Variety Of Workloads Research Articles

External Workload Evolution and Comparison across a Pre-Season in Belgian Professional Football Players: A Pilot Study

The pre-season plays a crucial role in the preparation of professional football players, as it allows for an extensive focus on training sessions compared to the more congested schedules during the in-season period, especially in professional football leagues. This study aimed to describe the workload during a 6-week pre-season in Belgian professional football players and to analyse and compare the workloads for players in each microcycle according to several variables of external workload (e.g., distance covered at some velocities). Seventeen male Belgian professional football players competing in the second division of the Belgian league system participated in the study. Throughout the 6 weeks, the players were closely monitored during both training sessions and friendly matches using Global Positioning System (GPS) devices. Several parameters, including total distance covered and distance at different velocities, were recorded. Accelerating and decelerating distances, as well as the number of sprints, were also captured. Statistical analysis was based on a repeated measures ANOVA, percentage dynamics, and effect size calculations. The results obtained showed a progressive increase in the distance travelled at different intensities from week 1 (i.e., lower values) to week 3 (i.e., higher values), with reductions in these values in week 6, prior to the start of the official competition. Similarly, the peak of accelerations and decelerations were observed in week 2 and week 3, with decrements at the end of the pre-season period. This comprehensive investigation attempts to shed light on the effects and dynamic changes in external workload during the crucial pre-season, contributing valuable insights for coaches and practitioners in football conditioning and training programs, especially concerning optimal preparation for the beginning of the league’s season.

POEM: Performance Optimization and Endurance Management for Non-volatile Caches

Non-volatile memories (NVMs) with their high storage density and ultra-low leakage power offer promising potential for redesigning the memory hierarchy in next-generation Multi-Processor Systems-on-Chip (MPSoCs). However, the adoption of NVMs in cache designs introduces challenges such as NVM write overheads and limited NVM endurance. The shared NVM cache in an MPSoC experiences requests from different processor cores and responses from the off-chip memory when the requested data is not present in the cache. Besides, upon evictions of dirty data from higher-level caches, the shared NVM cache experiences another source of write operations, known as writebacks . These sources of write operations: writebacks and responses, further exacerbate the contention for the shared bandwidth of the NVM cache, and create significant performance bottlenecks. Uncontrolled write operations can also affect the endurance of the NVM cache, posing a threat to cache lifetime and system reliability. Existing strategies often address either performance or cache endurance individually, leaving a gap for a holistic solution. This study introduces the Performance Optimization and Endurance Management (POEM) methodology, a novel approach that aggressively bypasses cache writebacks and responses to alleviate the NVM cache contention. Contrary to the existing bypass policies which do not pay adequate attention to the shared NVM cache contention, and focus too much on cache data reuse, POEM’s aggressive bypass significantly improves the overall system performance, even at the expense of data reuse. POEM also employs effective wear leveling to enhance the NVM cache endurance by careful redistribution of write operations across different cache lines. Across diverse workloads, POEM yields an average speedup of \(34\% \) over a naïve baseline and \(28.8\% \) over a state-of-the-art NVM cache bypass technique, while enhancing the cache endurance by \(15\% \) over the baseline. POEM also explores diverse design choices by exploiting a key policy parameter that assigns varying priorities to the two system-level objectives.

Learning-driven hybrid scaling for multi-type services in cloud

In order to deal with the fast changing requirements of container based services in clouds, auto-scaling is used as an essential mechanism for adapting the number of provisioned resources with the variable service workloads. However, the latest auto-scaling approaches lack the comprehensive consideration of variable workloads and hybrid auto-scaling for multi-type services. Firstly, the historical data based proactive approaches are widely used to handle complex and variable workloads in advance. The decision-making accuracy of proactive approaches depends on the prediction algorithm, which is affected by the anomalies, missing values and errors in the historical workload data, and the unexpected workload cannot be handled. Secondly, the trigger based reactive approaches are seriously affected by workload fluctuation which causes the frequent invalid scaling of service resources. Besides, due to the existence of scaling time, there are different completion delays of different scaling actions. Thirdly, the latest approaches also ignore the different scaling time of hybrid scaling for multi-type services including stateful services and stateless services. Especially, when the stateful services are scaled horizontally, the neglected long scaling time causes the untimely supply and withdrawal of resources. Consequently, all three issues above can lead to the degradation of Quality of Services (QoS) and the inefficient utilization of resources. This paper proposes a new hybrid auto-scaling approach for multi-type services to resolve the impact of service scaling time on decision making. We combine the proactive scaling strategy with the reactive anomaly detection and correction mechanism. For making a proactive decision, the ensemble learning model with the structure improved deep network is designed to predict the future workload. On the basis of the predicted results and the scaling time of different types of services, the auto-scaling decisions are made by a Deep Reinforcement Learning (DRL) model with heterogeneous action space, which integrates horizontal and vertical scaling actions. Meanwhile, with the anomaly detection and correction mechanism, the workload fluctuation and unexpected workload can be detected and handled. We evaluate our approach against three different proactive and reactive auto-scaling approaches in the cloud environment, and the experimental results show the proposed approach can achieve the better scaling behavior compared to state-of-the-art approaches.

