Journal articles: 'Scheduling tasks'

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Kaur, Ramandeep, and Navpreet Kaur. "A Study for VM Placement Schemes in Cloud." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 208. http://dx.doi.org/10.23956/ijarcsse.v7i8.52.

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The cloud computing can be essentially expressed as aconveyance of computing condition where distinctive assets are conveyed as a support of the client or different occupants over the web. The task scheduling basically concentrates on improving the productive use of assets and henceforth decrease in task fruition time. Task scheduling is utilized to allot certain tasks to specific assets at a specific time occurrence. A wide range of systems has been exhibited to take care of the issues of scheduling of various tasks. Task scheduling enhances the productive use of asset and yields less reaction time with the goal that the execution of submitted tasks happens inside a conceivable least time. This paper talks about the investigation of need, length and due date based task scheduling calculations utilized as a part of cloud computing.

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Ju, Jiubin, Yong Wang, and Yu Yin. "Scheduling PVM tasks." Journal of Computer Science and Technology 12, no. 2 (March 1997): 167–76. http://dx.doi.org/10.1007/bf02951336.

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Korst, Jan, Emile Aarts, and Jan Karel Lenstra. "Scheduling Periodic Tasks." INFORMS Journal on Computing 8, no. 4 (November 1996): 428–35. http://dx.doi.org/10.1287/ijoc.8.4.428.

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Ju, Jiubin, and Yong Wang. "Scheduling PVM tasks." ACM SIGOPS Operating Systems Review 30, no. 3 (July 1996): 22–31. http://dx.doi.org/10.1145/230908.230914.

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Turek, John, Joel L. Wolf, Krishna R. Pattipati, and Philip S. Yu. "Scheduling parallelizable tasks." ACM SIGMETRICS Performance Evaluation Review 20, no. 1 (June 1992): 225–36. http://dx.doi.org/10.1145/149439.133111.

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Barg-Walkow, Laura H., and Wendy A. Rogers. "Modeling Task Scheduling in Complex Healthcare Environments: Identifying Relevant Factors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 772–75. http://dx.doi.org/10.1177/1541931213601677.

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Multiple task coordination involves scheduling tasks, completing tasks, and integrating tasks into a workflow. Task scheduling can influence outcomes of safety, satisfaction, and efficiency when completing tasks. This is especially important in complex life-critical environments such as healthcare, which incurs many situations where there are multiple tasks and limited resources for addressing all tasks. One approach for understanding tasks coordination is the Strategic Task Overload Management (STOM) model, which is a model for task scheduling behavior. In this theoretical paper, we discuss how this model can be extended to a complex healthcare environment. There are additional considerations (e.g., time) which must be considered when applying this model to healthcare. Ultimately, understanding how emergency physicians make multiple task scheduling decisions will advance theories and models, such as STOM, which can then in turn be implemented to improve scheduling behaviors in complex healthcare environments.

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Lei, Zhenyang, Xiangdong Lei, and Jun Long. "Memory-Aware Scheduling Parallel Real-Time Tasks for Multicore Systems." International Journal of Software Engineering and Knowledge Engineering 31, no. 04 (April 2021): 613–34. http://dx.doi.org/10.1142/s0218194021400106.

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Shared resources on the multicore chip, such as main memory, are increasingly becoming a point of contention. Traditional real-time task scheduling policies focus on solely on the CPU, and do not take in account memory access and cache effects. In this paper, we propose parallel real-time tasks scheduling (PRTTS) policy on multicore platforms. Each set of tasks is represented as a directed acyclic graph (DAG). The priorities of tasks are assigned according to task periods Rate Monotonic (RM). Each task is composed of three phases. The first phase is read memory stage, the second phase is execution phase and the third phase is write memory phase. The tasks use locks and critical sections to protect data access. The global scheduler maintains the task pool in which tasks are ready to be executed which can run on any core. PRTTS scheduling policy consists of two levels: the first level scheduling schedules ready real-time tasks in the task pool to cores, and the second level scheduling schedules real-time tasks on cores. Tasks can preempt the core on running tasks of low priority. The priorities of tasks which want to access memory are dynamically increased above all tasks that do not access memory. When the data accessed by a task is in the cache, the priority of the task is raised to the highest priority, and the task is scheduled immediately to preempt the core on running the task not accessing memory. After accessing memory, the priority of these tasks is restored to the original priority and these tasks are pended, the preempted task continues to run on the core. This paper analyzes the schedulability of PRTTS scheduling policy. We derive an upper-bound on the worst-case response-time for parallel real-time tasks. A series of extensive simulation experiments have been performed to evaluate the performance of proposed PRTTS scheduling policy. The results of simulation experiment show that PRTTS scheduling policy offers better performance in terms of core utilization and schedulability rate of tasks.

