Job Completion Research Articles

Eventually-consistent federated scheduling for data center workloads

Data center schedulers operate at unprecedented scales today to accommodate the growing demand for computing and storage power. The challenge that schedulers face is meeting the requirements of scheduling speeds despite the scale. To do so, most scheduler architectures use parallelism. However, these architectures consist of multiple parallel scheduling entities that can only utilize partial knowledge of the data center’s state, as maintaining consistent global knowledge or state would involve considerable communication overhead. The disadvantage of scheduling without global knowledge is sub-optimal placements—tasks may be made to wait in queues even though there are resources available in zones outside the scope of the scheduling entity’s state. This leads to unnecessary queuing overheads and lower resource utilization of the data center.In this paper, we extend our previous work on Thiyyakat et al. (2023), a federated decentralized data center scheduling architecture that uses eventual consistency. The architecture utilizes both parallelism and an eventually consistent global state in each of its scheduling entities to make fast decisions in a scalable manner. In our work, we compare Megha with 3 scheduling architectures — Sparrow, Eagle, and Pigeon, using simulation. We also evaluate Megha’s prototype on a 123-node cluster and compare its performance with Pigeon’s prototype using cluster traces. The results of our experiments show that Megha consistently reduces delays in job completion time when compared to other architectures.

Design And Building Of The Utility Performance Monitoring System At PT. XYZ With Brainstorming Method

In general, utilities in engineering are devices or systems designed to provide basic needs or to help complete certain jobs or processes. In engineering, utilities generally refer to systems or devices designed to provide basic needs such as electricity, water, gas, or telecommunications. At PT. The brainstorming method is used to produce creative ideas that can be applied in developing utility monitoring systems. Various ideas are generated through discussion and debate between brainstorming participants. Then, these ideas are analyzed and selected to select those that best suit PT XYZ's needs. Therefore, the author created a utility performance monitoring system to make it easier for the engineering department to carry out work activities. This utility performance monitoring system was created using a JavaScript framework, namely AngularJS for the frontend and ExpressJS for the backend. Apart from that, the author also tested PT XYZ employees so that the system runs according to needs. Of the 10 respondents, more than 50% of the accumulated respondents stated that the utility performance monitoring system application was running satisfactorily and very satisfactorily, there were 10% to 30% of respondents who stated that it was running well and 10% of respondents said it was not working well. Based on the test results above, it was found that the utility monitoring system at PT XYZ could be categorized as a fairly informative information system for the engineering department.

Renewable-Aware Frequency Scaling Approach for Energy-Efficient Deep Learning Clusters

Recently, renewable energy has emerged as an attractive means to reduce energy consumption costs for deep learning (DL) job processing in modern GPU-based clusters. In this paper, we propose a novel Renewable-Aware Frequency Scaling (RA-FS) approach for energy-efficient DL clusters. We have developed a real-time GPU core and memory frequency scaling method that finely tunes the training performance of DL jobs while maximizing renewable energy utilization. We introduce quantitative metrics: Deep Learning Job Requirement (DJR) and Deep Learning Job Completion per Slot (DJCS) to accurately evaluate the service quality of DL job processing. Additionally, we present a log-transformation technique to convert our non-convex optimization problem into a solvable one, ensuring the rigorous optimality of the derived solution. Through experiments involving deep neural network (DNN) model training jobs such as SqueezeNet, PreActResNet, and SEResNet on NVIDIA GPU devices like RTX3060, RTX3090, and RTX4090, we validate the superiority of our RA-FS approach. The experimental results show that our approach significantly improves performance requirement satisfaction by about 71% and renewable energy utilization by about 31% on average, compared to recent competitors.

