Server Workloads Research Articles

Load Balancing Server and Homomorphic Encryption in Internet of Things

User demand for Internet of Things (IoT) services is ever increasing. The growing user demand can lead to an escalation of server workloads and faces the threat of theft of critical data. Consequently, a system is necessary to balance the server load and is protected with data encryption. In this study, we designed a system to share server workloads using load balancing methods. The load balancing technique uses open-source web server software. The system is equipped with data security using a homomorphic encryption algorithm from AES on the sender's side. The system embeds in an IoT telemedicine apparatus. During testing, we analyze the error requests that arrive at each server for the HTTP GET and POST methods. We also evaluate the speed of data encryption and decryption. The results showed that server load balancing reduces the number of error requests for the GET method by 97%. Meanwhile, the number of error requests for the POST method decreases by 66.75%. Observations reveal that the average homomorphic encryption speed, computation time, and decryption time are 15.66 ms, 764.18 µs, and 362.49 µs, respectively.

Gendered workload allocation in universities: A feminist analysis of practices and possibilities in a European University

AbstractThe negotiated allocation of teaching and institutional service workload in universities is a key determinant of the quantity and quality of all work for academic staff. There is abundant quantitative evidence that women and men experience differential outcomes from faculty, school, or departmental workload allocation processes and convincing theoretical explanations as to why this happens. We add to this knowledge through feminist analysis of a mixed methods case study of an academic unit in a European University, focusing on gendered dynamics in the workload allocation processes there. Our analysis follows the “sweaty concept” methodology proposed by Sara Ahmed as a means of developing feminist theory that is founded on embodied experiences of discomfort in worlds that are not welcoming. The concept we develop through this is “inequitable modeling”; it suggests that while workload allocation processes are understood by model designers as a managerial tool to enable transparency and fairness (forms of procedural equity), the managed, especially women, experience them as opaque and unfair (forms of lived inequity). We conclude by questioning the principles and outcomes of such tools in achieving gender equity, and then describe how a feminist approach to workload modeling and allocation might be implemented.

Leveling the Field: Gender Inequity in Academia During COVID-19

ABSTRACTThis article explores the differential impact of the COVID-19 pandemic on the productivity of male and female academics and whether the ongoing health crisis will exacerbate further the existing gender gap in academia in both the short and long terms. We present early evidence of the pandemic’s disproportionate effect on women’s research productivity using online survey data supplemented by interview data with regional and international female political scientists. The interviews and survey findings reveal gender disparities in perceived research productivity and service workloads during the pandemic. The results also shed initial light on the pandemic’s impact on the research productivity of academics who are parents, especially among women.

Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud.

In recent years, smartphone users are interested in large volumes to view live videos and sharing video resources over social media (e.g., Youtube, Netflix). The continuous streaming of video in mobile devices faces many challenges in network parameters namely bandwidth estimation, congestion window, throughput, delay, and transcoding is a challenging and time-consuming task. To perform these resource-intensive tasks via mobile is complicated, and hence, the cloud is integrated with smartphones to provide Mobile Cloud Computing (MCC). To resolve the issue, we propose a novel framework called delay aware bandwidth estimation and intelligent video transcoder in mobile cloud. In this paper, we introduced four techniques, namely, Markov Mobile Bandwidth Cloud Estimation (MMBCE), Cloud Dynamic Congestion Window (CDCW), Queue-based Video Processing for Cloud Server (QVPS), and Intelligent Video Transcoding for selecting Server (IVTS). To evaluate the performance of the proposed algorithm, we implemented a testbed using the two mobile configurations and the public cloud server Amazon Web Server (AWS). The study and results in a real environment demonstrate that our proposed framework can improve the QoS requirements and outperforms the existing algorithms. Firstly, MMBCE utilizes the well-known Markov Decision Process (MDP) model to estimate the best bandwidth of mobile using reward function. MMBCE improves the performance of 50% PDR compared with other algorithms. CDCW fits the congestion window and reduces packet loss dynamically. CDCW produces 40% more goodput with minimal PLR. Next, in QVPS, the M/M/S queueing model is processed to reduce the video processing delay and calculates the total service time. Finally, IVTS applies the M/G/N model and reduces 6% utilization of transcoding workload, by intelligently selecting the minimum workload of the transcoding server. The IVTS takes less time in slow and fast mode. The performance analysis and experimental evaluation show that the queueing model reduces the delay by 0.2 ms and the server’s utilization by 20%. Hence, in this work, the cloud minimizes delay effectively to deliver a good quality of video streaming on mobile.