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Nayak, Suvendu Chandan, and Chitaranjan Tripathy. "An Improved Task Scheduling Mechanism Using Multi-Criteria Decision Making in Cloud Computing." International Journal of Information Technology and Web Engineering 14, no. 2 (April 2019): 92–117. http://dx.doi.org/10.4018/ijitwe.2019040106.

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In this work, the authors propose Multi-criteria Decision-making to schedule deadline based tasks in cloud computing. The existing backfilling task scheduling algorithm could not handle similar tasks for scheduling. In backfilling algorithm, tasks are backfilled to provide ideal resources to schedule other deadline sensitive tasks. However, the task to be backfilled is selected on first come, first serve (FCFS) basis from scheduling queue. The scheduling performances require to be improved when, there are similar tasks. In this proposed work, the authors propose to implement MCDM technique, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to improve the performance of the backfilling algorithm through scheduling deadline sensitive tasks in cloud computing. It resolves the conflicts among the similar tasks that is used as a decision support system. The work is simulated with synthetic data sets based on slack values of the tasks. The performance results affirm the task completion and reduction in task rejection compared to the existing backfilling algorithm.

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Fu, Weina, Shuai Liu, and Gautam Srivastava. "Optimization of Big Data Scheduling in Social Networks." Entropy 21, no. 9 (September 17, 2019): 902. http://dx.doi.org/10.3390/e21090902.

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In social network big data scheduling, it is easy for target data to conflict in the same data node. Of the different kinds of entropy measures, this paper focuses on the optimization of target entropy. Therefore, this paper presents an optimized method for the scheduling of big data in social networks and also takes into account each task’s amount of data communication during target data transmission to construct a big data scheduling model. Firstly, the task scheduling model is constructed to solve the problem of conflicting target data in the same data node. Next, the necessary conditions for the scheduling of tasks are analyzed. Then, the a periodic task distribution function is calculated. Finally, tasks are scheduled based on the minimum product of the corresponding resource level and the minimum execution time of each task is calculated. Experimental results show that our optimized scheduling model quickly optimizes the scheduling of social network data and solves the problem of strong data collision.

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K. Jairam Naik, Dr, and B. Veda Vidhya. "A Group Tasks Scheduling Algorithm for Cloud Computing Networks based on QoS." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 53. http://dx.doi.org/10.14419/ijet.v7i4.6.20236.

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This article introduces a Novel Group-Tasks Scheduling Algorithm (NGTSA) which is used for allocating the tasks in the network of cloud computing by means of pertaining quality of services to gratify user’s desires. The tasks are categorized into five classes by the anticipated algorithm. Every one group contains the tasks with akin attributes (like, types of the users and tasks, size and latency of the task). Once the tasks are allocated to a precise group, scheduler starts assigning these tasks to accessible services. This assignment of tasks was performed in two steps: In Step-I is to decide which group tasks is to be scheduled foremost. Such decision will be based on the attributes of the tasks of each group. Hence, the groups which have higher task’s attribute values are scheduled foremost. Step-II is for taking internal decision that is which task from the selected group is scheduled foremost. This decision will be based on time needed for task’s execution. Therefore, the task which has the lowest time for execution will schedule foremost.

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Shi, Lei, Jing Xu, Lunfei Wang, Jie Chen, Zhifeng Jin, Tao Ouyang, Juan Xu, and Yuqi Fan. "Multijob Associated Task Scheduling for Cloud Computing Based on Task Duplication and Insertion." Wireless Communications and Mobile Computing 2021 (April 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/6631752.

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With the emergence and development of various computer technologies, many jobs processed in cloud computing systems consist of multiple associated tasks which follow the constraint of execution order. The task of each job can be assigned to different nodes for execution, and the relevant data are transmitted between nodes to complete the job processing. The computing or communication capabilities of each node may be different due to processor heterogeneity, and hence, a task scheduling algorithm is of great significance for job processing performance. An efficient task scheduling algorithm can make full use of resources and improve the performance of job processing. The performance of existing research on associated task scheduling for multiple jobs needs to be improved. Therefore, this paper studies the problem of multijob associated task scheduling with the goal of minimizing the jobs’ makespan. This paper proposes a task Duplication and Insertion algorithm based on List Scheduling (DILS) which incorporates dynamic finish time prediction, task replication, and task insertion. The algorithm dynamically schedules tasks by predicting the completion time of tasks according to the scheduling of previously scheduled tasks, replicates tasks on different nodes, reduces transmission time, and inserts tasks into idle time slots to speed up task execution. Experimental results demonstrate that our algorithm can effectively reduce the jobs’ makespan.

