Journal articles: 'Dynamic Scheduling Technique'

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Zhuang, Shufeng, Zhendong Yin, Zhilu Wu, and Xiaoguang Chen. "Dynamic Relay Satellite Scheduling Based on ABC-TOPSIS Algorithm." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3161069.

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Tracking and Data Relay Satellite System (TDRSS) is a space-based telemetry, tracking, and command system, which represents a research field of the international communication. The issue of the dynamic relay satellite scheduling, which focuses on assigning time resource to user tasks, has been an important concern in the TDRSS system. In this paper, the focus of study is on the dynamic relay satellite scheduling, whose detailed process consists of two steps: the initial relay satellite scheduling and the selection of dynamic scheduling schemes. To solve the dynamic scheduling problem, a new scheduling algorithm ABC-TOPSIS is proposed, which combines artificial bee colony (ABC) and technique for order preference by similarity to ideal solution (TOPSIS). The artificial bee colony algorithm is performed to solve the initial relay satellite scheduling. In addition, the technique for order preference by similarity to ideal solution is adopted for the selection of dynamic scheduling schemes. Plenty of simulation results are presented. The simulation results demonstrate that the proposed method provides better performance in solving the dynamic relay satellite scheduling problem in the TDRSS system.

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Mostafa, Samih M., and Hirofumi Amano. "Dynamic Round Robin CPU Scheduling Algorithm Based on K-Means Clustering Technique." Applied Sciences 10, no. 15 (July 26, 2020): 5134. http://dx.doi.org/10.3390/app10155134.

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Minimizing time cost in time-shared operating system is the main aim of the researchers interested in CPU scheduling. CPU scheduling is the basic job within any operating system. Scheduling criteria (e.g., waiting time, turnaround time and number of context switches (NCS)) are used to compare CPU scheduling algorithms. Round robin (RR) is the most common preemptive scheduling policy used in time-shared operating systems. In this paper, a modified version of the RR algorithm is introduced to combine the advantageous of favor short process and low scheduling overhead of RR for the sake of minimizing average waiting time, turnaround time and NCS. The proposed work starts by clustering the processes into clusters where each cluster contains processes that are similar in attributes (e.g., CPU service period, weights and number of allocations to CPU). Every process in a cluster is assigned the same time slice depending on the weight of its cluster and its CPU service period. The authors performed comparative study of the proposed approach and popular scheduling algorithms on nine groups of processes vary in their attributes. The evaluation was measured in terms of waiting time, turnaround time, and NCS. The experiments showed that the proposed approach gives better results.

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Abello, Manuel Blanco, and Zbigniew Michalewicz. "Multiobjective Resource-Constrained Project Scheduling with a Time-Varying Number of Tasks." Scientific World Journal 2014 (2014): 1–35. http://dx.doi.org/10.1155/2014/420101.

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In resource-constrained project scheduling (RCPS) problems, ongoing tasks are restricted to utilizing a fixed number of resources. This paper investigates a dynamic version of the RCPS problem where the number of tasks varies in time. Our previous work investigated a technique called mapping of task IDs for centroid-based approach with random immigrants (McBAR) that was used to solve the dynamic problem. However, the solution-searching ability of McBAR was investigated over only a few instances of the dynamic problem. As a consequence, only a small number of characteristics of McBAR, under the dynamics of the RCPS problem, were found. Further, only a few techniques were compared to McBAR with respect to its solution-searching ability for solving the dynamic problem. In this paper, (a) the significance of the subalgorithms of McBAR is investigated by comparing McBAR to several other techniques; and (b) the scope of investigation in the previous work is extended. In particular, McBAR is compared to a technique called, Estimation Distribution Algorithm (EDA). As with McBAR, EDA is applied to solve the dynamic problem, an application that is unique in the literature.

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SMANCHAT, Sucha, and Kanchana VIRIYAPANT. "Scheduling Dynamic Parallel Loop Workflow in Cloud Environment." Walailak Journal of Science and Technology (WJST) 15, no. 1 (August 4, 2016): 19–27. http://dx.doi.org/10.48048/wjst.2018.2267.

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Scientific workflows have been employed to automate large scale scientific experiments by leveraging computational power provided on-demand by cloud computing platforms. Among these workflows, a parallel loop workflow is used for studying the effects of different input values of a scientific experiment. Because of its independent loop characteristic, a parallel loop workflow can be dynamically executed as parallel workflow instances to accelerate the execution. Such execution negates workflow traversal used in existing works to calculate execution time and cost during scheduling in order to maintain time and cost constraints. In this paper, we propose a novel scheduling technique that is able to handle dynamic parallel loop workflow execution through a new method for evaluating execution progress together with a workflow instance arrival control and a cloud resource adjustment mechanism. The proposed technique, which aims at maintaining a workflow deadline while reducing cost, is tested using 3 existing task scheduling heuristics as its task mapping strategies. The simulation results show that the proposed technique is practical and performs better when the time constraint is more relaxed. It also prefers task scheduling heuristics that allow for a more accurate progress evaluation.

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Naeem, Huma, and Asif Masood. "An optimal dynamic threat evaluation and weapon scheduling technique." Knowledge-Based Systems 23, no. 4 (May 2010): 337–42. http://dx.doi.org/10.1016/j.knosys.2009.11.012.

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SURMA, DAVID R., EDWIN H. M. SHA, and NELSON PASSOS. "COMMUNICATION SCHEDULING WITH RE-ROUTING BASED ON STATIC AND HYBRID TECHNIQUES." Journal of Circuits, Systems and Computers 13, no. 05 (October 2004): 1039–64. http://dx.doi.org/10.1142/s0218126604001829.