Flexible silicon photonic architecture for accelerating distributed deep learning

The increasing size and complexity of deep learning (DL) models have led to the wide adoption of distributed training methods in datacenters (DCs) and high-performance computing (HPC) systems. However, communication among distributed computing units (CUs) has emerged as a major bottleneck in the training process. In this study, we propose Flex-SiPAC, a flexible silicon photonic accelerated compute cluster designed to accelerate multi-tenant distributed DL training workloads. Flex-SiPAC takes a co-design approach that combines a silicon photonic hardware platform with a tailored collective algorithm, optimized to leverage the unique physical properties of the architecture. The hardware platform integrates a novel wavelength-reconfigurable transceiver design and a micro-resonator-based wavelength-reconfigurable switch, enabling the system to achieve flexible bandwidth steering in the wavelength domain. The collective algorithm is designed to support reconfigurable topologies, enabling efficient all-reduce communications that are commonly used in DL training. The feasibility of the Flex-SiPAC architecture is demonstrated through two testbed experiments. First, an optical testbed experiment demonstrates the flexible routing of wavelengths by shuffling an array of input wavelengths using a custom-designed spatial-wavelength selective switch. Second, a four-GPU testbed running two DL workloads shows a 23% improvement in job completion time compared to a similarly sized leaf-spine topology. We further evaluate Flex-SiPAC using large-scale simulations, which show that Flex-SiPAC is able to reduce the communication time by 26% to 29% compared to state-of-the-art compute clusters under representative collective operations.

The role of key workplace elements in determining individual and organizational success in Jordan Tourism Board

The aim of this research was to investigate the impact of various work-related factors, such as task load, workers’ compensation, and organizational structure community on job success and the overall performance of the Jordan tourism board. The researchers conducted an analysis of 269 randomly selected samples from their study and discussed their findings to validate their hypotheses. The study revealed that factors like compensation levels, organizational structure, community, and task load significantly influence individual and organizational success. The first aspect examined was the task load, which was assessed by considering factors such as job completion rates, daily challenges faced, and the time required for tasks. Work-related compensation, encompassing factors like experience, skill, incentives, and rewards, emerged as the second most crucial factor after working hours. The third and most vital component to consider was the organization structure community, which includes communities of practice, collaborative efforts, and physical infrastructure. According to the research findings, improving employee performance can be achieved by reducing their task load, increasing work income, and developing the organization community that fosters teamwork and the formation of workgroups. These elements collectively impact overall productivity. The study provides valuable insights into an underexplored area, shedding light on how task load, compensation, and organizational structure community interplay. The research's focus on the Jordan tourism board is particularly significant, as it has the potential to help tourism companies enhance their operations and provide superior service to their customers.

Real-Time Information Access in Urban Environments: A User Interaction Study Using the Real-Time Information Test

In this study, "Real-Time Information Access in Urban Environments: A User Interaction Study Using the Real-Time Information Test," participant data revealed a diverse group with an average age of 31, a balanced gender distribution, varying education levels (40% Bachelor's, 20% Master's, 40% PhD), and an average of 6 years of experience with urban navigation. The findings of the Real-Time Information Test (RTIT) showed an average job completion time of 140 seconds and a low average error count of 1.2, demonstrating competency in interacting with real-time information systems. Furthermore, the User Satisfaction Survey found an average of 8.4 overall satisfaction ratings, 8.4 user-friendliness ratings, and 7.8 information accuracy ratings, indicating excellent user experiences. These results highlight user variety, increases in job efficiency and accuracy, and high user satisfaction, all of which contribute to a comprehensive knowledge of real-time information access in urban contexts, with implications for system advancements and urban planning.

Evaluating procedures in the NEH heuristic for the PFSP - SIST

The development and assessment of 48 heuristics for the sequence-independent setup time permutation flow shop problem (PFSP-SIST) are presented in this article. This contribution combines four tie-breaking solutions with twelve priority rules for the NEH heuristic fourth and first stage, respectively. Heuristics are evaluated on Ruiz and Allahverdi (2007) benchmark problem instances, that covers small, medium and large-size problems. The popular accelerations of Taillard were used in all tests, which were adapted to the sequence-independent setup time constraint. The aim is to reduce the longest job completion time, which is also referred to as makespan. Computation results show that using different tie-breaking strategies has a greater impact on performance than using different priority rules. The heuristics that presented the best results in relatively low computation time are those that use the FFs tie-breaking strategy procedure to the sequence-independent setup time problem.