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Yadav, Pradeep Kumar, M. P. Singh, and Harendra Kumar. "Scheduling Algorithm: Tasks Scheduling Algorithm for Multiple Processors with Dynamic Reassignment." Journal of Computer Systems, Networks, and Communications 2008 (2008): 1–9. http://dx.doi.org/10.1155/2008/578180.

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Distributed computing systems [DCSs] offer the potential for improved performance and resource sharing. To make the best use of the computational power available, it is essential to assign the tasks dynamically to that processor whose characteristics are most appropriate for the execution of the tasks in distributed processing system. We have developed a mathematical model for allocating “M” tasks of distributed program to “N” multiple processors (M>N) that minimizes the total cost of the program. Relocating the tasks from one processor to another at certain points during the course of execution of the program that contributes to the total cost of the running program has been taken into account. Phasewise execution cost [EC], intertask communication cost [ITCT], residence cost [RC] of each task on different processors, and relocation cost [REC] for each task have been considered while preparing a dynamic tasks allocation model. The present model is suitable for arbitrary number of phases and processors with random program structure.

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Wojciechowicz, Wojciech, and Michaël Gabay. "Scheduling High Multiplicity Coupled Tasks." Foundations of Computing and Decision Sciences 45, no. 1 (March 1, 2020): 47–61. http://dx.doi.org/10.2478/fcds-2020-0004.

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AbstractThe coupled tasks scheduling problem is class of scheduling problems, where each task consists of two operations and a separation gap between them. The high-multiplicity is a compact encoding, where identical tasks are grouped together, and the group is specified instead of each individual task. Consequently the encoding of a problem instance is decreased significantly. In this article we derive a lower bound for the problem variant as well as propose an asymptotically optimal algorithm. The theoretical results are complemented with computational experiment, where a new algorithm is compared with three other algorithms implemented.

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Cheng, Nian Sheng. "Intelligent Job Shop Scheduling in Auto Blanking Plant." Advanced Materials Research 476-478 (February 2012): 2023–27. http://dx.doi.org/10.4028/www.scientific.net/amr.476-478.2023.

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Job shop scheduling belongs to the large class of NP-complete (nondeterministic polynomial time complete) problems and the complexity of job shop scheduling characterizes many real-life situations. In auto blanking plant, intelligent scheduling system is needed to deal with the automation of production. The core structure of job shop scheduling system is designed. The scheduling rules such as mission-critical priority rule, first come first serve rule, the highest task priority rule and the urgent tasks priority rule are built with the definitions of express tasks, urgent tasks, middle urgent tasks and normal tasks according to the time features of tasks. The heuristic optimization algorithm is designed to follow the above rules. The intelligent logistics management system is built to optimize job shop scheduling in auto blanking plant.

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Patel, Dinkan, and Anjuman Ranavadiya. "REVIEW OF TASK SCHEDULING METHODS FOR REAL TIME TASKS IN CLOUD ENVIRONMENT." International Journal of Engineering Technologies and Management Research 5, no. 1 (February 7, 2020): 85–89. http://dx.doi.org/10.29121/ijetmr.v5.i1.2018.50.

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Cloud Computing is a type of Internet model that enables convenient, on-demand resources that can be used rapidly and with minimum effort. Cloud Computing can be IaaS, PaaS or SaaS. Scheduling of these tasks is important so that resources can be utilized efficiently with minimum time which in turn gives better performance. Real time tasks require dynamic scheduling as tasks cannot be known in advance as in static scheduling approach. There are different task scheduling algorithms that can be utilized to increase the performance in real time and performing these on virtual machines can prove to be useful. Here a review of various task scheduling algorithms is done which can be used to perform the task and allocate resources so that performance can be increased.

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Ecker, Klaus H. "Scheduling of resource tasks." European Journal of Operational Research 115, no. 2 (June 1999): 314–27. http://dx.doi.org/10.1016/s0377-2217(98)00226-4.

