Workload Balancing Research Articles

A Vehicle Routing Problem with Time Windows and Workload Balancing for COVID-19 Testers: A Case Study

Due to the COVID-19 pandemic, laboratories have faced unprecedented demand for in-home delivery test services. This drastic demand increase requires a rapid reaction from laboratories to manage their testers in order to respond to the high demand volume and avoid unnecessary costs. This study provides an optimization model based on the vehicle routing problem with time windows by considering the testers’ workload balancing to improve laboratories’ assignment and routing policies. A medical lab that has faced this situation for its in-home test services is taken as a real-world case in the current study. A mixed-integer programming model is solved for small instances using the CPLEX solver, and an adaptive large neighborhood search algorithm is implemented for large instances. Ultimately, the obtained solutions are compared to the real-world implementation of the lab on a dataset of six consecutive days, and the results are further discussed.

Work Balancing vs. Load Balancing in Network Ids Parallelization

Work Balancing vs. Load Balancing in Network Ids Parallelization

Resource Scheduling Strategy for Performance Optimization Based on Heterogeneous CPU-GPU Platform

In recent years, with the development of processor architecture, heterogeneous processors including Center processing unit (CPU) and Graphics processing unit (GPU) have become the mainstream. However, due to the differences of heterogeneous core, the heterogeneous system is now facing many problems that need to be solved. In order to solve these problems, this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies. To improve the performance of the system, this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks. The combination strategy consists of two sub-strategies, the first strategy improves the execution efficiency of tasks on the GPU by changing the thread organization structure. The second focuses on the working state of the efficient core and develops more reasonable workload balancing schemes to improve resource utilization of heterogeneous systems. The multi-task scheduling strategy obtains the execution efficiency of heterogeneous cores and global task information through the processing of task samples. Based on this information, an improved ant colony algorithm is used to quickly obtain a reasonable task allocation scheme, which fully utilizes the characteristics of heterogeneous cores. The experimental results show that the combination strategy reduces task execution time by 29.13% on average. In the case of processing multiple tasks, the multi-task scheduling strategy reduces the execution time by up to 23.38% based on the combined strategy. Both strategies can make better use of the resources of heterogeneous systems and significantly reduce the execution time of tasks on heterogeneous systems.

Practical Near-Data-Processing Architecture for Large-Scale Distributed Graph Neural Network

Graph Neural Networks have drawn tremendous attention in the past few years due to their convincing performance and high interpretability in various graph-based tasks like link prediction and node classification. With the ever-growing graph size in the real world, especially for industrial graphs at a billion-level, the storage of graphs can easily consume Terabytes so that the process of GNNs has to be processed in a distributed manner. As a result, the execution could be inefficient due to the expensive cross-node communication and irregular memory access. Various GNN accelerators have been proposed for efficient GNN processing. They, however, mainly focused on small and medium-size graphs, which is not applicable to large-scale distributed graphs. In this paper, we present a practical Near-Data-Processing architecture based on a memory-pool system for large-scale distributed GNNs. We propose a customized memory fabric interface to construct the memory pool for low-latency and high throughput cross-node communication, which can provide flexible memory allocation and strong scalability. A practical Near-Data-Processing design is proposed for efficient work offloading and bandwidth utilization improvement. Moreover, we also introduce a partition and scheduling scheme to further improve performance and achieve workload balance. Comprehensive evaluations demonstrate that the proposed architecture can achieve up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> higher training speed compared to two state-of-the-art distributed GNN frameworks: Deep Graph Library and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P^{3}$ </tex-math></inline-formula> , respectively.

Distributed Edge System Orchestration for Web-based Mobile Augmented Reality Services

The emergence of edge computing and 5G networks has fueled the growth of mobile Web AR. Although efforts have been made to improve the edge system efficiency for Web AR applications, efficient edge-assisted mobile Web AR services remain technically challenging. This paper presents EARNet, a distributed edge system orchestration approach for mobile Web AR in 5G networks. The design of EARNet makes three novel contributions. First, EARNet manages the edge network dynamics with respect to user mobility and their Web AR service requests by employing landmarks and grid index based edge node localization mechanisms. Second, EARNet takes into account both request serving performance and offloading cost in managing workload balance and quality of service and leverages dynamic hash and max heap mechanisms for efficient Web AR service lookup and AR computations. Third, EARNet designs the service migration schemes by optimizing several performance factors, such as message efficiency, scheduling latency, request density and locality of mobile users and edge nodes, and accuracy of Web AR services after migration. Experimental evaluations are conducted using the real base station deployment data in the Melbourne Central Business District (CBD) area. The results shows the effectiveness of the EARNet edge orchestration approach compared to several baseline approaches.

