Job Scheduling Research Articles

Job Scheduling in Hybrid Clouds With Privacy Constraints: A Deep Reinforcement Learning Approach

ABSTRACTWith the proliferation of cloud computing and the escalating demand for extensive data processing capabilities, an increasing number of enterprises are embracing hybrid cloud solutions. However, as more businesses move toward hybrid clouds, the need for effective solutions to privacy and security concerns becomes increasingly important. Although current scheduling approaches for cloud computing have addressed privacy protection to some extent, few have adequately considered the unique challenges posed by hybrid clouds. To address this gap, we propose a novel approach for scheduling jobs in hybrid clouds that prioritizes privacy protection. Our approach, called PH‐DRL, leverages Deep Reinforcement Learning (DRL) to intelligently allocate jobs to virtual machines, optimizing both privacy and Quality of Service (QoS), while minimizing response time. We present the detailed implementation of our approach and our experimental results demonstrate the superior performance of PH‐DRL in terms of privacy protection compared to existing methods.

Location-aware job scheduling for IoT systems using cloud and fog

The rapid growth of Internet of Things (IoT) applications generates vast volumes of high-speed data streams from numerous devices. Cloud computing solutions often handle and manage this data; however, for certain applications, the latency introduced by transmitting data from edge devices to the cloud may be unacceptable. This issue is exacerbated by the bandwidth constraints of public networks, which become significant barriers in large-scale IoT implementations. Consequently, effective resource management, service management, data storage, and power management require more robust infrastructure and complex protocols. An "intelligent gateway" based on fog computing can enhance the efficient utilization of cloud and network resources. Resource planning and management in a fog-cloud environment significantly impact system performance, particularly latency. This problem is known to be NP-hard. This paper addresses the challenge of lifetime optimization for scheduling data-intensive tasks in fog and cloud-based IoT systems. Initially, we propose an Integer Linear Programming (ILP) optimization model to frame the problem. Subsequently, we introduce a heuristic method named Data-Locality Aware Job Scheduling in Fog-Cloud (DLSFC), which is derived from the proposed formulation. The effectiveness of DLSFC is evaluated across various system characteristics, with results demonstrating that DLSFC achieves solutions within 85 % of the ideal outcome provided by the LP solver.

Efficient Job Scheduling in Cloud Environments using Reinforcement Learning Actor-Critic Models

Efficient Job Scheduling in Cloud Environments using Reinforcement Learning Actor-Critic Models

Job Involvement among ICDS Scheme Functionaries in India: Assessing Extension Needs for Enhanced Performance

Job involvement is psychological identification with one’s work as well as degree to which the job situation is central to person and his identity. Human behavior plays a pivotal role in maximizing organizational effectiveness irrespective of the technological support and their involvement in their job is critical for successful achievement of its objectives. ICDS (Integrated Child Development Services) is a comprehensive scheme of Govt. of India enriches human resource and tackles malnutrition and health problems in children below six years of age and their mothers. The services to the beneficiaries, who are pregnant women, Lactating Mothers, children under 5 years, adolescents and adult women, are provided at Centre called as Anganwadi. The grass-root level functionaries at the center are called Anganwadi workers (AWW) who executes ICDS scheme as per the Government guidelines to raise the level of health and nutrition, create proper mental, physical and social development of children below six years; to reduce mortality and malnutrition and dropouts. Since job involvement contributes to overall availability of human resource, the success of the ICDS programme extents to effective and successful working of AWWs. The study was conducted to know the job- involvement of AWWs and AWHs – the grass-root functionaries of ICDS scheme. The conducted in one of the blocks of Faizabad District situated in Eastern part of Uttar Pradesh state. One hundred AWWs were randomly selected from 182 AWWs in the block and administered interview schedule and standardized Job Involvement scale by Dr. A.P Singh. The data collected revealed that AWWs were highly involved (47%), moderately involved (37%) and rest (16%) had low involvement. Analysis further found that job involvement and income of the workers; educational qualification and work experience are significantly dependent.

