Data-Driven Intelligent Scheduling For Long Running Workloads In Large-Scale Datacenters

Cloud computing is becoming a fundamental facility of society today. Large-scale public or private cloud datacenters spreading millions of servers, as a warehouse-scale computer, are supporting most business of Fortune-500 companies and serving billions of users around the world. Unfortunately, modern industry-wide average datacenter utilization is as low as 6% to 12%. Low utilization not only negatively impacts operational and capital components of cost efficiency, but also becomes the scaling bottleneck due to the limits of electricity delivered by nearby utility. It is critical and challenge to improve multi-resource efficiency for global datacenters. Additionally, with the great commercial success of diverse big data analytics services, enterprise datacenters are evolving to host heterogeneous computation workloads including online web services, batch processing, machine learning, streaming computing, interactive query and graph computation on shared clusters. Most of them are long-running workloads that leverage long-lived containers to execute tasks. We concluded datacenter resource scheduling works over last 15 years. Most previous works are designed to maximize the cluster efficiency for short-lived tasks in batch processing system like Hadoop. They are not suitable for modern long-running workloads of Microservices, Spark, Flink, Pregel, Storm or Tensorflow like systems. It is urgent to develop new effective scheduling and resource allocation approaches to improve efficiency in large-scale enterprise datacenters. In the dissertation, we are the first of works to define and identify the problems, challenges and scenarios of scheduling and resource management for diverse long-running workloads in modern datacenter. They rely on predictive scheduling techniques to perform reservation, auto-scaling, migration or rescheduling. It forces us to pursue and explore more intelligent scheduling techniques by adequate predictive knowledges. We innovatively specify what is intelligent scheduling, what abilities are necessary towards intelligent scheduling, how to leverage intelligent scheduling to transfer NP-hard online scheduling problems to resolvable offline scheduling issues. We designed and implemented an intelligent cloud datacenter scheduler, which automatically performs resource-to-performance modeling, predictive optimal reservation estimation, QoS (interference)-aware predictive scheduling to maximize resource efficiency of multi-dimensions (CPU, Memory, Network, Disk I/O), and strictly guarantee service level agreements (SLA) for long-running workloads. Finally, we introduced a large-scale co-location techniques of executing long-running and other workloads on the shared global datacenter infrastructure of Alibaba Group. It effectively improves cluster utilization from 10% to averagely 50%. It is far more complicated beyond scheduling that involves technique evolutions of IDC, network, physical datacenter topology, storage, server hardwares, operating systems and containerization. We demonstrate its effectiveness by analysis of newest Alibaba public cluster trace in 2017. We are the first of works to reveal the global view of scenarios, challenges and status in Alibaba large-scale global datacenters by data demonstration, including big promotion events like Double 11 . Data-driven intelligent scheduling methodologies and effective infrastructure co-location techniques are critical and necessary to pursue maximized multi-resource efficiency in modern large-scale datacenter, especially for long-running workloads

Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

Failure to recognize samples from the classes unseen during training is a major limit of artificial intelligence (AI) in real-world implementation of retinal anomaly classification. To resolve this obstacle, we propose an uncertainty-inspired open-set (UIOS) model which was trained with fundus images of 9 common retinal conditions. Besides the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external testing set and non-typical testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicted high uncertainty scores, which prompted the need for a manual check, in the datasets of rare retinal diseases, low-quality fundus images, and non-fundus images. This work provides a robust method for real-world screening of retinal anomalies

Dietary Supplementation with Oleum Cinnamomi Improves Intestinal Functions in Piglets

The present study was to determine the efficacy of dietary supplementation with oleum cinnamomi (OCM) on growth performance and intestinal functions in piglets. Sixteen piglets (24-day-old) were randomly assigned to the control or OCM groups. Piglets in the control group were fed a basal diet, whereas piglets in the OCM group were fed the basal diet supplemented with 50 mg/kg OCM. On day 20 of the trial, blood samples and intestinal tissues were obtained from piglets. Compared with the control group, dietary OCM supplementation increased (p < 0.05) average daily feed intake, plasma insulin levels, villus width and villous surface area in the duodenum and jejunum, DNA levels and RNA/DNA ratios in the ileum, the abundance of Enterococcus genus and Lactobacillus genus in caecum digesta, mRNA levels for epithelial growth factor receptor (EGFR), Ras, extracellular signal-regulated kinase 1/2 (Erk1/2), b-cell lymphoma-extra large (Bcl-xL), villin, junctional adhesion molecule A (JAM-A), myxovirus resistance (MX) 1, MX2 and regenerating islet-derived protein 3 gamma (REG3G), and protein abundances of Ras and claudin-1, but decreased (p < 0.05) diarrhoea incidence; the abundances of Enterobacteriaceae family, Enterococcus genus, Lactobacillus genus, Bifidobacterium genus, and Clostrium coccoides in the colon digesta, and AMP-activated protein kinase (AMPK) mRNA levels and caspase-3 protein abundance in the jejunal mucosa of piglets. Taken together, these data indicate that dietary OCM supplementation modulates intestinal microbiota and improves intestinal function in weanling pigs. OCM is an effective feed additive and alternative to feed antibiotics for improving intestinal health in swine