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Amoura, Bampis, Kenyon, and Manoussakis. "Scheduling Independent Multiprocessor Tasks." Algorithmica 32, no. 2 (February 2002): 247–61. http://dx.doi.org/10.1007/s00453-001-0076-9.

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Mubeen, Aroosa, Muhammad Ibrahim, Nargis Bibi, Mohammad Baz, Habib Hamam, and Omar Cheikhrouhou. "Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing." Processes 9, no. 9 (August 26, 2021): 1514. http://dx.doi.org/10.3390/pr9091514.

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According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches.

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Xiong, Feng, Yi Ping Yuan, Yu Ying Wang, and Guang Wen Wang. "Task Scheduling in Multi-Process with Resource Constraints under MG Workflow." Advanced Materials Research 33-37 (March 2008): 1425–30. http://dx.doi.org/10.4028/www.scientific.net/amr.33-37.1425.

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In manufacturing Grid workflow, multiple tasks share a common and limited resource pool. In order to solve task scheduling in multi-process with resource constraints under MG workflow, the Task-Resource Constrained model is set up to descript the assignment relation between task and resource. The framework of the task scheduling and the scheduling policies are also presented that can readjust the tasks according to the priority rules and the time parameters in the process. Then we present a heuristic scheduling algorithm that includes multiple policies. The heuristic scheduling algorithm will update the critical path of DAG (Direct Acyclic Graph) and the beginning time of post-tasks. MG Workflow engine can dynamically schedule the resources according the task requirement. An example is given to illustrate the method at last.

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Zhang, Guo Quan, Guo Qing Shi, Hao Guang Zhao, and Yong Hong Chen. "A Parallel Test Task Scheduling of Integrated Avionics System Based on the Ant Colony Algorithm." Applied Mechanics and Materials 713-715 (January 2015): 2069–72. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2069.

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Parallel testing is the key to achieving parallel test task scheduling, and its core is allocating resources fairly and reasonably to the test tasks, then rearrange the execution order of the test tasks in meeting the priority relationship between the resource constraints and the conditions of the test tasks, making the whole test mission of this system can be completed in the shortest possible time and improving the test efficiency. The basic ant colony algorithm has been improved in this paper to fit the parallel test task scheduling and to obtain the task scheduling sequence that complete all testing tasks in shortest test time.

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Dai, Yanyan, and Xiangli Zhang. "A Synthesized Heuristic Task Scheduling Algorithm." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/465702.

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Aiming at the static task scheduling problems in heterogeneous environment, a heuristic task scheduling algorithm named HCPPEFT is proposed. In task prioritizing phase, there are three levels of priority in the algorithm to choose task. First, the critical tasks have the highest priority, secondly the tasks with longer path to exit task will be selected, and then algorithm will choose tasks with less predecessors to schedule. In resource selection phase, the algorithm is selected task duplication to reduce the interresource communication cost, besides forecasting the impact of an assignment for all children of the current task permits better decisions to be made in selecting resources. The algorithm proposed is compared with STDH, PEFT, and HEFT algorithms through randomly generated graphs and sets of task graphs. The experimental results show that the new algorithm can achieve better scheduling performance.

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Gong, Fanghai. "Workflow Scheduling Based on Mobile Cloud Computing Machine Learning." Wireless Communications and Mobile Computing 2021 (July 5, 2021): 1–13. http://dx.doi.org/10.1155/2021/9923326.

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In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

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Cheng, Yuxia, Zhiwei Wu, Kui Liu, Qing Wu, and Yu Wang. "Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method." Sustainability 11, no. 7 (March 27, 2019): 1826. http://dx.doi.org/10.3390/su11071826.

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Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exists a large optimization space to be explored. In this paper, we propose a trust-aware adaptive DAG tasks scheduling algorithm using the reinforcement learning and Monte Carlo Tree Search (MCTS) methods. The scheduling problem is defined using the reinforcement learning model. Efficient scheduling state space, action space and reward function are designed to train the policy gradient-based REINFORCE agent. The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities. Leveraging the algorithm’s capability of exploring long term reward, the proposed algorithm could achieve good scheduling policies while guaranteeing trusted tasks scheduled within trusted entities. Experimental results showed the effectiveness of the proposed algorithm compared with the classic HEFT/CPOP algorithms.

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Wang, Hui, Cheng Xu, Li Ning Zeng, and Yan Liu. "A Mixed Scheduling Algorithm about Hard Periodic and Soft Aperiodic Real-Time Tasks on Heterogeneous Multiprocessor." Advanced Materials Research 950 (June 2014): 209–13. http://dx.doi.org/10.4028/www.scientific.net/amr.950.209.

