- 著者
- Ping DU Akihiro NAKAO Satoshi MIKI Makoto INOUE
- 出版者
- The Institute of Electronics, Information and Communication Engineers
- 雑誌
- IEICE Transactions on Communications (ISSN:09168516)
- 巻号頁・発行日
- vol.E103.B, no.4, pp.422-430, 2020-04-01 (Released:2020-04-01)
- 参考文献数
- 20
- 被引用文献数
- 3
In the coming smart-home era, more and more household electrical appliances are generating more and more sensor data and transmitting them over the home networks, which are often connected to Internet through Point-to-Point Protocol over Ethernet (PPPoE) for desirable authentication and accounting. However, according to our knowledge, high-speed commercial home PPPoE router is still absent for a home network environment. In this paper, we first introduce and evaluate our programmable platform FLARE-DPDK for ease of programming network functions. Then we introduce our effort to build a compact 10Gbps software FLARE PPPoE router on a commercial mini-PC. In our implementation, the control plane is implemented with Linux PPPoE software for authentication-like signaling control. The data plane is implemented over FLARE-DPDK platform, where we get packets from physical network interfaces directly bypassing Linux kernel and distribute packets to multiple CPU cores for data processing in parallel. We verify our software PPPoE router in both lab and production network environment. The experimental results show that our FLARE software PPPoE router can achieve much higher throughput than a commercial PPPoE router tested in a production environment.
- 著者
- Akihiro NAKAO Ping DU
- 出版者
- The Institute of Electronics, Information and Communication Engineers
- 雑誌
- IEICE Transactions on Communications (ISSN:09168516)
- 巻号頁・発行日
- vol.E101.B, no.7, pp.1536-1543, 2018-07-01 (Released:2018-07-01)
- 参考文献数
- 29
- 被引用文献数
- 28
In this paper, we posit that, in future mobile network, network softwarization will be prevalent, and it becomes important to utilize deep machine learning within network to classify mobile traffic into fine grained slices, by identifying application types and devices so that we can apply Quality-of-Service (QoS) control, mobile edge/multi-access computing, and various network function per application and per device. This paper reports our initial attempt to apply deep machine learning for identifying application types from actual mobile network traffic captured from an MVNO, mobile virtual network operator and to design the system for classifying it to application specific slices.