Log in

Relevant bibliographies by topics / Workload scheduling / Journal articles

Journal articles on the topic 'Workload scheduling'

To see the other types of publications on this topic, follow the link: Workload scheduling.

Author: Grafiati

Published: 13 May 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Workload scheduling.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Bani-Mohammad, Saad. "The Effect of Real Workloads and Synthetic Workloads on the Performance of Job Scheduling for Non-Contiguous Allocation in 2D Mesh Multicomputers." International Journal of Distributed Systems and Technologies 6, no. 1 (January 2015): 53–68. http://dx.doi.org/10.4018/ijdst.2015010104.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The performance of non-contiguous allocation has been traditionally carried out by means of simulations based on synthetic workloads, and also it can be significantly affected by the job scheduling strategy used for determining the order in which jobs are selected for execution. To validate the performance of the non-contiguous allocation algorithms, there has been a need to evaluate the algorithm's performance based on a real workload trace. In this paper, the performance of the well-known Greedy Available Busy List (GABL) non-contiguous allocation strategy for 2D mesh-connected multicomputers is revisited considering several important job scheduling strategies based on a real workload trace, and the results are compared to those obtained from using a synthetic workload. The scheduling strategies used are the First-Come-First-Served (FCFS), Out-of-Order (OO), and Window-Based job scheduling strategies. These strategies have been selected because they are common and they have been used in related works (Ababneh & Bani-Mohammad, 2011). Extensive simulation results based on synthetic and real workload models indicate that the Window-Based job scheduling strategy can improve both overall system performance and fairness (i.e., maximum job waiting delays) by adopting a large job scheduling window. Moreover, the relative performance merits of the scheduling strategies when a real workload trace is used are in general compatible with those obtained when a synthetic workload is used.

2

Lüthi, Hans-Jakob, and Andrés Polyméris. "Scheduling to Minimize Maximum Workload." Management Science 31, no. 11 (November 1985): 1409–15. http://dx.doi.org/10.1287/mnsc.31.11.1409.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Andre, Anthony D., Susan T. Heers, Robert S. McCann, and Patricia A. Cashion. "Preview and Practice: Effects on Scheduling Behavior in a Simulated Flight Task." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 1 (October 1993): 132–36. http://dx.doi.org/10.1177/154193129303700131.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The present study examined pilot scheduling behavior in the context of simulated instrument flight. Over the course of the flight, pilots flew along specified routes while concurrently performing three different flight-related secondary tasks. Seven pilots flew the simulation with no preview of future workload conditions, while another seven received preview information in the form of both written instruction and practice. The results show evidence for both macro and micro scheduling strategies. Specifically, those pilots with preview of future workload demands adopted an efficient macro strategy of scheduling more of the difficult secondary tasks during the low workload phase of flight. Subjects in both groups engaged in micro scheduling strategies as a function of flight path workload and secondary task workload.

4

Zhang, Xiaomei, Kang Li, Xiang Hu Zhao, Tian Yan, and Yong Sun. "Multicore workload scheduling in JUNO[1]." EPJ Web of Conferences 214 (2019): 03048. http://dx.doi.org/10.1051/epjconf/201921403048.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The Jiangmen Underground Neutrino Observatory (JUNO) is going to apply parallel computing in its software to accelerate JUNO data processing and fully use capability of multi-core and manycore CPUs. Therefore, it is necessary for the JUNO distributed computing system to explore the way to support single-core and multi-core jobs in a consistent way. To support multi-core jobs, a series of changes to the job descriptions, scheduling, monitoring needs to be considered, in which the pilot-based scheduling for a hybrid of single-core and multi-core jobs is the most complicated part. Two scheduling modes and their efficiency are presented and compared in this paper, and also a way to optimize efficiency is provided.

5

Sodinapalli, Nagendra Prasad, Subhash Kulkarni, Nawaz Ahmed Sharief, and Prasanth Venkatareddy. "An efficient resource utilization technique for scheduling scientific workload in cloud computing environment." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 367. http://dx.doi.org/10.11591/ijai.v11.i1.pp367-378.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

<span lang="EN-US">Recently, number of data intensive workflow have been generated with growth of internet of things (IoT’s) technologies. Heterogeneous cloud framework has been emphasized by existing methodologies for executing these data-intensive workflows. Efficient resource scheduling plays a very important role provisioning workload execution on Heterogeneous cloud framework. Building tradeoff model in meeting energy constraint and workload task deadline requirement is challenging. Recently, number of multi-objective-based workload scheduling aimed at minimizing power budget and meeting task deadline constraint. However, these models induce significant overhead when demand and number of processing core increases. For addressing research problem here, the workload is modelled by considering each sub-task require dynamic memory, cache, accessible slots, execution time, and I/O access requirement. Thus, for utilizing resource more efficiently better cache resource management is needed. Here efficient resource utilization (ERU) model is presented. The ERU model is designed to utilize cache resource more efficiently and reduce last level cache failure and meeting workload task deadline prerequisite. The ERU model is very efficient when compared with standard resource management methodology in terms of reducing execution time, power consumption, and energy consumption for execution scientific workloads on heterogeneous cloud platform.</span>