Virtual LiDAR Simulation as a High Performance Computing Challenge: Toward HPC HELIOS++

The software HELIOS++ simulates the laser scanning of a given virtual scene that can be composed of different spatial primitives and 3D meshes with distinct granularity. The high computational cost of this type of simulation software demands efficient computational solutions. Classical solutions based on GPU are not well suited when irregular geometries compose the scene combining different primitives and physics models because they lead to different computation branches. In this paper, we explore the usage of parallelization strategies based on static and dynamic workload balancing and heuristic optimization strategies to speed up the ray tracing process based on a k-dimensional tree (KDT). Using HELIOS++ as our case study, we analyze the performance of our algorithms on different parallel computers, including the CESGA FinisTerrae-II supercomputer. There is a significant performance boost in all cases, with the decrease in computation time ranging from 89.5% to 99.4%. Our results show that the proposed algorithms can boost the performance of any software that relies heavily on a KDT or a similar data structure, as well as those that spend most of the time computing with only a few synchronization barriers. Hence, the algorithms presented in this paper improve performance, whether computed on personal computers or supercomputers.

Collaborative Resource Sharing Game Based Cloud-Edge Offload Computing Orchestration Scheme

In the foreseeable future, the rapid growth of mobile communications and cloud systems has substantially promoted edge computing paradigm. Although edge computing technology has been attracting much interest, most current research is application-specific without considering a control perspective of cloud providers that provides general-purpose edge services. In this study, we present a new cloud-edge computing orchestration scheme that integrates the different resource sharing problems to maximize benefits for offloading computing services. Specifically, we focus on two cooperative game solutions - <i>Sim bargaining solution</i> and <i>interval Banzhaf Value</i> - to effectively share the computing, communication and cache resources in the cloud-edge system platform. In the proposed scheme, we formalize two different cooperative games, and they work together and act cooperatively to satisfy contradictory requirements. Under widely different service request situations, we aim at accelerating an efficient resource orchestration through dynamic workload balancing. The primary goal of our cooperative game approach is to find optimal control decisions toward the appropriate system operation. Finally, we evaluate the proposed scheme in terms of several performance criteria from the numerical simulations. Experimental results show that we can achieve a mutually desirable solution with a good system performance balance while enhancing the resource efficiency better than the existing cloud-edge control protocols.

Task Scheduling–Review of Algorithms and Analysis of Potential Use in a Biological Wastewater Treatment Plant

The idea of task scheduling is to increase the efficiency of a system by minimising wasted time, evenly loading machines, or maximising the throughput of machines. Moreover, the use of appropriate scheduling algorithms often leads to a reduction in the energy costs of the process. Task scheduling problems are found in a variety of industrial areas, and their scale changes significantly depending on the problem. This review shows the extent to which task scheduling methods are used in industry. This paper presents methods and algorithms for solving task scheduling problems. In addition, an analysis of the possibility of using task scheduling methods to improve the efficiency of wastewater treatment plants was also conducted. This approach is based on the assumption of a balanced workload for multiple SBR reactors. Analysing the case of a wastewater treatment plant in Swarzewo City in northern Poland. The problem of scheduling tasks in a wastewater treatment plant has not been so far subject of research work.