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In massively parallel systems, the performance gains are often significantly diminished by the inherent communication overhead. This overhead is caused by the required message passing resulting from the task allocation scheme. In this paper, techniques to reduce this communication overhead by both scheduling the communication and determining the routing that the messages should take within a tightly-coupled processor network are presented. Using the recently developed Collision Graph model, static scheduling algorithms are derived which work at compile-time to determine the ordering and routing of the individual message transmissions. Since a priori knowledge about the network traffic required by static scheduling may not be available or accurate, this work also considers dynamic scheduling. A novel hybrid technique is presented which operates in a dynamic environment yet uses known information obtained by analyzing the communication patterns. Experiments performed show significant improvement over baseline techniques.

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Xu, Bo, Yi Hu, Menglan Hu, Feng Liu, Kai Peng, and Lan Liu. "Iterative Dynamic Critical Path Scheduling: An Efficient Technique for Offloading Task Graphs in Mobile Edge Computing." Applied Sciences 12, no. 6 (March 21, 2022): 3189. http://dx.doi.org/10.3390/app12063189.

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Recent years have witnessed a paradigm shift from centralized cloud computing to decentralized edge computing. As a key enabler technique in edge computing, computation offloading migrates computation-intensive tasks from resource-limited devices to nearby devices, optimizing service latency and energy consumption. In this paper, we investigate the problem of offloading task graphs in edge computing scenarios. Previous work based on list-scheduling heuristics is likely to suffer from severe processor time wastage due to intricate task dependencies and data transfer requirements. To this end, we propose a novel offloading algorithm, referred to as Iterative Dynamic Critical Path Scheduling (IDCP). IDCP minimizes the makespan by iteratively migrating tasks to keep shortening the dynamic critical path. Through IDCP, what is managed are essentially the sequences among tasks, including task dependencies and scheduled sequences on processors. Since we only schedule sequences here, the actual start time of each task is not fixed during the scheduling process, which effectively helps to avoid unfavorable schedules. Such flexibilities also offer us much space for continuous scheduling optimizations. Our experimental results show that our algorithm significantly outperforms existing list-scheduling heuristics in various scenarios, which demonstrates the effectiveness and competitiveness of our algorithm.

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Ghosh, Tarun Kumar, and Sanjoy Das. "Efficient Job Scheduling in Computational Grid Systems Using Wind Driven Optimization Technique." International Journal of Applied Metaheuristic Computing 9, no. 1 (January 2018): 49–59. http://dx.doi.org/10.4018/ijamc.2018010104.

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Computational Grid has been employed for solving complex and large computation-intensive problems with the help of geographically distributed, heterogeneous and dynamic resources. Job scheduling is a vital and challenging function of a computational Grid system. Job scheduler has to deal with many heterogeneous computational resources and to take decisions concerning the dynamic, efficient and effective execution of jobs. Optimization of the Grid performance is directly related with the efficiency of scheduling algorithm. To evaluate the efficiency of a scheduling algorithm, different parameters can be used, the most important of which are makespan and flowtime. In this paper, a very recent evolutionary heuristic algorithm known as Wind Driven Optimization (WDO) is used for efficiently allocating jobs to resources in a computational Grid system so that makespan and flowtime are minimized. In order to measure the efficacy of WDO, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are considered for comparison. This study proves that WDO produces best results.

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Göçgün, Yasin. "Dynamic scheduling with cancellations: an application to chemotherapy appointment booking." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 8, no. 2 (April 22, 2018): 161–69. http://dx.doi.org/10.11121/ijocta.01.2018.00469.

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We study a dynamic scheduling problem that has the feature of due dates and time windows. This problem arises in chemotherapy scheduling where patients from different types have specific target dates along with time windows for appointment. We consider cancellation of appointments. The problem is modeled as a Markov Decision Process (MDP) and approximately solved using a direct-search based approximate dynamic programming (ADP) tehnique. We compare the performance of the ADP technique against the myopic policy under diverse scenarios. Our computational results reveal that the ADP technique outperforms the myopic policy on majority of problem sets we generated.

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Sharma, Manik, and Smriti Smriti. "STATIC AND DYNAMIC BNP PARALLEL SCHEDULING ALGORITHMS FOR DISTRIBUTED DATABASE." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 1, no. 1 (December 30, 2011): 10–15. http://dx.doi.org/10.24297/ijct.v1i1.2601.

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Parallel processing is a technique of executing the multiple tasksconcurrently on different processors. Parallel processing is usedto solve the complex problems that require vast amount ofprocessing time. Task scheduling is one of the major problemsof parallel processing. The objective of this study is to analyzethe performance of static (HLFET) and dynamic (DLS) BNPparallel scheduling algorithm for allocating the tasks ofdistributed database over number of processors. In the wholestudy the focus will be given on measuring the impact ofnumber of processors on different metrics of performance likemakespan, speed up and processor utilization by using HLFETand DLS BNP task scheduling algorithms.

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Bierwirth, Christian, and Dirk C. Mattfeld. "Production Scheduling and Rescheduling with Genetic Algorithms." Evolutionary Computation 7, no. 1 (March 1999): 1–17. http://dx.doi.org/10.1162/evco.1999.7.1.1.

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A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed atreasonable runtime costs.

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Ktata, Ismail, Fakhreddine Ghaffari, Bertrand Granado, and Mohamed Abid. "Dynamic Application Model for Scheduling with Uncertainty on Reconfigurable Architectures." International Journal of Reconfigurable Computing 2011 (2011): 1–15. http://dx.doi.org/10.1155/2011/156946.