PENGARUH GAYA KEPEMIMPINAN, BEBAN KERJA, DAN PENGENDALIAN INTERNAL TERHADAP KINERJA KARYAWAN RUMAH SAKIT DI SIDOARJO

This research aims to determine employee performance at the Sidoarjo Regional General Hospital through independent variables including leadership style, workload, and internal control. This research is a type of quantitative research, where the source of testing is based on numbers and data as material for discussion. The population in this study were employees of the Sidoarjo Regional General Hospital. The sample obtained was 178 respondents. Data collection techniques use observation and questionnaires. The tests carried out in this research include validity tests, reliability tests, classical assumption tests, and hypothesis tests. This research uses multiple linear regression analysis methods. The results show that the variables of leadership style, workload and internal control influence employee performance at the Sidoarjo Regional General Hospital

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human–robot collaboration

Objective. Brain–computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC. Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials. Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed an increased frontal theta power and increasing trend in the central alpha and central beta power after the unexpected robot stopping compared to when the robot stopped correctly at the target. We also demonstrate that a popular convolution neural network model, EEGNet, could predict the amplitudes of PEN and Pe from the EEG data prior to the error. Significance. This prediction model could be instrumental in developing an online prediction model that could forewarn the system and operators of the diminished error awareness of the user, alluding to a potential safety breach in error-related potential-based BCI system for pHRC. Therefore, our work paves the way for embracing BCI technology in pHRC to optimally adapt the robot behavior for personalized user experience using real-time brain activity, enriching the quality of the interaction.

Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines

We present Limousine, a self-designing key-value storage engine, that can automatically morph to the near-optimal storage engine architecture shape given a workload, a cloud budget, and target performance. At its core, Limousine identifies the fundamental design principles of storage engines as combinations of learned and classical data structures that collaborate through algorithms for data storage and access. By unifying these principles over diverse hardware and three major cloud providers (AWS, GCP, and Azure), Limousine creates a massive design space of quindecillion (1048) storage engine designs the vast majority of which do not exist in literature or industry. Limousine contains a distribution-aware IO model to accurately evaluate any candidate design. Using these models, Limousine searches within the exhaustive design space to construct a navigable continuum of designs connected along a Pareto frontier of cloud cost and performance. If storage engines contain learned components, Limousine also introduces efficient lazy write algorithms to optimize the holistic read-write performance. Once the near-optimal design is decided for the given context, Limousine automatically materializes the corresponding design in Rust code. Using the YCSB benchmark, we demonstrate that storage engines automatically designed and generated by Limousine scale better by up to 3 orders of magnitude when compared with state-of-the-art industry-leading engines such as RocksDB, WiredTiger, FASTER, and Cosine, over diverse workloads, data sets, and cloud budgets.

Analysis of Factors Affecting the Occurrence of Burnout Syndrome among Healthcare Workers in Batakte Health Center, Kupang Regency

This study aims to analyze the factors that influence Burnout Syndrome in health workers at the Batakte Health Center, Kupang Regency. The method used is a quantitative research method with a cross sectional design. The sampling technique uses total sampling. The results showed that the results of the chi-square analysis showed no effect of age (p = 0.771), gender (p = 0.375), education (p = 1.000) on the incidence of burnout syndrome in health workers in the Working Area of the Batakte Health Center, Kupang Regency. There is an influence of marital status (p = 0.033), length of work (p = 0.002), workload (p = 0.001), social support (p = 0.002) on the incidence of burnout syndrome in health workers in the Working Area of the Batakte Health Center, Kupang Regency. Simultaneously, the variables of length of work (OR 6.352), workload (OR 6.647) and social support (OR 4.555) have an influence on the incidence of burnout syndrome in health workers in the Working Area of the Batakte Health Center, Kupang Regency. The workload variable is the most dominant variable that has an influence on the incidence of burnout syndrome in health workers with OR (7.163), p=0.000, CI=2.466-20.808. In conclusion, the factors that influence Burnout Syndrome in health workers are workload.