Arduino UNO Controller Based Semi Automated Wheelbarrow: The Ultimate Companion

When it comes to building projects, landscaping, and gardening, the correct equipment is required to complete jobs quickly and simply. The wheelbarrow is useful and timeless equipment for transporting big items. The Powered Wheelbarrow makes several journeys, hilly hikes, and tight bends simpler. The primary goal of this project is to design, build, and test a conventional wheelbarrow with better stability and efficiency. E mobility will increase production by replacing the old way. The wheelbarrow is linked to the Arduino board and the Bluetooth module in this project. Two DC motors are linked to the wheelbarrow’s wheels, producing power and assisting in the movement of the wheelbarrow. The Bluetooth module HC-05 is used to control it. It is used to alleviate the tension and workload of the person driving the wheelbarrow. Another goal met was the implementation of a reverse breaking and safety function, which attempts to keep people safe when transporting large items. As a result, the motorized wheelbarrow can reduce the operator’s workload.

Coalition theory based task scheduling algorithm using DLFC‐NN model

SummaryResource management and job scheduling are essential in today's cloud computing world. Due to task scheduling and users' diverse submission of large‐scale requests, co‐located VM instances negatively impacted the performance of leased VM instances. This workload further led to resource rivalry across co‐located VMs. In order to address the aforementioned problems, numerous strategies have been presented, however, they fail to take the asynchronous nature of the cloud environment into account. To address this issue, a novel “CTA using DLFC‐NN model” is proposed. This proposed approach combines the coalition theory and DLFC‐NN techniques by including IRT‐OPTICS for task size clustering, digital metrology based on ionized information (DMBII) for defect detection in virtue machines (VM), and the dynamic levy flight hamster optimization algorithm for processing time optimization of the clusters. However, the implementation of task scheduling in an online environment is limited by a number of presumptions or oversimplifications made by current scheduling systems. As a result, a unique coalition theory is applied to efficiently schedule activities. In addition, the DLFC‐NN model is used to reduce resource consumption, span time, and be highly accurate and energy‐efficient when working on both online and offline jobs. Nevertheless, while optimizing the clusters' overall execution time, earlier approaches only decreased the make‐span time for task scheduling. However, the DLFC‐NN model solves the computation problem by using a fully weighted bipartite graph and the pseudo method to determine the fitness of the least makespan time. The enhanced methodology used in this study reduces the scheduling cost and minimizes job completion times according to different task counts when compared to the existing techniques.

Approximation Algorithms for Multiprocessor Scheduling with Testing to Minimize the Total Job Completion Time

Approximation Algorithms for Multiprocessor Scheduling with Testing to Minimize the Total Job Completion Time

3. THE IMPACT OF ENGAGEMENT ON JOB COMPLETION’S LEVEL OF MEDICAL STAFF: A CASE AT THAI THUY GENERAL HOSPITAL, THAI BINH

Introduction: Healthcare workers play an important role in the delivery of healthcare around the world. The purpose of this analysis was to explore the impact of healthcare workers’ organizational commitment, controlling for demographic variables, on job satisfaction, particularly in the context of the mid-career health sector going through a stressful period in the fight against the COVID-19 pandemic.
 Methods: This study used a hospital assessment toolkit issued by the Vietnamese Ministry of Health, surveying 156 medical staffs directly providing health care services at Thai Thuy General Hospital, Thai Binh province.
 Results: The analysis results by the PLS-SEM structural equation model show that, when the cohesion of medical staff is improved, it will lead to a higher level of job completion. Besides, the medical staff must concurrently take on other jobs, which will negatively affect the level of job completion.
 Conclusion: This implies that the level of the job performance of healthcare workers can depend to a large extent on the organizational commitment and the medical staff’s single duty.

CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency

Cloud platforms are increasing their emphasis on sustainability and reducing their operational carbon footprint. A common approach for reducing carbon emissions is to exploit the temporal flexibility inherent to many cloud workloads by executing them in periods with the greenest energy and suspending them at other times. Since such suspend-resume approaches can incur long delays in job completion times, we present a new approach that exploits the elasticity of batch workloads in the cloud to optimize their carbon emissions. Our approach is based on the notion of "carbon scaling," similar to cloud autoscaling, where a job dynamically varies its server allocation based on fluctuations in the carbon cost of the grid's energy. We develop a greedy algorithm for minimizing a job's carbon emissions via carbon scaling that is based on the well-known problem of marginal resource allocation. We implement a CarbonScaler prototype in Kubernetes using its autoscaling capabilities and an analytic tool to guide the carbon-efficient deployment of batch applications in the cloud. We then evaluate CarbonScaler using real-world machine learning training and MPI jobs on a commercial cloud platform and show that it can yield i) 51% carbon savings over carbon-agnostic execution; ii) 37% over a state-of-the-art suspend-resume policy; and iii) 8 over the best static scaling policy.

A Novel Model for Dynamic Manufacturing Service Collaboration on Industrial Internet

Industrial Internet enables distributed manufacturing enterprises to efficiently and promptly respond to the requirements of stakeholders using a manufacturing service collaboration chain (MSCC) composed of networked enterprises. However, various dynamic uncertainties may interrupt the MSCC, such as device malfunctions, urgent order insertions, and dynamic logistics, resulting in inexactitude practical applications. This paper proposes a novel reliability-based dynamic manufacturing service collaboration optimization (R-DMSCO) model for uncertain manufacturing collaboration procedures on Industrial Internet. The RDMSCO model reformulates the MSCC reliability in the form of an expectation-standard deviation of uncertain job completion time described by discrete scenarios pertaining to the uncertain perturbation of processing time and logistics time. Subsequently, an enhanced multi-objective artificial bee colony (EMOABC) algorithm that embeds four improvements is intended to address the manufacturing service collaboration optimization (MSCO) problem. The experimental results demonstrate that EMOABC outperforms other typical multi-objective algorithms for MSCO problems. Additionally, the R-DMSCO model can cope with dynamic uncertainties with better robustness and stability than two other effective strategies for dynamic manufacturing service collaboration.

Transgressing proved patterns

Where does the whole hype about artificial intelligence (AI) come from? Is it the first time? Do we really have a true, general AI? If not, how soon shall we have it? How much will technology change everyday tasks, complete job descriptions, entire professions? Does it apply to scientific publishing and how? What do scientists really need in publishing, what can be done by AI and what is the job for a human?

A Hybrid Optimization Algorithm for Efficient Virtual Machine Migration and Task Scheduling Using a Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique

This To achieve optimal system performance in the quickly developing field of cloud computing, efficient resource management—which includes accurate job scheduling and optimized Virtual Machine (VM) migration—is essential. The Adaptive Multi-Agent System with Deep Deterministic Policy Gradient (AMS-DDPG) Algorithm is used in this study to propose a cutting-edge hybrid optimization algorithm for effective virtual machine migration and task scheduling. An sophisticated combination of the War Strategy Optimization (WSO) and Rat Swarm Optimizer (RSO) algorithms, the Iterative Concept of War and Rat Swarm (ICWRS) algorithm is the foundation of this technique. Notably, ICWRS optimizes the system with an amazing 93% accuracy, especially for load balancing, job scheduling, and virtual machine migration. The VM migration and task scheduling flexibility and efficiency are greatly improved by the AMS-DDPG technology, which uses a powerful combination of deterministic policy gradient and deep reinforcement learning. By assuring the best possible resource allocation, the Adaptive Multi-Agent System method enhances decision-making even more. Performance in cloud-based virtualized systems is significantly enhanced by our hybrid method, which combines deep learning and multi-agent coordination. Extensive tests that include a detailed comparison with conventional techniques verify the effectiveness of the suggested strategy. As a consequence, our hybrid optimization approach is successful. The findings show significant improvements in system efficiency, shorter job completion times, and optimum resource utilization. Cloud-based systems have unrealized potential for synergistic optimization, as shown by the integration of ICWRS inside the AMS-DDPG framework. Enabling a high-performing and sustainable cloud computing infrastructure that can adapt to the changing needs of modern computing paradigms is made possible by this strategic resource allocation, which is attained via careful computational utilization.