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Applications executed on embedded systems require dynamicity and flexibility according to user and environment needs. Dynamically reconfigurable architecture could satisfy these requirements but needs efficient mechanisms to be managed efficiently. In this paper, we propose a dedicated application modeling technique that helps to establish a predictive scheduling approach to manage a dynamically reconfigurable architecture named OLLAF. OLLAF is designed to support an operating system that deals with complex embedded applications. This model will be used for a predictive scheduling based on an early estimation of our application dynamicity. A vision system of a mobile robot application has been used to validate the presented model and scheduling approach. We have demonstrated that with our modeling we can realize an efficient predictive scheduling on a robot vision application with a mean error of 6.5%.

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R K, Srinivasa, and Hemantha Kumar A.R. "A Research Survey on Scheduling Techniques in lte-based Network and its State of Art." APTIKOM Journal on Computer Science and Information Technologies 3, no. 2 (July 1, 2018): 51–58. http://dx.doi.org/10.11591/aptikom.j.csit.116.

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The growing interest over the mobile based communication has attracted the Long-Term Evolution (LTE) technique which facilitates higher data transmission rates to its dynamic users. The LTE-based network involves with scheduling which governs the better communication at different traffic (large, small, medium) condition. The LTE caters up network demands and encounters some potential and unreported problems in presence of large traffic with uncertainty of its associated load. Thus, scheduling becomes most essential is must. This manuscript reviews the recent existing researches with their contribution along with significances and limitations. The paper also contributes in identify the state of art in research issues to identify the research gap towards scheduling techniques in LTE

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Md Isa, Mohd Nazrin, Sohiful Anuar Zainol Murad, Mohamad Imran Ahmad, Muhammad M. Ramli, and Rizalafande Che Ismail. "An Efficient Scheduling Technique for Biological Sequence Alignment." Applied Mechanics and Materials 754-755 (April 2015): 1087–92. http://dx.doi.org/10.4028/www.scientific.net/amm.754-755.1087.

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Computing alignment matrix score to search for regions of homology between biological sequences is time consuming task. This is due to the recursive nature of the dynamic programming-based algorithms such as the Smith-Waterman and the Needleman-Wunsch algorithmns. Typical FPGA-based protein sequencer comprises of two main logic blocks. One for computing alignment scores i.e. the processing element (PE), while another logic block for configuring the PE with coefficients. During alignment matrix computation, the logic block for configuring the PE are left unused until the time consuming alignment matrix computation finished. Therefore, a new technique, known as overlap computation and configuration (OCC) is proposed to minimize the time overhead for performing biological sequence alignment. The OCC technique simultaneously updating substitution matrix in a processing element (PE) systolic array, while computing alignment matrix scores. Results showed that, the sequencer achieves more than two order of magnitude speed-up higher compared to the state of the art, at negligible area overhead, if any.

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Abdel-Raheem, Mohamed, Cuauhtemoc Torres Cantu, and Xiaohui Wang. "Dynamic Contract Time Determination System for Highway Projects." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 5 (May 2020): 381–92. http://dx.doi.org/10.1177/0361198120915896.

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Texas Department of Transportation (TxDOT) projects have been experiencing significant delays. Some of these delays can be rooted to the inaccurate estimation of the contract time. This research presents a preliminary framework for the development of a computer-based system designed to determine a realistic contract duration for TxDOT projects. In addition to traditional deterministic scheduling, the system also performs probabilistic scheduling using the program evaluation and review technique. The system also incorporates an interactive database containing a list of various highway construction activities and their productivity rates; the database is used to estimate the durations of project activities. The system was deployed to reschedule some previous TxDOT projects, and the results were statistically compared and analyzed. The results show that this system can significantly improve the estimate of the contract time. This framework lays the foundation for the development of a more advanced contract time determination system based on probabilistic scheduling.

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Parvez, Iram, and Jianjian Shen. "Algorithms of approximate dynamic programming for hydro scheduling." E3S Web of Conferences 144 (2020): 01001. http://dx.doi.org/10.1051/e3sconf/202014401001.

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In hydro scheduling, unit commitment is a complex sub-problem. This paper proposes a new approximate dynamic programming technique to solve unit commitment. A new method called Least Square Policy Iteration (LSPI) algorithm is introduced which is efficient and faster in convergence. This algorithm takes the properties of widely used algorithm least square temporal difference (LSTD), enhance it further and make it useful for optimization problems. First value function is to find a fixed policy by using least square temporal difference Q (LSTDQ) algorithm which is similar to LSTD, then LSPI is introduced for making the policy iteration algorithm by using the results of LSTDQ. It combines the data efficiency of LSTDQ and policy-search efficiency of policy iteration.

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Zhou, Jian, Ai Min Wang, and Zhi Bing Lu. "An HCI Spatial Layout and Planning Technique for Ship Curved Block Construction." Applied Mechanics and Materials 401-403 (September 2013): 31–35. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.31.

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According to the study on the information technology status of domestic shipbuilding industry, aiming as the demand of full-vision and effective management mode of curved block construction scheduling, the HCI (Human-computer Interaction) spatial layout and planning technique of curved block is proposed. Through Several technical points and rich-client application, the graphics vector modeling of curved block and the dual-dimension block layout and planning dynamic display technology are achieved, then on these bases an HCI scheduling management technology with graphics transformation as core is put forward. Finally, a prototype system was designed and developed to verify in practice.

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Blanco Abello, Manuel, and Zbigniew Michalewicz. "Implicit memory-based technique in solving dynamic scheduling problems through Response Surface Methodology – Part I." International Journal of Intelligent Computing and Cybernetics 7, no. 2 (June 3, 2014): 114–42. http://dx.doi.org/10.1108/ijicc-12-2013-0053.

