Management of Streaming Multimedia Content Using Mobile Agent Technology on Pure P2P-based Distributed E-learning System
Management of Streaming Multimedia Content Using Mobile Agent Technology on Pure P2P-based Distributed E-learning System
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DOI：
10.1504/ijguc.2014.062928
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发表时间：
2013-03
期刊：
影响因子：
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通讯作者：
Masayuki Higashino;Tadafumi Hayakawa;Kenichi Takahashi;T. Kawamura;K. Sugahara
中科院分区：
文献类型：
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作者：
Masayuki Higashino;Tadafumi Hayakawa;Kenichi Takahashi;T. Kawamura;K. Sugahara
目前，许多数位学习系统被广泛地部署在教育学校中。典型的e-Learning系统是以客户端-服务器模型实现的。在客户端-服务器模型中，客户端的数量影响服务器的负载。为了减轻服务器的负载，我们开发了一个基于P2P的分布式e-Learning系统。该系统由多个移动的代理组成，这些代理管理学习内容和一些功能，如评分、显示问题和正确答案。当学习者请求内容时，具有其内容的移动的代理来到学习者的计算机，然后他/她可以开始学习。这里，移动的代理必须管理多媒体数据，该多媒体数据可能是巨大的数据量。因此，一个移动的代理必须迁移到学习者的节点与一个巨大的数据量。然后，学习者不能开始学习，直到移动的代理完成迁移。为了解决这个问题，我们将多媒体数据分成片段，并准备移动的代理来管理每个片段。由于每个移动的代理变得很小，学习者可以很快开始学习，而无需等待具有巨大多媒体数据量的移动的代理的迁移。然而，我们必须寻找一个移动的代理来管理它们的片段。因此，管理第n个片段的移动的代理将其位置通知给管理第（n+1）个片段的代理，反之亦然。由于每个代理知道管理下一个和前一个片段的移动的代理的位置，并且片段被高速缓存到学习者的节点中，所以学习者可以平滑地播放多媒体数据，而不需要找到管理保持下一个片段的移动的代理的位置。实验结果表明了该方法的有效性。
Nowadays, a lot of e-Learning systems are widely deployed in educational schools. Typical e-Learning systems are implemented as client-server model. In the client-server model, the number of clients affects on the load of the server. In order to reduce the load on the server, we developed a P2P-based distributed e-Learning system. The proposed system consists of a lot of mobile agents which manage study contents and some functions such as scoring, showing questions, and correct answers. When a learner requests content, a mobile agent who has its content comes to the learner's computer, and then he/she can start the study. Here, a mobile agent has to manage multimedia data, which may be a huge size of data. Thus, a mobile agent has to migrates to the learner's node with a huge size of data. Then, the learner cannot start the study until the mobile agent finished to migrate. In order to solve this problem, we divide multimedia data into fragments and prepare mobile agents which manages each fragments. Since each mobile agents become small, a learner can start the study soon without waiting for the migration of a mobile agent which has a huge size of multimedia data. We, however, have to search a mobile agent which manages their fragments. Therefore, a mobile agent which manages n-th fragments informs its location to the agent which manages (n+1)-th fragments, and vice versa. Since each agent knows the location of a mobile agent which manages next and previous fragment, and fragments are cached into learner's node, a learner can play multimedia data smoothly without finding the location of mobile agent which manages holding next fragment. Experiment results show the effectiveness of our method.