A Parallel Scheduling Algorithm of Multi-Core Processor Based on Task Clustering and Duplication

2012 ◽  
Vol 546-547 ◽  
pp. 1421-1426
Author(s):  
Xiao Hui Ma
Keyword(s):  
Task Scheduling ◽  
Time Complexity ◽  
Scheduling Algorithm ◽  
Optimal Time ◽  
Parallel Scheduling ◽  
Task Load ◽  
Task Duplication ◽  
Multi Core Processor ◽  
Task Clustering

This thesis presents a parallel scheduling algorithm of multi-core processor based on task clustering and duplication. This algorithm, using the strategy of task clustering, gives priority to the operation of thread nodes of the same process on the same processor and effectively reduces time complexity of task scheduling. In order to avoid the unbalanced task load on the processors, it will employ their ultimate values to control the load. Finally, for achieving the optimal time of task operations, this algorithm, with the adoption of task duplication strategy, looks for the key tasks and duplicates them so as to fully utilize the resources of each core on the processor and improve the efficiency of task scheduling. The analysis of the experiment shows that, with the increasing number of task scheduling, the time of task operation of this algorithm is always the least.

Based on Improved Genetic Algorithm for Task Scheduling of Heterogeneous Multi-Core Processor

2014 ◽  
Vol 1030-1032 ◽  
pp. 1671-1675
Author(s):  
Yue Qiu ◽  
Jing Feng Zang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Experimental Results ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
High Quality ◽  
Multi Core Processor ◽  
Better Than

This paper puts forward an improved genetic scheduling algorithm in order to improve the execution efficiency of task scheduling of the heterogeneous multi-core processor system and give full play to its performance. The attribute values and the high value of tasks were introduced to structure the initial population, randomly selected a method with the 50% probability to sort for task of individuals of the population, thus to get high quality initial population and ensured the diversity of the population. The experimental results have shown that the performance of the improved algorithm was better than that of the traditional genetic algorithm and the HEFT algorithm. The execution time of tasks was reduced.

Research and Implementation on Multi-Core Processor Task Scheduling Algorithm

2011 ◽  
Vol 58-60 ◽  
pp. 1732-1737
Author(s):  
Fu Zhao ◽  
Yong Ping Zhang
Keyword(s):  
Experimental Data ◽  
Task Scheduling ◽  
Load Balance ◽  
Scheduling Algorithm ◽  
Task Decomposition ◽  
Software Applications ◽  
Task Scheduling Algorithm ◽  
Multi Core Processor ◽  
A Current

This paper firstly proposes one of the problems software applications faced by in the era of multi-core CPU: task decomposition and scheduling, and then analyzes a current scheduling algorithm together with its shortcomings. On the basis, an optimized algorithm is given. The optimized algorithm reduces the error and improves the accuracy. It is easier to achieve the calculation load balance of multi-core CPU. Finally, a multi-core platform is build using Simics system simulator, and the optimized algorithm is tested on this platform. Experimental data proves the superiority of the algorithm.

scholarly journals HSIP: A Novel Task Scheduling Algorithm for Heterogeneous Computing

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Guan Wang ◽  
Yuxin Wang ◽  
Hui Liu ◽  
He Guo
Keyword(s):  
Task Scheduling ◽  
High Performance ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Selection Policy ◽  
Task Scheduling Algorithm ◽  
Task Duplication ◽  
Task Priority ◽  
Low Efficiency

High-performance heterogeneous computing systems are achieved by the use of efficient application scheduling algorithms. However, most of the current algorithms have low efficiency in scheduling. Aiming at solving this problem, we propose a novel task scheduling algorithm for heterogeneous computing named HSIP (heterogeneous scheduling algorithm with improved task priority) whose functionality relies on three pillars: (1) an improved task priority strategy based on standard deviation with improved magnitude as computation weight and communication cost weight to make scheduling priority more reasonable; (2) an entry task duplication selection policy to make the makespan shorter; and (3) an improved idle time slots (ITS) insertion-based optimizing policy to make the task scheduling more efficient. We evaluate our proposed algorithm on randomly generated DAGs, using some real application DAGs by comparison with some classical scheduling algorithms. According to the experimental results, our proposed algorithm appears to perform better than other algorithms in terms of schedule length ratio, efficiency, and frequency of best results.

Task Optimization Scheduling Algorithm in Embedded System Based on Internet of Things

2014 ◽  
Vol 513-517 ◽  
pp. 2398-2402
Author(s):  
Dian Hong Wu
Keyword(s):  
Internet Of Things ◽  
Embedded System ◽  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Task Duplication ◽  
System Task ◽  
Network Server ◽  
Heterogeneous Computing System

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.

scholarly journals Multijob Associated Task Scheduling for Cloud Computing Based on Task Duplication and Insertion

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lei Shi ◽  
Jing Xu ◽  
Lunfei Wang ◽  
Jie Chen ◽  
Zhifeng Jin ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Use Of Resources ◽  
Task Scheduling Algorithm ◽  
Task Duplication ◽  
Insertion Algorithm ◽  
Speed Up ◽  
Execution Order

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.

A Hybrid Task Scheduling Algorithm Based on Task Clustering

2020 ◽  
Vol 25 (4) ◽  
pp. 1518-1527 ◽  
Author(s):  
Qiao Tian ◽  
Jingmei Li ◽  
Di Xue ◽  
Weifei Wu ◽  
Jiaxiang Wang ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Scheduling Algorithm ◽  
Task Clustering

scholarly journals A Synthesized Heuristic Task Scheduling Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yanyan Dai ◽  
Xiangli Zhang
Keyword(s):  
Task Scheduling ◽  
Resource Selection ◽  
Scheduling Algorithm ◽  
Scheduling Problems ◽  
Task Scheduling Algorithm ◽  
Task Duplication ◽  
Current Task ◽  
The Impact ◽  
Made In ◽  
Selection Phase

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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