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.

Characterizing big data analytics workloads on POWER8 SMT processors

Big data analytics is emerging as one kind of the most important workloads in modern data centers. Hence,it is of great interest to identify the method of achieving the best performance for big data analytics workloads running on state-of-the-art SMT( simultaneous multithreading) processors,which needs comprehensive understanding to workload characteristics. This paper chooses the Spark workloads as the representative big data analytics workloads and performs comprehensive measurements on the POWER8 platform,which supports a wide range of multithreading. The research finds that the thread assignment policy and cache contention have significant impacts on application performance. In order to identify the potential optimization method from the experiment results,this study performs micro-architecture level characterizations by means of hardware performance counters and gives implications accordingly.

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.

Interference Analysis of Co-Located Container Workloads:A Perspective from Hardware Performance Counters

Workload characterization is critical for resource management and scheduling.Recently,with the fast development of container technique,more and more cloud service providers like Google and Alibaba adopt containers to provide cloud services,due to the low overheads.However,the characteristics of co-located diverse services(e.g.,interactive on-line services,off-line computing services)running in containers are still not clear.In this paper,we present a comprehensive analysis of the characteristics of co-located workloads running in containers on the same server from the perspective of hardware events.Our study quantifies and reveals the system behavior from the micro-architecture level when workloads are running in different co-location patterns.Through the analysis of typical hardware events,we provide recommended/unrecommended co-location workload patterns which provide valuable deployment suggestions for datacenter administrators.

Learning-Based Joint Service Caching and Load Balancing for MEC Blockchain Networks

Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure reward mechanism that can facilitate load balancing among MECS.In addition,intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of workloads.In this paper,we investigate a learningbased joint service caching and load balancing policy for optimizing the communication and computation resources allocation,so as to improve the resource utilization of MEC blockchain networks.We formulate the problem as a challenging long-term network revenue maximization Markov decision process(MDP)problem.To address the highly dynamic and high dimension of system states,we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network(DQN)approach.The simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.

Stress perceptions of nurses serving in home healthcare services during the COVID-19 pandemic:A qualitative research

Objective:To investigate the stress perceptions of nurses serving in home healthcare services during COVID-19.Methods:This study was qualitative research with a phenomenological design.Data were collected and recorded through in-depth interviews with 6 nurses working in MuşState Hospital,Home Healthcare Services Unit using a form consisting of 12 questions on an online platform between May 2021 and July 2021.The audio recordings were transcribed by the researcher and content analysis was performed by creating codes,categories,and themes.Results:The interviews yielded 10 categories and 59 sub-codes.These codes were addressed under the theme of"COVID-19 pandemic".Under this main theme,nurses expressed the problems they experienced in issues such as stress,support mechanisms,and family and social problems during COVID-19.They mentioned that they experienced high stress in this process,as well as social isolation and negative thoughts of society about them and that they could not spare time for themselves and their families.Conclusions:Nurses working in home healthcare services frequently express negativities such as high stress,isolation from society,and increased workload.Therefore,actions should be taken to raise awareness of society on these issues,increase the number of personnel,conduct more research,and share the results with the public.

Detection and Recuperation of Mental Fatigue

Mental fatigue is a complex state that results from prolonged cognitive activity. Symptoms of mental fatigue can include change in mood, motivation, and temporary deterioration of various cognitive functions involved in goal-directed behavior. Extensive research has been done to develop methods for recognizing physiological and psychophysiological signs of mental fatigue. This has allowed the development of many AI-based models to classify different levels of fatigue, using data extracted from eye-tracking device, EEG, or ECG. In this paper, we present an experimental protocol which aims to both generate/measure mental fatigue and provide effective strategies for recuperation via VR sessions paired with EEG and eye tracking devices. This paper first provides a comprehensive state-of-the-art of mental fatigue predictive factors, measurement methods, and recuperation strategies. Then the paper presents an experimental protocol resulting from the state-of-the-art to 1) generate and measure mental fatigue and 2) evaluate the effectiveness of virtual therapy for fatigue recuperation, using a virtual reality (VR) simulated environment. In our work, we successfully generated mental fatigue through completion of cognitive tasks in a virtual simulated environment. Participants showed significant decline in pupil diameter and theta/alpha score during the various cognitive tasks. We trained an RBF SVM classifier from Electroencephalogram (EEG) data classifying mental fatigue with 95% accuracy on the test set. Finally, our results show that the time allocated for virtual therapy did not improve pupil diameter in post-relaxation period. Further research on the impact of relaxation therapy on relaxation therapy should allocate time closer to the standard recovery time of 60 min.

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.

Interpretive Structural Modeling Based Assessment and Optimization of Cloud with Internet of Things (CloudIoT) Issues Through Effective Scheduling

Integrated CloudIoT is an emergingfield of study that integrates the Cloud and the Internet of Things(IoT)to make machines smarter and deal with real-world objects in a distributed manner.It collects data from various devices and analyses it to increase efficiency and productivity.Because Cloud and IoT are complementary technologies with distinct areas of application,integrating them is difficult.This paper identifies various CloudIoT issues and analyzes them to make a relational model.The Interpretive Structural Modeling(ISM)approach establishes the interrelationship among the problems identified.The issues are categorised based on driving and dependent power,and a hierarchical model is presented.The ISM analysis shows that scheduling is an important aspect and has both(driving and dependence)power to improve the performance of the CloudIoT model.Therefore,existing CloudIoT job scheduling algorithms are ana-lysed,and a cloud-centric scheduling mechanism is proposed to execute IoT jobs on a suitable cloud.The cloud implementation using an open-source framework to simulate Cloud Computing(CloudSim),based on the job’s workload,is pre-sented.Simulation results of the proposed scheduling model indicate better per-formance in terms of Average Waiting Time(AWT)and makespan than existing cloud-based scheduling approaches.

