• Journal of Information Processing Systems
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• 제2권1호
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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.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2004년도 추계 종합학술대회 논문집
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• pp.437-441
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• 2004

컴퓨터 시스템과 네트워크가 발달함에 따라 사용자의 요구가 다양해지고 있다. 다양한 사용자 요구를 충족시키기 위해 새로운 기술들이 개발되고 있으며, 스트리밍 기법을 이용하여 소프트웨어를 사용하는 소프트웨어 스트리밍 기술이 새롭게 등장하였다. 네트워크를 기반으로 하는 스트리밍 서비스에서는 네트워크의 고장이 발생하면 소프트웨어 실행에 필요한 실행 코드를 스트리밍 서버로부터 전송 받을 수 없기 때문에 더 이상 소프트웨어를 사용할 수 없다. 본 논문에서는 로컬 저장장치에 임시 저장된 실행 코드들을 각 기능별로 관리하여 네트워크 고장 이후에는 서버로부터 실행 코드를 받지 않고 기존에 전송 받은 실행 코드를 가지고 서비스를 지속적으로 제공하기 위한 클라이언트의 소프트웨어 실행 코드 관리 기법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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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.