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Sensors 2018, 18(10), 3267; https://doi.org/10.3390/s18103267

Enhancing the Isolation and Performance of Control Planes for Fog Computing

1
Department of Computer Science and Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
2
Institute of Convergence Technology, Korea Telecom Corporation, 70 Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon 34047, Korea
3
School of Electrical and Electronics Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea
*
Author to whom correspondence should be addressed.
Received: 16 August 2018 / Revised: 21 September 2018 / Accepted: 26 September 2018 / Published: 28 September 2018
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Abstract

Fog computing, which places computing resources close to IoT devices, can offer low latency data processing for IoT applications. With software-defined networking (SDN), fog computing can enable network control logics to become programmable and run on a decoupled control plane, rather than on a physical switch. Therefore, network switches are controlled via the control plane. However, existing control planes have limitations in providing isolation and high performance, which are crucial to support multi-tenancy and scalability in fog computing. In this paper, we present optimization techniques for Linux to provide isolation and high performance for the control plane of SDN. The new techniques are (1) separate execution environment (SE2), which separates the execution environments between multiple control planes, and (2) separate packet processing (SP2), which reduces the complexity of the existing network stack in Linux. We evaluate the proposed techniques on commodity hardware and show that the maximum performance of a control plane increases by four times compared to the native Linux while providing strong isolation. View Full-Text
Keywords: fog computing; software-defined networking; Linux network stack fog computing; software-defined networking; Linux network stack
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Lee, K.; Lee, C.; Hong, C.-H.; Yoo, C. Enhancing the Isolation and Performance of Control Planes for Fog Computing. Sensors 2018, 18, 3267.

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