Sequential-modular-based process flowsheeting software remains an indispensable tool for process design,control,and optimization.Yet,as the process industry advances in intelligent operation and maintenance,convention...Sequential-modular-based process flowsheeting software remains an indispensable tool for process design,control,and optimization.Yet,as the process industry advances in intelligent operation and maintenance,conventional sequential-modular-based process-simulation techniques present challenges regarding computationally intensive calculations and significant central processing unit(CPU)time requirements,particularly in large-scale design and optimization tasks.To address these challenges,this paper proposes a novel process-simulation parallel computing framework(PSPCF).This framework achieves layered parallelism in recycling processes at the unit operation level.Notably,PSPCF introduces a groundbreaking concept of formulating simulation problems as task graphs and utilizes Taskflow,an advanced task graph computing system,for hierarchical parallel scheduling and the execution of unit operation tasks.PSPCF also integrates an advanced work-stealing scheme to automatically balance thread resources with the demanding workload of unit operation tasks.For evaluation,both a simpler parallel column process and a more complex cracked gas separation process were simulated on a flowsheeting platform using PSPCF.The framework demonstrates significant time savings,achieving over 60%reduction in processing time for the simpler process and a 35%–40%speed-up for the more complex separation process.展开更多
Hardware/software（HW/SW） partitioning is one of the key processes in an embedded system.It is used to determine which system components are assigned to hardware and which are processed by software.In contrast with p...Hardware/software（HW/SW） partitioning is one of the key processes in an embedded system.It is used to determine which system components are assigned to hardware and which are processed by software.In contrast with previous research that focuses on developing efficient heuristic,we focus on the pre-process of the task graph before the HW/SW partitioning in this paper,that is,enumerating all the sub-graphs that meet the requirements.Experimental results showed that the original graph can be reduced to 67% in the worst-case scenario and 58% in the best-case scenario.In conclusion,the reduced task graph saved hardware area while improving partitioning speed and accuracy.展开更多
Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in im...Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility.展开更多
This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent...This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent policy networks.The graph of welding spots distribution is encoded using the graph attention network.Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks.The policy network is used to convert the large scale welding spots allocation problem to multiple small scale singlerobot welding path planning problems,and the path planning problem is quickly solved through existing methods.Then,the model is trained through reinforcement learning.In addition,the task balancing method is used to allocate tasks to multiple stations.The proposed algorithm is compared with classical algorithms,and the results show that the algorithm based on DRL can produce higher quality solutions.展开更多
The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ap...The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.展开更多
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
Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance...Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.展开更多
Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of...Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of task scheduling algorithms for homogeneous environments have been proposed, whereas, a few for heterogeneous environments can be found in the literature. A novel task scheduling algorithm for heterogeneous environments, called the heterogeneous critical task （HCT） scheduling algorithm is presented. By means of the directed acyclic graph and the gantt graph, the HCT algorithm defines the critical task and the idle time slot. After determining the critical tasks of a given task, the HCT algorithm tentatively duplicates the critical tasks onto the processor that has the given task in the idle time slot, to reduce the start time of the given task. To compare the performance of the HCT algorithm with several recently proposed algorithms, a large set of randomly generated applications and the Gaussian elimination application are randomly generated. The experimental result has shown that the HCT algorithm outperforms the other algorithm.展开更多
In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on pa...In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.展开更多
A new static task scheduling algorithm named edge-zeroing based on dynamic critical paths is proposed. The main ideas of the algorithm are as follows: firstly suppose that all of the tasks are in different clusters; s...A new static task scheduling algorithm named edge-zeroing based on dynamic critical paths is proposed. The main ideas of the algorithm are as follows: firstly suppose that all of the tasks are in different clusters; secondly, select one of the critical paths of the partially clustered directed acyclic graph; thirdly, try to zero one of graph communication edges; fourthly, repeat above three processes until all edges are zeroed; finally, check the generated clusters to see if some of them can be further merged without increasing the parallel time. Comparisons of the previous algorithms with edge-zeroing based on dynamic critical paths show that the new algorithm has not only a low complexity but also a desired performance comparable or even better on average to much higher complexity heuristic algorithms.展开更多
It is important to improve the development efficiency of decoupling a coupling task package according to the information relevancy relation between development tasks in the collaborative development process of complic...It is important to improve the development efficiency of decoupling a coupling task package according to the information relevancy relation between development tasks in the collaborative development process of complicated electronic products.In order to define the task coupling model in the development process,the weighted directed graph based on the information relevancy is established,and the correspondence between weighted directed graph model and numerical design structure matrix model of coupling tasks is introduced.The task coupling model is quantized,thereby the interactivity matrix of task package is built.A multi-goal task decoupling method based on improved genetic algorithm is proposed to decouple the task coupling model,which transforms the decoupling of task package into a multi-goal optimization issue.Then the improved genetic algorithm is used to solve the interactivity matrix of coupling tasks.Finally,the effectiveness of this decomposition method is proved by using the example of task package decoupling of collaborative development of a radar’s phased array antenna.展开更多
