A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

An investigation of the impact of various geographical scales for the specification of spatial dependence

Ecological studies are based on characteristics of groups of individuals, which are common in various disciplines including epidemiology. It is of great interest for epidemiologists to study the geographical variation of a disease by accounting for the positive spatial dependence between neighbouring areas. However, the choice of scale of the spatial correlation requires much attention. In view of a lack of studies in this area, this study aims to investigate the impact of differing definitions of geographical scales using a multilevel model. We propose a new approach -- the grid-based partitions and compare it with the popular census region approach. Unexplained geographical variation is accounted for via area-specific unstructured random effects and spatially structured random effects specified as an intrinsic conditional autoregressive process. Using grid-based modelling of random effects in contrast to the census region approach, we illustrate conditions where improvements are observed in the estimation of the linear predictor, random effects, parameters, and the identification of the distribution of residual risk and the aggregate risk in a study region. The study has found that grid-based modelling is a valuable approach for spatially sparse data while the SLA-based and grid-based approaches perform equally well for spatially dense data.

Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.

Regulation of P-fimbrial phase variation frequencies in Escherichia coli CFT073

Adherence of uropathogenic Escherichia coli to host tissue is required for infection and is mediated by fimbriae, such as pyelonephritis-associated pili (Pap). Expression of P fimbriae is regulated by phase variation, and to date, phase transition frequencies have been measured only for pap regulatory region constructs integrated into the E. coli K-12 chromosome. The aim of this work was to measure P phase transition frequencies in clinical isolates for the first time, including frequencies for the sequenced strain E. coli CFT073. P fimbriation and associated phase transition frequencies were measured for two E. coli clinical isolates and compared with levels for homologous pap constructs in E. coli K-12. Fimbriation and off-to-on transition frequencies were always higher in the clinical isolate. It was concluded that the regulatory inputs controlling papI expression are likely to be different in E. coli CFT073 and E. coli K-12 as (i) phase variation could be stimulated in E. coli K-12 by induction of papI and (ii) the level of expression of a papI::gfp+ fusion was higher in E. coli CFT073 than in E. coli K-12. Furthermore, phase transition frequencies for the two E. coli CFT073 pap clusters were shown to be different depending on the culture conditions, indicating that there is a hierarchy of expression depending on signal inputs.

Evidence of a conserved role for Chlamydia HtrA in the replication phase of the chlamydial developmental cycle

Identification of the HtrA inhibitor JO146 previously enabled us to demonstrate an essential function for HtrA during the mid-replicative phase of the Chlamydia trachomatis developmental cycle. Here we extend our investigations to other members of the Chlamydia genus. C. trachomatis isolates with distinct replicative phase growth kinetics showed significant loss of viable infectious progeny after HtrA was inhibited during the replicative phase. Mid-replicative phase addition of JO146 was also significantly detrimental to Chlamydia pecorum, Chlamydia suis and Chlamydia cavie. These data combined indicate that HtrA has a conserved critical role during the replicative phase of the chlamydial developmental cycle.

Palliative care phase : inter-rater reliability and acceptability in a national study

Background: The concept of palliative care consisting of five distinct, clinically meaningful, phases (stable, unstable, deteriorating, terminal and bereavement) was developed in Australia about 20 years ago and is used routinely for communicating clinical status, care planning, quality improvement and funding. Aim: To test the reliability and acceptability of revised definitions of Palliative Care Phase. Design: Multi-centre cross-sectional study involving pairs of clinicians independently rating patients according to revised definitions of Palliative Care Phase. Setting/participants: Clinicians from 10 Australian palliative care services, including 9 inpatient units and 1 mixed inpatient/community-based service. Results: A total of 102 nursing and medical clinicians participated, undertaking 595 paired assessments of 410 patients, of which 90.7% occurred within 2 h. Clinicians rated 54.8% of patients in the stable phase, 15.8% in the unstable phase, 20.8% in the deteriorating phase and 8.7% in the terminal phase. Overall agreement between clinicians’ rating of Palliative Care Phase was substantial (kappa = 0.67; 95% confidence interval = 0.61–0.70). A moderate level of inter-rater reliability was apparent across all participating sites. The results indicated that Palliative Care Phase was an acceptable measure, with no significant difficulties assigning patients to a Palliative Care Phase and a good fit between assessment of phase and the definition of that phase. The most difficult phase to distinguish from other phases was the deteriorating phase. Conclusion: Policy makers, funders and clinicians can be confident that Palliative Care Phase is a reliable and acceptable measure that can be used for care planning, quality improvement and funding purposes.

Improving national hospice/palliative care service symptom outcomes systematically through point-of-care data collection, structured feedback and benchmarking

PURPOSE Every health care sector including hospice/palliative care needs to systematically improve services using patient-defined outcomes. Data from the national Australian Palliative Care Outcomes Collaboration aims to define whether hospice/palliative care patients' outcomes and the consistency of these outcomes have improved in the last 3 years. METHODS Data were analysed by clinical phase (stable, unstable, deteriorating, terminal). Patient-level data included the Symptom Assessment Scale and the Palliative Care Problem Severity Score. Nationally collected point-of-care data were anchored for the period July-December 2008 and subsequently compared to this baseline in six 6-month reporting cycles for all services that submitted data in every time period (n = 30) using individual longitudinal multi-level random coefficient models. RESULTS Data were analysed for 19,747 patients (46 % female; 85 % cancer; 27,928 episodes of care; 65,463 phases). There were significant improvements across all domains (symptom control, family care, psychological and spiritual care) except pain. Simultaneously, the interquartile ranges decreased, jointly indicating that better and more consistent patient outcomes were being achieved. CONCLUSION These are the first national hospice/palliative care symptom control performance data to demonstrate improvements in clinical outcomes at a service level as a result of routine data collection and systematic feedback.