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Purpose – This is the first part of a two-part paper. The purpose of this paper is to report on methods that use the Response Surface Methodology (RSM) to investigate an Evolutionary Algorithm (EA) and memory-based approach referred to as McBAR – the Mapping of Task IDs for Centroid-Based Adaptation with Random Immigrants. Some of the methods are useful for investigating the performance (solution-search abilities) of techniques (comprised of McBAR and other selected EA-based techniques) for solving some multi-objective dynamic resource-constrained project scheduling problems with time-varying number of tasks. Design/methodology/approach – The RSM is applied to: determine some EA parameters of the techniques, develop models of the performance of each technique, legitimize some algorithmic components of McBAR, manifest the relative performance of McBAR over the other techniques and determine the resiliency of McBAR against changes in the environment. Findings – The results of applying the methods are explored in the second part of this work. Originality/value – The models are composite and characterize an EA memory-based technique. Further, the resiliency of techniques is determined by applying Lagrange optimization that involves the models.

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Lv, Ning, and Jing Li Zhou. "A Dynamic Load Balancing Method with Available Bandwidth Information." Applied Mechanics and Materials 667 (October 2014): 121–24. http://dx.doi.org/10.4028/www.scientific.net/amm.667.121.

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Network striping is a well-known technique that can transparently utilize parallel, redundant links in order to improve the performance and reliability of the network interface. However, most of existing scheduling schemes designed for load balancing do not consider the factor of available bandwidth of multi-link. To overcome this disadvantage, in this paper, we propose a novel method called Available Bandwidth based Dynamic Load Balancing (ABDLB) to address this issue. Using available bandwidth obtained by periodically probing, at the host, packets are proportionally allocated to multiple links according to the probed results. The simulation results demonstrate that our proposed algorithm achieves better performance than traditional scheduling methods.

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Schütze, N., M. de Paly, and U. Shamir. "Novel simulation-based algorithms for optimal open-loop and closed-loop scheduling of deficit irrigation systems." Journal of Hydroinformatics 14, no. 1 (April 23, 2011): 136–51. http://dx.doi.org/10.2166/hydro.2011.073.

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The scarcity of water compared with the abundance of land constitutes the main drawback within agricultural production. Besides the improvement of irrigation techniques a task of primary importance is solving the problem of intra-seasonal irrigation scheduling under limited seasonal water supply. An efficient scheduling algorithm has to take into account the crops' response to water stress at different stages throughout the growing season. Furthermore, for large-scale planning tools compact presentations of the relationship between irrigation practices and grain yield, such as crop water production functions, are often used which also rely on an optimal scheduling of the considered irrigation systems. In this study, two new optimization algorithms for single-crop intra-seasonal scheduling of deficit irrigation systems are introduced which are able to operate with general crop growth simulation models. First, a tailored evolutionary optimization technique (EA) searches for optimal schedules over a whole growing season within an open-loop optimization framework. Second, a neuro-dynamic programming technique (NDP) is used for determining optimal irrigation policy. In this paper, different management schemes are considered and crop-yield functions generated with both the EA and the NDP optimization algorithms compared.

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Tout, K. R., and D. J. Evans. "Parallel forward chaining technique with dynamic scheduling, for rule-based expert systems." Parallel Computing 18, no. 8 (August 1992): 913–30. http://dx.doi.org/10.1016/0167-8191(92)90037-8.

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P. Sudha, Ms, and Dr A. Rengarajan. "Priority based Dynamic Resource Allocation and Scheduling for Wimax Networks." International Journal of Engineering & Technology 7, no. 3.34 (September 1, 2018): 262. http://dx.doi.org/10.14419/ijet.v7i3.34.18983.

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WiMAX network provides effective internet communication between users with low expense and ease of deployment. It is used efficiently in small to medium enterprises. However, proficient resource allocation and scheduling is still a critical requirement in WiMAX networks due to the varying factors related to the network communication. If the network resources are not appropriately allocated, then there are possibilities for missing out critical data, or wasting more resources on transmitting less important data which in turn will have adverse affect on data transmission in the future stages. Hence, in this paper, we propose to develop a Priority Based Dynamic Resource Allocation and Scheduling for WiMAX Networks. In this technique, the incoming data are dynamically allocated, and then users are prioritized to identify the critical users. The allocation and scheduling is performed by considering user priority, robustness and also power consumption rate to ensure effective network performance.

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Priore, Paolo, Alberto Gómez, Raúl Pino, and Rafael Rosillo. "Dynamic scheduling of manufacturing systems using machine learning: An updated review." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, no. 1 (January 20, 2014): 83–97. http://dx.doi.org/10.1017/s0890060413000516.

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AbstractA common way of dynamically scheduling jobs in a manufacturing system is by implementing dispatching rules. The issues with this method are that the performance of these rules depends on the state the system is in at each moment and also that no “ideal” single rule exists for all the possible states that the system may be in. Therefore, it would be interesting to use the most appropriate dispatching rule for each instance. To achieve this goal, a scheduling approach that uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a literature review of the main machine learning based scheduling approaches from the last decade is presented.

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Yassa, Sonia, Rachid Chelouah, Hubert Kadima, and Bertrand Granado. "Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments." Scientific World Journal 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/350934.

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We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

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Kaur, Gurleen, and Anju Bala. "An efficient resource prediction–based scheduling technique for scientific applications in cloud environment." Concurrent Engineering 27, no. 2 (March 7, 2019): 112–25. http://dx.doi.org/10.1177/1063293x19832946.

