scholarly journals Multisensors Cooperative Detection Task Scheduling Algorithm Based on Hybrid Task Decomposition and MBPSO

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Changyun Liu ◽  
Xiangke Guo ◽  
Zhihui Li ◽  
Yingying Wang ◽  
Gang Wei
Keyword(s):  
Optimization Model ◽  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Binary Particle Swarm Optimization ◽  
Sigmoid Function ◽  
Scheduling Problem ◽  
Task Decomposition ◽  
Task Scheduling Algorithm ◽  
Cooperative Detection ◽  
Solution Accuracy

A multisensor scheduling algorithm based on the hybrid task decomposition and modified binary particle swarm optimization (MBPSO) is proposed. Firstly, aiming at the complex relationship between sensor resources and tasks, a hybrid task decomposition method is presented, and the resource scheduling problem is decomposed into subtasks; then the sensor resource scheduling problem is changed into the match problem of sensors and subtasks. Secondly, the resource match optimization model based on the sensor resources and tasks is established, which considers several factors, such as the target priority, detecting benefit, handover times, and resource load. Finally, MBPSO algorithm is proposed to solve the match optimization model effectively, which is based on the improved updating means of particle’s velocity and position through the doubt factor and modified Sigmoid function. The experimental results show that the proposed algorithm is better in terms of convergence velocity, searching capability, solution accuracy, and efficiency.

Task Scheduling in Heterogeneous Computing Systems Based on Machine Learning Approach

2020 ◽  
Vol 34 (12) ◽  
pp. 2051012
Author(s):  
Hui Xie ◽  
Li Wei ◽  
Dong Liu ◽  
Luda Wang
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Scheduling Problem ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Machine Learning Approach ◽  
Heterogeneous Computing Systems ◽  
Regression Theory

Task scheduling problem of heterogeneous computing system (HCS), which with increasing popularity, nowadays has become a research hotspot in this domain. The task scheduling problem of HCS, which can be described essentially as assigning tasks to the proper processor for executing, has been shown to be NP-complete. However, the existing scheduling algorithm suffers from an inherent limitation of lacking global view. Here, we reported a novel task scheduling algorithm based on Multi-Logistic Regression theory (called MLRS) in heterogeneous computing environment. First, we collected the best scheduling plans as the historical training set, and then a scheduling model was established by which we could predict the following schedule action. Through the analysis of experimental results, it is interpreted that the proposed algorithm has better optimization effect and robustness.

QoS-Constrained Resource Scheduling Base on Market Economy Models in Grid Computing

2013 ◽  
Vol 443 ◽  
pp. 599-602
Author(s):  
Lei Chen
Keyword(s):  
Market Economy ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Supply And Demand ◽  
Resource Scheduling ◽  
Data Access ◽  
Grid Environment ◽  
Task Scheduling Algorithm ◽  
User Tasks

The Grid task scheduling algorithm is proposed that takes the service quality of resource scheduling, time and cost together into consideration, so that it can better meet user tasks Quality of Service (QoS) requirements and make the complex grid environment open. On the basis of the price model drove by supply and demand in economy, we design the Grid task scheduling algorithm in the market economy model. The experiment results indicate the effectiveness of proposed algorithm in terms of usersQoS guarantee. It reduce data access latency and decrease bandwidth consumption.

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 Dynamic Task Scheduling Algorithm with Deadline Constraint in Heterogeneous Volunteer Computing Platforms

2019 ◽  
Vol 11 (6) ◽  
pp. 121 ◽  
Author(s):  
Ling Xu ◽  
Jianzhong Qiao ◽  
Shukuan Lin ◽  
Wanting Zhang
Keyword(s):  
Distributed Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Volunteer Computing ◽  
Scheduling Problem ◽  
Dynamic Task ◽  
Use Of Resources ◽  
Task Scheduling Algorithm ◽  
Deadline Constraint ◽  
Dynamic Task Scheduling

Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

Resource allocation of English intelligent learning system based on reinforcement learning

