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.

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.

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.

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.

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.

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.

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.

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.

Server Workload in an M/M/1 Queue with Bulk Arrivals and Special Delays

We consider a variant of M/M/1 where customers arrive singly or in pairs. Each single and one member of each pair is called primary;the other member of each pair is called secondary. Each primary joins the queue upon arrival. Each secondary is delayed in a separate area, and joins the queue when “pushed” by the next arriving primary. Thus each secondary joins the queue followed immediately by the next primary. This arrival/delay mechanism appears to be new in queueing theory. Our goal is to obtain the steady-state probability density function (pdf) of the workload, and related quantities of interest. We utilize a typical sample path of the workload process as a physical guide, and simple level crossing theorems, to derive model equations for the steady-state pdf. A potential application is to the processing of electronic signals with error free components and components that require later confirmation before joining the queue. The confirmation is the arrival of the next signal.

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.

Cognitive Workload Assessment of Aircraft Pilots

In this research, we study the cognitive workload of aircraft pilots during a simulated takeoff procedure. We propose a proof-of-concept setup environment to gather heart rate, pupil dilation, and brain cognitive workload data during an A320 takeoff within a simulator. Experiments were performed during which we collected 136 takeoffs across 13 pilots for more than 9 hours of time-series data. Moreover, this paper investigates the correlations between heart rate, pupil dilation, and cognitive workload during such exercise and found that a spike in cognitive load during a critical moment, such as an engine failure, augments a pilot’s heart rate and pupil dilation. Results show that a critical moment within a takeoff procedure increases a pilot’s cognitive load. Next, we used a stacked-LSTM model to predict cognitive workload 5 seconds into the future. The model was able to produce accurate predictions.

Ergonomic Evaluation of the Perceived Mental Workload among Tunisian University Hospital Caregivers

Objectives: To evaluate the perceived mental workload and to identify its individual and professional determinants among Tunisian caregivers in university hospitals. Methods: A cross-sectional study was carried out in two university hospitals of the center of Tunisia, which employ 1179 paramedics providing care activities. The survey was conducted within a representative sample, of 301 caregivers assigned to 28 medical and surgical services, randomly drawn, after being paired according to age, gender and work schedule. It included structured inquiry (socio-professional characteristics, occupational perceived workload) and validated questionnaires (Job Content Questionnaire of Karasek, SF12, WAI). Results: Mental workload was perceived as “moderate” by 145 caregivers (48.49%) and as “heavy” by 119 caregivers (40.61% of them). Among the sample, 209 paramedics endured at least once the fixed night work schedules along their respective careers, with an average of 5.63 ± 7.54 years. According to the WAI index, 39.59% of subjects considered their capacity of work as low. Moreover, job strain was noted in 19.79%. Indeed, work latitude was unsatisfactory among 41% of caregiver, 60.1% of them mentioned the absence of colleagues’ support. A feeling of job insecurity was reported by 73.72% of subjects. Multi-varied analytic study concluded that heavier perceived mental work load was correlated to individual determinants particularly age >45 years (p -3), overweight or obesity (p = 0.02), more than three depending children (p -3), lower degree obtained (p -3), working grade as nurse assistant (P = 0.001), lower quality of physical life (0.010) and expressed willingness to retire early (p = 0.003). Conclusion: The heavy perception of mental workload associated to care activities, is one of the reasons of premature departure among nurses from professional activity. Preventive actions on professional determinants of perceived workload can reduce this phenomenon.

Evaluation of the Adequacy between the Workload and the Number of State Midwives and Maieuticians Practicing in the Gynecology-Obstetrics Departments of the University Hospitals of Burkina Faso

Objective: To evaluate the adequacy between the workload and the number of midwives and state midwives practicing in the gynecology-obstetrics services of the university hospitals of Burkina Faso. Methodology: This is a multicenter study conducted in 4 university hospitals representing this country (for brevity referred here to as A, B, C, and D) from May 1, 2018, to April 30, 2019. The Workload Indicators of Staffing Need (WISN) method was used to analyze the workload. Results: Dystocic delivery was the activity that took the most time. Inpatient activities were the most performed in terms of volume. At the University Hospitals D and C, there was an over-staffing of midwives and maïeuticians with a ratio of 1.93 and 2.12 respectively. At University Hospital B, the workload was in line with the existing number of State midwives and maieuticians. A low workload pressure (04%) was found at the University Hospital A. Conclusion: In most of the University Hospital Centers, there was no match between the workload and the number of midwives and maïeuticians. These results highlight the need for redeplayment of midwives and maïeuticians in order to ensure good health coverage for midwives and maïeuticians.

Measurement and Evaluation of Pilot Mental Workload Based on Flight Simulation System

The digital simulation technology is adopted to establish the flight environment and scenario,which realizes the flight display instrument interface and unusual attitude recovery task.The humancomputer interaction(HCI) experiment is carried out in the flight simulation system,and the behavior performances and physiological parameters are recorded as the evaluation indices of mental workload.Participants are required to perform the unusual attitude recovery tasks and keep monitoring the target flight indicators.Once the unusual attitude occurs,the corresponding operation of steering wheel is expected to be taken immediately to response and eliminate it.The results show that most of behavior performances and physiological parameters are sensitive to mental workload and some of the indices are significantly correlative.

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.