• Journal of Information Processing Systems
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• v.2 no.1
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• pp.6-12
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• 2006

Peer-to-Peer (P2P) networks are attracting considerable research interest because of their scalability and high performance relative to cost. One of the important services on a P2P network is the streaming service. However, because each node in the P2P network is autonomous, it is difficult to provide a stable streaming service on the network. Therefore, for a stable streaming service on the P2P network, a fault-tolerant scheme must be provided. In this paper, we propose two new node selection schemes, Playback Node First (PNF) and Playback Node first with Prefetching (PNF-P) that can be used for a service migration-based fault-tolerant streaming service. The proposed schemes exploit the fact that the failure probability of a node currently being served is lower than that of a node not being served. Simulation results show that the proposed schemes outperform traditional node selection schemes.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.437-441
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• 2004

As the development of networks and computer systems, users want various services. To meet user's various needs, new technologies is developed and a software streaming technology, uses software with streaming technology, is newly coming out. If network trouble occurs, the software is not available because the streaming server can't send the binary code that software needs to be executed in the streaming service based on network environment. In this paper, we propose the management technology of software binary code in client that managing the binary codes classfied by the function kept in the local storage device, provides the service continuously after the network trouble with the given binary code previously not the binary code from the server.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.2977-2997
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• 2018

In big data era, fresh data grows rapidly every day. More than 30,000 gigabytes of data are created every second and the rate is accelerating. Many organizations rely heavily on real time streaming, while big data stream computing helps them spot opportunities and risks from real time big data. Storm, one of the most common online stream computing platforms, has been used for big data stream computing, with response time ranging from milliseconds to sub-seconds. The performance of Storm plays a crucial role in different application scenarios, however, few studies were conducted to evaluate the performance of Storm. In this paper, we investigate the performance of Storm under different application scenarios. Our experimental results show that throughput and latency of Storm are greatly affected by the number of instances of each vertex in task topology, and the number of available resources in data center. The fault-tolerant mechanism of Storm works well in most big data stream computing environments. As a result, it is suggested that a dynamic topology, an elastic scheduling framework, and a memory based fault-tolerant mechanism are necessary for providing high throughput and low latency services on Storm platform.