Details

Autor(en) / Beteiligte

• Murata, Ken T.
• Pavarangkoon, Praphan
• Inoue, Kenji
• Mizuhara, Takamichi
• Kagebayashi, Yuya
• Yamamoto, Kazunori
• Muranaga, Kazuya
• Kimura, Eizen
• Nagaya, Yoshiaki

Titel

Development of High-Performance and Flexible Protocol Handler for International Web Accesses

Ist Teil von

• 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2019, p.1958-1963

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• In this big data era, a high-speed web access technique is expected for sharing a variety of large-scale data, especially over an international network. There are several high-speed web access techniques, such as data caching, data compression, data visualization, and intelligent data processing. In addition, hypertext transfer protocol (HTTP)/1.1 provides a persistent connection for better throughput than the HTTP/1.0. However, using these techniques developed so far, there are many users who are not satisfied with the current web access speed. In this paper, we propose a novel protocol handler for international web accesses based on a high-speed data transfer protocol, namely high-performance and flexible protocol (HpFP). We first design our protocol handler on the basis of HTTP, and then implement it for the Internet Explorer browser in Windows operating system (OS). Using a laboratory experiment, we evaluate the performance of our protocol handler in terms of throughput on a real-time web for weather satellite data. The results show that the proposed protocol handler achieves higher throughput than the HTTP for website requests on international networks. The improvement gets more significant as either the latency or the packet loss ratio becomes higher. This implies that the proposed protocol handler especially devotes to international web accesses for big data.