A Study Resource Optimization Techniques Based Job Scheduling in Cloud Computing

Cloud computing has revolutionized the way businesses and individuals utilize computing resources. It offers on-demand access to a vast pool of virtualized resources, such as processing power, storage, and networking, through the Internet. One of the key challenges in cloud computing is efficiently scheduling jobs to maximize resource utilization and minimize costs. Job scheduling in cloud computing involves allocating tasks or jobs to available resources in an optimal manner. The objective is to minimize job completion time, maximize resource utilization, and meet various performance metrics such as response time, throughput, and energy consumption. Resource optimization techniques play a crucial role in achieving these objectives. Resource optimization techniques aim to efficiently allocate resources to jobs, taking into account factors like resource availability, job priorities, and constraints. These techniques utilize various algorithms and optimization approaches to make intelligent decisions about resource allocation. Research on resource optimization techniques for job scheduling in cloud computing is of significant importance due to the following reasons: Efficient Resource Utilization: Cloud computing environments consist of a large number of resources that need to be utilized effectively to maximize cost savings and overall system performance. By optimizing job scheduling, researchers can develop algorithms and techniques that ensure efficient utilization of resources, leading to improved productivity and reduced costs. Performance Improvement: Job scheduling plays a crucial role in meeting performance metrics such as response time, throughput, and reliability. By designing intelligent scheduling algorithms, researchers can improve the overall system performance, leading to better user experience and customer satisfaction. Scalability: Cloud computing environments are highly scalable, allowing users to dynamically scale resources based on their needs. Effective job scheduling techniques enable efficient resource allocation and scaling, ensuring that the system can handle varying workloads without compromising performance. Energy Efficiency: Cloud data centres consume significant amounts of energy, and optimizing resource allocation can contribute to energy conservation. By scheduling jobs intelligently, researchers can reduce energy consumption, leading to environmental benefits and cost savings for cloud service providers. Quality of Service (QoS): Cloud computing service providers often have service-level agreements (SLAs) that define the QoS requirements expected by users. Resource optimization techniques for job scheduling can help meet these SLAs by ensuring that jobs are allocated resources in a timely manner, meeting performance guarantees, and maintaining high service availability. Here in this research, we have used the method of the weighted product model (WPM). For the topic of Resource Optimization Techniques Based Job Scheduling in Cloud Computing For calculating the values of alternative and evaluation parameters. A variation of the WSM called the weighted product method (WPM) has been proposed to address some of the weaknesses of The WSM that came before it. The main distinction is that the multiplication is being used in place of additional. The terms "scoring methods" are frequently used to describe WSM and WPM Execution time on Virtual machine, Transmission time (delay)on Virtual machine, Processing cost of a task on virtual machine resource optimization techniques based on job scheduling play a crucial role in maximizing the efficiency and performance of cloud computing systems. By effectively managing and allocating resources, these techniques help minimize costs, reduce energy consumption, and improve overall system throughput. One of the key findings is that intelligent job scheduling algorithms, such as genetic algorithms, ant colony optimization,

GPARS: Graph predictive algorithm for efficient resource scheduling in heterogeneous GPU clusters