A heuristic algorithm for balancing workloads in coverage modeling

Location covering problems are important tools for supporting facility siting decisions in both the natural and built environment. However, facility workloads often vary significantly using classic coverage modeling approaches, because service allocations are not explicitly considered. In addition to capacitated extensions that are traditionally relied on, an explicit way to balance workloads in coverage modeling is to track workload difference between pairs of sited facilities, with a goal of minimizing variation. Such an approach provides an effective way to consider workload equity, but a byproduct is increased complexity and computationally difficulty in solving associated models by exact methods. This paper proposes a heuristic algorithm to address the computational difficulties in balancing workloads in coverage modeling. The proposed algorithm incorporates interchange along with simulated annealing, taking advantage of problem-specific knowledge to derive high-quality solutions in an efficient manner. Empirical studies demonstrate that the proposed algorithm is able to generate non-dominated solutions that effectively approximate the Pareto optimal frontier, but do so in a computationally efficient manner.

Work-life Balance Initiatives of Employees’ Women in Development Banks of Nepal: Employees’ Perspective

Work-life balance (WLB) is almost helping employees in maintaining healthy, rewarding lifestyles that will, in turn, lead to improvements in efficiency or prosperity. The study intended to explore the level of satisfaction of the working women on their personal commitments, to evaluate the workingwomen perception on support of senior officials and to identify employee’s women perspective on balance work load. This study has been based on descriptive as well as exploratory research designs were used with sample size of 40. Data was collected from four development sector banks in Nepal through structured schedules, using convenience sampling. The study provides an insight to academicians and policy makers in bank and other organizations into the needs of today’s employees and initiatives taken in public sector banks for work-life balance. Other organizations might adopt these practices for helping employees attain work-life balance and hence increase their job satisfaction and organizational productivity.

Lean Manufacturing: Efficiency improvement application in multi-product area of the Aerospace Industry

This research addresses the problem of leveling workloads in a multi-product final assembly area. In which it was found that 27.4% of the time is used for set up and the current distribution presents areas of opportunity. The target was to implement improvement actions to make use of resources more efficient in the production process in the aforementioned area through Lean Manufacturing tools. The results obtained consist of eight products generated with the support of lean manufacturing support tools such as SMED, Workload Balancing and MUDA waste identification, achieving important results among which productivity in the area stands out from 109% to 125%, as well as a reduction in set-up time from 17 min to 4.4 min.

PERBAIKAN SISTEM KERJA PADA DIVISI PACKING PRODUK RACK TROLLEY DENGAN METHOD TIME MEASUREMENT (MTM)

The work system is the most important thing in the company, especially in manufacturing companies that produce various kinds of kitchen utensils made from stainless steel 402 and stainless steel 201 according to market demand. Of the many existing products, there are products with the highest demand level but take a long time in the packing division, which only has one station. The MTM (Method Time Measurement) method is used to map work movements that focus on work elements in the packing division with mobile gastronomic rack trolley observation products. After observing and measuring, this division has two light work elements but the processing time is quite long, the sandpaper work element is 9.48 minutes with the clean lab work element 8.13 minutes so that in total there are 9 work elements with a time of 53 minutes, by combining the sandpaper and lab work elements. clean with rework mapping, the result is 9.97 minutes and there is a balance of workload between right-hand movement and left-hand movement, from combining these elements, the work element in the packing division becomes more maximal, namely 8 work elements with 37 minutes per product.

МОДЕЛІ ІЄРАРХІЧНОЇ БАГАТОАГЕНТНОЇ СИСТЕМИ ДЛЯ ВИКОНАННЯ БІЗНЕС-ПРОЦЕСІВ

Purpose. Developing a set of models which formally describe the operation environment of the organizational and technical system and the interaction of software agents of different roles in performing business operations. The pres-ence of such a set of models allows you to create information technology for monitoring and routing business processes and reduce costs for business operations. Methodology. The research methods are based on systems analysis methods. Findings. A set of models of hierarchical multi-agent system for business processes has been developed. The complex comprises a static description of the operation environment of the organizational and technical system, where there are models of business process, business operation and its components. Originality. Organization of total monitoring of the operation environment of the organizational and technical system, i.e. the current state of the processes of processing applications, queues and workstations requires the development of static and dynamic business process models. Based on the static description, a model of the dynamics of business processes promoting throughout many workstations has been developed. This makes possible to monitor the status of workstations, queues and applications for business opera-tions. In particular, a formal description of the software agent, its competencies and a model of interaction of three-level agents which perform the functions of business operation executors, monitors and dispatchers has been developed. Having such tools, the second important aspect is the development of a universal hierarchical structure of the multi-agent system, in which different agents perform the roles of performers, monitors and dispatchers. Such a structure should include the regulation of the agent functions, models of agents interaction at all three levels, ways of agents-people communication. Practical value. Tests of the monitoring and scheduling system in different conditions (electronic document management, manufacturing company, human resources management department) showed a decrease in time of business operations, losses on waiting and increase in rhythm of business processes. The results of the work has enabled the development of a queue management strategy, which has showed a reduction in time of operations and a more balanced workload.