6

Buttazzo, G. C., G. Lipari, M. Caccamo, and L. Abeni. "Elastic scheduling for flexible workload management." IEEE Transactions on Computers 51, no. 3 (March 2002): 289–302. http://dx.doi.org/10.1109/12.990127.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Tan, Kian-Lee, and Hongjun Lu. "Workload scheduling for multiple query processing." Information Processing Letters 55, no. 5 (September 1995): 251–57. http://dx.doi.org/10.1016/0020-0190(95)00088-t.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Mao, Li, Deyu Qi, Weiwei Lin, and Chaoyue Zhu. "A Self-Adaptive Prediction Algorithm for Cloud Workloads." International Journal of Grid and High Performance Computing 7, no. 2 (April 2015): 65–76. http://dx.doi.org/10.4018/ijghpc.2015040105.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

It is difficult to analyze the workload in complex cloud computing environments with a single prediction algorithm as each algorithm has its own shortcomings. A self-adaptive prediction algorithm combining the advantages of linear regression (LR) and a BP neural network to predict workloads in clouds is proposed in this paper. The main idea of the self-adaptive prediction algorithm is to choose the better prediction method of the future workload. Some experiments of prediction algorithms are conducted with workloads on the public cloud servers. The experimental results show that the proposed algorithm has a relatively high accuracy on the workload predictions compared with the BP neural network and LR. Furthermore, in order to use the proposed algorithm in a cloud data center, a dynamic scheduling architecture of cloud resources is designed to improve resource utilization and reduce energy consumption.

9

Prasad, V. Rajendra, Mike Graul, Perakath Benjamin, Richard Mayer, and Patrick D. Cahill. "Resource-Constrained Shop-Level Scheduling in a Shipyard." Journal of Ship Production 19, no. 02 (May 1, 2003): 65–75. http://dx.doi.org/10.5957/jsp.2003.19.2.65.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Ship production planning and scheduling at higher levels do not explicitly consider scheduling details at the level of individual workshops. However, the schedule of major events in ship production is collectively influenced by the actual shop-level, short-interval production schedules, which depend on resource and material availability and also on the due dates and priorities of the workloads. This necessitates development of robust, resource-constrained, shop-level scheduling systems that can support higher-level schedules in ship production. WorkShip (Knowledge Based Systems, Inc., College Station, TX) is a software tool for scheduling workload over short, regular intervals in workshops of a shipyard. It is driven by a powerful scheduling engine that is based on a generic model of resource-constrained job-shop scheduling and an efficient scheduling technique. Similar scheduling systems are being developed in other shops so that all systems can be used in tandem to support higher-level scheduling and help achieve optimal productivity for the shipyard.

10

WU, MIN-YOU. "PARALLEL INCREMENTAL SCHEDULING." Parallel Processing Letters 05, no. 04 (December 1995): 659–70. http://dx.doi.org/10.1142/s0129626495000588.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Parallel incremental scheduling is a new approach for load balancing. In parallel scheduling, all processors cooperate together to balance the workload. Parallel scheduling accurately balances the load by using global load information. In incremental scheduling, the system scheduling activity alternates with the underlying computation work. This paper provides an overview of parallel incremental scheduling. Different categories of parallel incremental scheduling are discussed.

11

Attiya, Ibrahim, Laith Abualigah, Doaa Elsadek, Samia Allaoua Chelloug, and Mohamed Abd Elaziz. "An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing." Mathematics 10, no. 7 (March 29, 2022): 1100. http://dx.doi.org/10.3390/math10071100.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The cloud computing paradigm is evolving rapidly to address the challenges of new emerging paradigms, such as the Internet of Things (IoT) and fog computing. As a result, cloud services usage is increasing dramatically with the recent growth of IoT-based applications. To successfully fulfill application requirements while efficiently harnessing cloud computing power, intelligent scheduling approaches are required to optimize the scheduling of IoT application tasks on computing resources. In this paper, the chimp optimization algorithm (ChOA) is incorporated with the marine predators algorithm (MPA) and disruption operator to determine the optimal solution to IoT applications’ task scheduling. The developed algorithm, called CHMPAD, aims to avoid entrapment in the local optima and improve the exploitation capability of the basic ChOA as its main drawbacks. Experiments are conducted using synthetic and real workloads collected from the Parallel Workload Archive to demonstrate the applicability and efficiency of the presented CHMPAD method. The simulation findings reveal that CHMPAD can achieve average makespan time improvements of 1.12–43.20% (for synthetic workloads), 1.00–43.43% (for NASA iPSC workloads), and 2.75–42.53% (for HPC2N workloads) over peer scheduling algorithms. Further, our evaluation results suggest that our proposal can improve the throughput performance of fog computing.