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Cloud computing makes scientists to run complex scientific applications. The research community is able to access on-demand compute resources within a short span of time instead of experiencing peak demand bottlenecks. As the demand for cloud resources is dynamic and volatile in nature, this in turn affects the availability of resources during scheduling. In order to allocate sufficient resources for scientific applications with different execution requirements, it is necessary to predict the appropriate set of resources. To attain this objective, a resource prediction–based scheduling technique has been introduced which automates the resource allocation for scientific application in virtualized cloud environment. First, the proposed prediction model is trained on the dataset generated by concurrently deploying tasks of a scientific application on cloud. Then, the resources are scheduled based on the output of proposed prediction technique. The main objective of resource prediction–based scheduling technique is to efficiently handle the resources for virtual machines in order to reduce the execution time, error rate, and improve the accuracy.

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Sutar, Sandeep Gajanan, and Kumarswamy S. "Efficient Scheduling of Jobs and Allocation of Resources in Cloud Computing." International Journal of Software Innovation 10, no. 1 (January 1, 2022): 1–13. http://dx.doi.org/10.4018/ijsi.307013.

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Due to the drastic utilization of clouds, a Proper and proficient allocation of resources in dynamically working environment of cloud systems turns into the challenging task. Different promising mechanisms have been created to work on the effectiveness of process of resource allocation. Yet at the same time there is some incompetency as far as resource allocation and job scheduling, when the systems become highly loaded. Hence, an effective algorithm for scheduling of jobs is needed to work on the proficiency of resource allocation activities. In this paper a advanced technique for scheduling of jobs is proposed for effective and unique process of allocation of resources in cloud computing. By making use of prediction-based techniques and mechanism of updating resource tables in dynamic manner, we achieve, better allocation of resources in the form of response time and completion of jobs. The experimental results demonstrate the effective outcomes compared to existing techniques, by achieving exactness in values for resource table updation.

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Zhou, Guanghui, Pingyu Jiang, and Shuichi Fukuda. "Using Mobile Agents to Schedule a Manufacturing Chain on the Internet." Concurrent Engineering 10, no. 4 (December 1, 2002): 311–23. http://dx.doi.org/10.1177/a032005.

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This paper is mainly concerned with putting forward a novel manufacturing chain model and a new scheduling model for manufacturing chains in networked manufacturing systems. The manufacturing chain model is formed by adopting extensive activity-network-diagram workflow technique. The scheduling model is built upon mobile agent technologies, e.g. aglets. It includes two different scheduling levels. One is defined as the process scheduling which implements the scheduling logic for distributive manufacturing sites or manufacturing cells based on a specific manufacturing task. The other is the node scheduling which realizes the scheduling logic for manufacturing equipments based on specific working procedures or working paces. High-level scheduling aglet templates are created to support the operations at the levels of manufacturing sites or manufacturing cells. To complement, low-level scheduling aglet templates are created to support the operations such as working procedures or working paces over manufacturing equipments. As a result, the model is able to deal with dynamic manufacturing chains and their scheduling.

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PRIORE, PAOLO, DAVID DE LA FUENTE, ALBERTO GOMEZ, and JAVIER PUENTE. "A review of machine learning in dynamic scheduling of flexible manufacturing systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 3 (June 2001): 251–63. http://dx.doi.org/10.1017/s0890060401153059.

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A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no single rule exists that is better than the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a review of the main machine learning-based scheduling approaches described in the literature is presented.

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Wen-Hsuan, Liang, Cheng Dun-Wei, Hsu Chih-Wei, Lee Chia-Wei, Keand Chih-Heng, Zomaya Albert Y., and Hsieh Sun-Yuan. "Dynamic Flow Scheduling Technique for Load Balancing in Fat-Tree Data Center Networks." International Journal of Performability Engineering 17, no. 6 (2021): 491. http://dx.doi.org/10.23940/ijpe.21.06.p1.491503.

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Yu-Kwong Kwok and I. Ahmad. "Dynamic critical-path scheduling: an effective technique for allocating task graphs to multiprocessors." IEEE Transactions on Parallel and Distributed Systems 7, no. 5 (May 1996): 506–21. http://dx.doi.org/10.1109/71.503776.

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Abdulhamid, Shafi’i Muhammad, Muhammad Shafie Abd Latiff, Syed Hamid Hussain Madni, and Mohammed Abdullahi. "Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm." Neural Computing and Applications 29, no. 1 (July 16, 2016): 279–93. http://dx.doi.org/10.1007/s00521-016-2448-8.

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Muhammad Hussain, Yan Gao, Falak Shair, and Sherehe Semba. "Optimal residential load scheduling under dynamic pricing and demand-side management." International Journal of Frontiers in Engineering and Technology Research 1, no. 2 (November 30, 2021): 001–18. http://dx.doi.org/10.53294/ijfetr.2021.1.2.0049.

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Balancing electricity consumption and generation in the residential market is essential for power grids. The imbalance of power scheduling between energy supply and demand would definitely increase costs to both the energy provider and customer. This paper proposes a control function to normalize the peak cost and customer discomfort. In this work, we modify an optimization power scheduling scheme by using the inclined-block rate (IBR) and real-time price (RTP) technique to achieve a desired trade-off between electricity payment and consumer discomfort level. For discomfort, an average time delay between peak and off-peak is proposed to minimize waiting time. The simulation results present our model more practical and realistic with respect to the consumption constrained at peak hours.

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SHI, WEISONG, and ZHIMIN TANG. "LOAD BALANCING IN HOME-BASED SOFTWARE DSMS." International Journal of Foundations of Computer Science 12, no. 03 (June 2001): 307–24. http://dx.doi.org/10.1142/s0129054101000503.