Tangible digital twin with shared visualization for collaborative air traffic management operations

In recent years, digital twin technology has become increasingly popular and been widely applied across different industries including air traffic management (ATM). While previous studies on digital twin in ATM focused on factors such as system architecture, synthetic data generation and transfer, and real-time simulation, there is currently a lack of research on visualization and interaction design of digital twin from human factors’ perspective. Moreover, recent advances in virtual and mixed reality (VR/MR) technology give potential for designing interactive digital twin in 3D space that can assist human operators in performing their duties. Therefore, this research aims to explore visualization and interaction design of an MR-based digital twin prototype that can assist air traffic controllers (ATCOs) in carrying out ground control tasks. Specifically, MR headsets (Microsoft HoloLens 2) are leveraged to project out-of-tower view of airport traffic onto a 3D printed airport model at a 1:1 scale. The spatially aligned tangible system enables ATCOs to perform typical ATM operations by directly touching the 3D printed airport model. In addition, collaborative features are implemented into the system, which enable multiple ATCOs to have a shared view of the airport traffic and collaboratively perform ATM operations when wearing the MR headsets. Finally, a user study was conducted with the recruitment of ten licensed ATCOs to investigate usability of the system and ATCOs’ workload, situational awareness and trust when using the system. The feedback from the ATCOs in the user study suggested that: (1) The visual and interactive design were acceptable; (2) The ATCOs indicated varied workload under different scenarios; (3) The ATCOs developed a high level of shared situational awareness with shared view of the system; and (4) The ATCOs were open and confident to use the system in its’ mature form. The findings show that the MR-based tangible airport digital tower system has high potential in collaborative ATM operations by improving user performance and user experience.

The Influence of Motivation, Work Discipline and Workload on Employee Performance at MAN 2 Mojokerto

The aim of this research is to determine the influences that can influence employee performance at MAN 2 Mojokerto which include work motivation, work discipline and workload, either partially, simultaneously or dominantly. The research method used in this research is a quantitative method involving all ASN and non-ASN employees in the MAN 2 Mojokerto area, totaling 123 people. With the probability sampling method and using saturated sampling techniques. The data collection technique used was a questionnaire. The data analysis method used is hypothesis testing, multiple linear regression analysis, coefficient of determination. Based on the research results, it shows that the work motivation variable has a significant effect on employee performance at MAN 2 Mojokerto. The Work Discipline variable has a significant effect on employee performance at MAN 2 Mojokerto. The Workload variable has a significant effect on employee performance at MAN 2 Mojokerto. The variables of motivation, discipline and workload simultaneously have a significant effect on employee performance at MAN 2 Mojokerto. The work discipline variable is an independent variable that has a dominant influence on employee performance at MAN 2 Mojokerto.

User-defined weight based multi objective task scheduling in cloud using whale optimization algorithm

Cloud computing has revolutionized the IT landscape, providing scalable, on-demand computing resource. For efficiency in cloud environments, it is essential for modern organizations, where objectives often include cost reduction, resource consumption, operational efficiency and load balancing etc, to implement multi objective solutions. Single-objective systems can fail in handling dynamic and diverse workloads. This study introduces the Multi-Objective Whale Optimization-Based Scheduler (WOA-Scheduler) for efficient task scheduling in cloud computing environments. Leveraging the Whale Optimization Algorithm (WOA), the scheduler optimizes multiple objectives simultaneously, including cost, time, and load balancing. A key feature of the WOA-Scheduler is its flexibility in accommodating user-defined weights for different objectives, allowing organizations to prioritize optimization goals based on their specific requirements. Comparative analysis across various cloud environments demonstrates the superiority of the WOA-Scheduler over traditional single-objective approaches. By achieving a better balance between cost, time, and resource utilization, the scheduler enhances overall performance. Moreover, its multi-objective optimization capabilities enable dynamic adjustment of task assignments in response to changing workload conditions, ensuring efficient resource utilization and workload distribution. Overall, the WOA-Scheduler offers a customizable and adaptable solution for addressing the complexities of modern cloud services, ultimately improving performance and efficiency.