Minimizing the number of late jobs and total late work with step-learning

We study single-machine scheduling problems with step-learning, where an improvement in processing time is experienced if a job is started at, or after, a job-dependent learning-date. We consider minimizing two functions: the number of late jobs and the total late work, and we show that when at least a common due-date or common learning-date is assumed, the problem is NP-hard in the ordinary sense; however, when both are arbitrary, the problem becomes strongly NP-hard. For each of the problems where at least one of the dates is assumed to be common, we analyze the structure of an optimal job schedule with and without idle time and propose pseudo-polynomial time dynamic programming algorithms. We also show that the problem of minimizing the weighted number of late jobs with step-learning can be solved with a minor change to the algorithms for the unweighted case. In addition to this, we show that when a common due-date is assumed and no idle time is allowed, the problem of minimizing the total late work is equivalent to that of minimizing the makespan. Furthermore, we provide a more efficient algorithm to solve the problem of minimizing makespan under the assumption of a common learning-date than the one in the existing literature. Lastly, we show that our analysis can also be applied to the case of step-deterioration, where instead, the processing times of jobs increase at a given date.

Prevalence of sleep disorders and association with various occupations among Indian population aged ≥ 45 years: Insight from Longitudinal Ageing Study in India (LASI)

ABSTRACT Introduction: By scrutinizing the impact of job schedules, workplace culture and stress levels, our study aims to determine the prevalence of sleep disorders among various occupations and its associated factors. The findings promise to enhance the overall well-being, sleep quality and productivity in various occupational settings. Methods: This is an analytical cross-sectional study involving secondary data from the nationally representative Longitudinal Ageing Study in India (LASI Wave I 2017-18) of 35 Indian states and union territories with exception of Sikkim. The study included 59441 Indian volunteers who older than 45 years. Multivariable logistic regression analysis was conducted to study the association of sleep disorders among various occupations while accounting for other associated factors. Results: Prevalence of sleep disorders was higher among the females (12.66%), unemployed (13.34%), ≥ 60-yr age group (13.45%), those who lived in rural areas (12.35%), those who were illiterate (12.80%) and reported themselves to be widowed, separated or divorced (14.54%), people with no health insurance (11.74%), smokers (12.11%), without physical activity (12.67%) and also rated themselves as poor in the self-rated health status (26.77%). Alcohol consumption was not statistically significant factor for sleep disorders. Conclusions: There is an emerging need for targeted screening, prompt detection and early management of insomnia and other sleep disorders among the high-risk groups. Provision of accessible and affordable screening tools, training skills for early diagnosis at primary care level and/or proper referral services to higher centres form the cornerstone for adequate management of sleep disorders.

Minimizing Costs in Fog Nodes Through Effective Resource and Task Scheduling

Background: This system comprises the fog control node, access node, and Internet of Things (IoT) application. This study aims to reduce scheduling-related cloud costs. A user scheduling method and an economical task are carried out for this reason. The algorithm for job scheduling is constructed to determine the best strategy to handle several jobs in a fog access node. Reallocation technology ultimately reduces delays in both time and service. For analytical objectives, extensive simulations were run and performance metrics were compared with those of other currently in use methods. It was discovered that the suggested technique lowers task latency and offers extreme cost-effectiveness by improving the concurrent capability in the fog node, users, and task scheduling with improved performance statistics that made it possible. Materials and Methods: The research paper explores fog computing, a three-layered architecture consisting of IoT devices, fog computing, and cloud computing. The fog computing layer sits between cloud networks and Internet of Things (IoT) devices, providing fast access to these devices and handling computation locally at the network edge. Results: The research paper focuses on reducing cloud costs in scheduling. Although the proposed system showed many similarities in execution times compared to the diverse early completion-time method, it consistently lags behind the heterogeneous method by 5 to 10%. Conclusion: This research aims to reduce the cloud scheduling costs for Internet of Things (IoT) devices. A user-based scheduling algorithm and economical task allocation are implemented. A constraint-based and user-defined strategy is recommended.

