Shared Pseudomonas aeruginosa genotypes are common in Australian cystic fibrosis centres

Recent molecular-typing studies suggest cross-infection as one of the potential acquisition pathways for Pseudomonas aeruginosa in patients with cystic fibrosis (CF). In Australia, there is only limited evidence of unrelated patients sharing indistinguishable P. aeruginosa strains. We therefore examined the point-prevalence, distribution, diversity and clinical impact of P. aeruginosa strains in Australian CF patients nationally. 983 patients attending 18 Australian CF centres provided 2887 sputum P. aeruginosa isolates for genotyping by enterobacterial repetitive intergenic consensus-PCR assays with confirmation by multilocus sequence typing. Demographic and clinical details were recorded for each participant. Overall, 610 (62%) patients harboured at least one of 38 shared genotypes. Most shared strains were in small patient clusters from a limited number of centres. However, the two predominant genotypes, AUST-01 and AUST-02, were widely dispersed, being detected in 220 (22%) and 173 (18%) patients attending 17 and 16 centres, respectively. AUST-01 was associated with significantly greater treatment requirements than unique P. aeruginosa strains. Multiple clusters of shared P. aeruginosa strains are common in Australian CF centres. At least one of the predominant and widespread genotypes is associated with increased healthcare utilisation. Longitudinal studies are now needed to determine the infection control implications of these findings.

A Lightweight Tool for Anomaly Detection in Cloud Data Centres

Cloud data centres are critical business infrastructures and the fastest growing service providers. Detecting anomalies in Cloud data centre operation is vital. Given the vast complexity of the data centre system software stack, applications and workloads, anomaly detection is a challenging endeavour. Current tools for detecting anomalies often use machine learning techniques, application instance behaviours or system metrics distribu- tion, which are complex to implement in Cloud computing environments as they require training, access to application-level data and complex processing. This paper presents LADT, a lightweight anomaly detection tool for Cloud data centres that uses rigorous correlation of system metrics, implemented by an efficient corre- lation algorithm without need for training or complex infrastructure set up. LADT is based on the hypothesis that, in an anomaly-free system, metrics from data centre host nodes and virtual machines (VMs) are strongly correlated. An anomaly is detected whenever correlation drops below a threshold value. We demonstrate and evaluate LADT using a Cloud environment, where it shows that the hosting node I/O operations per second (IOPS) are strongly correlated with the aggregated virtual machine IOPS, but this correlation vanishes when an application stresses the disk, indicating a node-level anomaly.

The VINEYARD Approach: Versatile, Integrated, Accelerator-Based, Heterogeneous Data Centres.

Emerging web applications like cloud computing, Big Data and social networks have created the need for powerful centres hosting hundreds of thousands of servers. Currently, the data centres are based on general purpose processors that provide high flexibility buts lack the energy efficiency of customized accelerators. VINEYARD aims to develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by employing typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). This programming framework will, further, allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer high flexibility and energy efficiency. VINEYARD will foster the expansion of the soft-IP core industry, currently limited in the embedded systems, to the data-centre market. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases (a) a bio-informatics application for high-accuracy brain modeling, (b) two critical financial applications, and (c) a big-data analysis application.