107 research outputs found

    Towards predictive runtime modelling of Kubernetes microservices

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    Kubernetes is one of the major container management platforms utilised by Cloud Service Providers offering to host applications and services. As cloud based services become more prevalent, platform providers are faced with an increasingly complex problem of trying to meet contracted performance levels. Providers must strike a balance between management of resource allocations and contractual obligations to ensure that their service is profitable, while offering competitive pricing rates for contracts. This research explores performance modelling of microservice application tenants within the Kubernetes container management platform. We present a self-adaptive architecture to achieve modelling at runtime. We establish the potential for automated classification of cloud systems, and utilise a hybridised modelling approach to verify system properties and evaluate performance. We achieve this through the modelling of components as Extended Finite State Machines in WATERS, from which we automate the generating of performance models using the PEPA syntax

    Rural AI: Serverless-powered federated learning for remote applications

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    With increasing connectivity to support digital services in urban areas, there is a realization that demand for offering similar capability in rural communities is still limited. To unlock the potential of Artificial Intelligence (AI) within rural economies, we propose Rural AI—the mobilization of serverless computing to enable AI in austere environments. Inspired by problems observed in New Zealand, we analyze major challenges in agrarian communities and define their requirements. We demonstrate a proof-of-concept Rural AI system for cross-field pasture weed detection that illustrates the capabilities serverless computing offers to traditional federated learning

    Cumulative versus transient shoreline change : dependencies on temporal and spatial scale

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    Author Posting. © American Geophysical Union, 2011. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 116 (2011): F02014, doi:10.1029/2010JF001835.Using shoreline change measurements of two oceanside reaches of the North Carolina Outer Banks, USA, we explore an existing premise that shoreline change on a sandy coast is a self-affine signal, wherein patterns of change are scale invariant. Wavelet analysis confirms that the mean variance (spectral power) of shoreline change can be approximated by a power law at alongshore scales from tens of meters up to ∼4–8 km. However, the possibility of a power law relationship does not necessarily reveal a unifying, scale-free, dominant process, and deviations from power law scaling at scales of kilometers to tens of kilometers may suggest further insights into shoreline change processes. Specifically, the maximum of the variance in shoreline change and the scale at which that maximum occurs both increase when shoreline change is measured over longer time scales. This suggests a temporal control on the magnitude of change possible at a given spatial scale and, by extension, that aggregation of shoreline change over time is an important component of large-scale shifts in shoreline position. We also find a consistent difference in variance magnitude between the two survey reaches at large spatial scales, which may be related to differences in oceanographic forcing conditions or may involve hydrodynamic interactions with nearshore geologic bathymetric structures. Overall, the findings suggest that shoreline change at small spatial scales (less than kilometers) does not represent a peak in the shoreline change signal and that change at larger spatial scales dominates the signal, emphasizing the need for studies that target long-term, large-scale shoreline change.Our thanks to the NSF (grant EAR‐04‐ 44792) for funding this researc

    In vitro characterisation of fresh and frozen sex-sorted bull spermatozoa

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    peer-reviewedThis study sought to compare the in vitro characteristics of fresh and frozen non-sorted (NS) and sex-sorted (SS) bull spermatozoa. Experiment 1: Holstein–Friesian ejaculates (n = 10 bulls) were split across four treatments and processed: (1) NS fresh at 3 × 106 spermatozoa, (2) X-SS frozen at 2 × 106 spermatozoa, (3) X-SS fresh at 2 × 106 spermatozoa and (4) X-SS fresh at 1 × 106 spermatozoa. NS frozen controls of 20 × 106 spermatozoa per straw were sourced from previously frozen ejaculates (n = 3 bulls). Experiment 2: Aberdeen Angus ejaculates (n = 4 bulls) were split across four treatments and processed as: (1) NS fresh 3 × 106 spermatozoa, (2) Y-SS fresh at 1 × 106 spermatozoa, (3) Y-SS fresh at 2 × 106 spermatozoa and (4) X-SS fresh at 2 × 106 spermatozoa. Controls were sourced as per Experiment 1. In vitro assessments for progressive linear motility, acrosomal status and oxidative stress were carried out on Days 1, 2 and 3 after sorting (Day 0 = day of sorting. In both experiments SS fresh treatments had higher levels of agglutination in comparison to the NS fresh (P < 0.001), NS frozen treatments had the greatest PLM (P < 0.05) and NS spermatozoa exhibited higher levels of superoxide anion production compared with SS spermatozoa (P < 0.05). Experiment 1 found both fresh and frozen SS treatments had higher levels of viable acrosome-intact spermatozoa compared with the NS frozen treatments (P < 0.01).ACCEPTEDpeer-reviewe

    Memoirs of Stephen Burroughs. [Two lines of verse] : Copy right secured.

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