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This paper studies the problem about scheduling composition of periodic real-time tasks and aperiodic soft real-time tasks in heterogeneous multiprocessor environment. It analyzes the response time of each task in periodic real-time task set and the influence factor about the response time of aperiodic soft real-time task. We use a new mixed scheduling algorithm--UEDF and Task-Centric with Slack Defragmentation algorithm (TCSD) to schedule hybrid task set which consist of the periodic real-time tasks and aperiodic soft real-time tasks. It can improve the timeliness of aperiodic soft real-time tasks response, so the ratio of aperiodic tasks to meet soft deadline will increase.

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Teng, Shaohua, Wei Zhang, Haibin Zhu, Xiufen Fu, Jiangyi Su, and Baoliang Cui. "A Least-Laxity-First Scheduling Algorithm of Variable Time Slice for Periodic Tasks." International Journal of Software Science and Computational Intelligence 2, no. 2 (April 2010): 86–104. http://dx.doi.org/10.4018/jssci.2010040105.

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The LLF (Least Laxity First) scheduling algorithm assigns a priority to a task according to its executing urgency. The smaller the laxity value of a task is, the sooner it needs to be executed. When two or more tasks have same or approximate laxity values, LLF scheduling algorithm leads to frequent switches among tasks, causes extra overhead in a system, and therefore, restricts its application. The least switch and laxity first scheduling algorithm is proposed in this paper by searching out an appropriate common divisor in order to improve the LLF algorithm for periodic tasks.

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Chaudhuri, Pranay, and Jeffrey Elcock. "Process Scheduling in Heterogeneous Multiprocessor Systems Using Task Duplication." International Journal of Business Data Communications and Networking 6, no. 1 (January 2010): 58–69. http://dx.doi.org/10.4018/jbdcn.2010010104.

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Scheduling tasks in heterogeneous parallel and distributed computing environments continues to be a challenging problem. In this paper, the authors investigate the Heterogeneous Earliest Finish Time (HEFT) algorithm, along with alternative scheduling policies for task prioritising phases and the Critical Path on a Processor (CPOP) for scheduling tasks on a heterogeneous multiprocessor system. The authors show that by combining the HEFT algorithm selection policy with the task duplication strategy, it is possible to further reduce the schedule length produced by both HEFT and CPOP. The process scheduling algorithm presented in this paper compares favourably with other algorithms that use a similar strategy. The proposed algorithm has a time complexity of ?(¦V¦2(p + d)), whererepresents the number of tasks, p represents the number of processors and d the maximum in-degree of tasks.

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Potluri, Sirisha, and Katta Subba Rao. "Optimization model for QoS based task scheduling in cloud computing environment." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 1081. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp1081-1088.

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Shortest job first task scheduling algorithm allocates task based on the length of the task, i.e the task that will have small execution time will be scheduled first and the longer tasks will be executed later based on system availability. Min- Min algorithm will schedule short tasks parallel and long tasks will follow them. Short tasks will be executed until the system is free to schedule and execute longer tasks. Task Particle optimization model can be used for allocating the tasks in the network of cloud computing network by applying Quality of Service (QoS) to satisfy user’s needs. The tasks are categorized into different groups. Every one group contains the tasks with attributes (types of users and tasks, size and latency of the task). Once the task is allocated to a particular group, scheduler starts assigning these tasks to accessible services. The proposed optimization model includes Resource and load balancing Optimization, Non-linear objective function, Resource allocation model, Queuing Cost Model, Cloud cost estimation model and Task Particle optimization model for task scheduling in cloud computing environement. The main objectives identified are as follows. To propose an efficient task scheduling algorithm which maps the tasks to resources by using a dynamic load based distributed queue for dependent tasks so as to reduce cost, execution and tardiness time and to improve resource utilization and fault tolerance. To develop a multi-objective optimization based VM consolidation technique by considering the precedence of tasks, load balancing and fault tolerance and to aim for efficient resource allocation and performance of data center operations. To achieve a better migration performance model to efficiently model the requirements of memory, networking and task scheduling. To propose a QoS based resource allocation model using fitness function to optimize execution cost, execution time, energy consumption and task rejection ratio and to increase the throughput. QoS parameters such as reliability, availability, degree of imbalance, performance and SLA violation and response time for cloud services can be used to deliver better cloud services.

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Wu, Dian Hong. "Task Optimization Scheduling Algorithm in Embedded System Based on Internet of Things." Applied Mechanics and Materials 513-517 (February 2014): 2398–402. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2398.