Evaluating the impact of cloud computing on accounting firms: A review of efficiency, scalability, and data security

Cloud computing has emerged as a transformative force, revolutionizing the landscape for accounting firms. This comprehensive review delves into the profound impact of cloud computing on accounting firms, focusing on key dimensions such as efficiency, scalability, and data security. Examining the shift from traditional infrastructure to cloud-based solutions, the review navigates through the tangible benefits and potential challenges that cloud adoption brings to the accounting domain. Efficiency stands out as a cornerstone of cloud computing's influence on accounting firms. The agility and accessibility offered by cloud-based platforms streamline routine tasks, facilitating seamless collaboration among accounting professionals. The scalability afforded by cloud services empowers firms to dynamically adjust their computing resources, adapting to fluctuations in workload and business demands. This ensures that accounting firms can efficiently handle diverse workloads without being constrained by rigid infrastructure limitations. Scalability further intersects with efficiency, enabling accounting firms to optimize resource allocation and enhance overall productivity. The scalability of cloud solutions aligns with the dynamic nature of the accounting profession, allowing firms to scale up during peak seasons and scale down during lulls, ultimately fostering cost-effectiveness and operational agility. However, the review also critically evaluates the nuances of data security in the cloud computing paradigm. Addressing concerns related to data privacy, confidentiality, and compliance, the review navigates the intricate landscape of securing financial data in a cloud-based environment. It probes into the robustness of encryption protocols, authentication mechanisms, and compliance frameworks, ensuring a comprehensive understanding of the security implications inherent in cloud adoption by accounting firms. In conclusion, this review encapsulates the multifaceted impact of cloud computing on accounting firms. Efficiency gains and scalability advantages are juxtaposed against the imperative of fortifying data security. This examination provides a roadmap for accounting professionals, offering insights into harnessing the full potential of cloud technologies while ensuring the integrity and security of sensitive financial data. As accounting firms increasingly pivot towards cloud adoption, this review serves as a strategic guide, equipping practitioners with the knowledge to navigate the evolving landscape of cloud computing in the realm of accounting.

Modeling heart rate of individual and team manual handling with one hand using generalized additive mixed models

ObjectivesDespite the fact that team manual handling is common in different working environments, the previous studies in this regard, particularly those with a physiological approach are quite limited. The present study is an attempt to model the heart rate (HR) of individual and team manual handling with one hand.MethodsTwenty-five young men (aged 21.24±1.42 year) volunteered for this study. The experiments included individual and two-person handling of the load with three different weights with and without height difference. The participants’ HR was registered at the end of the route by a chest-strap pulse monitor and a polar watch according to the manufacturer's recommendation. A multivariate Generalized Additive Mixed Model (MGAMM) was used for modeling heart rate based on explanatory variables of workload, carry method, HRrest, body weight, height, knee height, shoulder height, elbow height, and hand height. The significance level of the tests was considered as <0.05.ResultsBased on the MGAMM, the average HR (bpm) of participants increased as the workload increased (P<0.001). Handling the load with a taller person increased the HR compared to shorter partner (P<0.001). Moreover, the nonlinear associations of the resting HR (P<0.001), body weight (P<0.001), height (P<0.001), and the height of elbow, hand and knee (P<0.001) were statistically significant. The adjusted R2 of the model was 0.89 indicating that about 90 percent of the variations observed in HR could be explained by the variables in the model. This was greater than the model considering only linear effects (R2 =0.60).ConclusionThe model obtained in this study can predict the heart rate of individual and team one-handed handling with high validity. The MGAMM can be used in modeling heart rate in manual handling.

Optimizing Resource Management for Shared Microservices: A Scalable System Design

A common approach to improving resource utilization in data centers is to adaptively provision resources based on the actual workload. One fundamental challenge of doing this in microservice management frameworks, however, is that different components of a service can exhibit significant differences in their impact on end-to-end performance. To make resource management more challenging, a single microservice can be shared by multiple online services that have diverse workload patterns and SLA requirements. We present an efficient resource management system, namely Erms, for guaranteeing SLAs with high probability in shared microservice environments. Erms profiles microservice latency as a piece-wise linear function of the workload, resource usage, and interference. Based on this profiling, Erms builds resource scaling models to optimally determine latency targets for microservices with complex dependencies. Erms also designs new scheduling policies at shared microservices to further enhance resource efficiency. Experiments across microservice benchmarks as well as trace-driven simulations demonstrate that Erms can reduce SLA violation probability by 5× and more importantly, lead to a reduction in resource usage by 1.6×, compared to state-of-the-art approaches.