A new heuristic approach that resembles the evolution of interpersonal relationships in human society is put forward for the problem of scheduling multitasks represented by a directed acyclic graph. The algorithm incl...A new heuristic approach that resembles the evolution of interpersonal relationships in human society is put forward for the problem of scheduling multitasks represented by a directed acyclic graph. The algorithm includes dynamic-group, detachgraph and front-sink components. The priority rules used are new. Relationship number, potentiality, weight and merge degree are defined for cluster＇s priority, and task potentiality for tasks＇ priority. Experiments show the algorithm could get good result in short time. The algorithm produces another optimal solution for the classic MJD benchmark. Its average performance is better than five latter-day representative algorithms, especially six benchmarks of the nines.展开更多
In order to reduce the scheduling makespan of a workflow,three list scheduling algorithms,namely,level and out-degree earliest-finish-time(LOEFT),level heterogeneous selection value(LHSV),and heterogeneous priority ea...In order to reduce the scheduling makespan of a workflow,three list scheduling algorithms,namely,level and out-degree earliest-finish-time(LOEFT),level heterogeneous selection value(LHSV),and heterogeneous priority earliest-finish-time(HPEFT)are proposed.The main idea hidden behind these algorithms is to adopt task depth,combined with task out-degree for the accurate analysis of task prioritization and precise processor allocation to achieve time optimization.Each algorithm is divided into three stages:task levelization,task prioritization,and processor allocation.In task levelization,the workflow is divided into several independent task sets on the basis of task depth.In task prioritization,the heterogeneous priority ranking value(HPRV)of the task is calculated using task out-degree,and a non-increasing ranking queue is generated on the basis of HPRV.In processor allocation,the sorted tasks are assigned one by one to the processor to minimize makespan and complete the task-processor mapping.Simulation experiments through practical applications and stochastic workflows confirm that the three algorithms can effectively shorten the workflow makespan,and the LOEFT algorithm performs the best,and it can be concluded that task depth combined with out-degree is an effective means of reducing completion time.展开更多
基金supported by the National Key Research and Development Program of China(2022YFB3305900)the National Natural Science Foundation of China(Key Program)(62136003)+2 种基金the National Natural Science Foundation of China(62394345)the Major Science and Technology Projects of Longmen Laboratory(LMZDXM202206)the Fundamental Research Funds for the Central Universities.
文摘Sequential-modular-based process flowsheeting software remains an indispensable tool for process design,control,and optimization.Yet,as the process industry advances in intelligent operation and maintenance,conventional sequential-modular-based process-simulation techniques present challenges regarding computationally intensive calculations and significant central processing unit(CPU)time requirements,particularly in large-scale design and optimization tasks.To address these challenges,this paper proposes a novel process-simulation parallel computing framework(PSPCF).This framework achieves layered parallelism in recycling processes at the unit operation level.Notably,PSPCF introduces a groundbreaking concept of formulating simulation problems as task graphs and utilizes Taskflow,an advanced task graph computing system,for hierarchical parallel scheduling and the execution of unit operation tasks.PSPCF also integrates an advanced work-stealing scheme to automatically balance thread resources with the demanding workload of unit operation tasks.For evaluation,both a simpler parallel column process and a more complex cracked gas separation process were simulated on a flowsheeting platform using PSPCF.The framework demonstrates significant time savings,achieving over 60%reduction in processing time for the simpler process and a 35%–40%speed-up for the more complex separation process.
基金Supported by the National Natural Science Foundation of China (60970016,61173032)
文摘Hardware/software（HW/SW） partitioning is one of the key processes in an embedded system.It is used to determine which system components are assigned to hardware and which are processed by software.In contrast with previous research that focuses on developing efficient heuristic,we focus on the pre-process of the task graph before the HW/SW partitioning in this paper,that is,enumerating all the sub-graphs that meet the requirements.Experimental results showed that the original graph can be reduced to 67% in the worst-case scenario and 58% in the best-case scenario.In conclusion,the reduced task graph saved hardware area while improving partitioning speed and accuracy.
基金supported by the Talent Fund of Beijing Jiaotong University(No.2023XKRC028)CCFLenovo Blue Ocean Research Fund and Beijing Natural Science Foundation under Grant(No.L221003).
文摘Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility.
基金National Key Research and Development Program of China,Grant/Award Number:2021YFB1714700Postdoctoral Research Foundation of China,Grant/Award Number:2024M752364Postdoctoral Fellowship Program of CPSF,Grant/Award Number:GZB20240525。
文摘This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent policy networks.The graph of welding spots distribution is encoded using the graph attention network.Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks.The policy network is used to convert the large scale welding spots allocation problem to multiple small scale singlerobot welding path planning problems,and the path planning problem is quickly solved through existing methods.Then,the model is trained through reinforcement learning.In addition,the task balancing method is used to allocate tasks to multiple stations.The proposed algorithm is compared with classical algorithms,and the results show that the algorithm based on DRL can produce higher quality solutions.