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Embedded system has been widely used in the network, server, etc., and it has a good application prospect with the development of Internet of things. In the embedded heterogeneous computing system, task scheduling is the key to deciding the system performance. For multi-task scheduling, the current scheduling algorithm is mostly based on task duplication, without a full consideration of the correlation between the predecessor task and its subsequent tasks. Based on modeling the multi-frame task scheduling problem in the heterogeneous embedded system, this paper analyzes the availability of tasks through the design of genetic algorithm, so as to verify the algorithm's feasibility, which is of important guiding significance for the multi-task scheduling in the embedded heterogeneous computing system.

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Saydam, Berkay, Cem Orhan, Niyazi Toker, and Mansur Turasan. "Optimisation of scheduled tasks by real-time measurement and correlation." New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 12 (April 30, 2020): 36–43. http://dx.doi.org/10.18844/gjpaas.v0i12.4984.

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For functional safety, the scheduler should perform all time critical tasks in an order and within predefined deadlines in embedded systems. Scheduling of time critical tasks is determined by estimating their worst-case execution times. To justify the model design of task scheduling, it is required to simulate and visualise the task execution and scheduling maps. This helps to figure out possible problems before deploying the schedule model to real hardware. The simulation tools which are used by companies in an industry perform scheduling simulation and visualisation of all time critical tasks to design and verify the model. All of them lack the capability of comparing simulation results versus real results to achieve the optimised scheduling design. This sometimes leads the overestimated worst-case execution times and increased system cost. The aim of our study is to decrease the system cost with optimisation of scheduled tasks via using the static analysing method. Keywords: Schedule visualisation, scheduler optimisation, functional safety, real-time systems, scheduler.

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Wang, Peng, Jun Feng Zhang, and Xue Chen. "Towards Efficient Task Scheduling on 2D Reconfigurable FPGAs." Advanced Materials Research 850-851 (December 2013): 961–64. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.961.

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With the rapid development of dynamical partial reconfiguration technology, FPGA (Field Programmable Gate Array) is able to allow independent tasks to be executed concurrently without interfering with each other, which increases its flexibility and performance, but on the other hand leads to multi-task scheduling problem. The task scheduling in this paper is the problem of task sequencing, which adjusts the entering sequence of the incoming tasks with the consideration of the attributes of tasks and the utilization of FPGA. A conditional preemption based task sequencing method is proposed that allows tasks that arrive later to be executed in advance as long as the previous tasks can still be guaranteed to enter the FPGA on time. Simulations show that these methods can effectively decrease the waiting ratio of task sets, thus improving the flexibility and utilization of FPGA.

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Oliveira, R. S., and J. S. Fraga. "Scheduling Imprecise Computation Tasks with Intra-Task / Inter-Task Dependence." IFAC Proceedings Volumes 29, no. 5 (November 1996): 63–68. http://dx.doi.org/10.1016/s1474-6670(17)46356-7.

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Dieffenbach, Robert M. "Combinatorial Scheduling." Mathematics Teacher 83, no. 4 (April 1990): 269–73. http://dx.doi.org/10.5951/mt.83.4.0269.

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Small projects like putting up a swing set and big projects like building a house all consist of interrelated tasks. Some of these tasks must be done in sequence, but often several tasks can be going on at once.

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ZHANG, Ran, Zhuan WANG, and Yu-xin AN. "Research on Multi-objective Dynamic Task Scheduling of Cross-aisles Multi-layer Shuttle Truck Storage System Based on Elite Strategy." MATEC Web of Conferences 325 (2020): 05002. http://dx.doi.org/10.1051/matecconf/202032505002.

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In order to improve the operating efficiency of the cross-aisles shuttle truck system and the responsiveness to the disturbance of the return task, this paper first proposes a dynamic task scheduling process for the shuttle truck, using an event trigger mechanism. Each time a warehouse return task is generated, the corresponding scheduling optimization algorithm is triggered according to the current task combination mode to re-optimize the task sequence. Secondly, a task sequencing model is established under three combined task modes: full outbound / full return tasks, task mode with in-transit return tasks under the compound operation mode, task mode without in-transit return tasks under the compound operation mode. A fast non-dominated sorting genetic algorithm scheduling algorithm with elite strategy for compound operation mode is proposed. Finally, MATLAB was used to compile the simulation experiment system. The simulation analysis results show that the method can effectively improve the system’s operating efficiency, which verifies the feasibility and effectiveness of the scheduling method.

