Design and deployment of secure, robust, and resilient SDN Controllers

The scale of the Software-Defined Network (SDN) Controller design problem has become apparent with the expansion of SDN deployments. Initial SDN deployments were small-scale, single controller environments for research and usecase testing. Today, enterprise deployments requiring multiple controllers are gathering momentum e.g. Google’s backbone network, Microsoft’s public cloud, and NTT’s edge gateway. Third-party applications are also becoming available e.g. HP SDN App Store. The increase in components and interfaces for the evolved SDN implementation increases the security challenges of the SDN controller design. In this work, the requirements of a secure, robust, and resilient SDN controller are identified, stateof-the-art open-source SDN controllers are analyzed with respect to the security of their design, and recommendations for security improvements are provided. This contribution highlights the gap between the potential security solutions for SDN controllers and the actual security level of current controller designs.

Inexact-aware architecture design for ultra-low power bio-signal analysis

This study introduces an inexact, but ultra-low power, computing architecture devoted to the embedded analysis of bio-signals. The platform operates at extremely low voltage supply levels to minimise energy consumption. In this scenario, the reliability of static RAM (SRAM) memories cannot be guaranteed when using conventional 6-transistor implementations. While error correction codes and dedicated SRAM implementations can ensure correct operations in this near-threshold regime, they incur in significant area and energy overheads, and should therefore be employed judiciously. Herein, the authors propose a novel scheme to design inexact computing architectures that selectively protects memory regions based on their significance, i.e. their impact on the end-to-end quality of service, as dictated by the bio-signal application characteristics. The authors illustrate their scheme on an industrial benchmark application performing the power spectrum analysis of electrocardiograms. Experimental evidence showcases that a significance-based memory protection approach leads to a small degradation in the output quality with respect to an exact implementation, while resulting in substantial energy gains, both in the memory and the processing subsystem.

Photon nanojet lens: Design, fabrication and characterization

In this paper, a novel nanolens with super resolution, based on the photon nanojet effect through dielectric nanostructures in visible wavelengths, is proposed. The nanolens is made from plastic SU-8, consisting of parallel semi-cylinders in an array. This paper focuses on the lens designed by numerical simulation with the finite-difference time domain method and nanofabrication of the lens by grayscale electron beam lithography combined with a casting/bonding/lift-off transfer process. Monte Carlo simulation for injected charge distribution and development modeling was applied to define the resultant 3D profile in PMMA as the template for the lens shape. After the casting/bonding/lift-off process, the fabricated nanolens in SU-8 has the desired lens shape, very close to that of PMMA, indicating that the pattern transfer process developed in this work can be reliably applied not only for the fabrication of the lens but also for other 3D nanopatterns in general. The light distribution through the lens near its surface was initially characterized by a scanning near-field optical microscope, showing a well defined focusing image of designed grating lines. Such focusing function supports the great prospects of developing a novel nanolithography based on the photon nanojet effect.