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Load balancing is a critical issue for achieving good performance in parallel and distributed systems. However, this issue is neglected in the research area of software DSMs in the past decade. Based on the observation that scientific applications can be classified into two categories: iterative and non-iterative, we propose two dynamic scheduling schemes for these two cases respectively in this paper. For iterative scientific applications, a dynamic task migration technique is proposed which characterizes itself with integrating computation migration and data migration together. An affinity-based self scheduling (ABS) is proposed for non-iterative scientific applications, which take both the static and dynamic processor affinity into consideration when scheduling. The target experiment platform is a state-of-the-art home-based DSM system named JIAJIA. Performance evaluation results show that the novel task migration scheme improves the performance ranging from 36% to 50% compared with a static task allocation scheme in a metacomputing environment, and performs better than traditional task (computation-only) migration approach about 12.5% for MAT, and 37.5% for SOR and EM3D. Higher resource utilization is achieved via the new task migration scheme too. In comparison with other loop scheduling schemes, the ABS achieves the best performance among all scheduling schemes in a metacomputing environment because of the reduction of synchronization overhead and the great improvement of waiting time resulting from load imbalance.

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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.

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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.

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Jaybhaye, Sangita M., and Vahida Z. Attar. "Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing." Vietnam Journal of Computer Science 07, no. 02 (February 26, 2020): 179–96. http://dx.doi.org/10.1142/s2196888820500104.

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Cloud services are used to achieve diverse computing needs such as cost, security, scalability, and availability. Acceleration evolution in the distributed and cloud domains is common for large and dynamic workflows deployment. Resources and task mapping depend on the user’s objectives such as reduction in cost or execution completion within the stipulated time in consideration with certain quality of services. Multiple virtual machine instances can be launched by defining different configurations such as operating system, server types, and applications. Though workflow scheduling is an NP-Hard problem, variety of decision-making techniques are available for optimum resource allocation. In this research paper, different algorithms are studied and compared with evolutionary approaches. Workflow scheduling using genetic algorithm is implemented and discussed. This paper aims to design a decision-making technique to optimize resources of cloud. It is an adaptive scheduling to maximize profit by reducing execution time. The approach implemented is useful to cloud service providers to maximize profit and resource efficiency in their services.

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Singh Anjanaa, Parwat, N. Naga Maruthia, Sagar Gujjunooria, and Madhu Orugantib. "Runtime Parallelization of Static and Dynamic Irregular Array of Array References." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 150. http://dx.doi.org/10.14419/ijet.v7i4.6.20452.

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The advancement of computer systems such as multi-core and multiprocessor systems resulted in much faster computing than earlier. However, the efficient utilization of these rich computing resources is still an emerging area. For efficient utilization of computing resources, many optimization techniques have been developed, some techniques at compile time and some at runtime. When all the information required for parallel execution is known at compile time, then optimization compilers can reasonably parallelize a sequential program. However, optimization compiler fails when it encounters compile time unknowns in the program. A conventional solution for such problem can be performing parallelization at runtime. In this article, we propose three different solutions to parallelize a loop having an irregularity in the array of array references, with and without dependencies. More specifically, we introduce runtime check based parallelization technique for the static irregular references without dependency, Inspector-Executor based parallelization technique for static irregular references with dependencies, and finally Speculative parallelization technique (BitTLS) for dynamic irregular references with dependencies. For pro ling the runtime information, shared and private data structures are used. To detect the dependencies between footprints and for synchronization of threads at runtime, we use bit level operations. A window based scheduling policy is employed to schedule the iterations to the threads.

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Alsheikhy, Ahmed A. "Dynamic approach to minimize overhead and response time in scheduling periodic real-time tasks." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 4 (April 2021): 75–81. http://dx.doi.org/10.21833/ijaas.2021.04.009.

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In real-time systems, a task or a set of tasks needs to be executed and completed successfully within a predefined time. Those systems require a scheduling technique or a set of scheduling methods to distribute the given task or the set of tasks among different processors or on a processor. In this paper, a new novel scheduling approach to minimize the overhead from context switching between several periodic tasks is presented. This method speeds up a required response time while ensuring that all tasks meet their deadline times and there is no deadline miss occurred. It is a dynamic-priority technique that works either on a uniprocessor or several processors. In particular, it is proposed to be applied on multiprocessor environments since many applications run on several processors. Various examples are presented within this paper to demonstrate its optimality and efficiency. In addition, several comparison experiments with an earlier version of this approach were performed to demonstrate its efficiency and effectiveness too. Those experiments showed that this novel approach sped up the execution time from 15% to nearly around 46%. In addition, it proved that it reduced the number of a context switch between tasks from 12% to around 50% as shown from simulation tests. Furthermore, this approach delivered all tasks/jobs successfully and ensured there was no deadline miss happened.

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Mugeraya, Sanath, and Kailas Devadkar. "Dynamic Task Scheduling and Resource Allocation for Microservices in Cloud." Journal of Physics: Conference Series 2325, no. 1 (August 1, 2022): 012052. http://dx.doi.org/10.1088/1742-6596/2325/1/012052.

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Abstract With the emergence of new companies and the expansion of the information technology sector, the need for Cloud Computing becomes apparent. Currently, the enterprises are rapidly transitioning from monolithic architecture to microservice-driven architecture. This research study has discovered that all task scheduling algorithms were designed for a specific (set) number of virtual machines, which resulted in the bottleneck problem, where multiple tasks were assigned to the microservice scheduler and the execution time of processing the tasks was significantly increased. Therefore, to address this issue, a novel model was designed based on the number of tasks and accordingly the number of virtual machines were dynamically generated to send the tasks to the microservice scheduler one by one, and the difficulties with execution time were also addressed. The study also discovered that due to the multiple workloads on the microservices, resource allocation becomes extremely difficult. To address this issue, containerized microservices were discovered. Here, the microservices would be distributed in containers. To implement the dynamic work scheduling technique, a cloud microservice translator would be developed, where a user may upload a text file and quickly get it dynamically translated. The main aim of this research work is to improve the task scheduling and resource allocation in microservices.

