Autoscaling Method for Docker Swarm Towards Bursty Workload

The autoscaling mechanism of cloud computing can automatically adjust computing resources according to user needs, improve quality of service (QoS) and avoid over-provision. However, the traditional autoscaling methods suffer from oscillation and degradation of QoS when dealing with burstiness. Therefore, the autoscaling algorithm should consider the effect of bursty workloads. In this paper, we propose a novel AmRP (an autoscaling method that combines reactive and proactive mechanisms) that uses proactive scaling to launch some containers in advance, and then the reactive module performs vertical scaling based on existing containers to increase resources rapidly. Our method also integrates burst detection to alleviate the oscillation of the scaling algorithm and improve the QoS. Finally, we evaluated our approach with state-of-the-art baseline scaling methods under different workloads in a Docker Swarm cluster. Compared with the baseline methods, the experimental results show that AmRP has fewer SLA violations when dealing with bursty workloads, and its resource cost is also lower

Research hotspots and trends of fresh e-commerce in China: A knowledge mapping analysis based on bibliometrics

The fresh e-commerce industry has seen a sudden and substantial rise since the outbreak of COVID-19. The rapid development of this industry calls for a comprehensive and systematic review of its research status, hotspots and future trends, which will have significant implications for researchers in related fields. This paper first conducts a current situation analysis of the core literature on fresh e-commerce retrieved from four databases – CNKI, CSSCI, Wanfang and VIP – to categorize the research status of fresh e-commerce in three dimensions: the year of publication, article sources, and distribution of subjects. CiteSpace is then used to perform a bibliometric analysis of the data and to create visualized knowledge maps. The results show that the research on fresh e-commerce can be divided into three stages: rapid development (2012-2015), exploration and transformation (2016-2019), maturity and upgrade (2020-present). At each stage, the research evolves toward diversity and maturity with policy developments and changes in the external environment. Cold chain logistics, business models, freshness-keeping of products and e-commerce are ongoing research hotspots in fresh produce e-commerce, while later studies focus more on the transformation and upgrade of products, logistics, distribution and platforms to better serve consumers’ consumption habits and environmental requirements. This study provides valuable insights for researchers and enterprises who are engaged in the industry and for those who are interested in the development of fresh e-commerce in China

Sales forecasting of stores in shopping malls: A study based on external data and transaction data

To improve the forecast accuracy of the sales of stores in shopping malls, this paper proposes a prediction method based on deep learning that comprehensively considers the external data, such as online review data of shopping mall stores, weather data, weekday/weekend data, and historical transaction data of the stores. To begin with, the online review data of the stores are pre-trained with BERT (Bidirectional Encoder Representations from Transformers) to complete the multi-label sentiment classification and obtain the intensity index of perceived sentiment of reviews. The index, together with other external data, such as online ratings, weather, weekday/weekend differences, and historical transactions of the stores, is pre-processed. At last, the Long Short-Term Memory (LSTM) and the Attention models are used to predict the sales volume of stores in a certain shopping mall. The results show that the addition of external data – weather, weekday/weekend, online ratings and intensity index of sentiment of reviews – to the historical sales data-based model can effectively improve the forecast accuracy of store sales

Ab initio study of electron mean free paths and thermoelectric properties of lead telluride

Last few years have witnessed significant enhancement of thermoelectric figure of merit of lead telluride (PbTe) via nanostructuring. Despite the experimental progress, current understanding of the electron transport in PbTe is based on either band structure calculation using first principles with constant relaxation time approximation or empirical models, both relying on adjustable parameters obtained by fitting experimental data. Here, we report parameter-free first-principles calculation of electron and phonon transport properties of PbTe, including mode-by-mode electron-phonon scattering analysis, leading to detailed information on electron mean free paths and the contributions of electrons and phonons with different mean free paths to thermoelectric transport properties in PbTe. Such information will help to rationalize the use and optimization of nanostructures to achieve high thermoelectric figure of merit

Measurement of the total cross section and ρ -parameter from elastic scattering in pp collisions at √s=13 TeV with the ATLAS detector

In a special run of the LHC with β⋆= 2.5 km, proton–proton elastic-scattering events were recorded at s=13 TeV with an integrated luminosity of 340μb-1 using the ALFA subdetector of ATLAS in 2016. The elastic cross section was measured differentially in the Mandelstam t variable in the range from - t= 2.5 · 10 - 4 GeV 2 to - t= 0.46 GeV 2 using 6.9 million elastic-scattering candidates. This paper presents measurements of the total cross section σtot , parameters of the nuclear slope, and the ρ -parameter defined as the ratio of the real part to the imaginary part of the elastic-scattering amplitude in the limit t→ 0 . These parameters are determined from a fit to the differential elastic cross section using the optical theorem and different parameterizations of the t-dependence. The results for σtot and ρ are σtot(pp→X)=104.7±1.1mb,ρ=0.098±0.011. The uncertainty in σtot is dominated by the luminosity measurement, and in ρ by imperfect knowledge of the detector alignment and by modelling of the nuclear amplitude

Measurement of exclusive pion pair production in proton–proton collisions at √s=7 TeV with the ATLAS detector

The exclusive production of pion pairs in the process pp→ ppπ+π- has been measured at s=7TeV with the ATLAS detector at the LHC, using 80μb-1 of low-luminosity data. The pion pairs were detected in the ATLAS central detector while outgoing protons were measured in the forward ATLAS ALFA detector system. This represents the first use of proton tagging to measure an exclusive hadronic final state at the LHC. A cross-section measurement is performed in two kinematic regions defined by the proton momenta, the pion rapidities and transverse momenta, and the pion–pion invariant mass. Cross-section values of 4.8±1.0(stat)-0.2+0.3(syst)μb and 9±6(stat)-2+2(syst)μb are obtained in the two regions; they are compared with theoretical models and provide a demonstration of the feasibility of measurements of this type

Constraints on the Higgs boson self-coupling from single- and double-Higgs production with the ATLAS detector using pp collisions at root s=13 TeV

Constraints on the Higgs boson self-coupling are set by combining double-Higgs boson analyses in the bb over bar bb over bar , bb over bar & tau;+& tau;- and bb over bar & gamma; & gamma; decay channels with single-Higgs boson analyses targeting the & gamma;& gamma;, Z Z*, W W *, & tau;+& tau;- and bb over bar decay channels. The data used in these analyses were recorded by the ATLAS detector at the LHC in proton-proton collisions at & RADIC;s = 13 TeV and correspond to an integrated luminosity of 126-139 fb-1. The combination of the double-Higgs analyses sets an upper limit of & mu;HH < 2.4 at 95% confidence level on the double-Higgs production cross-section normalised to its Standard Model prediction. Combining the single-Higgs and double-Higgs analyses, with the assumption that new physics affects only the Higgs boson self-coupling (& lambda;HHH), values outside the interval -0.4 < & kappa;& lambda; = (& lambda;HHH/& lambda;SM H H H ) < 6.3 are excluded at 95% confidence level. The combined single-Higgs and double-Higgs analyses provide results with fewer assumptions, by adding in the fit more coupling modifiers introduced to account for the Higgs boson interactions with the other Standard Model particles. In this relaxed scenario, the constraint becomes -1.4 < & kappa;& lambda; < 6.1 at 95% CL. & COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/). Funded by SCOAP3