HUBUNGAN BEBAN KERJA DAN MASA KERJA DENGAN KELUHAN NYERI LEHER PADA PENJAHIT DI LEMBAGA LATIHAN KERJA LUBUK PAKAM TAHUN 2020

Complaints of neck pain are a complaint on the part of the skeletal muscle as a result of forced movement and receiving heavy loads for a long time. Tailoring is one of the jobs that has a high risk of experiencing neck pain complaints. The rotation of the spine when the body is bending is a factor in neck pain complaints that are often complained of by tailors. This work activity will indirectly endanger health. This study aims to determine the relationship between workload and work period with complaints of neck pain in tailors at the Lubuk Pakam Training Institute in 2020. The type of research used is quantitative with a cross sectional design. The population of this study were 30 workers at the Lubuk Pakam training institute using the total sampling technique. Data collection by questionnaire. Data analysis used the chi-square test at the level of confidence level of 95% and α = 0.05. The results showed that there was a significant relationship between workload (p = 0.024) and length of service (p-0.016) with complaints of neck pain in tailors at the Lubuk Pakam Training Institute. It is advisable for tailors to pay more attention to health and safety while working without forcing the body to work and regulating good working hours, adequate rest, stretching while working and paying attention to the dangers that can occur in the work environment

Proactive auto-scaling for cloud environments using temporal convolutional neural networks

Auto-scaling systems can dynamically scale the required resources for cloud-based services at runtime. This is an effective mechanism, enabling services to adapt to environmental changes. These systems establish the foundation for achieving elasticity in the modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, cloud auto-scaling systems are one of the most complex and sophisticated created artifacts, aiming to achieve self-aware, self-adaptive, and dependable runtime scaling. To find an effective solution to this problem, an accurate prediction of the required amount of workload as well as the system metrics for future time periods are needed. Various solutions have already been proposed to tackle this problem. Many solutions make use of machine learning, statistical, and ensemble methods. In this paper, we view the auto-scaling problem as a sequence model and apply the convolutional neural networks to predict the future workload of cloud services. Also, by using neural networks, we obtain a mapping between the predicted workload as well as the real-time and future amounts of the required resources. We have also proposed a decision-making mechanism that takes into account different and sometimes conflicting user criteria resulting in the best-compromised decision. To this aim, we have used TOPSIS as a multi-criteria decision-making method for the decision-making component. In the evaluation section, we have examined the amount of prediction error, the amount of service level agreement violations, as well as the amount of resources' under-utilization. Evaluations demonstrate that the proposed approach for predicting the workload shows a 4 percent improvement over the existing approaches.

A Benchmarking application on Workload and Performance forecasting of micro services

Micro services are increasingly understood as the ideal architectural framework for building large cloud applications within and beyond organizational boundaries. These micro services architectures scale up the application, but are expensive to work on, so pay attention to workflow planning and workflow planning. However, this issue is not very clear. In this work, we are developing independent micro services workflows suitable for modeling and prediction methods and designing three-step game models for based applications. Solved the problem of designing micro services based applications to reduce end-to-end delays under user-specific limitations (MAWS-BC) and recommended micro services routing algorithms. The design process and estimation methods are improved and adequate. The experimental results produced by a well-known micro service bank cover a wide variety of statistical analyzes and the production utility of graphic design is shown by a large comparison copy compared to current algorithms.

An End-to-End Learning-Based Metadata Management Approach for Distributed File Systems

Current distributed file systems are designed to support PB-scale even EB-scale data storage. Metadata service, which manages file attribute information and the global namespace tree, is crucial to system performance. Distributed metadata management, using multiple metadata servers (MDS's) to store metadata, provides effective approaches to alleviate the workload of a single server. However, maintaining good metadata locality and keeping load balancing among MDS's is a nontrivial problem. In this paper, we present the first machine learning based model called DeepHash, which leverages the neural network to learn a locality preserving hashing (LPH) mapping scheme. DeepHash first converts the metadata nodes to feature vectors by the network embedding technology. Due to the absence of training labels, i.e., the hash values of metadata nodes, we design a pair loss function with distinctive characters to train DeepHash, and introduce the sampling strategy to improve the training efficiency. Besides, we propose an efficient algorithm to dynamically balance the workload and adopt the cache model to improve query efficiency. The experiments on the Amazon EC2 platform demonstrate that the DeepHash can preserve the metadata locality meanwhile maintaining a high load balancing, which denotes the effectiveness and efficiency of DeepHash compared with traditional and state-of-the-art schemes.