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Dhanesh, Lavanya, and P. Murugesan. "A Novel Approach in Scheduling Of the Real- Time Tasks In Heterogeneous Multicore Processor with Fuzzy Logic Technique For Micro-grid Power Management." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 1 (March 1, 2018): 80. http://dx.doi.org/10.11591/ijpeds.v9.i1.pp80-88.

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Scheduling of tasks based on real time requirement is a major issue in the heterogeneous multicore systemsfor micro-grid power management . Heterogeneous multicore processor schedules the serial tasks in the high performance core and parallel tasks are executed on the low performance cores. The aim of this paper is to implement a scheduling algorithm based on fuzzy logic for heterogeneous multicore processor for effective micro-grid application. Real – time tasks generally have different execution time and dead line. The main idea is to use two fuzzy logic based scheduling algorithm, first is to assign priority based on execution time and deadline of the task. Second , the task which has assigned higher priority get allotted for execution in high performance core and remaining tasks which are assigned low priority get allotted in low performance cores. The main objective of this scheduling algorithm is to increase the throughput and to improve CPU utilization there by reducing the overall power consumption of the micro-grid power management systems. Test cases with different task execution time and deadline were generated to evaluate the algorithms using MATLAB software.

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Xu, Yan. "Task Scheduling Algorithm Research in Grid Computing." Applied Mechanics and Materials 380-384 (August 2013): 2841–44. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2841.

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This paper studies effective task scheduling problem in the process of grid computing. Generally, task scheduling in the process of grid computing can be realized in shorter time, which guarantees the efficiency of task scheduling in grid computing. Traditional algorithm can not fully consider the resources load balance in calculating task scheduling in grid computing, resulting in network resources idleness. Finally, it can't reasonably use network resources. In order to avoid the above defects, this paper proposes a task scheduling method in grid computing based on double fitness particle swarm optimization algorithm. In the process of grid computing, channel perception method is applied to forecast the amount of grid computing tasks in the channel so as to provide the basis for task scheduling in grid computing. Realize task scheduling in grid computing by the use of double fitness particle swarm optimization algorithm. Experimental results show that under the condition of larger tasks of grid computing, the performance of task scheduling in grid computing by using the algorithm presented in this paper is superior to the traditional particle swarm optimization algorithm and can get ideal task scheduling result.

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Agarwal, Mohit, and Gur Mauj Saran Srivastava. "A PSO Algorithm Based Task Scheduling in Cloud Computing." International Journal of Applied Metaheuristic Computing 10, no. 4 (October 2019): 1–17. http://dx.doi.org/10.4018/ijamc.2019100101.

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Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.

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Agrawal, Amrit, and Pranay Chaudhuri. "An Algorithm for Task Scheduling in Heterogeneous Distributed Systems Using Task Duplication." International Journal of Grid and High Performance Computing 3, no. 1 (January 2011): 89–97. http://dx.doi.org/10.4018/jghpc.2011010105.

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Task scheduling in heterogeneous parallel and distributed computing environment is a challenging problem. Applications identified by parallel tasks can be represented by directed-acyclic graphs (DAGs). Scheduling refers to the assignment of these parallel tasks on a set of bounded heterogeneous processors connected by high speed networks. Since task assignment is an NP-complete problem, instead of finding an exact solution, scheduling algorithms are developed based on heuristics, with the primary goal of minimizing the overall execution time of the application or schedule length. In this paper, the overall execution time (schedule length) of the tasks is reduced using task duplication on top of the Critical-Path-On-a-Processor (CPOP) algorithm.

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38

Udvanshi, Pankaj. "Scheduling of Real Time Tasks." IOSR Journal of Engineering 03, no. 6 (June 2013): 44–58. http://dx.doi.org/10.9790/3021-03624458.

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Korst, Jan, Emile Aarts, and Jan Karel Lenstra. "Scheduling Periodic Tasks with Slack." INFORMS Journal on Computing 9, no. 4 (November 1997): 351–62. http://dx.doi.org/10.1287/ijoc.9.4.351.

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Tzung-Pei Hong, Cheng-Ming Huang, and Kun-Ming Yu. "LPT scheduling for fuzzy tasks." Fuzzy Sets and Systems 97, no. 3 (August 1998): 277–86. http://dx.doi.org/10.1016/s0165-0114(96)00357-0.

