网络计算环境中基于智能算法的任务调度研究
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摘要
智能优化算法是近几年发展的一类仿生算法,具有自组织、自学习、自适应、多点并行及有指导搜索等特点,已被广泛应用于工程技术、非线性优化、结构性设计、并行计算和社会科学等领域。本文利用智能优化算法能够较好地解决复杂问题的优点,研究网络计算中任务分配与调度(Task Matching and Scheduling)问题。任务分配与调度是充分利用网络计算潜力的关键技术,也是NP-hard问题,该问题的解决对于高性能计算的应用与发展具有十分重要的意义。
针对智能优化算法的普适性和具体问题的特殊性,提出算法的改进策略和具体操作算子的设计;基于进化算法的共性,对进化算法用于调度问题的算法设计进行深入研究。主要研究内容如下:
(1)针对蚁群优化(Ant Colony Optimization,ACO)算法擅长解决离散问题,但信息素设计比较困难的特点,提出利用调度任务图静态和动态属性作为启发信息的策略,设计了相关算法,实验证明了该算法的优良性能。通过分析算法的性能,研究了启发信息的选取原则和实施方案。在此基础上设计了并行蚁群调度算法,并在MPICH支撑平台上实现,研究了不同并行方式对调度性能的影响,以及并行群体中信息交换策略和信息交换频率的难题,进一步提高了算法的性能和速度。
(2)分析进化算法在优化问题中的应用特点,提出了调度问题解空间的不同编码方式和解码方法,研究了算法搜索过程中有效的进化操作。以微分算法为例,对两种调度编码方式分别设计同构系统调度算法,并比较两种方式的调度性能。在算法实现过程中,根据问题设计特殊的交叉、变异算子,并通过随机拓扑排序方法获得初始群体,综合局部搜索策略,加速算法收敛,提高运行质量和全局搜索能力。实验表明,两种编码方式都能有效解决调度问题,且基于任务排列的方式优于基于任务优先级的方式。
(3)针对任务排列编码方式较优的特点,提出了基于粒子群优化(Particle Swarm Optimization,PSO)算法的异构调度算法,设计具有问题特征的进化算子,保证算法能在可接受的时间内提供高质量的解,避免了现有算法中采用平均值而导致调度不合理的缺点。量子行为粒子群优化算法是对标准PSO的一种改进,参数少,理论上能保证解全局收敛。针对任务优先级编码方式容易实现的特点,综合调度问题的空间信息,设计了混合量子行为粒子群的调度算法,研究提高算法性能的策略,实验验证了算法的有效性。
(4)针对网格任务调度难点,分析网格异构环境中任务分配与调度的关键问题,研究了网格计算模型下静态任务分配与调度的算法;同时根据网格环境中资源自治和作业动态变化的特性,设计了动态自适应的禁忌搜索算法,在任务调度过程中,自适应地调整算法参数,实时响应网格的动态变化。最后在GridSim环境中仿真实现,并取得了满意的结果。
Intelligent optimization algorithms are a class of bionic algorithms and are well characterized by its self-organizing, self-learning, self-adaptive, implicit parallelism and guided search, etc. These algorithms have the preponderance over solving complex issues and have been widely used in engineering technology, nonlinear optimization, structural design, parallel computing, social science as well as many other fields. By employing intelligent optimization algorithms, this paper studies task matching and scheduling for network computing. In order to make full use of the potential power of network computing system, tasks matching and scheduling is one of the critical challenges in this filed and is also NP-hard. Solving the problem is of great significance to the development and application of high performance computing.
Considering the universality of intelligent optimization algorithms against the particularity of specific issues, the proposed algorithm adjusts evolution strategies and designs special operators. Based on common characteristics of the evolutionary algorithms, scheduling techniques and strategies for the network computing are further investigated. The main contents are as follows:
(1) Ant Colony Optimization (ACO) algorithm is good at solving discrete problems, but the pheromone is very difficult to select. Using static and dynamic properties of task graphs as heuristic informations, the corresponding algorithms are designed and the excellent performances of these algorithms are demonstrated by the experiment results. By analyzing the performances of the proposed algorithms, the selection strategies and the implementation principles of heuristic informations are further studied. Moreover, a parallel scheduling algorithm is designed and implemented under the MPICH supporting environment. Distinct parallel techniques are analyzed to find how the parallel performance is affected. The strategy and frequency of the information exchanges among the parallel colonies are studied to further improve the performance and speed.