文摘The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.
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supported by the National Natural Science Foundation of China (61104180)the National Basic Research Program of China(973 Program) (97361361)
文摘Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.
文摘Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of task scheduling algorithms for homogeneous environments have been proposed, whereas, a few for heterogeneous environments can be found in the literature. A novel task scheduling algorithm for heterogeneous environments, called the heterogeneous critical task （HCT） scheduling algorithm is presented. By means of the directed acyclic graph and the gantt graph, the HCT algorithm defines the critical task and the idle time slot. After determining the critical tasks of a given task, the HCT algorithm tentatively duplicates the critical tasks onto the processor that has the given task in the idle time slot, to reduce the start time of the given task. To compare the performance of the HCT algorithm with several recently proposed algorithms, a large set of randomly generated applications and the Gaussian elimination application are randomly generated. The experimental result has shown that the HCT algorithm outperforms the other algorithm.
基金supported by Science and technology fund of China General Administration of Civil Aviation ( No: MY0421808)National Natural Science Foundation of China ( No: 60832011 )Tianjin scientific and technological research program ( No: 06YFGZGX00700)
基金The National Natural Science Foundation of China(No.61741102,61471164,61601122)the Fundamental Research Funds for the Central Universities(No.SJLX_160040)
文摘In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.
文摘A new static task scheduling algorithm named edge-zeroing based on dynamic critical paths is proposed. The main ideas of the algorithm are as follows: firstly suppose that all of the tasks are in different clusters; secondly, select one of the critical paths of the partially clustered directed acyclic graph; thirdly, try to zero one of graph communication edges; fourthly, repeat above three processes until all edges are zeroed; finally, check the generated clusters to see if some of them can be further merged without increasing the parallel time. Comparisons of the previous algorithms with edge-zeroing based on dynamic critical paths show that the new algorithm has not only a low complexity but also a desired performance comparable or even better on average to much higher complexity heuristic algorithms.
基金supported by the National Defense Basic Research Program of China (No. A1120131044)
文摘It is important to improve the development efficiency of decoupling a coupling task package according to the information relevancy relation between development tasks in the collaborative development process of complicated electronic products.In order to define the task coupling model in the development process,the weighted directed graph based on the information relevancy is established,and the correspondence between weighted directed graph model and numerical design structure matrix model of coupling tasks is introduced.The task coupling model is quantized,thereby the interactivity matrix of task package is built.A multi-goal task decoupling method based on improved genetic algorithm is proposed to decouple the task coupling model,which transforms the decoupling of task package into a multi-goal optimization issue.Then the improved genetic algorithm is used to solve the interactivity matrix of coupling tasks.Finally,the effectiveness of this decomposition method is proved by using the example of task package decoupling of collaborative development of a radar’s phased array antenna.
基金Supported by the National Natural Science Foundation of China (7047107)the Ph.D. Programs Foundation of Ministry of Education of China (20020487046)
文摘A new heuristic approach that resembles the evolution of interpersonal relationships in human society is put forward for the problem of scheduling multitasks represented by a directed acyclic graph. The algorithm includes dynamic-group, detachgraph and front-sink components. The priority rules used are new. Relationship number, potentiality, weight and merge degree are defined for cluster＇s priority, and task potentiality for tasks＇ priority. Experiments show the algorithm could get good result in short time. The algorithm produces another optimal solution for the classic MJD benchmark. Its average performance is better than five latter-day representative algorithms, especially six benchmarks of the nines.
基金The Natural Science Foundation of Hunan Province(No.2018JJ2153)the Scientific Research Fund of Hunan Provincial Education Department(No.18B356)+1 种基金the Foundation of the Research Center of Hunan Emergency Communication Engineering Technology(No.2018TP2022)the Innovation Foundation for Postgraduate of the Hunan Institute of Science and Technology(No.YCX2018A06).
文摘In order to reduce the scheduling makespan of a workflow,three list scheduling algorithms,namely,level and out-degree earliest-finish-time(LOEFT),level heterogeneous selection value(LHSV),and heterogeneous priority earliest-finish-time(HPEFT)are proposed.The main idea hidden behind these algorithms is to adopt task depth,combined with task out-degree for the accurate analysis of task prioritization and precise processor allocation to achieve time optimization.Each algorithm is divided into three stages:task levelization,task prioritization,and processor allocation.In task levelization,the workflow is divided into several independent task sets on the basis of task depth.In task prioritization,the heterogeneous priority ranking value(HPRV)of the task is calculated using task out-degree,and a non-increasing ranking queue is generated on the basis of HPRV.In processor allocation,the sorted tasks are assigned one by one to the processor to minimize makespan and complete the task-processor mapping.Simulation experiments through practical applications and stochastic workflows confirm that the three algorithms can effectively shorten the workflow makespan,and the LOEFT algorithm performs the best,and it can be concluded that task depth combined with out-degree is an effective means of reducing completion time.