Nonlinear distortion mitigation by machine learning of SVM classification for PAM-4 and PAM-8 modulated optical interconnection

We demonstrated a support vector machine (SVM) based machine learning method to mitigate modulation nonlinearity distortion for PAM-4 and PAM-8 vertical cavity surface emitter laser multi-mode fiber (VCSEL-MMF) optical link. Simulations at 100 Gb/s data rate and experimental work at 60 Gb/s data rate were carried out. We achieved a significant improvement in bit error rate (BER) when complete binary tree SVMs (CBT-SVMs) are applied for both PAM-4 and PAM-8 signals. Quantitative analysis of the sensitivity gain versus modulation nonlinearity distortion is presented with experimentally verification. The results indicate that CBT-SVMs have better performance for PAM-8 compared to PAM-4. The sensitivity gain increases almost linearly with the increase of eye-linearity (increase of modulation nonlinearity distortion). Up to 2.5-dB sensitivity improvement is achieved by the proposed CBT-SVMs at eye-linearity of 1.72 for PAM-4.</p

Genome-Wide Assessment of Runs of Homozygosity in Chinese Wagyu Beef Cattle

Runs of homozygosity (ROH) are continuous homozygous regions that generally exist in the DNA sequence of diploid organisms. Identifications of ROH leading to reduction in performance can provide valuable insight into the genetic architecture of complex traits. Here, we evaluated genome-wide patterns of homozygosity and their association with important traits in Chinese Wagyu beef cattle. We identified a total of 29,271 ROH segments from 462 animals. Within each animal, an average number of ROH was 63.36 while an average length was 62.19 Mb. To evaluate the enrichment of ROH across genomes, we initially identified 280 ROH regions by merging ROH events across all individuals. Of these, nine regions containing 154 candidate genes, were significantly associated with six traits (body height, chest circumference, fat coverage, backfat thickness, ribeye area, and carcass length; p &lt; 0.01). Moreover, we found 26 consensus ROH regions with frequencies exceeding 10%, and several regions overlapped with QTLs, which are associated with body weight, calving ease, and stillbirth. Among them, we observed 41 candidate genes, including BCKDHB, MAB21L1, SLC2A13, FGFR3, FGFRL1, CPLX1, CTNNA1, CORT, CTNNBIP1, and NMNAT1, which have been previously reported to be related to body conformation, meat quality, susceptibility, and reproductive traits. In summary, we assessed genome-wide autozygosity patterns and inbreeding levels in Chinese Wagyu beef cattle. Our study identified many candidate regions and genes overlapped with ROH for several important traits, which could be unitized to assist the design of a selection mating strategy in beef cattle

Machine learning adaptive receiver for PAM-4 modulated optical interconnection based on silicon microring modulator

Modulation nonlinearity can severely distort multi-level modulation, and signal processing to mitigate the distortion is highly desirable. In this work, we demonstrated a machine learning method for adaptive detection of 4-level pulse amplitude modulation (PAM-4) signals modulated by silicon micro-ring modulator (Si-MRM). The very limited linear modulation range of Si-MRM leads to serious modulation nonlinearity distortion for high-level modulations like PAM-4 with the consideration of wavelength drift. Our approach is based on the support vector machine (SVM) method which can learn the nonlinear distortion of Si-MRM during PAM-4 modulation. Thus, the detection can be made adaptive for PAM-4 signals with nonlinear levels and level dependent noise. The modulation nonlinearity distortion of PAM-4 has been characterized in terms of level deviation (LD) with respect to wavelength drift. Up to 2.7-dB receiver sensitivity gain is obtained at about 26% LD by using the proposed SVM machine learning method. The receiver sensitivity-float range can be squeezed to be within 0.3 dB even with up to 30% LD which indicates a stable detection of PAM-4 signals along with wavelength drift. Up to 3.63-dB receiver sensitivity improvement has been experimentally achieved at 50 Gbps for PAM-4 signals modulated by a Si-MRM and after 2-km standard single mode fiber (SSMF) transmission. The stable operation of Si-MRM is very difficult and very important. The proof-of-concept results indicate the very promising capability of machine learning method for stable detection of PAM-4 signals modulated by Si-MRM, which is of great significance for practical application of Si-MRM in optical interconnection.</p