Efficient resource scheduling in heterogeneous graphics processing unit (GPU) clusters are critical for maximizing system performance and optimizing resource utilization. However, prior research in resource scheduling algorithms typically employed machine learning (ML) algorithms to estimate job durations or GPU utilization in the cluster based on training progress and task speed. Regrettably, these studies often overlooked the performance variations among different GPU types within these clusters, as well as the presence of spatiotemporal correlations among jobs. To address these limitations, this paper introduces the graph predictive algorithm for efficient resource scheduling (GPARS) designed specifically for heterogeneous clusters. GPARS leverages spatiotemporal correlations among jobs and utilizes graph attention networks (GANs) for precise job duration prediction. Building upon the prediction results, we develop a dynamic objective function to allocate suitable GPU types for newly submitted jobs. By conducting a comprehensive analysis of Alibaba’s heterogeneous GPU cluster, we delve into the impact of GPU capacity and type on job completion time (JCT) and resource utilization. Our evaluation, using real traces from Alibaba and Philly, substantiates the effectiveness of GPARS. It achieves a remarkable 10.29% reduction in waiting time and an average improvement of 7.47% in resource utilization compared to the original scheduling method. These findings underscore GPARS’s superior performance in enhancing resource scheduling within heterogeneous GPU clusters.

The Accessibility and Usability of Online Job Applications for Screen Reader Users.

This study investigated the accessibility and usability of online job applications by individuals with visual impairments who use screen readers. Three blind, experienced screen reader users on the research team attempted to complete online job applications from 30 randomly selected Fortune 500 companies, with 90 application trials attempted. Each tester used a different screen reader and browser combination. The testers experienced significant barriers to completing the online job applications: 50 of 90 (55.6%) attempts were successful, but 23 of the 30 (76.7%) sites had critical issues that prevented at least one tester from completing the application. Testers found 694 accessibility issues and gave an average ease of use rating of 3.60 (SD=1.05) out of 5 for the job application sites. Twenty-six of the job application sites were created by eight job application software vendors, with significant variation in accessibility across different applications using the same vendor. The accessibility and usability issues identified with the majority of online job applications evaluated present an unnecessary barrier to employment for screen reader users. While the 55.6% success rate found in this study is considerably higher compared to previous research, only 23.3% of the job application sites were accessible to all three screen readers. Job application software vendors and companies that create their own online job applications should take a Universal Design approach by following the WCAG guidelines and implementing recommended solutions to increase the accessibility of their applications. Software vendors are encouraged to only offer accessible options for creating online job applications, such that companies would not be able to create an inaccessible form.

Peningkatan motivasi kerja dengan penilaian prestasi kerja , beban kerja dan lingkungan kerja pada dinas perhubungan kabupaten buleleng

The results of this study indicate that work performance has a positive and significant effect on work motivation at the Buleleng Regency Transportation Service with the ability of employees to work together with other employees to improve their work results by helping each other in each job to achieve the expected goals in an effort to increase their motivation at work because each job given can be completed properly. Workload has a positive and significant effect on work motivation at the Buleleng Regency Transportation Service with workload received by employees and proven by the most common way to complete the work given and employee responsibilities have been arranged based on the working hour schedule set by superiors, with responsibilities given to employees based on the employee's working hours and obligations distributed to employees who have the choice to do their work. The work environment has a positive and significant effect on work motivation at the Buleleng Regency Transportation Service with a good employee work environment which will be proven by the way employees work in carrying out each activity or activity provided whether they are able to be completed properly, a good work environment will help employees complete every job given to them.

Gender Differences in the Geographic Breadth of Job Search: Examining Job Applications

Abstract Where one lives and works is increasingly important in shaping economic opportunities. Yet, women, particularly partnered women, are less likely than men to relocate for a job, potentially serving as a key force in the production of gender labor market stratification. We examine why women’s geographic job mobility is lower than men’s, building on theoretical insights about the structural features of the labor market as well as how households make decisions. Our contribution arises from examining job applications rather than completed job moves. This enables us to examine the behavior of job seekers independent of employers’ hiring decision-making that may shape the findings in scholarship that focuses on completed job moves. We draw on an original dataset that captures detailed, prospective information on the job applications submitted by a national, probability-based sample of job seekers. Our findings indicate that even at the application stage, partnered women – but not women who have never been married – are less likely than comparable men to apply for a job requiring a move. This pattern holds even after accounting for structural features of the labor market. Theories of gendered household dynamics appear to better explain our findings for partnered individuals than theories of household economic maximization.