2020 ◽  
pp. 1-14
Author(s):  
Jin Jingbo
Keyword(s):  
Scheduling Algorithm ◽  
Dynamic Environment ◽  
Learning System ◽  
System Structure ◽  
Scheduling Problem ◽  
Research Gaps ◽  
System Operation ◽  
Simulation Experiments ◽  
Decision Algorithm ◽  
Task Scheduling Algorithm

The communication energy efficiency of the English intelligent learning system is affected by many factors. In order to improve the system operation efficiency, it is necessary to carry out analysis from the perspective of the mobile edge server and the system structure. However, there are still many research gaps and deficiencies. Based on a dynamic and heterogeneous English intelligent learning system, this paper designs an adaptive offloading decision algorithm ADCO and an online spring slide task scheduling algorithm SSLS. Moreover, this paper has proposed corresponding solutions to the computational offloading problem and online scheduling problem in a dynamic environment and compared and analyzed the performance of this research algorithm through simulation experiments. The research results show that the method proposed in this paper has certain effects.

Task scheduling algorithm based on reliability perception in cloud computing

2020 ◽  
Vol 13 ◽  
Author(s):  
Kuang Yuejuan ◽  
Luo Zhuojun ◽  
Ouyang Weihao
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Pso Algorithm ◽  
Cloud Services ◽  
Task Scheduling Algorithm ◽  
Cloud Resource ◽  
Communication Links ◽  
The Impact

Background: In order to obtain reliable cloud resources, reduce the impact of resource node faults in cloud computing environment and reduce the fault time perceived by the application layer, a task scheduling model based on reliability perception is proposed. Methods: The model combines the two-parameter weibull distribution and analyzes various interaction relations between parallel tasks to describe the local characteristics of the failure rules of resource nodes and communication links in different periods.The model is added into the particle swarm optimization (pso) algorithm, and an adaptive inertial weighted pso resource scheduling algorithm based on reliability perception is obtained. Results: Simulation results show that when A increases to 0.3, the average scheduling length of the task increases rapidly.When it is 0.4-0.6, the growth rate is relatively slow.When greater than 0.8, the average scheduling length increases sharply.It can be seen that the r-pso algorithm proposed in this paper can accurately estimate the relevant parameters of cloud resource failure rule, and the generated resource scheduling scheme has better fitness, and the optimization effect is more significant with the increase of the number of tasks. Conclusion: With only a small amount of time added, the reliability of cloud services is greatly improved.

A Novel Task-Scheduling Algorithm of Cloud Computing Based on Particle Swarm Optimization

2021 ◽  
Vol 13 (2) ◽  
pp. 1-15
Author(s):  
Zhou Wu ◽  
Jun Xiong
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Particle Swarm ◽  
Resource Scheduling ◽  
Computing System ◽  
Swarm Optimization ◽  
Task Scheduling Algorithm ◽  
Cloud Computing System

With the characteristics of low cost, high availability, and scalability, cloud computing has become a high demand platform in the field of information technology. Due to the dynamic and diversity of cloud computing system, the task and resource scheduling has become a challenging issue. This paper proposes a novel task scheduling algorithm of cloud computing based on particle swarm optimization. Firstly, the resource scheduling problem in cloud computing system is modeled, and the objective function of the task execution time is formulated. Then, the modified particle swarm optimization algorithm is introduced to schedule applications' tasks and enhance load balancing. It uses Copula function to explore the relation of the random parameters random numbers and defines the local attractor to avoid the fitness function to be trapped into local optimum. The simulation results show that the proposed resource scheduling and allocation model can effectively improve the resource utilization of cloud computing and greatly reduce the completion time of tasks.

scholarly journals Optimization model for QoS based task scheduling in cloud computing environment

2020 ◽  
Vol 18 (2) ◽  
pp. 1081
Author(s):  
Sirisha Potluri ◽  
Katta Subba Rao
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Optimization Model ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Cloud Services ◽  
Allocation Model ◽  
Task Scheduling Algorithm ◽  
And Task

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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