Development and implementation of effective Web Scraping methods for automated data collection and processing using Python

With each passing year, the process of digitalization in society is accelerating, leading to a significant increase in demand for processed and analyzed information. In today's world, data has become a valuable resource, and the ability to quickly find and analyze large amounts of information is a key competitive advantage for companies, researchers, and analysts. In this context, web scraping has become an important tool, enabling the efficient collection of data from various online sources for further analysis and informed decision-making. This paper examines the latest advancements in the development and implementation of effective web scraping methods for automatic data collection and processing using Python. The use of the latest Python libraries, such as BeautifulSoup, Selenium, and Scrapy, allows for high-speed and accurate data collection from various web sources, particularly in secondary markets. The proposed algorithms reduce the risk of site blocking, ensuring the stability and reliability of data collection in various situations. Additionally, the paper places great emphasis on automating the data collection process through the development of automated scripts and the implementation of job scheduling programs, such as cron jobs. This ensures continuous database updates and the collection of new information without manual intervention. Special attention is given to the processing and cleaning of collected data, particularly in the development of methods for filtering out unnecessary information, duplicates, and noise, which enhances data quality. The efficient use of the collected data demonstrates its value for market analysis, demand assessment, and quality forecasting, highlighting the importance of the proposed method. The research includes examples of real-world data use cases in various fields such as marketing, economics, and business analysis. A comparative analysis of different data collection methods is also provided, allowing for the assessment of the effectiveness and reliability of the proposed solutions.

Workplace Risk Associated Activities: Case Study of Medical Laboratory Science Council of Nigeria (MLSCN)

In the study of the Workplace Risk Associated Activities, Medical Laboratory Science Council of Nigeria (MLSCN) Case Study, a descriptive survey research design was adopted. The research method used for the primary data collection was structured closed and opened–ended questionnaire created with Epi-Info version7 based on the research aim and objectives of the study. The collected data was analysed using Epi-Info version7. Results indicated that: high number of person-to-person contacts (83.3%), high number of respondents/staff encounter with sharp materials during work (55.7%), presence of snakes (3.8%) and rodents (39.2%) In or around offices, presence of mosquitoes bite (43.0%), low number of immunized respondents/staff (24.4%), low number of trained respondents/staff on workplace risks (33.3%), working space (67.1%), job schedule orientation (66.7%), respondents/staff that cover their mouth when coughing or sneezing (66.6%) and respondents/staff that wash hands before eating in the office (89.6%). These trends amount to high risks with high likelihood and consequences calling for urgent attention. Though the organization scored high (98.7%) in assess control by making sure that all offices had lock and keys and were locked when leaving office, in switching off electrical appliances when leaving office (94.8%), washing hands before eating in office (89.6%) and in work environment cleanliness and conduciveness (88.5%) yet more hard work is needed to enable attainment of 100% in all workplace safety areas. The logistic regression analysis of training (outcome variable) and covariates: washing hands before eating in the office, covering mouth when coughing or sneezing and switching off light when leaving office indicated significant results (p<0.5) with likelihood of about 90.0% There are high workplace risks associated activities in MLSCN calling for urgent attention.

DuCFF: A Dual-Channel Feature-Fusion Network for Workload Prediction in a Cloud Infrastructure

Cloud infrastructures are designed to provide highly scalable, pay-as-per-use services to meet the performance requirements of users. The workload prediction of the cloud plays a crucial role in proactive auto-scaling and the dynamic management of resources to move toward fine-grained load balancing and job scheduling due to its ability to estimate upcoming workloads. However, due to users’ diverse usage demands, the changing characteristics of workloads have become more and more complex, including not only short-term irregular fluctuation characteristics but also long-term dynamic variations. This prevents existing workload-prediction methods from fully capturing the above characteristics, leading to degradation of prediction accuracy. To deal with the above problems, this paper proposes a framework based on a dual-channel temporal convolutional network and transformer (referred to as DuCFF) to perform workload prediction. Firstly, DuCFF introduces data preprocessing technology to decouple different components implied by workload data and combine the original workload to form new model inputs. Then, in a parallel manner, DuCFF adopts the temporal convolution network (TCN) channel to capture local irregular fluctuations in workload time series and the transformer channel to capture long-term dynamic variations. Finally, the features extracted from the above two channels are further fused, and workload prediction is achieved. The performance of the proposed DuCFF’s was verified on various workload benchmark datasets (i.e., ClarkNet and Google) and compared to its nine competitors. Experimental results show that the proposed DuCFF can achieve average performance improvements of 65.2%, 70%, 64.37%, and 15%, respectively, in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared (R2) compared to the baseline model CNN-LSTM.