Optimal resource scheduling for SMEs using cloud in manufacturing sectors

Defining on a common policy which effectively suits all the production units, stakeholders and suppliers is very difficult to figure-out in highly populated countries like India. Manufacturing industries are ineffective in managing services, this is due to the communication gap between stakeholders. Approximately 48 million small and medium businesses are registered in India, according to a report by the Ministry of Micro, Small and Medium Enterprises (MSME). Among these numbers, SMEstend to contribute 40% of the total exports to India's industrial output, which is nearly 45%. As scheduling process majorly impacts on the time and cost of job completion, it is a critical part of the project. To efficiently schedule resources related to the casting industry, we propose a hybrid Ant Colony Optimization strategy. The efficiency of this enhanced algorithm was simulated to verify its effectiveness and the results show that, the proposed model minimizes both cost and time. It also maintains workload balance to maintain optimum resource utilization.

AN ICOR APPROACH TOWARDS SHIP MAINTENANCE SOFTWARE DEVELOPMENT

Ship maintenance is one of the key processes to improve system performance and reliability onboard. Maintenance onboard ships is required to be performed in a planned manner because safe and efficient operation of a ship is very much depending on equipment reliability in operational level. A planned maintenance system (PMS), mainly supported with software, is established to monitor the maintenance implementations on board ships. This study aims to assess the effectiveness of onboard maintenance and limited with the procedures and specific softwares employed for the maintenance on board. Specifically, the process analysis approach is developed to execute the existing ship PMS phases based on four dimensions such as input, control, output, resources (ICOR). Besides the critical points to be improved in ship maintenance, this research reveals recommendations to increase the functionality of selected software in terms of workload balance, smart scheduling and safe working environment. To demonstrate the functional improvements, an illustrative case study on an auxiliary diesel generator maintenance routines are conducted. The contributions of the study are expected in both ship operations management and marine software development.

Sparse and dense matrix multiplication hardware for heterogeneous multi-precision neural networks

In this paper, we present hardware accelerators created with high-level synthesis techniques for sparse and dense matrix multiplication operations. The cores can operate with different precisions and are designed to be integrated in a heterogeneous CPU-FPGA system for Edge AI applications. The methodology involves quantization-sparsity aware training and it is applied to a case study consisting of human activity classification. We initially investigate the effects of quantization and sparsity on the accuracy of neural networks with convolution, dense and recurrent layers observing better tolerance to pruning when recurrent layers are present. Then, we propose the hardware accelerators that can switch precision at run-time and work with any matrix size up to a maximum configured at compile time. We compare the performance of these accelerators at different levels of precision and sparsity levels and create a performance model to enable workload balancing. The results show that the proposed sparse matrix multipliers can outperform dense multipliers when sparsity levels are higher than 70% and the improvements are more evident when higher precision arithmetic or structural pruning is used. Additionally, sparsity levels as high as 99% can maintain the level of accuracy required in the network especially when recurrent layers are deployed. Overall, the balance between sparse and dense performance depends on matrix shape, precision, structural pruning and sparsity levels and performance modelling can be used to balance concurrent execution in a heterogeneous configuration.

Mentoring of nursing students-A comparative study of Japan and five European countries.

