A Configurable Packet Classification Architecture for Software-Defined Networking

Network management tools must be able to monitor and analyze traffic flowing through network systems. According to the OpenFlow protocol applied in Software-Defined Networking (SDN), packets are classified into flows that are searched in flow tables. Further actions, such as packet forwarding, modification, and redirection to a group table, are made in the flow table with respect to the search results. A novel hardware solution for SDN-enabled packet classification is presented in this paper. The proposed scheme is focused on a label-based search method, achieving high flexibility in memory usage. The implemented hardware architecture provides optimal lookup performance by configuring the search algorithm and by performing fast incremental update as programmed the software controller.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.

Electron-phonon thermalization in a scalable method for real-time quantum dynamics

We present a quantum simulation method that follows the dynamics of out-of-equilibrium many-body systems of electrons and oscillators in real time. Its cost is linear in the number of oscillators and it can probe time scales from attoseconds to hundreds of picoseconds. Contrary to Ehrenfest dynamics, it can thermalize starting from a variety of initial conditions, including electronic population inversion. While an electronic temperature can be defined in terms of a nonequilibrium entropy, a Fermi-Dirac distribution in general emerges only after thermalization. These results can be used to construct a kinetic model of electron-phonon equilibration based on the explicit quantum dynamics.

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.

A Scalable Runtime for the ECOSCALE Heterogeneous Exascale Hardware Platform

Exascale computation is the next target of high performance computing. In the push to create exascale computing platforms, simply increasing the number of hardware devices is not an acceptable option given the limitations of power consumption, heat dissipation, and programming models which are designed for current hardware platforms. Instead, new hardware technologies, coupled with improved programming abstractions and more autonomous runtime systems, are required to achieve this goal. This position paper presents the design of a new runtime for a new heterogeneous hardware platform being developed to explore energy efficient, high performance computing. By combining a number of different technologies, this framework will both simplify the programming of current and future HPC applications, as well as automating the scheduling of data and computation across this new hardware platform. In particular, this work explores the use of FPGAs to achieve both the power and performance goals of exascale, as well as utilising the runtime to automatically effect dynamic configuration and reconfiguration of these platforms.