Performance Prediction Based Workload Scheduling in Co-Located Cluster

Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competition between batch jobs and online services,co-location frequently impairs the performance of online services.This study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for co-locatedworkloads.The performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on randomforest.On-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs.When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling strategy.Compared with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average.Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.

The Relation between Mental Workload and Face Temperature in Flight Simulation

In this research, we study the relationship between mental workload and facial temperature of aircraft participants during a simulated takeoff flight. We conducted experiments to comprehend the correlation between work and facial temperature within the flight simulator. The experiment involved a group of 10 participants who played the role of pilots in a simulated A-320 flight. Six different flying scenarios were designed to simulate normal and emergency situations on airplane takeoff that would occur in different levels of mental workload for the participants. The measurements were workload assessment, face temperatures, and heart rate monitoring. Throughout the experiments, we collected a total of 120 instances of takeoffs, together with over 10 hours of time-series data including heart rate, workload, and face thermal images and temperatures. Comparative analysis of EEG data and thermal image types, revealed intriguing findings. The results indicate a notable inverse relationship between workload and facial muscle temperatures, as well as facial landmark points. The results of this study contribute to a deeper understanding of the physiological effects of workload, as well as practical implications for aviation safety and performance.

Analytical workload dependence of self-heating effect for SOI MOSFETs considering two-stage heating process

Dynamic self-heating effect(SHE)of silicon-on-insulator(SOI)MOSFET is comprehensively evaluated by ultrafast pulsed I-V measurement in this work.It is found for the first time that the SHE complete heating response and cooling response of SOI MOSFETs are conjugated,with two-stage curves shown.We establish the effective thermal transient response model with stage superposition corresponding to the heating process.The systematic study of SHE dependence on workload shows that frequency and duty cycle have more significant effect on SHE in first-stage heating process than in the second stage.In the first-stage heating process,the peak lattice temperature and current oscillation amplitude decrease by more than 25 K and 4%with frequency increasing to 10 MHz,and when duty cycle is reduced to 25%,the peak lattice temperature drops to 306 K and current oscillation amplitude decreases to 0.77%.Finally,the investigation of two-stage(heating and cooling)process provides a guideline for the unified optimization of dynamic SHE in terms of workload.As the operating frequency is raised to GHz,the peak temperature depends on duty cycle,and self-heating oscillation is completely suppressed.

Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation

Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier’s accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.

HTAP数据库系统数据共享模型和优化策略

混合事务与分析处理数据库系统(HTAP)因其在一套系统上可以同时处理混合负载而逐渐获得大众认可.为了不影响在线事务处理(OLTP)业务的写入性能,HTAP数据库系统往往会通过维护数据多版本或额外副本的方式来支持在线分析处理(OLAP)任务,从而引入了TP/AP端版本的数据一致性问题.同时,HTAP数据库系统面临资源隔离下实现高效数据共享的核心挑战,且数据共享模型的设计综合权衡了业务对性能和数据新鲜度之间的要求.因此,为了系统地阐释现有HTAP数据库系统数据共享模型及优化策略,首先根据TP生成版本与AP查询版本的差异,通过一致性模型定义数据共享模型,将HTAP数据共享的一致性模型分为3类,分别为线性一致性,顺序一致性与会话一致性.然后,梳理数据共享模型的全流程,即从数据版本标识号分配,数据版本同步,数据版本追踪3个核心问题出发,给出不同一致性模型的实现方法.进一步,以典型的HTAP数据库系统为例对具体实现进行深入的阐释.最后,针对数据共享过程中涉及的版本同步、追踪、回收等模块的优化策略进行归纳和分析,并展望数据共享模型的优化方向,指出数据同步范围自适应,数据同步周期自调优和顺序一致性的新鲜度阈值约束控制是提高HTAP数据库系统性能和新鲜度的可能手段.