(2) In response to characteristics of evolutionary algorithms in solving combinatory problems, different solution encoding and decoding methods are investigate and feasible evolution operation is designed for the search process of the algorithms. Based on Differential Evolution algorithm (DE), the scheduling algorithms of the two encoding structures are designed for homogeneous system and the scheduling performances of two methods are compared. Special crossover and mutation operator are designed to fit in with the specific problem, and initial value is obtained by stochastic topological sorting for permutation-based method. In order to improve the solution quality and the global search capability, local search strategy is integrated to accelerate the convergence of the algorithms. Experiments show that the two encoding methods can effectively solve the scheduling problem, and permutation-based method is superior to priority-based method.
(3) Based on permutation-based method, the particle swarm optimization (PSO) is utilized to tackle with the scheduling problem in heterogeneous environments. By designing specific-problem operators, the algorithm can be guaranteed to provide high-quality solutions in acceptable time and avoid using the mean values which led to the unreasonable schedule in other algorithms. Quantum-behaved PSO (QPSO) algorithm is a modification of the standard PSO algorithm, and it has fewer parameters to control and can be demonstrated mathematically to be a global convergent algorithm. Combining spatial information, hybrid quantum-behaved PSO algorithm for tasks scheduling is proposed based on priority-based method to improve the performance and tests verify the effectiveness of the proposed algorithm.
(4) Considering the key issues of tasks matching and scheduling in dynamic heterogeneous environments, a static scheduling algorithms is proposed under the model of Grid computing; On the basis of dynamic characteristics of jobs and the autonomy of resources in grid environments, a dynamic self-adaptive tabu search algorithm for tasks scheduling is developed, which can self-adaptively adjust algorithm parameters, and make a real-time response to the dynamic changes of the grid. Finally, implementing the scheduling algorithm in GridSim simulation environment, satisfactory results are obtained.
针对智能优化算法的普适性和具体问题的特殊性,提出算法的改进策略和具体操作算子的设计;基于进化算法的共性,对进化算法用于调度问题的算法设计进行深入研究。主要研究内容如下:
(1)针对蚁群优化(Ant Colony Optimization,ACO)算法擅长解决离散问题,但信息素设计比较困难的特点,提出利用调度任务图静态和动态属性作为启发信息的策略,设计了相关算法,实验证明了该算法的优良性能。通过分析算法的性能,研究了启发信息的选取原则和实施方案。在此基础上设计了并行蚁群调度算法,并在MPICH支撑平台上实现,研究了不同并行方式对调度性能的影响,以及并行群体中信息交换策略和信息交换频率的难题,进一步提高了算法的性能和速度。
(2)分析进化算法在优化问题中的应用特点,提出了调度问题解空间的不同编码方式和解码方法,研究了算法搜索过程中有效的进化操作。以微分算法为例,对两种调度编码方式分别设计同构系统调度算法,并比较两种方式的调度性能。在算法实现过程中,根据问题设计特殊的交叉、变异算子,并通过随机拓扑排序方法获得初始群体,综合局部搜索策略,加速算法收敛,提高运行质量和全局搜索能力。实验表明,两种编码方式都能有效解决调度问题,且基于任务排列的方式优于基于任务优先级的方式。
(3)针对任务排列编码方式较优的特点,提出了基于粒子群优化(Particle Swarm Optimization,PSO)算法的异构调度算法,设计具有问题特征的进化算子,保证算法能在可接受的时间内提供高质量的解,避免了现有算法中采用平均值而导致调度不合理的缺点。量子行为粒子群优化算法是对标准PSO的一种改进,参数少,理论上能保证解全局收敛。针对任务优先级编码方式容易实现的特点,综合调度问题的空间信息,设计了混合量子行为粒子群的调度算法,研究提高算法性能的策略,实验验证了算法的有效性。
(4)针对网格任务调度难点,分析网格异构环境中任务分配与调度的关键问题,研究了网格计算模型下静态任务分配与调度的算法;同时根据网格环境中资源自治和作业动态变化的特性,设计了动态自适应的禁忌搜索算法,在任务调度过程中,自适应地调整算法参数,实时响应网格的动态变化。最后在GridSim环境中仿真实现,并取得了满意的结果。
Intelligent optimization algorithms are a class of bionic algorithms and are well characterized by its self-organizing, self-learning, self-adaptive, implicit parallelism and guided search, etc. These algorithms have the preponderance over solving complex issues and have been widely used in engineering technology, nonlinear optimization, structural design, parallel computing, social science as well as many other fields. By employing intelligent optimization algorithms, this paper studies task matching and scheduling for network computing. In order to make full use of the potential power of network computing system, tasks matching and scheduling is one of the critical challenges in this filed and is also NP-hard. Solving the problem is of great significance to the development and application of high performance computing.