Machine learning approaches to predict the execution time of the meteorological simulation software COSMO

AbstractPredicting the execution time of weather forecast models is a complex task, since these models are usually performed on High Performance Computing systems that require large computing capabilities. Indeed, a reliable prediction can imply several benefits, by allowing for an improved planning of the model execution, a better allocation of available resources, and the identification of possible anomalies. However, to make such predictions is usually hard, since there is a scarcity of datasets that benchmark the existing meteorological simulation models. In this work, we focus on the runtime predictions of the execution of the COSMO (COnsortium for SMall-scale MOdeling) weather forecasting model used at the Hydro-Meteo-Climate Structure of the Regional Agency for the Environment and Energy Prevention Emilia-Romagna. We show how a plethora of Machine Learning approaches can obtain accurate runtime predictions of this complex model, by designing a new well-defined benchmark for this application task. Indeed, our contribution is twofold: 1) the creation of a large public dataset reporting the runtime of COSMO run under a variety of different configurations; 2) a comparative study of ML models, which greatly outperform the current state-of-practice used by the domain experts. This data collection represents an essential initial benchmark for this application field, and a useful resource for analyzing the model performance: better accuracy in runtime predictions could help facility owners to improve job scheduling and resource allocation of the entire system; while for a final user, a posteriori analysis could help to identify anomalous runs.

Chronotype and Lifestyle Schedule in Healthy Rural Population in South-Eastern Region of Chhattisgarh

Chronotype is morningness and eveningness preference of humans. The human biological clock is primarily entrained by sunlight but can show the highly flexible phase-shifting capacity of the biological clock allows a person to adjust to new time zones, shift work, temperature changes, seasonal changes, job schedules, social obligations, and other urgent activities. The aim of the current study was to identify the chronotype of interior rural residents who have limited access to electricity. 2117 healthy subjects (aged between 6 to 75 years) willingly participated in the study. All the subjects were provided with biographical information sheets. Chronotype was subjectively assessed using Horne and Östberg questionnaire [1].Result revealed predominant morningness in the studied population. Evening-types more exposed to nighttime artificial light as well as screen time. In addition, body surface area (BSA) of evening-types was also significantly higher than that in other chronotypes. Further the sleeping and waking hours significantly delayed in evening-type as compared to morning-type and intermediate-type, irrespective of work days and free days. Present study provides insights to the chronotype of rural populations living under semi-modernized conditions. Absence of light at nights, limited screen time, and less facility of electricity could be a reason for predominant morningness among the population. Further eveningtypes with higher BSA are seems to be more susceptible to disturbed sleep-wake pattern.

Hub production scheduling problem with mold availability constraints

This research delves into a scheduling challenge inherent in the wheel hub casting process, a critical stage in automotive wheel manufacturing. The process involves shaping molten metal into specific wheel hub designs using dedicated molds. Diverse customer demands necessitate a unique mold for each wheel hub design, posing a scheduling challenge due to the limited availability of these expensive and long-lasting molds. There are 2 machines available for casting. Each wheel hub design requires a specific mold. These molds are expensive, long-lasting, and need periodic maintenance. The objective function is to minimize the total time (makespan) to complete all casting jobs. Only one job requiring a specific mold can be processed at a time (due to mold limitations). Molds are unavailable during maintenance periods. The challenge lies in scheduling jobs considering both machine availability and mold constraints to minimize the overall production time. We formulate a mathematical model using mixed integer programming to precisely represent the problem and its constraints. Inspired by the Longest Processing Time (LPT) rule, we propose a heuristic named LRTPT to efficiently schedule jobs on the two machines. For large-scale problems, we employ a novel SIAIS algorithm to find near-optimal solutions effectively. We also present a dedicated branch and bound algorithm specifically tailored to solve smaller-sized instances of the problem. This algorithm leverages two lower bounds and several dominance properties to expedite the search for the optimal solution. To evaluate the effectiveness of our proposed approaches, we conduct a series of comprehensive experiments. The results demonstrate that the branch and bound algorithm significantly outperforms the widely used optimization solver CPLEX for smaller problems. Furthermore, the SIAIS algorithm proves to be more efficient than existing scheduling heuristics.

