Acceleration and visualization of Dynamic Network Optimization

With the emerging prevalence of smart phones and 4G LTE networks, the demand for faster-better-cheaper mobile services anytime and anywhere is ever growing. The Dynamic Network Optimization (DNO) concept emerged as a solution that optimally and continuously tunes the network settings, in response to varying network conditions and subscriber needs. Yet, the DNO realization is still at infancy, largely hindered by the bottleneck of the lengthy optimization runtime. This paper presents the design and prototype of a novel cloud based parallel solution that further enhances the scalability of our prior work on various parallel solutions that accelerate network optimization algorithms. The solution aims to satisfy the high performance required by DNO, preliminarily on a sub-hourly basis. The paper subsequently visualizes a design and a full cycle of a DNO system. A set of potential solutions to large network and real-time DNO are also proposed. Overall, this work creates a breakthrough towards the realization of DNO.

A parallel formulation for the simulation of a generic branch predictor

A parallel formulation for the simulation of a branch prediction algorithm is presented. This parallel formulation identifies independent tasks in the algorithm which can be executed concurrently. The parallel implementation is based on the multithreading model and two parallel programming platforms: pthreads and Cilk++. Improvement in execution performance by up to 7 times is observed for a generic 2-bit predictor in a 12-core multiprocessor system.

Fast adaptive real-time classification for data streams with concept drift

An important application of Big Data Analytics is the real-time analysis of streaming data. Streaming data imposes unique challenges to data mining algorithms, such as concept drifts, the need to analyse the data on the fly due to unbounded data streams and scalable algorithms due to potentially high throughput of data. Real-time classification algorithms that are adaptive to concept drifts and fast exist, however, most approaches are not naturally parallel and are thus limited in their scalability. This paper presents work on the Micro-Cluster Nearest Neighbour (MC-NN) classifier. MC-NN is based on an adaptive statistical data summary based on Micro-Clusters. MC-NN is very fast and adaptive to concept drift whilst maintaining the parallel properties of the base KNN classifier. Also MC-NN is competitive compared with existing data stream classifiers in terms of accuracy and speed.

Revisiting the ‘Venturi effect’ in passage ventilation between two non-parallel buildings

A recent study conducted by Blocken et al. (Numerical study on the existence of the Venturi effect in passages between perpendicular buildings. Journal of Engineering Mechanics, 2008,134: 1021-1028) challenged the popular view of the existence of the ‘Venturi effect’ in building passages as the wind is exposed to an open boundary. The present research extends the work of Blocken et al. (2008a) into a more general setup with the building orientation varying from 0° to 180° using CFD simulations. Our results reveal that the passage flow is mainly determined by the combination of corner streams. It is also shown that converging passages have a higher wind-blocking effect compared to diverging passages, explained by a lower wind speed and higher drag coefficient. Fluxes on the top plane of the passage volume reverse from outflow to inflow in the cases of α=135°, 150° and 165°. A simple mathematical expression to explain the relationship between the flux ratio and the geometric parameters has been developed to aid wind design in an urban neighborhood. In addition, a converging passage with α=15° is recommended for urban wind design in cold and temperate climates since the passage flow changes smoothly and a relatively lower wind speed is expected compared with that where there are no buildings. While for the high-density urban area in (sub)tropical climates such as Hong Kong where there is a desire for more wind, a diverging passage with α=150° is a better choice to promote ventilation at the pedestrian level.

A parallel data assimilation model for oceanographic observations

In this paper we describe the development of a program that aims at the optimal integration of observed data in an oceanographic model describ