An intelligent collaborative inference approach of service partitioning and task offloading for deep learning based service in mobile edge computing networks

AbstractAs the rapid evolution of smart devices and real‐time applications, many new kinds of computation‐intensive services have been emerged successively and the corresponding requirements have been growing dramatically. Extended from cloud computing, mobile edge computing (MEC) is a novel technology which can provide powerful computing resource at the proximity of resource‐restrained mobile devices. Thus, it enables collaboration between edge server and mobile device, which can improve the quality of experience for users. In this article, we propose an intelligent collaborative inference (ICI) approach for real‐time computation‐intensive services in MEC network, which can achieve intelligent service partitioning and partial task offloading. Since machine learning algorithms have been applied in many applications with the advancement of big data and computing power, we focus on the services based on deep‐learning. Particularly, we research a service based on Pose‐Net model to achieve human pose estimation in the field of computer vision. And we design relevant ICI algorithm to achieve fine‐grained video stream processing in consideration of video service requirement, deep neural network (DNN) model structure, mobile device capability, wireless network condition, and cooperative server workload. Based on Python programming language and TensorFlow library, we test the ICI approach with some practical simulation parameters on real hardware platforms. The experiment results show that the presented ICI approach have superior performance in terms of service frame rate and client energy consumption than other benchmark approaches.

Retrospective analysis of Syrian refugee patients admitted to a hospital adult emergency service

Introduction: Due to wars and political changes, large-scale migration can occur from countries. Millions of people leave their places of residence and take refuge in other countries and affect the sociological, economic and health systems of the country they go to. Methods: Syrians under temporary protection, who applied to Sakarya University Training and Research Hospital adult emergency room between 01/03/2018-31/12/2019, were examined retrospectively. Thus, it was aimed to examine the effect of Syrian refugees on emergency services. Results: A total of 4310 Syrian patient applications are made, of which 2710 are male and 1600 are female. 3607 (83.7%) of the patients applied to the green outpatient clinic, 175 of the patients (4.1%) are forensic cases. 151 (86.3%) of forensic cases belong to male Syrian patients. Most of the patients are young adults, the average patient age is 30.8 ± 10.71 and the median age is 28. Conclusion: It was concluded that Syrians mostly apply to the green clinic in the adult emergency department, male and young patients apply more, forensic cases occupy a small proportion among these applications, and generally non-emergency cases use the emergency service. Refugees can be informed about applying to primary health care in non-emergency situations and organizing health infrastructure in this direction can reduce the emergency service workload.

Calidad de vida relacionada con la salud en trabajadores de unidades de cuidados intensivos

ObjectiveTo analyse health-related quality of life in a sample of doctors and nurses from 2 intensive care units in the city of Buenos Aires, Argentina. MethodAn analytical, cross-sectional and quantitative study. The Spanish version of the SF-36 questionnaire was used, which comprises 8 dimensions grouped into 2 components (physical and mental) and evaluates the health status of respondents on a scale from 0 to 100; the higher the score, the better the perception of health status. ResultsNinety-five professionals from 2 intensive care units participated, 58% from a private institution and the rest from a public institution. The majority were women (57%), doctors (37%) with an average age of 42 years and with a maximum undergraduate degree and degree (59%). Of the dimensions making up health-related quality of life, the vitality dimension was perceived poorest with an average of 53.3, and physical function was the best evaluated at 87.8. The physical component dimensions were better evaluated than those of the mental component. The variables sex (male), age (>40 years), presence of chronic diseases, qualification (medical), hourly workload (>40h per week) and years of service (>11 years), seem to have a negative influence on the perception of health-related quality of life. ConclusionsStrategies need to be implemented to reduce the impact of work activity on the mental and physical health of health professionals. Strategies for health promotion and the prevention of known psychosocial and ergonomic risks are required, and workloads and duration of working hours need to be adjusted to preserve the integrity of health workers.