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Chen, Lin, Deshi Ye, and Guochuan Zhang. "Approximating the Optimal Algorithm for Online Scheduling Problems via Dynamic Programming." Asia-Pacific Journal of Operational Research 32, no. 01 (February 2015): 1540011. http://dx.doi.org/10.1142/s0217595915400114.

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Very recently Günther et al. [E. Günther, O. Maurer, N. Megow and A. Wiese (2013). A new approach to online scheduling: Approximating the optimal competitive ratio. In Proc. 24th Annual ACM-SIAM Symp. Discrete Algorithms (SODA).] initiate a new systematic way of studying online problems by introducing the competitive ratio approximation scheme (simplified as competitive schemes in this paper), which is a class of algorithms {Aϵ|ϵ > 0} with a competitive ratio at most ρ*(1 + ϵ), where ρ* is the best possible competitive ratio over all online algorithms. Along this line, Günther et al. [E. Günther, O. Maurer, N. Megow and A. Wiese (2013). A new approach to online scheduling: Approximating the optimal competitive ratio. In Proc. 24th Annual ACM-SIAM Symp. Discrete Algorithms (SODA).] provide competitive schemes for several online over time scheduling problems like Qm|rj, (pmtn)|∑wjcj, while the running times are polynomial if the number of machines is a constant. In this paper, we consider the classical online scheduling problems, where jobs arrive in a list. We present competitive schemes for Rm‖C max and [Formula: see text], where the running times are polynomial if the number of machines is a constant. Specifically, we are able to derive a competitive scheme for P‖C max which runs in polynomial time even if the number of machines is an input. Our method is novel and more efficient than that of Günther et al. [E. Günther, O. Maurer, N. Megow and A. Wiese (2013). A new approach to online scheduling: Approximating the optimal competitive ratio. In Proc. 24th Annual ACM-SIAM Symp. Discrete Algorithms (SODA).] Indeed, by utilizing the standard rounding technique for the off-line scheduling problems, we reformulate the online scheduling problem into a game on an infinite graph, through which we arrive at the following key point: Assuming that the best competitive ratio is ρ*, for any online algorithm there exists a list of a polynomial number of jobs showing that its competitive ratio is at least ρ* - O(ϵ). Interestingly such a result is achieved via a dynamic programming algorithm. Our framework is also applicable to other online problems.

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S. Raj, Dr Jennifer. "Machine Learning Implementation in Cognitive Radio Networks with Game-Theory Technique." IRO Journal on Sustainable Wireless Systems 2, no. 2 (May 18, 2020): 68–75. http://dx.doi.org/10.36548/jsws.2020.2.002.

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Significant enhancement of spectrum utilization can be performed by means of Cognitive Radio technology. A game theory based Cognitive Radio Network with Dynamic Spectrum Allocation model is proposed in this paper. M|M|1 queuing model is implemented along with Preemptive Resume Priority for accommodation of all the cases. An Incremental Weights-Decremental Ratios (IW-DR) algorithm based on priority-based scheduling is used for supplementing this theory. Regression models are used for restructuring and improving the efficiency of the system.

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Wu, L. H., Xin Chen, X. D. Chen, and Qing Xin Chen. "The Research on Proactive-Reactive Scheduling Framework Based on Real-Time Manufacturing Information." Materials Science Forum 626-627 (August 2009): 789–94. http://dx.doi.org/10.4028/www.scientific.net/msf.626-627.789.

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Because of the dynamic and uncertain conditions of a real manufacturing system, many unforeseen events (e.g., machine breakdown, job revision, urgent jobs arrival, etc.) may lead to numerous schedule disruptions during schedule execution. In this paper, we present a new mixed technique that combines a proactive approach with a reactive approach to deal with scheduling problem under uncertainty. In the proactive phase, we build a robust baseline schedule that minimizes the schedules distance defined as the sum of the absolute deviations between the baseline and expected schedules. The robust baseline schedule contains some built-in flexibility in order to minimize the need of complex search procedures for the reactive scheduling approach. Based on the real-time manufacturing information getting online from the manufacturing execution system (MES), we then develop a reactive scheduling procedure to quickly revise the disrupted schedule when unexpected events occurring. The proposed framework integrated proactive and reactive approach is applied to molds production management system. The experimental results show that this mixed technique is more efficient than another technique when the duration of operation is uncertain.

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Han, Sangchul. "A Simple and Aggressive Dynamic Voltage/Frequency Scaling Technique for EDZL Scheduling on Multiprocessors." International Journal of Emerging Trends in Engineering Research 8, no. 8 (August 25, 2020): 4201–5. http://dx.doi.org/10.30534/ijeter/2020/27882020.

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Ahmad, Z., A. I. Jehangiri, M. Iftikhar, A. I. Umer, and I. Afzal. "Data-Oriented scheduling with Dynamic-Clustering fault-tolerant technique for Scientific Workflows in Clouds." Proceedings of the Institute for System Programming of the RAS 31, no. 2 (2019): 121–36. http://dx.doi.org/10.15514/ispras-2018-31(2)-9.

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Ahmad, Z., A. I. Jehangiri, M. Iftikhar, A. I. Umer, and I. Afzal. "Data-Oriented scheduling with Dynamic-Clustering fault-tolerant technique for Scientific Workflows in Clouds." Proceedings of the Institute for System Programming of the RAS 31, no. 2 (2019): 121–36. http://dx.doi.org/10.15514/ispras-2019-31(2)-9.

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Ahmad, Z., A. I. Jehangiri, M. Iftikhar, A. I. Umer, and I. Afzal. "Data-Oriented Scheduling with Dynamic-Clustering Fault-Tolerant Technique for Scientific Workflows in Clouds." Programming and Computer Software 45, no. 8 (December 2019): 506–16. http://dx.doi.org/10.1134/s0361768819080097.