Pengaruh Pengalaman, Pelatihan, dan Beban Kerja terhadap Kemampuan Auditor dalam Pendeteksian Kecurangan

This research was conducted to examine the effect of experience, training, and workload on the auditor's ability to detect fraud. In this study using the type of research in the form of quantitative data with a causal relationship approach. The population in this study were auditors who worked at KAP KKSP with a total of 35 respondents. The sampling technique used in this research is nonprobability sampling using saturated sampling technique. Data collection techniques in this study used questionnaires and interviews with auditors as primary data. For data that is processed using multiple linear regression analysis to test the hypothesis. Based on the results of data processing, it can be concluded that the experience variable has a positive effect on the auditor's ability to detect fraud, the training variable has a positive effect on the auditor's ability to detect fraud, and the workload variable has no effect on the auditor's ability to detect fraud.&#x0D; Keywords: Experience, Training, Workload, Auditor's Ability in Fraud Detection.

Pengaruh Kepemimpinan, Motivasi Kerja Dan Beban Kerja Terhadap Kinerja Karyawan Pada PT. Kurnia Berdikari Sejahtera

The Influence of Leadership, Work Motivation and Workload on Employee Performance at PT. Kurnia Berdikari Sejahtera. This research aims to find out how much influence Leadership, Work Motivation and Workload have on Employee Performance which has an impact on PT. Kurnia Berdikari Sejahtera. This type of research is quantitative. The population in this study were all employees of PT. Kurnia Berdikari Sejahtera, located in Bekasi City. The sampling technique uses Probability Sampling (Simple Random Sampling). Data were collected through distributing questionnaires directly and via google form to 110 respondents, with data processing using SPSS version 27. Data that met the requirements was obtained using validity and reliability tests and classical assumption tests. The data analysis techniques used are multiple linear regression analysis, t statistical test (partial), F test (simultaneous) and coefficient of determination test (R²). Based on the results of this research, it is stated that leadership, work motivation and workload simultaneously have a significant effect on the performance of PT employees. Kurnia Berdikari Sejahtera. And the Leadership variable partially has a positive and significant influence on employee performance variables, the Work Motivation variable partially has a positive and significant influence on employee performance and the Workload variable has a positive and significant influence on employee performance. Based on this, it can be concluded that it is proven that leadership, work motivation and workload have a positive influence on employee performance. Each level of leadership, work motivation and workload has a very important role in employee performance results.

The Influence of Communication Leadership and Workload on Employee Performance at the Banyudono District Office, Boyolali Regency

This research aims to determine the influence of (1) Leadership (2) Communication (3) Workload on Employee Performance at the Banyudono District Office, both partially and simultaneously. Thisresearch is quantitative using primary and secondary data. Data quality analysis testing uses validity tests and reliability tests. This research uses a survey method by distributing questionnaires. From a population of 30 people, all were sampled using saturated sampling techniques. The analysis used is multiple linear regression. This research shows that leadership partially influences employee performance by 25.6%. Communication partially influences employee performance by 46.1%. Workload fiber partially influences employee performance by 23.1%. Leadership, communication, and workload simultaneously or together also have an influence on employee performance, shown by a significant value of 0.000 0.05. Meanwhile, the coefficient of determination test results have an Adjusted R Square value of 89.5%, which means that employee performance variables are influenced by leadership, communication and workload variables and the remaining 10.5% is influenced by other variables.

The Influence of Workload and Organizational Culture on Quality of Nursing Work Life : A Case Study

A high quality of nursing work life is important for a hospital to achieve high nurse performance and grow profits in attracting and retaining nurses. Increased workload and unsupportive organizational culture can affect the balance of nurses' lives at work and home, so the quality of nursing work life could be better. This study aims to determine and analyze the workload, organizational culture, and quality of nursing work life and the partial or simultaneous influence of workload and organizational culture on the quality of nursing work life at Arjawinangun Regional General Hospital. This study used analytical survey research methods with a cross-sectional approach. The respondents in this study were 73 nurses at Arjawinangun Regional General Hospital who were selected using random sampling techniques. The independent variables studied in this study were workload (X1) and organizational culture (X2), while the dependent variable was the quality of nursing work life (Y). Information about research variables was obtained by distributing questionnaires and then analyzed using the SPSS program. The results of this study concluded that the workload variables were in the low category, the organizational culture variables were in the high category, and the quality of nursing work life was in the high category. Both partially and simultaneously, workload and organizational culture affect the quality of nursing work life at Arjawinangun Regional General Hospital. Workload variables influence nurses' work-life quality variables less than organizational culture variables.