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Epstein, Leah, and Rob Van Stee. "Online scheduling of splittable tasks." ACM Transactions on Algorithms 2, no. 1 (January 2006): 79–94. http://dx.doi.org/10.1145/1125994.1125999.

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Papazachos, Zafeirios C., and Helen D. Karatza. "Scheduling of frequently communicating tasks." International Journal of Communication Systems 25, no. 2 (March 29, 2011): 146–57. http://dx.doi.org/10.1002/dac.1260.

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Lin, J. F., and S. J. Chen. "Scheduling parallel tasks on hypercubes." Electronics Letters 30, no. 11 (1994): 841. http://dx.doi.org/10.1049/el:19940591.

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Lee, Jongwon, Sungyoung Lee, and Hyungill Kim. "Scheduling of hard aperiodic tasks." ACM SIGPLAN Notices 30, no. 11 (November 1995): 7–19. http://dx.doi.org/10.1145/216633.216647.

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Blazewicz, J., M. Drozdowski, D. de Werra, and J. Weglarz. "Deadline scheduling of multiprocessor tasks." Discrete Applied Mathematics 65, no. 1-3 (March 1996): 81–95. http://dx.doi.org/10.1016/0166-218x(95)00020-r.

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Wang, Qingzhou, and Kam Hoi Cheng. "List scheduling of parallel tasks." Information Processing Letters 37, no. 5 (March 1991): 291–97. http://dx.doi.org/10.1016/0020-0190(91)90222-4.

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Drozdowski, Maciej. "Scheduling multiprocessor tasks — An overview." European Journal of Operational Research 94, no. 2 (October 1996): 215–30. http://dx.doi.org/10.1016/0377-2217(96)00123-3.

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Dong, Xinyang, Gang Chen, Mingsong Lv, Weiguang Pang, and Wang Yi. "Flexible Mixed-Criticality Scheduling with Dynamic Slack Management." Journal of Circuits, Systems and Computers 30, no. 10 (August 2021): 2150306. http://dx.doi.org/10.1142/s0218126621503060.

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Mixed-criticality (MC) system has attracted a lot of research attention in the past few years for its resource efficiency. Recent work attempted to provide a new MC model, the so-called Flexible Mixed-Criticality (FMC) task model, to relax the pessimistic assumptions in classic MC scheduling. However, in FMC, the behavior of MC tasks is still analyzed in offline stage. The run-time behavior such as dynamic slack has not yet been studied in FMC scheduling framework. In this paper, we present a utilization-based slack scheduling framework for FMC tasks. In particular, we monitor task execution on run time and collect dynamic slacks generated by task early completion. And these slacks can then be used either by high-criticality tasks to reduce mode-switches, or by low-criticality tasks so that less suspensions are triggered with more execution time, and thus quality of service is improved. We evaluate our approach with extensive simulations, and experiment results demonstrate the effectiveness of the proposed approaches.

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ZOMAYA, ALBERT Y., and GERARD CHAN. "EFFICIENT CLUSTERING FOR PARALLEL TASKS EXECUTION IN DISTRIBUTED SYSTEMS." International Journal of Foundations of Computer Science 16, no. 02 (April 2005): 281–99. http://dx.doi.org/10.1142/s0129054105002991.

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The scheduling problem deals with the optimal assignment of a set of tasks to processing elements in a distributed system such that the total execution time is minimized. One approach for solving the scheduling problem is task clustering. This involves assigning tasks to clusters where each cluster is run on a single processor. This paper aims to show the feasibility of using Genetic Algorithms for task clustering to solve the scheduling problem. Genetic Algorithms are robust optimization and search techniques that are used in this work to solve the task-clustering problem. The proposed approach shows great promise to solve the clustering problem for a wide range of clustering instances.

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Tao, Qian, Bo Pan, and Wen Quan Cui. "Task Scheduling of Cloud Computing in Weapon Network System." Applied Mechanics and Materials 610 (August 2014): 695–98. http://dx.doi.org/10.4028/www.scientific.net/amm.610.695.

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In recent years, the rapid development of cloud computing brings significant innovation in the whole IT industry. For the local tasks scheduling on each computational node of the top model of weapon network, an open task scheduling framework was introduced a task accept control scheme based on the tasks based on load balancing, quality of service (QoS) and an improved constant bandwidth server algorithm was presented. The result of simulation shows that the scheduling policies can improve the schedule speed when the number of tasks increases and can meet the demand better for the real time requirementsof the tactical training evaluation system for complexity and Large-scale.

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Journal articles on the topic 'Scheduling tasks'

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