Considering the universality of intelligent optimization algorithms against the particularity of specific issues, the proposed algorithm adjusts evolution strategies and designs special operators. Based on common characteristics of the evolutionary algorithms, scheduling techniques and strategies for the network computing are further investigated. The main contents are as follows:
(1) Ant Colony Optimization (ACO) algorithm is good at solving discrete problems, but the pheromone is very difficult to select. Using static and dynamic properties of task graphs as heuristic informations, the corresponding algorithms are designed and the excellent performances of these algorithms are demonstrated by the experiment results. By analyzing the performances of the proposed algorithms, the selection strategies and the implementation principles of heuristic informations are further studied. Moreover, a parallel scheduling algorithm is designed and implemented under the MPICH supporting environment. Distinct parallel techniques are analyzed to find how the parallel performance is affected. The strategy and frequency of the information exchanges among the parallel colonies are studied to further improve the performance and speed.
(2) In response to characteristics of evolutionary algorithms in solving combinatory problems, different solution encoding and decoding methods are investigate and feasible evolution operation is designed for the search process of the algorithms. Based on Differential Evolution algorithm (DE), the scheduling algorithms of the two encoding structures are designed for homogeneous system and the scheduling performances of two methods are compared. Special crossover and mutation operator are designed to fit in with the specific problem, and initial value is obtained by stochastic topological sorting for permutation-based method. In order to improve the solution quality and the global search capability, local search strategy is integrated to accelerate the convergence of the algorithms. Experiments show that the two encoding methods can effectively solve the scheduling problem, and permutation-based method is superior to priority-based method.
(3) Based on permutation-based method, the particle swarm optimization (PSO) is utilized to tackle with the scheduling problem in heterogeneous environments. By designing specific-problem operators, the algorithm can be guaranteed to provide high-quality solutions in acceptable time and avoid using the mean values which led to the unreasonable schedule in other algorithms. Quantum-behaved PSO (QPSO) algorithm is a modification of the standard PSO algorithm, and it has fewer parameters to control and can be demonstrated mathematically to be a global convergent algorithm. Combining spatial information, hybrid quantum-behaved PSO algorithm for tasks scheduling is proposed based on priority-based method to improve the performance and tests verify the effectiveness of the proposed algorithm.
(4) Considering the key issues of tasks matching and scheduling in dynamic heterogeneous environments, a static scheduling algorithms is proposed under the model of Grid computing; On the basis of dynamic characteristics of jobs and the autonomy of resources in grid environments, a dynamic self-adaptive tabu search algorithm for tasks scheduling is developed, which can self-adaptively adjust algorithm parameters, and make a real-time response to the dynamic changes of the grid. Finally, implementing the scheduling algorithm in GridSim simulation environment, satisfactory results are obtained.
引文
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