The Effect of Work Environment, Workload and Financial Incentives on Employee Performance at PT. Indofood CBP Sukses Makmur Tbk

The purpose of this study is to determine whether there is an influence of the Work Environment, Workload and Financial Incentives on Employee Performance at PT. Indofood CBP Sukses Makmur Tbk. By paying attention to the level of HR in the company, it is expected to make a real contribution to improving employee performance results. This research method uses a quantitative approach with correlational or causal studies. The research site is located on Jalan Raya Cikopo, Cikopo, Kec. Bungursari, Kab. Purwakarta, West Java 41181. The subjects of this research are individual employees who work at PT. Indofood CBP Sukses Makmur Tbk. This study uses primary data with data techniques using a questionnaire via google form to 72 employees. The instrument test uses validity and reliability tests. The data analysis technique used is multiple regression analysis which is operated through the SPSS 23 program. The results showed that there was a relationship between the variables of Work Environment, Workload and Financial Incentives on Employee Performance at PT. Indofood CBP Success Makmur. Partially significant effect on the relationship between the dependent variable (Y) and the independent variable (X). Suggestions that need to be considered for the company’s internal parties to continue to maintain the quality of employee performance by paying attention to all supporting aspects, namely variable X.

FluidKV: Seamlessly Bridging the Gap between Indexing Performance and Memory-Footprint on Ultra-Fast Storage

Our extensive experiments reveal that existing key-value stores (KVSs) achieve high performance at the expense of a huge memory footprint that is often impractical or unacceptable. Even with the emerging ultra-fast byte-addressable persistent memory (PM), KVSs fall far short of delivering the high performance promised by PM's superior I/O bandwidth. To find the root causes and bridge the huge performance/memory-footprint gap, we revisit the architectural features of two representative indexing mechanisms (single-stage and multi-stage) and propose a three-stage KVS called FluidKV. FluidKV effectively consolidates these indexes by fast and seamlessly running incoming key-value request stream from the write-concurrent frontend stage to the memory-efficient backend stage across an intermediate stage. FluidKV also designs important enabling techniques, such as thread-exclusive logging, PM-friendly KV-block structures, and dual-grained indexes, to fully utilize both parallel-processing and high-bandwidth capabilities of ultra-fast storage hardware while reducing the overhead. We implemented a FluidKV prototype and evaluated it under a variety of workloads. The results show that FluidKV outperforms the state-of-the-art PM-aware KVSs, including ListDB and FlatStore with different indexes, by up to 9× and 3.9× in write and read throughput respectively, while cutting up to 90% of the DRAM footprint.

Pengaruh Beban Kerja dan Stres Kerja Terhadap Kinerja Karyawan Pada PT Asian Agri Singingi Hilir Kabupaten Kuantan Singingi

The purpose of this study was to determine and analyze the effect of workload and work stress on employee performance partially and simultaneously at PT Asian Agri Singingi Hilir. The population in this study amounted to 40 people taken from production employees, while the sample in this study amounted to 40 people using the census method. The analysis in this study used multiple linear regression. Based on the results of the study where the tcount&gt; ttable value is and the significance level is 0.026 &lt;0.05, which means that workload partially affects the performance of PT Asian Agri Singingi Hilir office staff employees. Workload with a significance level of 0.00 &lt;0.05 which means that workload partially affects the performance of employees of the office staff of PT Asian Agri Singingi Hilir. Workload and work stress have a simultaneous influence on employee performance at PT Asian Agri Singingi Hilir with a calculated F value of 33.931&gt; F-tab of 3.25 with sig. (0,000) &lt; 0.05. Adjusted R Square of 0.628 or 62.8%, this means that 62.8% of the employee performance variable can be explained or influenced by workload and work stress variables. While the remaining 37.2% is influenced by other variables not examined.