double X{ get {return this.x; } set {this.x=value;} } private double y; public double Y{ get {return this.y; } set {this.y=value;} } public void Execute() { this.y=Math.Exp(-this.x*this.x); } } } 19. The class WorkflowRuntimeService is the base class and the others are the derived classes. These two processes could be performed in parallel at different computing node in cloud environment, which illustrates the parallelism of process level execution in the cloud workflow system. Transition Rule of 3DWFN. Secondly, cloud workflow concentrates more on data and resources and not just the control flow. HTC systems need to be robust and to reliably operate over a long time scale. Cloud computing has developed as a mainstream for hosting big data applications by its ability to provide the illusion of infinite resources. Parameter sweep applications are a specific class of embarrassingly parallel applications for which the tasks are identical in their nature and differ only by the specific parameters used to execute them.