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Giglio, Davide, and Riccardo Minciardi. "A dynamic programming-based technique for multi-class job scheduling on a single machine." IFAC Proceedings Volumes 37, no. 18 (September 2004): 201–6. http://dx.doi.org/10.1016/s1474-6670(17)30746-2.

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Wang, Shen, Jun Wu, and Yutao Zhang. "Consumer preference–enabled intelligent energy management for smart cities using game theoretic social tie." International Journal of Distributed Sensor Networks 14, no. 4 (April 2018): 155014771877323. http://dx.doi.org/10.1177/1550147718773235.

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In smart cities, balanced energy usage is a classic scheduling target in decision-making of power systems. To handle multiple energy consumers, energy management is usually built based on game theory. Despite their effectiveness, they do not consider consumer preferences, which are however important in developing salient scheduling frameworks. For the first time, this work explores consumer preference–based social networking in computing-optimized schedules to facilitate the incorporation in energy management. We propose the consumer preference driven intelligent energy management technique for smart cities using game theoretic social tie. In our technique, social communities are constructed based on the preference of electricity usage. To support dynamic decisions in the consumer preference–induced game, community pricing strategy is adjusted during each time period through leveraging cooperative game theory. The simulation results demonstrate the effectiveness and efficiency of the proposed intelligent energy management technique.

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Jin, Liangliang, Chaoyong Zhang, Xinyu Shao, and Xudong Yang. "A study on the impact of periodic and event-driven rescheduling on a manufacturing system: An integrated process planning and scheduling case." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, no. 3 (August 6, 2016): 490–504. http://dx.doi.org/10.1177/0954405416629585.

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The integration of process planning and scheduling is a very important problem because it proposes a new idea for improving the performance of a manufacturing system. At present, most existing studies on this problem are static, which assumes that all the jobs to be processed are available in the beginning. However, the practical processing situation is dynamic, such as new job arrivals. Since dynamic production situations are different with static cases, it is important to study the characteristics of actual production situations. In this article, the characteristics of dynamic integrated process planning and scheduling problem with job arrivals are studied. A novel mixed integer linear programming model is established to accommodate new job arrivals, and three criteria (makespan, stability, and tardiness) are considered. New periodic and event-driven rescheduling strategies are presented. In the proposed strategy, newly added jobs together with uncompleted jobs will be rescheduled by non-dominated sorting genetic algorithm-II to obtain the optimal Pareto front when the rescheduling procedure is triggered. The entropy-based weight assigning method together with the Technique for Order of Preference by Similarity to Ideal Solution method is adopted to determine an appropriate schedule among the resultant non-dominated solutions. A set of well-known benchmark instances is employed to investigate the characteristics of the dynamic integrated process planning and scheduling problem with random job arrivals. Experimental results show that the length of a scheduling interval, the number of newly added jobs, and the shop utilization have an important influence on the efficiency of a manufacturing system.

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Joshi, Aparna Shashikant, and Shyamala Devi Munisamy. "In-depth analysis of dynamic degree load balancing technique in public cloud for heterogeneous cloudlets." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (August 1, 2022): 1119. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1119-1126.

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Load balancing is one of the challenges of the distributed computing worldview. With the enormous development in clients and their interest for different administrations on the distributed computing stage, compelling or productive asset usage in the cloud climate has turned into an urgent concern. Load balancing is critical to keeping cloud computing running smoothly. This study examines the research using four scheduling algorithms: dynamic degree balance CPU based (D2B_CPU), dynamic degree balanced membership based (D2B_Membership), dynamic degree memory balanced allocation (D2MBA) and hybrid dynamic degree balance (HDDB) algorithm. Central processing unit (CPU) utilisation, bandwidth utilisation, and memory utilisation are used as performance measures to verify the performance of these algorithms. The CloudSim simulation programme was used to simulate these algorithms. The primary goal of this work is to aid in the future construction of new algorithms by researching the behaviour of various existing algorithms.

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Sangani, Sahil. "Scheduling Algorithms in Map Reduce." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 8 (August 10, 2019): 01–06. http://dx.doi.org/10.17762/ijritcc.v7i8.5342.

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Data generated in the past few years cannot be efficiently manipulated with the traditional way of storing techniques as it is a large-scale dataset, and it can be structured, semi-structured, or unstructured. To deal with this kind of enormous dataset Hadoop framework is used, which supports the processing of large dataset in a distributed computing environment. Hadoop uses a technique named as MapReduce for processing and generating a large dataset with a parallel distributed algorithm on a cluster. It automatically handles failures and data loss due to its fault-tolerance property. The scheduler is a pluggable component of the MapReduce framework. Hadoop MapReduce framework uses various scheduler as per the requirements of the task. FIFO (First In First Out) is a default algorithm used by Hadoop, in which the jobs are executed in the order of their arrival. This paper will discuss myriad of schedulers such as FIFO, Capacity Scheduler, LATE Scheduler, Fair Scheduler, Delay Scheduler, Deadline Constraint Scheduler, and Resource Aware Scheduler. Besides these schedulers, we also conducted study of comparison of schedulers like Round Robin, Weighted Round Robin, Self-adaptive Reduce Scheduling (SARS), Self-adaptive MapReduce Scheduling (SAMR), Dynamic Priority Scheduling, Learning Scheduling, Classification & Optimization-based Scheduler (COSHH), Network-Aware, Match-matching, and Energy-Aware Scheduler. Hopefully, this study will enhance the understanding of the specific schedulers and stimulate other developers and consumers to make accurate decisions for their specific research interests.

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Journal articles on the topic 'Dynamic Scheduling Technique'

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