An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

In cloud data centers,the consolidation of workload is one of the phases during which the available hosts are allocated tasks.This phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement(SLA).To consolidate the workloads,the hosts are segregated into three categories:normal hosts,under-loaded hosts,and over-loaded hosts based on their utilization.It is to be noted that the identification of an extensively used host or underloaded host is challenging to accomplish.Thresh-old values were proposed in the literature to detect this scenario.The current study aims to improve the existing methods that choose the underloaded hosts,get rid of Virtual Machines(VMs)from them,andfinally place them in some other hosts.The researcher proposes a Host Resource Utilization Aware(HRUAA)Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud environment.The mechanism presented in this study is contrasted with existing mechanisms empirically.The results attained from the study estab-lish that numerous hosts can be shut down,while at the same time,the user's workload requirement can also be met.The proposed method is energy-efficient in workload consolidation,saves cost and time,and leverages active hosts.

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.

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.

双元制高等教育背景下的“控制技术”实验教学改革初探——以合肥学院为例

实验教学对于大学生实践能力的提高具有极其重要的作用。合肥学院借鉴德国双元制大学教育模式,设立了机械设计制造及其自动化专业(双元制)。以“控制技术”实验教学改革为基础,对双元制高等教育背景下的实验教学改革进行了初步探索,对加快双元制专业在中国高等教育领域的本土化进程做出了有益尝试。

An SDN/NFV Based Framework for Management and Deployment of Service Based 5G Core Network

The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5 G era, service based architecture is introduced into mobile networks. The monolithic network elements(e.g., MME, PGW, etc.) are split into smaller network functions to provide customized services. However, the management and deployment of network functions in service based 5 G core network are still big challenges. In this paper, we propose a novel management architecture for 5 G service based core network based on NFV and SDN. Combined with SDN, NFV and edge computing, the proposed framework can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration of network functions and optimal workload allocation. Simulations are conducted to show that the proposed framework and algorithm are effective in terms of reducing network operating cost.

The Optimization Method of the Sector Partition Based on Metamorphic Voronoi Polygon

Voronoi diagram is founded by using computational geometry based on originaldistribution of the waypoints, and then the elements from Voronoi diagram are metamorphosed by usingthe rule for airsppce partition, and the controller's workload is accounted in each element that ismade up of Metamorphic Voronoi polygon. Then in accordance with the rule about balance ofcontroller's workload, Simulated Annealing algorithm (SA) is used to achieve the optimization ofcombination of those elements , and the new resolution has satisfied the restriction of two rulesfor airspace partition. Therefore, the boundaries of the aggregates of these elements are theoptimal borderlines of sectors. The result of actual airspace design example validates therationality of the sector optimization method presented in this paper.

Air Traffic Volume and Air Traffic Control Human Errors

Navigable airspaces are becoming more crowded with increasing air traffic, and the number of accidents caused by human errors is increasing. The main objective of this paper is to evaluate the relationship between air traffic volume and human error in air traffic control (ATC). First, the paper identifies categories and elements of ATC human error through a review of existing literature, and a study through interviews and surveys of ATC safety experts. And then the paper presents the results of an experiment conducted on 52 air traffic controllers sampled from the Korean ATC organization to find out if there is any relationship between traffic volume and air traffic controller human errors. An analysis of the experiment clearly showed that several types of ATC human error are influenced by traffic volume. We hope that the paper will make its contribution to aviation safety by providing a realistic basis for securing proper manpower and facility in accordance with the level of air traffic volume.

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.

Self-endangering:A qualitative study on psychological mechanisms underlying nurses’burnout in long-term care

Objectives To develop a more specific understanding of psychological mechanisms in the development of burnout in long-term care as a basis for potential new intervention strategies aiming at improving nurses’mental health.Methods Two qualitative studies with thematic analysis were conducted.In Study 1,we conducted eight group interviews with 110 nurses from May–July 2019 in the context of workshops at eight nursing homes in Germany.In Study 2,we supplemented these with semi-structured interviews with 14 executives at German nursing homes in December 2019.Results The thematic analysis in Study 1 identified three main themes:causes of challenges,employees’opportunities for change,and organisational opportunities for change.Thematic analysis in Study 2 identified three main themes:job motives,reasons for filling in for others,and employee self-care.Further,our results show that the need to stand in for colleagues,in particular,is one of the greatest challenges for geriatric caregivers.In dealing with these challenges we found that self-endangering behaviour—a diminished ability to say no when asked to fill in or to do work overtime—was an important antecedent of nurses’burnout.Further,high levels of altruistic motivation and identification with the team or organisation were associated with self-endangering behaviour in the presence of adverse working conditions.Low levels of self-worth are a further risk factor for self-endangering.Conclusions Our findings are at odds with some core tenets of classic models of job demands and burnout that construe motivation and identification as resources.Our results show the need of a holistic intervention program in nursing including individual coaching,team-based interventions and organisational development processes.Employees themselves should be sensitized to this issue and supported in the long term,and politicians should create structures that do not encourage this behaviour any further.

空中交通管制员工作负荷研究

This paper is focusing on workload leading to stress and fatigue,which,so far,have resulted in real or potential accidents,incidents or errors,not been explored but with impact on the controller everyday,studying workload curve on the real situations based on operational units,e.g. Zhengzhou area control center,and doing certain calculations on the number of air traffic controller needed in accordance with the air traffic to be handled. Lastly,some strategies are put forward.