ICU成人护理工时评估体系与NAS相关性和一致性的临床研究

目的:分析重症监护病房(ICU)成人护理工时评估体系与护理活动评估量表(NAS)之间的相关性,探讨二者之间的共性和差异,以期对我国今后ICU护理工作量的评价提供参考。方法:选取某三级医院ICU病人200例,应用ICU成人护理工时评估体系与NAS测定其护理工作量,应用Spearman、多元线性回归进行相关分析,采用Bland-Altman法对二者进行一致性分析。结果:ICU成人护理工时评估体系与NAS总分的相关系数为0.869(P<0.001)。其中直接护理工时与NAS总分的相关系数为0.869,间接护理工时与NAS总分无相关性。基础护理和ICU专科护理总体上与NAS 7个维度存在线性相关(P<0.001)。其中:基础护理与基础活动、通气支持、心血管支持和营养支持存在相关性(P<0.05);ICU专科护理与基础活动、通气支持、心血管支持、肾脏支持和ICU特殊干预存在相关关系(P<0.05)。两种工具测量值96%的差值点均落在95%的一致性界限内(-1.07,1.07),二者一致性较高。结论:ICU成人护理工时评估体系与NAS相关性、一致性均较高,可互换使用,较好反映ICU护理工作量;目前国内测定ICU护理工作量的“金标准”尚未统一,建议临床护理研究者、管理者参考本研究结果进行ICU护理工作量测定的研究,推进护理工作量研究领域的进一步发展。

Energy-Aware Scheduling Scheme Using Workload-Aware Consolidation Technique in Cloud Data Centres

To reduce energy consumption in cloud data centres,in this paper,we propose two algorithms called the Energy-aware Scheduling algorithm using Workload-aware Consolidation Technique(ESWCT) and the Energyaware Live Migration algorithm using Workload-aware Consolidation Technique(ELMWCT).As opposed to traditional energy-aware scheduling algorithms,which often focus on only one-dimensional resource,the two algorithms are based on the fact that multiple resources(such as CPU,memory and network bandwidth)are shared by users concurrently in cloud data centres and heterogeneous workloads have different resource consumption characteristics.Both algorithms investigate the problem of consolidating heterogeneous workloads.They try to execute all Virtual Machines(VMs) with the minimum amount of Physical Machines(PMs),and then power off unused physical servers to reduce power consumption.Simulation results show that both algorithms efficiently utilise the resources in cloud data centres,and the multidimensional resources have good balanced utilizations,which demonstrate their promising energy saving capability.

Multicenter application of a nursing workload measurement scale in adult hospitalization units

Objectives:The study aimed to the multicenter application of a nursing workload measurement scale in the internal medicine and surgery adults hospitalization units.Methods:The study design was a multicenter,observational,and descriptive study.A multicenter application of the MIDENF®nursing workload measurement scale was carried out,which consists of 21 items,and covers the four nursing functions(patient care items,teaching,manager,and researcher),in units of hospitalization of adults of internal medicine and surgery of four different hospitals.Each item contains one or more of the nursing interventions of Nursing Interventions Classification(NIC)and has an assigned time,after comparing the real time it takes to perform each intervention with the North American Nursing Diagnosis Association(NANDA)standardized time.The study was carried out during nine months of the year 2020,measuring two days each month in the three work shifts(morning,evening,and night)to all patients admitted on the days of measurement in the indicated units.Results:The descriptive and inferential analysis of 11,756 completed scales,5,695 in general surgery and 6,061 in internal medicine,showed a greater care load for the two units during the morning shift(227,034 min in general surgery,261,835 min in internal medicine),especially in the items of“self-care”,“medication”,“common invasive procedures”,“fluid therapy”,and“patient and family support”,while the managerial function was similar during the three work shifts in the two units studied,getting values between 57,348 and 62,901min.In the analysis by shift and unit,statistical significance was obtained both for the total workload and the four nursing functions(P<0.001).Conclusions:It is shown that the use of validated scales with the standardized language of nursing functions,adapted to the units,provides objective information to adjust the nursing staff to the real situation of care in any hospital and unit where it is applied,improving quality and patient safety.