12

Raby, Mireille, and Christopher D. Wickens. "Planning and Scheduling in Flight Workload Management." Proceedings of the Human Factors Society Annual Meeting 34, no. 1 (October 1990): 71–75. http://dx.doi.org/10.1177/154193129003400116.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Templon, J. A., C. Acosta-Silva, J. Flix Molina, A. C. Forti, A. Pérez-Calero Yzquierdo, and R. Starink. "Scheduling multicore workload on shared multipurpose clusters." Journal of Physics: Conference Series 664, no. 5 (December 23, 2015): 052038. http://dx.doi.org/10.1088/1742-6596/664/5/052038.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Yzquierdo, A. Perez-Calero, J. Balcas, J. Hernandez, F. Aftab Khan, J. Letts, D. Mason, and V. Verguilov. "CMS readiness for multi-core workload scheduling." Journal of Physics: Conference Series 898 (October 2017): 052030. http://dx.doi.org/10.1088/1742-6596/898/5/052030.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Prasad S, Nagendra, and Subash Kulkarni S. "Quality and energy optimized scheduling technique for executing scientific workload in cloud computing environment." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (February 1, 2021): 1039. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp1039-1047.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

<p class="Abstract">Modern BigData data-intensive and scientific workload execution is challenging. The major issues are reliable processing, performance efficiency and energy efficacy perquisite of BigData processing framework. This work assume self-aware MC architectures that autonomously adjust or optimize their performance to accommodate users quality of service (QoS) performance requirement, job execution performance, energy efficiency, and resource accessibility. Extensive workload scheduling has been presented to minimize energy consumption in cloud computing (CC) environment. However, the existing workload scheduling model induces higher amount of interaction cost between inter-processors communications. Further, due to poor resource utilization, routing inefficiency these existing model induces higher energy cost and fails to meet workload QoS prerequisite. For overcoming research challenges, this paper presented quality and energy optimized scheduling (QEOS) technique for executing data-intensive workload by employing dynamic voltage and frequency scaling (DVFS) technique. Experiment outcome shows QEOS model attains good trade-off between system performance and energy consumption in multi-core cloud computing (CC) architectures when compared with existing model.</p>

16

Banu, Shifaliya, and M. Prabakar. "Ecope: Task Aware Workload Elastic Scheduling and Customization for Infrastructure." International Journal of Emerging Research in Management and Technology 6, no. 6 (June 29, 2018): 52. http://dx.doi.org/10.23956/ijermt.v6i6.244.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

In the past several years, the development in non functional requirement such as CPU and memory has been done. Due to the workload characteristics the energy efficiency of non functional component has made a large coverage. We develop Ecope to attain energy proportionality for different methods of services of virtual machine in data centres’ decrease non functional energy for servers in large data centers. Demonstrate three input methods to illustrate our concept to real world services such as file processing, backend services and content processing. These services are applying on virtual machine in large data centers. In short, our aim is to recognize the preeminent non functional configuration among various workloads.

17

Ba, Li, Mingshun Yang, Xinqin Gao, Yong Liu, Zhoupeng Han, Erbao Xu, and Yan Li. "A Mathematical Model and Self-Adaptive NSGA-II for a Multiobjective IPPS Problem Subject to Delivery Time." Mathematical Problems in Engineering 2020 (August 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/6012737.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Process planning and scheduling are two important components of manufacturing systems. This paper deals with a multiobjective just-in-time integrated process planning and scheduling (MOJIT-IPPS) problem. Delivery time and machine workload are considered to make IPPS problem more suitable for manufacturing environments. The earliness/tardiness penalty, maximum machine workload, and total machine workload are objectives that are minimized. The decoding method is a crucial part that significantly influences the scheduling results. We develop a self-adaptive decoding method to obtain better results. A nondominated sorting genetic algorithm with self-adaptive decoding (SD-NSGA-II) is proposed for solving MOJIT-IPPS. Finally, the model and algorithm are proven through an example. Furthermore, different scale examples are tested to prove the good performance of the proposed method.

