To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony sy...To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.展开更多
Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,...Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.展开更多
A Genetic Algorithm-Ant Colony Algorithm(GA-ACA),which can be used to optimize multi-Unit Under Test(UUT)parallel test tasks sequences and resources configuration quickly and accurately,is proposed in the paper.With t...A Genetic Algorithm-Ant Colony Algorithm(GA-ACA),which can be used to optimize multi-Unit Under Test(UUT)parallel test tasks sequences and resources configuration quickly and accurately,is proposed in the paper.With the establishment of the mathematic model of multi-UUT parallel test tasks and resources,the condition of multi-UUT resources mergence is analyzed to obtain minimum resource requirement under minimum test time.The definition of cost efficiency is put forward,followed by the design of gene coding and path selection project,which can satisfy multi-UUT parallel test tasks scheduling.At the threshold of the algorithm,GA is adopted to provide initial pheromone for ACA,and then dual-convergence pheromone feedback mode is applied in ACA to avoid local optimization and parameters dependence.The practical application proves that the algorithm has a remarkable effect on solving the problems of multi-UUT parallel test tasks scheduling and resources configuration.展开更多
This document explains and demonstrates how to construct the applied writing training in network system. This system which is used in network consists of four modules, including task module, structure training module,...This document explains and demonstrates how to construct the applied writing training in network system. This system which is used in network consists of four modules, including task module, structure training module, text training module and study evaluation module. With the advantages of instantaneity, interactivity, authenticity and adequation of learning resources, the applied writing abilities of the students can be improved effectively by using the network system. And the problems during the process of applied writing teaching, for example, the teaching contents lose contract with social life, the textbook contents are lack of innovation, and writing training goes against cognizing system, can be solved effectively.展开更多
A Dominant Resource Fairness （DRF） based scheme for job scheduling in distributed cloud computing systems which was modeled as multi-job scheduling and multi-resource allocation coupling problem is proposed, where t...A Dominant Resource Fairness （DRF） based scheme for job scheduling in distributed cloud computing systems which was modeled as multi-job scheduling and multi-resource allocation coupling problem is proposed, where the resource pool is constructed from a large number of distributed heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, storage and bandwidth. By introducing dominant resource share of jobs and virtual machines, the multi-job scheduling and multi-resource allocation joint mechanism significantly improves the cloud system＇s resource utilization, yet with a substantial reduction of job completion times. We show through experiments and case studies the superior performance of the algorithms in practice.展开更多
Because of the anonymity and openness of E-commerce, the on-line transaction and the selection of network resources meet new challenges. For this reason, a trust domain-based multi-agent model for network resource sel...Because of the anonymity and openness of E-commerce, the on-line transaction and the selection of network resources meet new challenges. For this reason, a trust domain-based multi-agent model for network resource selection is presented. The model divides the network into numbers of trust domains and prevents the inconsistency of information maintained by different agents through the periodical communication among the agents. The model enables consumers to receive the response from the agents much quicker because the trust values of participators are evalUated and updated dynamically and timely after the completion of each transaction. In order to make users choose the best matching services and give users with trusted services, the model takes into account the similarity between services and the service providers＇ recognition to the services. Finally, the model illustrates the effectiveness and feasibility according to the experiment.展开更多
Power consumption is the main challenge to expand the wireless sensor network, since the active nodes are vulnerable to energy consumption. This paper proposes the tasks scheduling and distribution energy management m...Power consumption is the main challenge to expand the wireless sensor network, since the active nodes are vulnerable to energy consumption. This paper proposes the tasks scheduling and distribution energy management mechanism of roles dormant cells to curb excessive consumption of energy in the performance of duplicated tasks. Experimental results verify the proposal existing low energy adaptive clustering hierarchy （LEACH） protocol with SRDC approach establishes a low-energy communication structure and increases the lifetime.展开更多
With the growth of maritime activities,the number of computationally complex applications is growing exponentially.Mobile edge computing(MEC)is widely recognized as a viable option to address the substantial need for ...With the growth of maritime activities,the number of computationally complex applications is growing exponentially.Mobile edge computing(MEC)is widely recognized as a viable option to address the substantial need for wireless communications and compute-intensive operations in maritime environments.To reduce the processing load and meet the demands of mobile terminals for high bandwidth,low latency and multiple access,MEC systems with unmanned aerial vehicles(UAVs)have been proposed and extensively explored.In this paper,a maritime MEC network that employs a top-UAV(T-UAV)for task offloading supported by digital twin(DT)is considered.To explore the task offloading strategy employed by the edge server,the flight trajectory and resource allocation strategy of the T-UAV is studied in detail.The objective of this study is to minimize latency costs while ensuring that the energy of the T-UAV is sufficient to fulfill services.In order to accomplish this objective,the joint optimization problem is described as a Markov decision process(MDP).To overcome this problem,the priority-based reinforcement learning(RL)algorithm for computation offloading and trajectory planning(PRL-COTP)is developed.The simulation results demonstrate that the proposed approach can significantlyreduce the overall cost of the system in comparison to other benchmarks.展开更多
文摘To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.