Assessment of Effects of Concomitant Exposure to Sound, Heat, and Physical Workload Changes on Physiological Parameters in Five Different Combination Modes, a Controlled Laboratory Study

Exposure to sound,heat,and increased physical workload can change physiological parameters.This study was conducted to evaluate the effect of concomitant exposure to sound,heat,and physical workload changes on physiological parameters in controlled laboratory conditions.This experimental crosssectional study was conducted in 35 male university students with a mean age of 25.75 years and a mean BMI of 22.69 kg/m2.Systolic and diastolic blood pressure and heart rate were measured after 15 min rest in the laboratory,5 and 10 min after starting the experiment,and then after 20 min in controlled laboratory conditions in five combination modes.The combination modes were(Sound:65 dB,WBGT:22°C,Speed:1.7,Slope:10%),(Sound:65 dB,WBGT:22°C,Speed:3.4,Slope:14%),(Sound:95 dB,WBGT:22°C,Speed:1.7,Slope:10%),(Sound:65 dB,WBGT:32°C,Speed:1.7,Slope:10%),and(Sound:95 dB,WBGT:32°C,Speed:3.4,Slope:14%).Mixed model analysis and paired t-test were applied for analysis.The results showed that the mean physiological parameters(Systolic and diastolic blood pressure and heart rate)increased when different combination modes worsened(Sound from 65 to 95 dB,WBGT from 22°C to 32°C,speed from 1.7 to 3.4,and slope from 10%to 14%,and when sound:95 dB,WBGT:32°C,Speed:3.4,and Slope:14%).Moreover,the mean changes of systolic and diastolic blood pressure were significant in all conditions when compared with the reference condition(Sound:65 dB,WBGT:22°C,Speed:1.7,and Slope:10%).The mean heart rate changes were also significant except for exposure to the second condition(Sound:65 dB,WBGT:22°C,Speed:3.4,Slope:14%)and the third condition(Sound:95 dB,WBGT:22°C,Speed:1.7,Slope:10%).Exposure to hazardous levels of sound,heat,and workload has adverse effects on physiological parameters.Concomitant exposure to all three hazards has a synergistic effect and increases the adverse effect.

DISCRIMINATION OF MENTAL WORKLOAD LEVELS IN HUMAN SUBJECTS WITH FUNCTIONAL NEAR-INFRARED SPECTROSCOPY

We have applied functional near-infrared spectroscopy(fNIRS)to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex.We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks.To quantify the potential of fNIRS for mental workload discrimination,we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin(HbO2)and deoxyhemoglobin(HbR)concentration changes associated with the working memory tasks.We have found classification success rates in the range of 44%-72%,which are significantly higher than the corresponding chance level(for random data)of 19.1%.This work shows the potential of fNIRS for mental workload classification,especially when more parameters(rather than just the amplitude of concentration changes used here)and more sophisticated classification algorithms(rather than the simple 3-nearest neighbor algorithm used here)are considered and optimized for this application.

Measuring Mental Workload Using ERPs Based on FIR,ICA,and MARA

Mental workload is considered to be strongly linked to human performance,and the ability to measure it accurately is key for balancing human health and work.In this study,brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload.In addition,a finite impulse response(FIR)filter,independent component analysis(ICA),and multiple artifact rejection algorithms(MARAs)were used to filter event-related potentials(ERPs).Then,the data consisting of ERPs,subjective ratings of mental workload,and task performance,were analyzed through the use of variance and Spearman’s correlation during a simulated computer task.We found that participants responded faster and performed better in the easy task condition,followed by the medium and high-difficulty conditions,which verifies the validity of the ERP filtering.Moreover,larger P2 and P3 waveforms were evoked as the task difficulty increased,and a higher task difficulty elicited a more enhanced N300.Correlation analysis revealed a negative relationship between the amplitude of P3 and the subjective ratings,and a positive relationship between the P3 amplitude and accuracy.The results presented in this paper demonstrate that a combination of FIR,ICA,and MARA methods can filter ERPs in the non-invasive real-time measurement of workload.Additionally,frontocentral P2,N3,and parietal P3 components showed differences between genders.The proposed measurement of mental workload can be useful for real-time identification of mental states and can be applied to human-computer interaction in the future.