18

Lee, Jaehak, and Heonchang Yu. "I/O Strength-Aware Credit Scheduler for Virtualized Environments." Electronics 9, no. 12 (December 10, 2020): 2107. http://dx.doi.org/10.3390/electronics9122107.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

With the evolution of cloud technology, the number of user applications is increasing, and computational workloads are becoming increasingly diverse and unpredictable. However, cloud data centers still exhibit a low I/O performance because of the scheduling policies employed, which are based on the degree of physical CPU (pCPU) occupancy. Notably, existing scheduling policies cannot guarantee good I/O performance because of the uncertainty of the extent of I/O occurrence and the lack of fine-grained workload classification. To overcome these limitations, we propose ISACS, an I/O strength-aware credit scheduler for virtualized environments. Based on the Credit2 scheduler, ISACS provides a fine-grained workload-aware scheduling technique to mitigate I/O performance degradation in virtualized environments. Further, ISACS uses the event channel mechanism in the virtualization architecture to expand the scope of the scheduling information area and measures the I/O strength of each virtual CPU (vCPU) in the run-queue. Then, ISACS allocates two types of virtual credits for all vCPUs in the run-queue to increase I/O performance and concurrently prevent CPU performance degradation. Finally, through I/O load balancing, ISACS prevents I/O-intensive vCPUs from becoming concentrated on specific cores. Our experiments show that compared with existing virtualization environments, ISACS provides a higher I/O performance with a negligible impact on CPU performance.

19

Kumar, K. Dinesh, and E. Umamaheswari. "HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network." Cybernetics and Information Technologies 20, no. 4 (November 1, 2020): 55–73. http://dx.doi.org/10.2478/cait-2020-0047.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

AbstractFor cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach’s effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.

20

Sari, Rafika, Khairunnisa Fadhilla Ramdhania, and Rakhmat Purnomo. "Team-Teaching-Based Course Scheduling Using Genetic Algorithm." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 10, no. 1 (March 26, 2022): 55–66. http://dx.doi.org/10.33558/piksel.v10i1.4416.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Scheduling problems occur in various fields, e.g., education, health institutions, transportation, sports, etc. Main scheduling problems in education is course scheduling which creates schedules for students and lecturers. In this study, course scheduling allocates the lecturers in the form of team teaching and courses into the class and a certain time to even out the workload of lecturers per day and a group of students per day in one week without breaking the constraint. The method used in this research is a genetic algorithm where Universitas Bhayangkara Jakarta Raya as the case study. The genetic algorithm process is done by getting several candidate solutions that undergo a process of selection, mutation, and crossing over to produce chromosomes with the best fitness values. The objective function in this research is minimizing the average variance of the workload of lecturers and students per day in one week. The parameters used in genetic algorithm are determined based on the Design of Experiments mechanism (DOE). The optimal parameter values used to run the program are as: population size = 50, with probability of crossing over = 0.4 and probability of mutation = 0.008. The results of scheduling with genetic algorithms show that the value of the workload variance lecturers and students by considering team teaching is better than actual scheduling. The application of the genetic algorithm method results in a decrease in the standard value deviation of the workload of lecturers and a group of students in one week is 0.114 (3.68%) and 3.11 (55.7%). In addition, course scheduling uses a genetic algorithm with consider team teaching better than genetic algorithm without considering team teaching because there is no class schedule that clashes in real conditions.

21

Zhang, Qi, You Lin Ruan, and Feng Gao. "Temperature-Aware Scheduling Algorithm for Multi-Core System." Applied Mechanics and Materials 536-537 (April 2014): 703–7. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.703.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

High temperature will affect reliability and performance of multicore system. In this paper, we propose a temperature-aware task scheduling algorithm for real-time multi-core systems, which combines the DVFS and energy balancing by analyzing workload information and multicore utilization. At first, calculate average utilization ratio of tasks. Secondly, balancing strategy according to the workload is proposed for uniform temperature distribution on the cores. Finally, adapt the HR-2 and DVFS to scheduling tasks in each core. Simulation results show that the proposed scheduling algorithm obtains a better effect in temperature and energy-saving than other algorithms.

22

Akram, Shoaib, Alexandros Papakonstantinou, Rakesh Kumar, and Deming Chen. "A Workload-Adaptive and Reconfigurable Bus Architecture for Multicore Processors." International Journal of Reconfigurable Computing 2010 (2010): 1–22. http://dx.doi.org/10.1155/2010/205852.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Interconnection networks for multicore processors are traditionally designed to serve a diversity of workloads. However, different workloads or even different execution phases of the same workload may benefit from different interconnect configurations. In this paper, we first motivate the need for workload-adaptive interconnection networks. Subsequently, we describe an interconnection network framework based on reconfigurable switches for use in medium-scale (up to 32 cores) shared memory multicore processors. Our cost-effective reconfigurable interconnection network is implemented on a traditional shared bus interconnect with snoopy-based coherence, and it enables improved multicore performance. The proposed interconnect architecture distributes the cores of the processor into clusters with reconfigurable logic between clusters to support workload-adaptive policies for inter-cluster communication. Our interconnection scheme is complemented by interconnect-aware scheduling and additional interconnect optimizations which help boost the performance of multiprogramming and multithreaded workloads. We provide experimental results that show that the overall throughput of multiprogramming workloads (consisting of two and four programs) can be improved by up to 60% with our configurable bus architecture. Similar gains can be achieved also for multithreaded applications as shown by further experiments. Finally, we present the performance sensitivity of the proposed interconnect architecture on shared memory bandwidth availability.

