A constraints-based resource discovery model for multi-provider cloud environments

Abstract Abstract Increasingly infrastructure providers are supplying the cloud marketplace with storage and on-demand compute resources to host cloud applications. From an application user’s point of view, it is desirable to identify the most appropriate set of available resources on which to execute an application. Resource choice can be complex and may involve comparing available hardware specifications, operating systems, value-added services (such as network configuration or data replication) and operating costs (such as hosting cost and data throughput). Providers’ cost models often change and new commodity cost models (such as spot pricing) can offer significant savings. In this paper, a software abstraction layer is used to discover the most appropriate infrastructure resources for a given application, by applying a two-phase constraints-based approach to a multi-provider cloud environment. In the first phase, a set of possible infrastructure resources is identified for the application. In the second phase, a suitable heuristic is used to select the most appropriate resources from the initial set. For some applications a cost-based heuristic may be most appropriate; for others a performance-based heuristic may be of greater relevance. A financial services application and a high performance computing application are used to illustrate the execution of the proposed resource discovery mechanism. The experimental results show that the proposed model can dynamically select appropriate resouces for an application’s requirements. </jats:sec

SLA-aware resource management

The management of infrastructure resources in a large-scale environment such as Grid Computing is a challenging task and places significant demands on re- source discovery, scheduling and the underlying communication channels. The fulfilment of the business goals and service quality in such an environment requires an infrastructure to cope with changes in demand and infrastructure performance. In this paper, we propose an abstract service-oriented framework for SLA-aware dynamic resource management. The framework provides self-managing, self-configuration and self-healing strategies in order to support autonomic and ambient service management. We study an SLA negotiation process at the infrastructure resource layer, live migration for resource re-provisioning, a multi-layer architecture framework to monitor infrastructure resources and a harmonized interface to access arbitrary sources of infrastructure resources based on SLA requirements. Resource usage will be optimized according to the provider policies and SLA requirements

Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis

Background: Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes. Methods & findings Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04–1.06, per HbA1c-raising allele, p = 3 × 10−29); whereas GS-E was not (OR = 1.00, 95% CI 0.99–1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66–0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38–0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI 0.55–0.74) of African American adults with T2D to remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants. Conclusions: As G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses

T2D prediction, glycemic genetic score.

<p>Forest plot of association between glycemic genetic score with incident T2D over a decade-long follow-up period, by ancestry. MESA (European and Asian ancestry) and the <i>G6PD</i> variant (rs1050828) in ARIC (European and African American) were not included in the discovery GWAS analysis. Effect estimates were combined in a fixed effects meta-analysis. Overall effect estimate: 1.05, 95% CI 1.04–1.06, <i>p</i> = 2.5 × 10⁻²⁹. ARIC, Atherosclerosis Risk in Communities Study; ES, Effect Size; FHS, Framingham Heart Study; GWAS, genome-wide association study; G6PD, glucose-6-phosphate dehydrogenase; I-Squared, Higgin's I-squared statistic, a measure of heterogeneity; MESA, Multiethnic Study of Atherosclerosis; SCHS, Singapore Chinese Health Study; T2D, type 2 diabetes.</p