A scalable blackbox-oriented e-learning system based on desktop grid over private cloud
Introduction
E-learning is a rapid growing and important application in the Internet and is widely used in many enterprises and educational communities [1]. With the continuous evolution of the Internet, there is an increasing demand for incorporating online practice or online certification into traditional e-learning systems. This work focuses on a web-based computer language e-learning system that allows users to upload their programs to the server where the code is parsed, performed and evaluated.
The client–server computing model provides the economical and robust medium for adaptive learning management. However, system non-responsiveness is often experienced since the client request rate may exceed the fixed server capacity. A traditional solution is to build a dedicated server cluster with a task dispatcher. Since users can submit tasks intermittently in an e-learning system, a server may stay idle during non-training time.
Today, many research works have been devoted to various e-learning systems. Instead of developing new functionalities, this work attempts to explore the feasibility of building a low-cost and scalable e-learning system via user cooperation. The idea is to gather computing resources from e-learning users by using the recently emerged volunteer computing and cloud computing techniques [2], [3], [4], [5], [6].
Volunteer computing is a type of distributed computing that allows people donating their computing resources to certain projects [7], [8]. BOINC is a volunteer computing platform that has demonstrated its efficacy for large scientific research projects in fields as biology, medicine, astrophysics, engineering and climate study [9], [10]. By breaking a monolithic job into a large number of units and distributing into different desktop computers called workers, BOINC can perform jobs efficiently via massive parallelism. Cloud computing [2] is a computing paradigm that describes a model of supplement, consumption, and delegation of computing resources to the users on-demand. Cloud computing platform is usually viewed as a management system of a pool of virtualized resources [2]. Virtualization brings many benefits for manipulating computing resources across complex and heterogeneous environments.
In this work, the e-learning desktop grid refers to a VM infrastructure built on top of the user’s computers, in which each user is not only a generator of tasks, but also a volunteer to solving tasks. The following main issues are addressed.
Promotion. Motivating has been always critical for volunteer computing. A naive restrict approach is to prohibit users from submitting new tasks unless they contribute their resources proportionally. In this paper, we adopt the non-restrict approach which motivates users via user credits in the e-learning web site.
Performance. In the e-learning private cloud, user-perceived response time is a major concern since the workers are voluntary and can disconnect spontaneously. The servers’ responsiveness directly depends on the ratio between the number of online learners and the number of online volunteers, which can be controlled via the restrict or non-restrict promotion policies mentioned above. We introduce some high capability official workers and develop a new scheduling algorithm to integrate the official and volunteer workers efficiently.
Security. Unlike normal volunteer computing platform where tasks are normally generated in authenticated servers, the tasks in the e-learning system are initiated by those in-trained learners. Executing the e-learning tasks may lead to unexpected results in the volunteer’s computer. In addition, to prevent plagiarism, volunteers must be prohibited from inspecting the code delegated to their computers.
To the best of our knowledge, the notion of gathering computing resources from e-learning users to maintain system responsiveness is original. There are several existing web-based e-learning systems that provide automated and distributed online judge functionalities [11], [12], [13]. These systems employ more servers as the system scale increases. In this paper, the e-learning desktop grid achieves scalability by involving user computers. Most cloud computing platforms assume that VM hosts are directly controlled by a front-end server, which is not applicable in volunteer computing environment. In [14], [15], the authors developed the tools for automated deployment of VMs to the computers owned by anonymous volunteers. Compared to their works, our deployment component is more complex since the system has to maintain a membership mapping between the e-learning system and the volunteer computing platform.
The proposed architecture allows participants not only to donate their resources but also to use others’ as well. When employing a broker, the participants form a desktop grid community that helps members to solve overloaded large-scale tasks such as computational science projects or artificial intelligence game tree searches. Developing a resource broker that can fairly and efficiently assign resource for the desktop grid community will be a future work.
The remaining of this paper is organized as follows. Section 2 introduces the backgrounds of e-learning systems. Section 3 discusses the system architecture and implementation issues. Section 3.4 discusses the scheduler for assigning tasks among official and volunteer workers. Finally, conclusion remarks are made in Section 5.
Section snippets
Blackbox-oriented e-learning
This work models an e-learning system as a blackbox-oriented system—a platform representing the learning contents as a composition of blackboxes, which are software components whose internals cannot be viewed. The term “blackbox” indicates that the system only considers the input/output of learner behaviors and ignores completely other details. This model has been significantly adopted in many organizations and is referred by different terminologies, such as outcome-based education or
System architecture
This subsection gives an overview and depicts the specific requirements for the e-learning desktop grid. The system contains an e-learning web server, a BOINC server and the worker control components, as illustrated in Fig. 2. Depending on which server is registered, a user can be an e-learning user, a BOINC user, or both. The computers owned by e-learning users and BOINC users are respectively referred to as user computers (UCs) and workers. The workers are further categorized into volunteer
Scheduling algorithm for official and volunteer workers
The traditional scheduler does not distinguish official and volunteer workers which are considered having obvious complementary properties. This section develops a new scheduling algorithm that improves the task response times by trying to maximize the effect of high capability official workers.
Conclusion remarks
The traditional web-based e-learning system suffers from unstable workloads and the security risk of incorporating external executable objects. This paper addressed these issues and covered with the recently emerged desktop grid and cloud computing techniques. Unlike the normal volunteer computing model where the identities of users are known, the learning users are motivated to be volunteers to share the computations. We have developed the components to integrate the e-learning system and
Acknowledgment
This work was supported in part by the National Science Council of the Republic of China (Taiwan) under Contracts NSC 101-2221-E-029-034 and NSC 102-2221-E-029-029.
Lung-Pin Chen is an associate professor of Department of computer science and information engineering at Tung-Hai University, Taiwan. He received his B.S. from Soochow University in September 1991, M.S. from National Chung-Cheng University in September 1993, and Ph.D. from National Chiao-Tung University in January 1999, all in computer science. His research interests include distributed algorithm, internet computing, SOA, and pervasive computing.
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Lung-Pin Chen is an associate professor of Department of computer science and information engineering at Tung-Hai University, Taiwan. He received his B.S. from Soochow University in September 1991, M.S. from National Chung-Cheng University in September 1993, and Ph.D. from National Chiao-Tung University in January 1999, all in computer science. His research interests include distributed algorithm, internet computing, SOA, and pervasive computing.
Jien-An Lin received the M.S.degree in Computer Science and Information Engineering from Tunghai University, Tai-Chung, Taiwan, in 2009. His research interests include distributed system, volunteer computing, and database system.
Kuan-Ching Li is a Professor of Department of Computer Science and Information Engineering (CSIE) of Providence University in Taiwan. He serves as the Editor-in-chief of International Journal of Computational Science and Engineering (IJCSE). He is a Fellow of IET society and is a Senior Member of IEEE computer society.
Ching-Hsien (Robert) Hsu received the B.S. and Ph.D. degrees in Computer Science from Tung Hai University and Feng Chia University, Taiwan, in 1995 and 1999, respectively. He is currently a professor of the department of Computer Science and Information Engineering at Chung Hua University, Taiwan. Dr. Hsu is the editor-in-chief of International Journal of Grid and High Performance Computing (IJGHPC) and serving in a number of journal editorial boards. His research interests include Parallel and distributed computing, cloud/grid computing, P2P computing, Pervasive computing, Services computing, RFID and smart homes.
Zhi-Xian Chen received the M.S.degree of management information science from Tunghai University, Taiwan, in July 2012. His research interests include parallel and distributed system, volunteer computing and cloud computing.