Kairos: Preemptive Data Center Scheduling Without Runtime Estimates

The vast majority of data center schedulers use task runtime estimates to improve the quality of their scheduling decisions. Knowledge about runtimes allows the schedulers, among other things, to achieve better load balance and to avoid head-of-line blocking. Obtaining accurate runtime estimates is, however, far from trivial, and erroneous estimates lead to sub-optimal scheduling decisions. Techniques to mitigate the effect of inaccurate estimates have shown some success, but the fundamental problem remains. This paper presents Kairos, a novel data center scheduler that assumes no prior information on task runtimes. Kairos introduces a distributed approximation of the Least Attained Service (LAS) scheduling policy. Kairos consists of a centralized scheduler and per-node schedulers. The per-node schedulers implement LAS for tasks on their node, using preemption as necessary to avoid head-of-line blocking. The centralized scheduler distributes tasks among nodes in a manner that balances the load and imposes on each node a workload in which LAS provides favorable performance. We have implemented Kairos in YARN. We compare its performance against the YARN FIFO scheduler and Big-C, an open-source state-of-the-art YARN-based scheduler that also uses preemption. Compared to YARN FIFO, Kairos reduces the median job completion time by 73% and the 99th percentile by 30%. Compared to Big-C, the improvements are 37% for the median and 57% for the 99th percentile. We evaluate Kairos at scale by implementing it in the Eagle simulator and comparing its performance against Eagle. Kairos improves the 99th percentile of short job completion times by up to 55% for the Google trace and 85% for the Yahoo trace

Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe.

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project GenTORE and Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle

Genome-wide association studies of fertility and calving traits in Brown Swiss cattle using imputed whole-genome sequences

BACKGROUND: The detection of quantitative trait loci has accelerated with recent developments in genomics. The introduction of genomic selection in combination with sequencing efforts has made a large amount of genotypic data available. Functional traits such as fertility and calving traits have been included in routine genomic estimation of breeding values making large quantities of phenotypic data available for these traits. This data was used to investigate the genetics underlying fertility and calving traits and to identify potentially causative genomic regions and variants. We performed genome-wide association studies for 13 functional traits related to female fertility as well as for direct and maternal calving ease based on imputed whole-genome sequences. Deregressed breeding values from ~1000-5000 bulls per trait were used to test for associations with approximately 10 million imputed sequence SNPs. RESULTS: We identified a QTL on BTA17 associated with non-return rate at 56 days and with interval from first to last insemination. We found two significantly associated non-synonymous SNPs within this QTL region. Two more QTL for fertility traits were identified on BTA25 and 29. A single QTL was identified for maternal calving traits on BTA13 whereas three QTL on BTA19, 21 and 25 were identified for direct calving traits. The QTL on BTA19 co-localizes with the reported BH2 haplotype. The QTL on BTA25 is concordant for fertility and calving traits and co-localizes with a QTL previously reported to influence stature and related traits in Brown Swiss dairy cattle. CONCLUSION: The detection of QTL and their causative variants remains challenging. Combining comprehensive phenotypic data with imputed whole genome sequences seems promising. We present a QTL on BTA17 for female fertility in dairy cattle with two significantly associated non-synonymous SNPs, along with five additional QTL for fertility traits and calving traits. For all of these we fine mapped the regions and suggest candidate genes and candidate variants

Hemostasis, endothelial stress, inflammation, and the metabolic syndrome.

Obesity and the metabolic syndrome (MS) are two of the pressing healthcare problems of our time. The MS is defined as increased abdominal obesity in concert with elevated fasting glucose levels, insulin resistance, elevated blood pressure, and plasma lipids. It is a key risk factor for type 2 diabetes mellitus (T2DM) and for cardiovascular complications and mortality. Here, we review work demonstrating that various aspects of coagulation and hemostasis, as well as vascular reactivity and function, become impaired progressively during chronic ingestion of a western diet, but also acutely after meals. We outline that both T2DM and cardiovascular disease should be viewed as inflammatory diseases and describe that chronic overload of free fatty acids and glucose can trigger inflammatory pathways directly or via increased production of ROS. We propose that since endothelial stress and increases in platelet activity precede inflammation and overt symptoms of the MS, they are likely the first hit. This suggests that endothelial activation and insulin resistance are probably causative in the observed chronic low-level metabolic inflammation, and thus both metabolic and cardiovascular complications linked to consumption of a western diet

Dietary carbohydrates impair healthspan and promote mortality.