Risk Aware Stochastic Placement of Cloud Services

Allocating the right amount of resources to each service in any of the datacenters in a cloud environment is a very difficult task. This task becomes much harder due to the dynamic nature of the workload and the fact that while long term statistics about the demand may be known, it is impossible to predict the exact demand in each point in time. As a result, service providers either over allocate resources and hurt the service cost efficiency, or run into situation where the allocated local resources are insufficient to support the current demand. In these cases, the service providers deploy overflow mechanisms such as redirecting traffic to a remote datacenter or temporarily leasing additional resources (at a higher price) from the cloud infrastructure owner. The additional cost is in many cases proportional to the amount of overflow demand. In this paper we study this approach and develop a novel mechanism to assign services to datacenters based on the available resources in each datacenter and the distribution of the demand for each service. We use comprehensive analysis to prove that the overall overflow cost is almost optimal for arbitrary demand distributions, as long as there are no dependencies among the services. We further show, using simulation based on real data that the scheme performs very well on realistic service workloads.

The Take-off for Private Consultants in Child Protection Investigations—How Did Sweden Get Here?

Abstract The use of private consultants in child protection investigations in Sweden is an evolving practice that occurs under the radar; it is not in the political agenda and it is obscure in relation to legislation. The aim of this article is to analyse the contextual forces, facilitators and intentions behind the ongoing implementation of privatisation in child protection investigations. For this article, both quantitative and qualitative data for the years 2013–2017 were obtained through a telephone questionnaire. Managers of 32 social services departments were interviewed. Statistical data of the year 2018 collected by the National Board of Health and Welfare was also included in the analysis. Applying a mixed-method design, the article shows that some prerequisites in legislation make it possible for privatisation to develop. There are societal changes that act as contextual forces, and there are structures in place from earlier stages of the development of privatisation that facilitate the new practice. Also, there are professional intentions that drive the process. The implementation of private consultants in child protection takes place during a period of high workload for social services. The article concludes with a discussion about the consequences for children and families of this new trend in privatisation.

Joint optimization of network selection and task offloading for vehicular edge computing

Taking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

HUBUNGAN BEBAN KERJA FISIK, MENTAL, DAN KEBIASAAN SARAPAN TERHADAP TINGKAT KELELAHAN KERJA PADA KURIR EKSPEDISI PT POS INDONESIA KOTA SEMARANG

The role of human resources is very important to carry out work processes and achieve company goals. Companies need to pay attention to special skills or competencies to achieve goals. To produce optimal performance, human resources need protection in the form of occupational safety and health. To achieve this, companies need to pay attention to the factors that affect worker fatigue. The factors that influence worker fatigue come from two factors, namely internal factors (age, gender, nutritional status, health status, psychological state of the workforce), and external factors (workload and years of service, physical work environment). This study aims to analyze the relationship between physical workload, mental workload, and breakfast habits for PT. Pos Indonesia Semarang City. This research was a quantitative study using the coss sectional method. The instruments used were the IFRC Questionnaire to examine Work Fatigue, the NASA TLX questionnaire to examine mental workload and physical workload using workload measurements based on the level of Calorie Requirements. The population in this study were 121 employees. The sampling technique used was incidental sampling technique, namely the technique of determining the sample by chance, given the conditions that made it impossible to meet respondents directly during the pandemic, this technique was applied to anyone who was willing to fill out a questionnaire while still paying attention to the existing sample criteria. . Based on the results of the Spearman rank statistical test, it was found that there was no relationship between age (0.101> ρ value), physical workload (0.584> ρ value), and breakfast habits (0.982> ρ value) and work fatigue. However, there was a significant relationship between work fatigue on expedition couriers with their mental workload.

Large-scale parallel server system with multi-component jobs

A broad class of parallel server systems is considered, for which we prove the steady-state asymptotic independence of server workloads, as the number of servers goes to infinity, while the system load remains sub-critical. Arriving jobs consist of multiple components. There are multiple job classes, and each class may be of one of two types, which determines the rule according to which the job components add workloads to the servers. The model is broad enough to include as special cases some popular queueing models with redundancy, such as cancel-on-start and cancel-on-completion redundancy. Our analysis uses mean-field process representation and the corresponding mean-field limits. In essence, our approach relies almost exclusively on three fundamental properties of the model: (a) monotonicity, (b) work conservation and (c) the property that, on average, “new arriving workload prefers to go to servers with lower workloads.”

Health professional-educator collaboration in the delivery of school-based tiered support services: A qualitative case study.