Integrated Evaluation of Air Traffic Controller Workload Based on Matter-Element Analysis

A model for evaluating the controller workload was presented based on matter-element analysis, particularly from a mansystem engineering perspective. On the basis of a questionnaire survey, 18 kinds of indexes which influence the controller workload were determined. By establishing the classical field and node field of the controller workload, the correlation function of the controller workload grade was obtained; then the correlation degree and estimated grade of controller workload were given. A case study verifies the feasibility of the proposed evaluation method.

Synthetic Workload Generation for Cloud Computing Applications

We present techniques for characterization, modeling and generation of workloads for cloud computing applications. Methods for capturing the workloads of cloud computing applications in two different models - benchmark application and workload models are described. We give the design and implementation of a synthetic workload generator that accepts the benchmark and workload model specifications generated by the characterization and modeling of workloads of cloud computing applications. We propose the Georgia Tech Cloud Workload Specification Language (GT-CWSL) that provides a structured way for specification of application workloads. The GT-CWSL combines the specifications of benchmark and workload models to create workload specifications that are used by a synthetic workload generator to generate synthetic workloads for performance evaluation of cloud computing applications.

Workload Allocation Based on User Mobility in Mobile Edge Computing

Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all things.By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile devices.However,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in depth.In this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user.We obtain the next possible location through the user's past location information,and receive the next access server according to the grid matrix.Furthermore,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the task.In the experiments,the results demonstrate a high accuracy of our proposed model.

Fractional Rider Deep Long Short Term Memory Network for Workload Prediction-Based Distributed Resource Allocation Using Spark in Cloud Gaming

The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour. The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it streamed the scenes as video series to play through network. Therefore, cloud gaming is a capable approach, which quickly increases the cloud computing platform. Obtaining enhanced user experience in cloud gaming structure is not insignificant task because user anticipates less response delay and high quality videos. To achieve this, cloud providers need to be able to accurately predict irregular player workloads in order to schedule the necessary resources. In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming. The workload of each resource is computed based on developed Fractional Rider Deep LSTM network. Moreover, resource allocation is performed by fractional Rider-based Harmony Search Algorithm (Rider-based HSA). This Fractional Rider-based HSA is developed by combining Fractional calculus (FC), Rider optimization algorithm (ROA) and Harmony search algorithm (HSA). Moreover, the developed Fractional Rider Deep LSTM is developed by integrating FC and Rider Deep LSTM. In addition, the multi-objective parameters, namely gaming experience loss QE, Mean Opinion Score (MOS), Fairness, energy, network parameters, and predictive load are considered for efficient resource allocation and workload prediction. Additionally, the developed workload prediction model achieved better performance using various parameters, like fairness, MOS, QE, energy and delay. Hence, the developed Fractional Rider Deep LSTM model showed enhanced results with maximum fairness, MOS, QE of 0.999, 0.921, 0.999 and less energy and delay of 0.322 and 0.456.

Comprehensive Assessment of Pilot Mental Workload in Various Levels

Physiological measures indexed by fixation frequency and total fixation time,task performance based on n-back accuracy,subjective assessment based on NASA Task Load Index(NASA-TLX) were used to measure the Mental Workload(MW) in different levels which were induced by vision-related flight task combined with auditory cognitive load. 16 healthy novice pilots were recruited to complete a monitoring task based on Head-Up Display(HUD) and an auditory n-back task which was used to manipulate the Mental Workload Level(MWL) in flight simulation environment. In our experiment,fixation frequency,average saccade time,blink rate and average pupil diameter were sensitive to MW. What's more,a comprehensive assessment method of pilot mental workload based on various measures was advocated. At last,a Fisher projection function based on the Fisher discrimination method and a three-level discriminate model established by the Bayes discrimination method were built up,the original validation and cross-validation methods of both models were 97. 92% and 95. 83% respectively,which could discriminate various Mental Workload Levels(MWLs) ideally.