基金ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. The research has been partly supported by National Natural Science Foundation of China No. 61272528 and No. 61034005, and the Central University Fund （ID-ZYGX2013J073）.
文摘Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.
基金supported by“11th Five-year Projects”pre-research projects fund of the National Arming Department
文摘A Genetic Algorithm-Ant Colony Algorithm(GA-ACA),which can be used to optimize multi-Unit Under Test(UUT)parallel test tasks sequences and resources configuration quickly and accurately,is proposed in the paper.With the establishment of the mathematic model of multi-UUT parallel test tasks and resources,the condition of multi-UUT resources mergence is analyzed to obtain minimum resource requirement under minimum test time.The definition of cost efficiency is put forward,followed by the design of gene coding and path selection project,which can satisfy multi-UUT parallel test tasks scheduling.At the threshold of the algorithm,GA is adopted to provide initial pheromone for ACA,and then dual-convergence pheromone feedback mode is applied in ACA to avoid local optimization and parameters dependence.The practical application proves that the algorithm has a remarkable effect on solving the problems of multi-UUT parallel test tasks scheduling and resources configuration.
文摘This document explains and demonstrates how to construct the applied writing training in network system. This system which is used in network consists of four modules, including task module, structure training module, text training module and study evaluation module. With the advantages of instantaneity, interactivity, authenticity and adequation of learning resources, the applied writing abilities of the students can be improved effectively by using the network system. And the problems during the process of applied writing teaching, for example, the teaching contents lose contract with social life, the textbook contents are lack of innovation, and writing training goes against cognizing system, can be solved effectively.
文摘A Dominant Resource Fairness （DRF） based scheme for job scheduling in distributed cloud computing systems which was modeled as multi-job scheduling and multi-resource allocation coupling problem is proposed, where the resource pool is constructed from a large number of distributed heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, storage and bandwidth. By introducing dominant resource share of jobs and virtual machines, the multi-job scheduling and multi-resource allocation joint mechanism significantly improves the cloud system＇s resource utilization, yet with a substantial reduction of job completion times. We show through experiments and case studies the superior performance of the algorithms in practice.
基金Supported by the National Natural Science Foundation of China （No. 60873203 ）, the Natural Science Foundation of Hebei Province （No F2008000646） and the Guidance Program of the Department of Science and Technology in Hebei Province （No. 72135192）.
文摘Because of the anonymity and openness of E-commerce, the on-line transaction and the selection of network resources meet new challenges. For this reason, a trust domain-based multi-agent model for network resource selection is presented. The model divides the network into numbers of trust domains and prevents the inconsistency of information maintained by different agents through the periodical communication among the agents. The model enables consumers to receive the response from the agents much quicker because the trust values of participators are evalUated and updated dynamically and timely after the completion of each transaction. In order to make users choose the best matching services and give users with trusted services, the model takes into account the similarity between services and the service providers＇ recognition to the services. Finally, the model illustrates the effectiveness and feasibility according to the experiment.
文摘Power consumption is the main challenge to expand the wireless sensor network, since the active nodes are vulnerable to energy consumption. This paper proposes the tasks scheduling and distribution energy management mechanism of roles dormant cells to curb excessive consumption of energy in the performance of duplicated tasks. Experimental results verify the proposal existing low energy adaptive clustering hierarchy （LEACH） protocol with SRDC approach establishes a low-energy communication structure and increases the lifetime.
基金Foundation items:National Natural Science Foundation of China(Nos.62301307 and 62072096)Shanghai Pujiang Program,China(No.23PJD041)Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission,China(No.CGA60)。
文摘With the growth of maritime activities,the number of computationally complex applications is growing exponentially.Mobile edge computing(MEC)is widely recognized as a viable option to address the substantial need for wireless communications and compute-intensive operations in maritime environments.To reduce the processing load and meet the demands of mobile terminals for high bandwidth,low latency and multiple access,MEC systems with unmanned aerial vehicles(UAVs)have been proposed and extensively explored.In this paper,a maritime MEC network that employs a top-UAV(T-UAV)for task offloading supported by digital twin(DT)is considered.To explore the task offloading strategy employed by the edge server,the flight trajectory and resource allocation strategy of the T-UAV is studied in detail.The objective of this study is to minimize latency costs while ensuring that the energy of the T-UAV is sufficient to fulfill services.In order to accomplish this objective,the joint optimization problem is described as a Markov decision process(MDP).To overcome this problem,the priority-based reinforcement learning(RL)algorithm for computation offloading and trajectory planning(PRL-COTP)is developed.The simulation results demonstrate that the proposed approach can significantlyreduce the overall cost of the system in comparison to other benchmarks.