23

Huang, Kuo-Chan. "Minimizing Waiting Ratio for Dynamic Workload on Parallel Computers." Parallel Processing Letters 16, no. 04 (December 2006): 441–53. http://dx.doi.org/10.1142/s0129626406002769.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This paper proposes waiting ratio as a basis in evaluating various scheduling methods for dynamic workloads consisting of multi-processor jobs on parallel computers. We evaluate commonly used methods as well as several methods proposed in this paper by simulation studies. The results indicate that some commonly used methods do not improve the waiting ratios as expected by intuition, while some methods proposed in this paper do greatly improve waiting ratios more than 10 times for some workload data, promising in leading to more reasonable waiting time and better user's satisfaction.

24

Kim, Taeseok, Hyokyung Bahn, and Youjip Won. "A Pruning-Based Disk Scheduling Algorithm for Heterogeneous I/O Workloads." Scientific World Journal 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/940850.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

In heterogeneous I/O workload environments, disk scheduling algorithms should support different QoS (Quality-of-Service) for each I/O request. For example, the algorithm should meet the deadlines of real-time requests and at the same time provide reasonable response time for best-effort requests. This paper presents a novel disk scheduling algorithm called G-SCAN (Grouping-SCAN) for handling heterogeneous I/O workloads. To find a schedule that satisfies the deadline constraints and seek time minimization simultaneously, G-SCAN maintains a series of candidate schedules and expands the schedules whenever a new request arrives. Maintaining these candidate schedules requires excessive spatial and temporal overhead, but G-SCAN reduces the overhead to a manageable level via pruning the state space using two heuristics. One is grouping that clusters adjacent best-effort requests into a single scheduling unit and the other is the branch-and-bound strategy that cuts off inefficient or impractical schedules. Experiments with various synthetic and real-world I/O workloads show that G-SCAN outperforms existing disk scheduling algorithms significantly in terms of the average response time, throughput, and QoS-guarantees for heterogeneous I/O workloads. We also show that the overhead of G-SCAN is reasonable for on-line execution.

25

KRINGS, AXEL W., and MOSHE DROR. "REAL-TIME DISPATCHING: SCHEDULING STABILITY AND PRECEDENCE." International Journal of Foundations of Computer Science 10, no. 03 (September 1999): 313–27. http://dx.doi.org/10.1142/s012905419900023x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This paper introduces a new graph theoretical concept called strong precedence which is used to address the problem of scheduling instability is non-preemptive static list scheduling. Scheduling instability occurs when a reduction in task duration of one or more tasks causes other tasks to miss their deadline. This problem has been addressed in the past by introducing additional precedence constraints into the precedence graph representing the workload, or by limiting the depth the dispatchers scan at run-time. We present an alternative stabilization approach based on the concept of strong and weak precedence. By defining a strong precedence relation on selected subgraphs, the workload becomes inherently stable without requiring the introduction of new edges into the graph.

26

Volante, W. G., M. Merz, K. Stowers, and P. A. Hancock. "Sleep, Workload and Boredom." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 1833–37. http://dx.doi.org/10.1177/1541931213601418.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

As human spaceflight evolves toward long duration space missions (LDSM), it becomes increasingly important to design mission specifications and crew schedules that account for fluctuations in cognitive and psychomotor workload. Such schedules should optimize both sleep and workload to maintain high levels of mission performance. Effective sleep and workload scheduling tools are thus imperative for success, as they facilitate enhanced sleep quality and adjustable workload profiles for superior task performance. Here we examine issues related to sleep in space by taking two approaches: (1) completion of a systematic literature analysis, and (2) completion of interviews with Subject Matter Experts. Both of these approaches are summarized, with key findings and implications discussed.