Trailblazer: A proactive caching, blockchain, minimum content retrieval latency, and energy cost-aware resource and job orchestration scheme for publisher-subscriber based 6G ICN services

With the rise of information-centric networks (ICN), the user can access the caching content from nearby caching nodes rather than the remote content server through in-network caching facilities. The existing articles did not look into minimum caching content retrieval latency, user energy consumption, user financial cost, and maximum service provider profit-aware proper resource orchestration and job scheduling policies all at once, taking into account multiple publishers and subscriber-based caching jobs, blockchain, user-owned and service provider cache, pre-caching, neighbor collaboration, and available resources. To suppress these challenges, this article offers a proactive pre-caching, collaboration, blockchain, minimum latency-aware resource, and job scheduling policy for 6G ICN services that takes into account publishers’ and subscribers’ caching job requests, user-owned and service provider cache, available resources, and deadline. The experimental results visualized that at least 16% content retrieval delay, 66% energy, and 22% service provider profit gain are attained in the proposed scheme over the existing techniques.

Joint optimization of job scheduling, condition-based maintenance planning, and spare parts ordering for degrading production systems

The system degrades gradually due to intrinsic properties and the external environment while processing scheduling jobs. Maintenance plays an important role in restoring system performance during the job scheduling process. In contrast to traditional integration studies of scheduling and maintenance, this study further considers spare parts inventory in integrated decision-making. By integrating spare parts ordering information, engineers can meticulously organize maintenance and spare parts replenishment, thereby bolstering system reliability and ensuring smooth production scheduling. This paper presents a mathematical model that jointly optimizes job scheduling, condition-based maintenance planning, and spare parts ordering for deteriorating production systems. The system's degradation state and spare parts inventory state are identified and revealed at the end of the scheduling job. A condition-based maintenance and spare parts ordering policy is triggered based on the captured joint state. Subsequently, we developed a joint optimization model to determine the optimal maintenance thresholds, spare parts ordering thresholds, and job sequences, aimed at minimizing the total weighted expected cost within the scheduling horizon. Finally, an effective genetic algorithm is proposed, and a practical case study involving a rolling mill system is utilized to validate the proposed policy.

Bilinear branch and check for unspecified parallel machine scheduling with shift consideration

This paper tackles the complex challenge of team formations, assignments, and job schedules within the static Unspecified Parallel Machine Flexible Resource Scheduling problem, specifically incorporating shift considerations. In existing literature, teams are often simplified as machines that operate continuously throughout the day without any interruptions. However, in reality, teams require breaks between shifts and cannot work continuously within a day. Therefore, we introduce shift considerations to ensure that teams do not work in consecutive shifts. We consider flexible workers, capable of performing any job, who are distributed among different teams in different shifts to undertake various jobs. The number of teams in each shift is a decision variable. The duration of each job is determined by the number of workers in a team assigned to it. The objective function is to minimize the makespan, representing the overall schedule completion time, while adhering to precedence constraints. We formulate an integer linear programming model for the proposed problem and develop a novel bilinear branch and check algorithm that introduces valid bilinear inequalities to accelerate convergence. The numerical results confirm that our algorithm’s performance optimally solves problems up to 35 jobs within a reasonable timeframe, surpassing the efficiency of the branch and cut method of IBM CPLEX and the classical branch and check algorithm.