The prospective cohort study, named PURE, found that in >135,000 participants from 18 countries, nutritive carbohydrates increase human mortality, whereas dietary fat reduces it, requesting a fundamental change of current nutritional guidelines. Experimental evidence from animal models provides synergizing mechanistic concepts as well as pharmacological options to mimic low-carb or ketogenic diets

Gut peptide agonism in the treatment of obesity and diabetes.

Obesity is a global healthcare challenge that gives rise to devastating diseases such as the metabolic syndrome, type-2 diabetes (T2D), and a variety of cardiovascular diseases. The escalating prevalence of obesity has led to an increased interest in pharmacological options to counteract excess weight gain. Gastrointestinal hormones such as glucagon, amylin, and glucagon-like peptide-1 (GLP-1) are well recognized for influencing food intake and satiety, but the therapeutic potential of these native peptides is overall limited by a short half-life and an often dose-dependent appearance of unwanted effects. Recent clinical success of chemically optimized GLP-1 mimetics with improved pharmacokinetics and sustained action has propelled pharmacological interest in using bioengineered gut hormones to treat obesity and diabetes. In this article, we summarize the basic biology and signaling mechanisms of selected gut peptides and discuss how they regulate systemic energy and glucose metabolism. Subsequently, we focus on the design and evaluation of unimolecular drugs that combine the beneficial effects of selected gut hormones into a single entity to optimize the beneficial impact on systems metabolism

Short-term feeding of a ketogenic diet induces more severe hepatic insulin resistance than an obesogenic high-fat diet

Despite being a relevant healthcare issue and heavily investigated, the aetiology of type 2 diabetes (T2D) is still incompletely understood. It is well established that increased endogenous glucose production (EGP) leads to a progressive increase in glucose levels, causing insulin resistance and eventual loss of glucose homeostasis. The consumption of high carbohydrate, high‐fat, western style diet (HFD) is linked to the development of T2D and obesity, whereas the consumption of a low carbohydrate, high‐fat, ketogenic diet (KD) is considered healthy. However, several days of carbohydrate restriction are known to cause selective hepatic insulin resistance. In the present study, we compare the effects of short‐term HFD and KD feeding on glucose homeostasis in mice. We show that, even though KD fed animals appear to be healthy in the fasted state, they exhibit decreased glucose tolerance to a greater extent than HFD fed animals. Furthermore, we show that this effect originates from blunted suppression of hepatic glucose production by insulin, rather than impaired glucose clearance and tissue glucose uptake. These data suggest that the early effects of HFD consumption on EGP may be part of a normal physiological response to increased lipid intake and oxidation, and that systemic insulin resistance results from the addition of dietary glucose to EGP‐derived glucose.ISSN:0022-3751ISSN:1469-779

Grating-based X-ray dark-field imaging : a new paradigm in radiography

Grating-based X-ray dark-field contrast is an emerging new imaging modality that is demonstrating particularly high potential for radiography. The signal in dark-field X-ray imaging is determined by small-angle X-ray scattering at structures typically below the spatial resolution of the imaging setup. Thus, this technique not only yields complementary information but also visualizes information that lies under the resolution limit for conventional, absorption-based radiography. Grating-based X-ray dark-field imaging has been shown to be feasible with both synchrotron radiation and conventional X-ray tubes. Lung, breast, and bone imaging have been identified as the applications promising the main impact, but other applications are on the horizon. Specifically, dark-field radiography has been used to detect pulmonary emphysema and assesses its regional distribution in mice and holds promise to improve the visualization of micro-calcifications in mammography and yields information about bone microstructure. Further technical developments are required to make the technique suitable for clinical use

Lifetime feed efficiency and deep phenotypes from scarce feed intake records using the mechanistic LiGAPS-Dairy model

Ideally, selection for feed efficiency requires deep phenotyping of net efficiency, or lifetime recording of intake and all energy sinks across environments. However, recording of feed intake is scarce. Therefore, net efficiency is often defined as a simplistic linear equation, e.g. RFI. We tested the use of the mechanistic LiGAPS-Dairy model to derive nine deep phenotypes with a dataset for 1,228 dairy cows, combining feed intake, yield and liveweight data, with ration, weather, cow and farm data. Mismatch between data recording and model assumptions made this process time consuming, but allowing for missing parities and further automation should improve this quickly. We managed for 206 cows to estimate the deep phenotypes. Heritability and phenotypic correlations between the nine traits were estimated. When the pipeline is finished, the mechanistic LiGAPS-Dairy model will enable us to derive a more comprehensive breeding goal, more closely resembling net efficiency, whilst utilising scarce records