Muscle endurance measurement using a progressive workload and a constant workload by maximal voluntary contraction

Muscle endurance measurement using a progressive workload method may reduce pain sensation in the subject. This study aimed to examine the relationships between force-time parameters during sustained static gripping as measured by maximal voluntary contraction (MVC) using either a progressive workload (PW) or a constant workload (CW). Sixteen subjects performed sustained static gripping with 7 gradually increasing relative demand values of 20% to 80% MVC and sustained static gripping by MVC. The staging of progressive workload was 10 s for 20% MVC, 20 s each for 30, 40, 50, 60, and 70% MVC, and 10 s for 80% MVC. The forces exerted at 120 s in the CW and PW methods were at around the 23-27% MVC level. Peak force, final force, and force during the last 30 s for the PW method evaluated muscle endurance after 1 min and showed high correlations (r = 0.746 ? 0.895). Significant correlations (r = 0.575 ? 0.605) were found between time to 40% MVC in the CW method and peak force, final force, and force in the last 30 s in the PW method group. The peak force in the PW method may be useful for evaluating muscle endurance with a short testing time and without high pain sensation.

Study of fault injection system based on software

A software fault injection system SFIS is designed,which consists of the target system plus a fault injector,fault library,workload,data collector,and data analyzer. A serial communication mechanism is adopted to simulate the factual work environment. Then a fault model is built for single particle event,which can be denoted as FM=(FL,FT). FL stands for fault location,and FT stands for fault type. The fault model supports three temporal faults: transient,intermittent,and permanent. During the experiments implemented by SFIS,the software interruption method is adopted to inject transient faults,and step trace method is adopted to inject permanent faults into the target system. The experiment results indicate that for the injected transient code segment faults,2.8 % of them do not affect the program output,80.1% of them are detected by the built-in error detection in the system,and 17.1% of them are not detected by fault detection mechanism. The experiment results verify the validity of the fault injection method.

Distribution Calculation Model of Armored Equipment Maintenance Workload

The divisions of the typical army maintenance organization's tasks in wartime are discussed.Two distribution models of armored equipment maintenance objects are presented:one is calculated by maintenance workload and the other is calculated by maintenance time.Combined with the division of maintenance time limit for the land force's maintenance institutions,the probability distribution of the maintenance object which is produced from the typical armored equipment's technical failure and battle damage in every repair organization is obtained.A new way for the study of the distribution law of battle damage is supplied,which has an active function to improve the accuracy of technical support program.

PhiBench 2.0: characterizing data analytics workloads on Intel Knights Landing

With high computational capacity, e.g. many-core and wide floating point SIMD units, Intel Xeon Phi shows promising prospect to accelerate high-performance computing(HPC) applications. But the application of Intel Xeon Phi on data analytics workloads in data center is still an open question. Phibench 2.0 is built for the latest generation of Intel Xeon Phi(KNL, Knights Landing), based on the prior work PhiBench(also named BigDataBench-Phi), which is designed for the former generation of Intel Xeon Phi(KNC, Knights Corner). Workloads of PhiBench 2.0 are delicately chosen based on BigdataBench 4.0 and PhiBench 1.0. Other than that, these workloads are well optimized on KNL, and run on real-world datasets to evaluate their performance and scalability. Further, the microarchitecture-level characteristics including CPI, cache behavior, vectorization intensity, and branch prediction efficiency are analyzed and the impact of affinity and scheduling policy on performance are investigated. It is believed that the observations would help other researchers working on Intel Xeon Phi and data analytics workloads.