Scheduling in a Simple Assembly Robotics Cell to Minimize Earliness and Tardiness

Background: Robotics Assembly Cells (RAC) have been designed to meet the flexibility requirements demanded by today's globalized market. The objective is to manufacture a vast variety of products at a low cost, which requires equipment with a high level of flexibility, such as robots. The need to schedule a great variety of jobs in an RAC is a very relevant issue, as efficiency and productivity depend on the sequence in which jobs are scheduled. Studies around this matter have developed models with analytical and heuristic approaches, as well as simulation methods and genetic algorithms, seeking to improve performance measures based mainly on time, utilization, and costs. Method: The purpose of this article is to formulate an exact mathematical model using mixed-integer linear programming (MILP) to optimize small scheduling problems. The objective is to minimize the measure of performance related to the tardiness and earliness of jobs. This optimization aims to mitigate the effects of delays in product deliveries, queue times, and work-in-process inventory in subsequent processes. Doing so facilitates adherence to agreed-upon delivery deadlines and prevents bottlenecks in the assembly cell. Results: The proposed mathematical model generates optimal solutions to the job scheduling problem in the assembly cell, which serves as a case study. This addresses the need to minimize tardiness to meet delivery deadlines or minimize earliness while avoiding an increase in work-in-process inventories. The model ensures that optimal scheduling decisions are made to optimize both delivery performance and inventory levels. Conclusions: Due to the NP-hard complexity of the scheduling problem under study, the proposed mathematical model demonstrates computational efficiency in solving scheduling problems with fewer than 20 jobs. The model is designed to handle such smaller-scale problems within a reasonable computational time frame, considering the inherent complexity of the scheduling problem.

Industrial Relation Climate and Provision of Labour Welfare Measures in Manufacturing Sector

The study was conducted on a sample population of 150 employees of two manufacturing units based in West Bengal, India. Mostly permanent workers at plant level and employees of the zonal offices were considered for the purpose of the study. The overall objectives of the study focus on finding the general causes of job dissatisfaction and industrial fatigue among workers in the manufacturing sector, including the problem of industrial relations and getting proper statutory labour welfare benefits. Improper job scheduling, overburden of work and job complexities were some of the major factors of industrial fatigues that had been detected from structured questionnaire survey. Though majority of the employees revealed that the general industrial climate was good, in many cases employees were not aware of the claim settlement procedure about various provisions under the Gratuity Act and ESI Act leading to delays and payments not being received in time. This study also established the fact that overall industrial relation climate, availability of welfare measures and awareness level of the employees about different social security schemes varies with the educational level, total tenure of the employees in the present company and total years of working experience of the employees in the manufacturing sector. Lastly, lack of proper trade union to reflect employee grievances, proper training and engagement programmes are the other causes of disengagement and attrition of employees in the manufacturing sector, as also reflected from this study.

Status of Organizational Justice Dimensions: Evidence from Banks and Insurance Companies

This study examines the current state of organizational justice based on the perspectives of managers and employees in Nepalese banks and insurance companies. It also examines the differences in organizational justice dimensions. It uses a survey method, with questionnaires serving as the major tool. A total of 480 questionnaires (30 in each of the 16 institutions, including 8 commercial banks and 8 insurance companies) were distributed, and 327 (68.10%) of the completed and returned copies were used for the study. The findings demonstrate that employees generally rate their job schedule as adequate, their income as appropriate, and their workload and job requirements as reasonable. The findings indicate that employees have a positive perception of procedural justice. They also indicate that employees believe management treats them with respect, dignity, and honesty. The results suggest that the three dimensions of organizational justice differ significantly among Nepalese respondents. However, all three justice dimensions are crucial for organizational success. Therefore, companies' primary priorities should be to improve organizational justice. They should motivate employees to put up a significant effort for the benefit of the companies.

Library Service Usage, Barriers, and Strategies to Enhance Patronage Among Working Students

This study aimed to investigate the challenges faced by working students in patronizing library services in Nigeria and to propose tailored solutions to enhance their patronage. A descriptive survey research design was used to collect data from 110 working undergraduate students, which was then analyzed using descriptive statistics. The findings showed that busy job schedules, limited time on campus, and the fact that they mainly come to the campus for lectures are the primary reasons why working students rarely patronize the library. However, most of them expressed the need for library access tailored to their unique needs, which includes remote or distance approach. The preferred methods for accessing library services among working students include selective dissemination of e-books to their emails and having librarians available on a dedicated WhatsApp number. Additionally, they indicated openness to the idea of using chatbots to respond to their library requests. The study concluded that the use of technology such as email, instant messenger, and chatbots can address the challenges hindering library patronage by working students. Thus, the study recommends that library administrators invest in developing and adopting these technologies to meet the demands of working students in Nigeria.