27

Ghose, Anirban, Lokesh Dokara, Soumyajit Dey, and Pabitra Mitra. "A Framework for OpenCL Task Scheduling on Heterogeneous Multicores." Parallel Processing Letters 27, no. 03n04 (December 2017): 1750008. http://dx.doi.org/10.1142/s0129626417500086.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

We present an intelligent scheduling framework which takes as input a set of OpenCL kernels and distributes the workload across multiple CPUs and GPUs in a heterogeneous multicore platform. The framework relies on a Machine Learning (ML) based frontend that analyzes static program features of OpenCL kernels and predicts the ratio in which kernels are to be distributed across CPUs and GPUs. The framework provides such static analysis information along with system state information like runtime availability details of computing cores using well defined programming interfaces. Such interfaces are to be utilized by a user specified scheduling strategy. Given such a scheduling strategy, the framework generates device specific binaries and dispatches them across multiple devices in the heterogeneous platform as per the strategy. We test our scheduling framework extensively using different OpenCL task mixes of varying sizes and computational nature. Along with the scheduling framework, we propose a set of novel partition-aware scheduling strategies for heterogeneous multicores. Our proposed approach yields considerably better results in terms of schedule makespan when compared with the current state of the art ML based methods for scheduling of OpenCL workloads across heterogeneous multicores.

28

Liu, Yongkui, Xun Xu, Lin Zhang, Long Wang, and Ray Y. Zhong. "Workload-based multi-task scheduling in cloud manufacturing." Robotics and Computer-Integrated Manufacturing 45 (June 2017): 3–20. http://dx.doi.org/10.1016/j.rcim.2016.09.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Nagaraju, Sabout, and Latha Parthiban. "Real-Time Workload Scheduling (RTWS) Algorithm for Cloud." Journal of Information 1, no. 1 (2015): 36–52. http://dx.doi.org/10.18488/journal.104/2015.1.1/104.1.36.52.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Bijdekerke, Paul, Dirk Verellen, Koen Tournel, Vincent Vinh-Hung, Ferdi Somers, Peggy Bieseman, and Guy Storme. "TomoTherapy: Implications on daily workload and scheduling patients." Radiotherapy and Oncology 86, no. 2 (February 2008): 224–30. http://dx.doi.org/10.1016/j.radonc.2007.10.036.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Gulati, Divya P., Changkyu Kim, Simha Sethumadhavan, Stephen W. Keckler, and Doug Burger. "Multitasking workload scheduling on flexible core chip multiprocessors." ACM SIGARCH Computer Architecture News 36, no. 2 (May 2008): 46–55. http://dx.doi.org/10.1145/1399972.1399981.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Tsakalozos, Konstantinos, Vassilis Stoumpos, Kostas Saidis, and Alex Delis. "Adaptive disk scheduling with workload-dependent anticipation intervals." Journal of Systems and Software 82, no. 2 (February 2009): 274–91. http://dx.doi.org/10.1016/j.jss.2008.06.025.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Wang, Yanhua, Jianzhong Qiao, Shukuan Lin, and Tinglei Zhao. "An Approximate Optimal Solution to GPU Workload Scheduling." Computing in Science & Engineering 20, no. 5 (September 2018): 63–76. http://dx.doi.org/10.1109/mcse.2018.110145709.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Moray, Neville, Mohamed I. Dessouky, Brian A. Kijowski, and Ravi Adapathya. "Strategic Behavior, Workload, and Performance in Task Scheduling." Human Factors: The Journal of the Human Factors and Ergonomics Society 33, no. 6 (December 1991): 607–29. http://dx.doi.org/10.1177/001872089103300602.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Moon, Ilkyeong, Sanghyup Lee, Moonsoo Shin, and Kwangyeol Ryu. "Evolutionary resource assignment for workload-based production scheduling." Journal of Intelligent Manufacturing 27, no. 2 (January 21, 2014): 375–88. http://dx.doi.org/10.1007/s10845-014-0870-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Aupy, Guillaume, Manu Shantharam, Anne Benoit, Yves Robert, and Padma Raghavan. "Co-scheduling algorithms for high-throughput workload execution." Journal of Scheduling 19, no. 6 (August 30, 2015): 627–40. http://dx.doi.org/10.1007/s10951-015-0445-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Syafitri, Adelia, and Syarif Hidayat. "Perancangan Model Penjadwalan Teknisi pada Perawatan BD Check Pesawat Airbus dengan Memperhitungkan Beban Kerja." JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI 7, no. 1 (February 7, 2022): 39. http://dx.doi.org/10.36722/sst.v7i1.870.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