This study aimed to explore mentoring competence in nursing student mentors during clinical practice by identifying different mentor profiles and connections between different competence areas among five European countries and Japan. The study implemented a cross-sectional design in Finland, Italy, Lithuania, Slovenia, Spain, and Japan during 2016 and 2019. In total, 6208 mentors were invited, and 1862 participated from 58 healthcare organizations. The data were collected with a survey questionnaire by including background question items with the Mentor Competence Instrument. K-clustering and structural equation modeling were used for data analysis. Four mentor profiles, A (43%), B (30%), C (18%), and D (9%), were identified according to the seven mentoring competence areas with high statistical significance (p < 0.001). Higher mentoring competence (mean >3.50) was observed among Finnish, Lithuanian, and Slovenian mentors with university education in nursing, older ages, more work experience, and previous education in mentoring. Lower competence (mean <2.49) was observed among Japanese and Italian mentors with diplomas in nursing, younger ages, less work experience, and no previous education in mentoring. Mentoring requires motivated, highly competent mentors since mentoring is a critical aspect of nursing education. Mentoring roles should be given to nurses with higher education and mentoring training. Younger, less experienced nurses without formal mentoring training may need support from senior nurses when performing mentoring roles and could also facilitate a more balanced workload between patient care and mentoring for senior nurses.

Blockchain-IoT-Driven Nursing Workforce Planning for Effective Long-Term Care Management in Nursing Homes.

Due to the global ageing population, the increasing demand for long-term care services for the elderly has directed considerable attention towards the renovation of nursing homes. Although nursing homes play an essential role within residential elderly care, professional shortages have created serious pressure on the elderly service sector. Effective workforce planning is vital for improving the efficacy and workload balance of existing nursing staff in today's complex and volatile long-term care service market. Currently, there is lack of an integrated solution to monitor care services and determine the optimal nursing staffing strategy in nursing homes. This study addresses the above challenge through the formulation of nursing staffing optimisation under the blockchain-internet of things (BIoT) environment. Embedding a blockchain into IoT establishes the long-term care platform for the elderly and care workers, thereby decentralising long-term care information in the nursing home network to achieve effective care service monitoring. Moreover, such information is further utilised to optimise nursing staffing by using a genetic algorithm. A case study of a Hong Kong nursing home was conducted to illustrate the effectiveness of the proposed system. We found that the total monthly staffing cost after using the proposed model was significantly lower than the existing practice with a change of −13.48%, which considers the use of heterogeneous workforce and temporary staff. Besides, the care monitoring and staffing flexibility are further enhanced, in which the concept of skill substitution is integrated in nursing staffing optimisation.

Optimization of Cutting Tool Allocation to Enhance Workload Balance and Total Completion Time in Parallel-Type FMS

This study aims to build a machine scheduling method that involves the cutting tool management in parallel-type flexible manufacturing systems. These systems consist of multi-axis CNC machine tools and are equipped with an automated tool changer and a large-capacity tool magazine. The target scheduling problem could be described as a multi-objective parallel scheduling problem. We consider the availability of cutting tools stored in the magazine as so-called “machine eligibility,” and propose a two-phase scheduling method for tool allocation and job sequencing on machines to minimize the workload balance between machines and the total completion time. Two mathematical models for tool allocation are provided: a machine-eligibility-based model and an enhanced version of the model that considers each cutting tool. A series of computational experiments demonstrates the effectiveness of the proposed method. We also clarify the relationship between schedule performance measures and job routing flexibility in the system.

Computing Resource Optimization of Big Data in Optical Cloud Radio Access Networked Industrial Internet of Things

Optical cloud radio access network (O-CRAN) is an emerging solution for IIoT, where numerous different devices/nodes are networked together. O-CRAN provides pool of shareable computing facility, equipped with hundreds of general-purpose processor (GPP). The GPPs process massive big data exerted by nodes via remote radio heads (RRHs), regarded as RRH-requests, which are bandwidth-intensive and deadline-constrained digitized base-band signals. Computing resource (CR) optimization has been widely investigated in O-CRAN. However, the existing optimizations may not guarantee workload and thermal balance among the active GPPs while satisfying RRH-request's deadline, which are necessary to efficiently leverage virtualization GPP capacity in a manner that provides the greatest uniform CR utilization (CRU). Due to varying network-load a single optimal solution does not exist. Therefore, in this article, we propose a modified-first-fit decreasing (MFFD) algorithm to obtain a suboptimal solution for each time_stage. The MFFD evenly assigns RRH-requests among GPPs that maximizes individual CRU uniformly contrasting with FFD.