Educators and health professionals support the learning and participation of diverse children in school environments. Tiered approaches to service delivery may assist these efforts through consideration of universal supports that are useful to all children, targeted supports for some children and individualized supports for the smallest number of children. This study explored how an interprofessional team worked with educators to develop and implement tiered services in two school communities where many families experience economic and social disadvantages. Using a participatory action research approach and qualitative case study methods, the research and stakeholder teams jointly designed and conducted this study in two schools during the 2017-2018 school year. Data collected included weekly logs written by the interprofessional team members and 16 interviews conducted with team members, parents, educators and administrators. The team provided a variety of services to individual students, groups, whole classes and the school community. Collaboration and communication were needed to define roles and expectations and to plan and share student information. Reported benefits included timely service, capacity building and student goal achievement. The main barriers were related to service fragmentation, time and workload. Recommendations included clearer direction about expectations and improved coordination within the systems that offer services. Further research should include exploration of comparative cases with varying contexts, the inclusion of child perspectives and direct observation.

The collaborative consistent vehicle routing problem with workload balance

The rising competition in the logistics sector forces companies to be more economically efficient. One of the major sources of inefficiency is the incomplete usage of available resources, such as vehicles’ capacities. Mechanism that allow to better exploit such resources by enabling carrier collaborations are on the rise. Our study examines a centrally organized multi-period collaborative vehicle routing problem, where carriers can exchange customers who have to be serviced on a regular basis. Collaborations, where carriers serve frequent customers, are supposed to face the problem of (i) time consistency in terms of visiting time, and of (ii) service consistency. The latter ensures that customers are visited by the same collaboration partner throughout the whole planning horizon. Additionally, carriers might only be willing to enter a collaboration if a minimum market share can be guaranteed. In order to take all these issues into account, we introduce the collaborative vehicle routing problem with time and service consistency and workload balance. The mathematical model including several valid inequalities is presented. In a computational study, we solve small-sized instances to optimality. In order to tackle larger instances, we propose an efficient and effective matheuristic and an iterated local search algorithm. We show that both methods reach near optimal solutions within very short computational times. Managerial insights on the cost of imposing time and service consistency as well as workload balancing constraints are presented and discussed. We show that, according to our computational study, consistency constraints can be imposed for almost no additional cost, while workload balancing constraints do not have any negative effect on the total collaboration profit. This is a meaningful insight and might be a strong argument for carriers to enter collaborations.

EPRO-based individual follow-up care for women treated for early breast cancer: impact on service use and workflows.

The increasing population of breast cancer survivors highlights the need to (re)consider how we utilize available services for survivorship care in oncology clinics. Electronic Patient-Reported Outcomes (ePROs) can be used to identify patients' individual care needs and triage them to the right services. We examined the impact on service use, workflow and workload following the introduction of an ePRO-based individual follow-up (PIFU) for women treated for early breast cancer. A multi-method approach was used. In a pilot randomized controlled trial, the use of consultations, telephone calls, and specialist referrals were systematically recorded. Comparison was done between PIFU and standard follow-up care (SFU). Focus group interviews with nurse navigators evaluated the impact on workflow and workload qualitatively. The 64 women randomized to attend SFU used a mean of 3.8 (95% CI: 3.5-4.1) planned consultations during the 2-year study period compared with a mean of 1.9 consultations (95% CI: 1.4-2.4) for the 60 women randomized to PIFU (P < 0.001). Urgent appointments were more frequent in SFU (mean of 0.47 vs 0.22 per patient, P = 0.03). No statistically significant differences were observed in the use of telephone calls and specialist referrals. The nurse navigators did not experience an increase in their workload, but implementation of PIFU may require a re-structured workflow. The ePRO-based individual follow-up could change organization of care and re-allocate services for those in need of it. ePRO-based individual follow-up could potentially ensure more time for those most in need of face-to-face care.

A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers

The energy demand of data centers is increasing globally with the increasing demand for computational resources to ensure the quality of services. It is important to quantify the required resources to comply with the computational workloads at the rack-level. In this paper, a novel reliability index called loss of workload probability is presented to quantify the rack-level computational resource adequacy. The index defines the right-sizing of the rack-level computational resources that comply with the computational workloads, and the desired reliability level of the data center investor. The outage probability of the power supply units and the workload duration curve of servers are analyzed to define the loss of workload probability. The workload duration curve of the rack, hence, the power consumption of the servers is modeled as a function of server workloads. The server workloads are taken from a publicly available data set published by Google. The power consumption models of the major components of the internal power supply system are also presented which shows the power loss of the power distribution unit is the highest compared to the other components in the internal power supply system. The proposed reliability index and the power loss analysis could be used for rack-level computational resources expansion planning and ensures energy-efficient operation of the data center.