<p><strong>PT GMF AeroAsia is engaged in the maintenance, repair, and overhaul (MRO) of aircraft. It is known that GMF is the largest domestic MRO company at 30% while 70% is owned by foreign MRO companies. Based on the company's vision to make the company a world-class MRO, it is necessary to develop a business scheme in terms of capability, quantity, and quality. However, the scheduling of technicians in carrying out aircraft maintenance is currently unable to meet the demand every hour due to the uncertain flight schedule of the aircraft every day so that the workload of each technician exceeds the normal workload. The calculation of the workload using the full-time equivalent method showed that the workload of technicians was overloaded so that an additional number of technicians was needed, as well as analyzing the causes and consequences of the excessive workload of technicians using the fishbone diagram method and modeling the formulation of the technician scheduling problem that works in maintenance before departure check Airbus aircraft in the form of integer linear programming with decision variables such as the number of technicians, the number of shifts and the number of working days, then the constraint function is the constraints to be achieved in modeling, and the objective function is to minimize the number of technicians employed. Furthermore, the model is implemented using the LINGO 19.0 software so that it is found that the model can produce an optimal technician schedule in scheduling.</strong></p><p><strong><em>Keywords</em></strong> - <em>Fishbone Diagram, Full Time Equivalent, Integer Linear Programming, Lingo Software, Matchematical Modeling, Scheduling, Workload</em><em></em></p>

38

Hanabaratti, Kavita D., and Sunil F. Rodd. "Evolutionary Computing based Web Service Composition Technique for Scheduling of Workload under Cloud Environment." Indian Journal of Science and Technology 15, no. 2 (January 12, 2022): 69–80. http://dx.doi.org/10.17485/ijst/v15i2.1806.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Chen, James C., Ling Huey Su, Gary C. Chao, Chih Cheng Chen, Tzu Wei Peng, Cheng Ju Sun, Jui Wei Chien, and Hui Chien Chien. "Advanced Planning and Scheduling for Color Filter Fabrication Plants." Key Engineering Materials 450 (November 2010): 361–64. http://dx.doi.org/10.4028/www.scientific.net/kem.450.361.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This research proposed Advanced Planning and Scheduling (APS) system to effectively and efficiently balance machine loading for color filter fabrication plants in Thin Film Transistor - Liquid Crystal Display industry. APS uses six modules to estimate future equipment loading and calculate order due dates according to capacity limits by taking into account the due date and size of orders, as well as the capacity, loading, and yield of fabrication plants. These six modules include: Order Priority Module, WIP-Pulling Module, Order Release Module, Material Management Module, Workload Accumulation Module, and Workload Balance Module. A lot’s start processing time at each production step is selected leading to the best workload balance. Simulation and experimental design are used to evaluate the performance of APS. Production manager can use APS to improve color filter fabrication plants’ productivity and competitiveness.

40

Lakhani, B., and A. Agrawal. "A Task Scheduling Approach for Cloud Environments Employing Evolutionary Algorithms." Journal of Scientific Research 13, no. 2 (May 1, 2021): 423–38. http://dx.doi.org/10.3329/jsr.v13i2.49944.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

One of the key challenges in the domain of cloud computing is task scheduling and estimation of cloud workloads for time critical applications pertaining to constrained cloud resources. While effective task scheduling is necessary for balancing the load, workload forecasting is necessary to plan in advance the requirements of cloud platforms based on previous data so as to effectively utilize cloud resources. Often it is challenging to gather sufficient information about the tasks and hence allocating the tasks to virtual machines (VMs) in the most optimal way is non-trivial. In this paper, a hybrid task scheduling approach is proposed based on evolutionary algorithms. The first approach is the amalgamation of bat and particle swarm optimization (PSO) techniques. The scheduling approach also combines the processing time preemption (PTP) approach to schedule the source intensive tasks which allows to reduce the response time of the proposed system. The second approach is a machine learning based approach employing gradient descent with momentum (GDM). The evaluation of the proposed system has been done based on the response time and mean square error of the system.

41

Qiu, Yeliang, Congfeng Jiang, Yumei Wang, Dongyang Ou, Youhuizi Li, and Jian Wan. "Energy Aware Virtual Machine Scheduling in Data Centers." Energies 12, no. 4 (February 17, 2019): 646. http://dx.doi.org/10.3390/en12040646.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Power consumption is a primary concern in modern servers and data centers. Due to varying in workload types and intensities, different servers may have a different energy efficiency (EE) and energy proportionality (EP) even while having the same hardware configuration (i.e., central processing unit (CPU) generation and memory installation). For example, CPU frequency scaling and memory modules voltage scaling can significantly affect the server’s energy efficiency. In conventional virtualized data centers, the virtual machine (VM) scheduler packs VMs to servers until they saturate, without considering their energy efficiency and EP differences. In this paper we propose EASE, the Energy efficiency and proportionality Aware VM SchEduling framework containing data collection and scheduling algorithms. In the EASE framework, each server’s energy efficiency and EP characteristics are first identified by executing customized computing intensive, memory intensive, and hybrid benchmarks. Servers will be labelled and categorized with their affinity for different incoming requests according to their EP and EE characteristics. Then for each VM, EASE will undergo workload characterization procedure by tracing and monitoring their resource usage including CPU, memory, disk, and network and determine whether it is computing intensive, memory intensive, or a hybrid workload. Finally, EASE schedules VMs to servers by matching the VM’s workload type and the server’s EP and EE preference. The rationale of EASE is to schedule VMs to servers to keep them working around their peak energy efficiency point, i.e., the near optimal working range. When workload fluctuates, EASE re-schedules or migrates VMs to other servers to make sure that all the servers are running as near their optimal working range as they possibly can. The experimental results on real clusters show that EASE can save servers’ power consumption as much as 37.07%–49.98% in both homogeneous and heterogeneous clusters, while the average completion time of the computing intensive VMs increases only 0.31%–8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22%. The job completion time can be saved by 53.80%.

42

Shim, Sang Oh. "Line Level Scheduling by Integrating Area Level Scheduling in Manufacturing Systems." Applied Mechanics and Materials 267 (December 2012): 83–86. http://dx.doi.org/10.4028/www.scientific.net/amm.267.83.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

In current automated manufacturing systems, production managers try to implement more efficient and effective scheduling methods which play important roles in supply chain by maximizing utilization of the installed equipments and throughput as well as meeting customers' various demands. In relation to enhancing capabilities and responding demands, some raising issues are discussed, which are transportation of lots between manufacturing lines, management of urgent jobs and workload balancing among areas. In this research, by discussing these scheduling issues, a new concept and its design, which is called as line-level scheduling, are suggested for advanced planning and scheduling systems in the manufacturing systems.

43

Mahmood, Basharat, Naveed Ahmad, Majid Iqbal Khan, and Adnan Akhunzada. "Dynamic Priority Real-Time Scheduling on Power Asymmetric Multicore Processors." Symmetry 13, no. 8 (August 13, 2021): 1488. http://dx.doi.org/10.3390/sym13081488.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The use of real-time systems is growing at an increasing rate. This raises the power efficiency as the main challenge for system designers. Power asymmetric multicore processors provide a power-efficient platform for building complex real-time systems. The utilization of this efficient platform can be further enhanced by adopting proficient scheduling policies. Unfortunately, the research on real-time scheduling of power asymmetric multicore processors is in its infancy. In this research, we have addressed this problem and added new results. We have proposed a dynamic-priority semi-partitioned algorithm named: Earliest-Deadline First with C=D Task Splitting (EDFwC=D-TS) for scheduling real-time applications on power asymmetric multicore processors. EDFwC=D-TS outclasses its counterparts in terms of system utilization. The simulation results show that EDFwC=D-TS schedules up to 67% more tasks with heavy workloads. Furthermore, it improves the processor utilization up to 11% and on average uses 14% less cores to schedule the given workload.

44

Lin, Fei, Yang Yang, Shihua Wang, Yudi Xu, Hong Ma, and Ritai Yu. "Urban public bicycle dispatching optimization method." PeerJ Computer Science 5 (October 14, 2019): e224. http://dx.doi.org/10.7717/peerj-cs.224.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%–50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

45

Limna, Thanathip, and Pichaya Tandayya. "Workload scheduling for Nokkhum video surveillance as a service." Multimedia Tools and Applications 77, no. 1 (January 11, 2017): 1363–89. http://dx.doi.org/10.1007/s11042-016-4225-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Sorkhoh, Ibrahim, Dariush Ebrahimi, Ribal Atallah, and Chadi Assi. "Workload Scheduling in Vehicular Networks With Edge Cloud Capabilities." IEEE Transactions on Vehicular Technology 68, no. 9 (September 2019): 8472–86. http://dx.doi.org/10.1109/tvt.2019.2927634.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Zhou, Quan, Guohui Li, and Jianjun Li. "Improved Carry-in Workload Estimation for Global Multiprocessor Scheduling." IEEE Transactions on Parallel and Distributed Systems 28, no. 9 (September 1, 2017): 2527–38. http://dx.doi.org/10.1109/tpds.2017.2679195.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Urgaonkar, Rahul, Shiqiang Wang, Ting He, Murtaza Zafer, Kevin Chan, and Kin K. Leung. "Dynamic service migration and workload scheduling in edge-clouds." Performance Evaluation 91 (September 2015): 205–28. http://dx.doi.org/10.1016/j.peva.2015.06.013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Ayoub, Raid, Krishnam Indukuri, and Tajana Simunic Rosing. "Temperature Aware Dynamic Workload Scheduling in Multisocket CPU Servers." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 30, no. 9 (September 2011): 1359–72. http://dx.doi.org/10.1109/tcad.2011.2153852.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Luo, Wenchang, and Feng Liu. "On single-machine scheduling with workload-dependent maintenance duration." Omega 68 (April 2017): 119–22. http://dx.doi.org/10.1016/j.omega.2016.06.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles