WO2019196127A1 - 一种云计算任务分配方法、装置、设备及存储介质 - Google Patents
一种云计算任务分配方法、装置、设备及存储介质 Download PDFInfo
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Definitions
- the present invention belongs to the field of computer technologies, and in particular, to a cloud computing task allocation method, device, device and storage medium.
- Cloud computing is a convenient and flexible computing model. It is a shared pool of computing resources (such as network, server, storage, and application services) that can be accessed and used over the network. The core idea is to unify a large number of computing resources. Management and scheduling so that these computing resources are allocated on demand in the network like water in everyday life. Cloud computing task allocation refers to processing a large number of scheduling tasks under limited cloud computing resources according to the complexity of the tasks requested by the user.
- An object of the present invention is to provide a cloud computing task allocation method, apparatus, device, and storage medium, which are intended to solve the problem that the performance of the cloud computing task allocation method in the prior art is poor, and it is difficult to ensure the response time and completion time of the cloud computing task. Shorter question.
- the present invention provides a cloud computing task allocation method, the method comprising the following steps:
- the cloud computing task is allocated to the virtual machine in the cloud environment according to the optimal allocation path.
- the present invention provides a cloud computing task allocation apparatus, the apparatus comprising:
- a model building unit configured to: when receiving a cloud task allocation request of the user, construct a cloud task allocation model according to the cloud computing task to be allocated in the cloud task allocation request;
- An ant colony optimization unit configured to perform, by using the cloud task assignment model and a preset ant colony algorithm, a first preset number of times of allocation of the cloud computing task to generate the first preset number of intermediate allocations path;
- a genetic evolution unit configured to perform a second predetermined number of evolutions on the intermediate distribution path by a preset genetic algorithm to generate an optimal distribution path of the cloud computing task
- a task allocation unit configured to allocate the cloud computing task to the virtual machine in the cloud environment according to the optimal allocation path.
- the present invention also provides a cloud computing device comprising a memory, a processor, and a computer program stored in the memory and operable on the processor, the processor executing the computer program
- a cloud computing device comprising a memory, a processor, and a computer program stored in the memory and operable on the processor, the processor executing the computer program
- the present invention also provides a computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the steps as described in the cloud computing task assignment method described above .
- the invention constructs a cloud task allocation model, optimizes the allocation of the cloud computing task through the cloud task allocation model and the ant colony algorithm, and generates a first preset number of intermediate allocation paths.
- the second predetermined number of times of evolution of the intermediate allocation path is performed by the genetic algorithm, and the optimal allocation path of the cloud computing task is generated, and the cloud computing task is allocated to the virtual machine according to the optimal allocation path, thereby submitting a large number of independent cloud computing in the user.
- the hybrid group intelligent algorithm combined by ant colony and genetic algorithm effectively improves the performance of cloud computing task allocation, ensures the short response time and completion time of cloud computing tasks, and improves the service quality and users of cloud computing platform. Experience.
- FIG. 1 is a flowchart of an implementation of a cloud computing task allocation method according to Embodiment 1 of the present invention
- FIG. 2 is a schematic structural diagram of a cloud computing task allocation apparatus according to Embodiment 2 of the present invention.
- FIG. 3 is a schematic diagram of a preferred structure of a cloud computing task allocation apparatus according to Embodiment 2 of the present invention.
- FIG. 4 is a schematic structural diagram of a cloud computing device according to Embodiment 3 of the present invention.
- Embodiment 1 is a diagrammatic representation of Embodiment 1:
- FIG. 1 is a flowchart showing an implementation process of a cloud computing task allocation method according to Embodiment 1 of the present invention. For convenience of description, only parts related to the embodiment of the present invention are shown, which are as follows:
- step S101 when the cloud task assignment request of the user is received, the cloud task assignment model is constructed according to the cloud computing task to be allocated in the cloud task allocation request.
- the present invention is applicable to a cloud computing platform.
- the cloud computing tasks to be allocated are obtained from the cloud task allocation requests, and according to the cloud computing tasks, the cloud task allocation model can be constructed.
- the cloud task assignment model since multiple virtual machines are distributed on the host in the cloud environment, the cloud computing task assignment allocates the virtual machines to the cloud computing tasks, and the cloud task assignment model can be constructed by establishing the task virtual machine pairing process. .
- step S102 the cloud task assignment model and the preset ant colony algorithm are used to perform a first preset number of optimizations on the allocation of the cloud computing tasks, and generate a first preset number of intermediate distribution paths.
- the first preset number is a preset maximum number of iterations (or the maximum number of optimizations) of the ant colony algorithm
- each task virtual machine pair in the task virtual machine model is respectively set as a corresponding node.
- Ant colonies in ant colony algorithms need to find an optimal path between these nodes.
- the pheromone between the paired nodes of each task virtual machine may be initialized first, and the pheromone matrix in the ant colony algorithm is generated, and then the ants in the ant colony algorithm go out of the corresponding paths in the task virtual pairing node, and the paths are
- the local optimal path is set as the intermediate allocation path, and according to the running time required by the cloud computing task on the virtual machine, the pheromone on the path that each ant passes may be updated to guide the subsequent ant colony optimization through the pheromone. process.
- each ant in the ant colony algorithm selects the next node to be visited in the task virtual machine pairing node by using a preset probability formula when the ant is out of the corresponding path in the task virtual pairing node, thereby improving the path of the ant colony algorithm.
- Search ability For example, when the task virtual machine pairing node (a i , v j ) is located in the searchable task table of the ant k in the t-th optimization process, the probability that the ant k walks to the task virtual machine pairing node (a i , v j ) for:
- ⁇ ij (t) and ⁇ ij (t) represent the resource pheromone concentration and the intrinsic properties of the resource (such as calculation and communication ability) in the t-th optimization process, respectively, ⁇ and ⁇ respectively represent the importance degree of the pheromone, The importance of the inherent attributes of the resource, AllowedTasks(t) represents the searchable task table of the ant in the t-th optimization process, and vms is an optional virtual machine.
- the probability that the ant k walks to the task virtual machine pairing node (a i , v j ) is 0.
- the ant colony algorithm simulates the ant colony phenomenon in the natural environment, so the pheromone update includes the pheromone left by the ants, and the natural volatilization of the pheromone.
- the update formula of the pheromone matrix is expressed as:
- rho is the default volatilization factor
- Delta is the pheromone left by the ant
- Delta Q/max(costVm)
- Q is the preset weight parameter
- costVm[1...M] is the path of the ant.
- the time consumption of each virtual machine, max(costVm) is the longest time consumption of the time consumption of each virtual machine on the path through which the ant passes, so that the running time of the cloud computing task on the virtual machine is on the corresponding path of each ant.
- the pheromone is updated to effectively improve the accuracy of the pheromone update.
- each ant passing the local optimal path releases the pheromone again on the local optimal path according to a preset optimal path pheromone update formula, thereby indirectly Increasing the pheromone of the global path optimal path is beneficial to quickly find the global optimal path.
- the updated value of the pheromone in the optimal path pheromone update formula can be expressed as:
- determining whether the optimization number of the ant colony algorithm reaches the first preset number is to complete the optimization process of the ant colony algorithm, and obtain a first preset number of intermediate allocation paths, otherwise continue to pass the ant colony algorithm.
- the ants go out of the corresponding path in the task virtual pairing node.
- step S103 the intermediate distribution path is subjected to a second predetermined number of evolutions by a preset genetic algorithm to generate an optimal distribution path of the cloud computing task.
- the second preset number is the maximum number of iterations (or the maximum number of evolutions) of the genetic algorithm.
- each generation of the ant colony algorithm takes a long time, and the genetic algorithm costs each generation. The time is shorter, so the second preset number is set to be greater than the first preset number, thereby improving the effect of the cloud computing task allocation while reducing the time cost of the cloud computing task allocation.
- the intermediate allocation path obtained by the ant colony algorithm may be first encoded to obtain the current population of the genetic algorithm, and then the current population is evolved according to a preset fitness value function to generate a next generation population, and the genetics is determined. Whether the number of evolutions of the algorithm reaches the second preset number is to set the optimal individual in the next generation population as the optimal distribution path of the cloud computing task allocation, otherwise the next generation population is set as the current population, and the current population continues to be performed. evolution.
- the task virtual machine paired nodes in each intermediate allocation path are sorted, and a virtual machine sequence of each intermediate allocation path is generated, and the virtual machine sequences are Set as the individual population of the current population in the genetic algorithm, so as to realize the combination of ant colony algorithm and genetic algorithm in cloud computing task allocation.
- the fitness function used is:
- peNum is the number of hosts of a single virtual machine
- costPe is the cost of a single host
- costPerMem is the cost of a single host
- costPerStorage and costPerBw are respectively the memory cost, memory cost and bandwidth cost in the cloud computing environment
- ram size and bw are respectively cloud The amount of memory, memory size, and bandwidth in the computing environment.
- step S104 the cloud computing task is allocated to the virtual machine in the cloud environment according to the optimal allocation path.
- the cloud computing experiment can be simulated in the cloud computing simulation tool and the distributed system simulator of the cloud computing environment (such as CloudSim tool) to reduce the cloud computing task allocation research. Test thresholds and costs.
- the ant colony algorithm optimizes the allocation of the cloud computing task, generates a first preset number of intermediate allocation paths, and performs a second predetermined number of evolutions on the intermediate allocation path by the genetic algorithm to generate a cloud computing.
- the optimal allocation path of the task, the hybrid group intelligent algorithm combined by ant colony and genetic algorithm combines the good robustness and solution search ability of the ant colony algorithm, and the global space search ability and parallelism of the genetic algorithm, effectively improving the efficiency.
- the performance of the cloud computing task allocation ensures the short response time and completion time of the cloud computing task, thereby improving the service quality and user experience of the cloud computing platform.
- Embodiment 2 is a diagrammatic representation of Embodiment 1:
- FIG. 2 shows a structure of a cloud computing task allocation apparatus according to Embodiment 2 of the present invention. For convenience of description, only parts related to the embodiment of the present invention are shown, including:
- the model building unit 21 is configured to construct a cloud task allocation model according to the cloud computing task to be allocated in the cloud task allocation request when receiving the cloud task allocation request of the user.
- the cloud computing tasks to be allocated are obtained from the cloud task allocation requests, and according to the cloud computing tasks, the cloud task allocation model may be constructed.
- the cloud task assignment model since multiple virtual machines are distributed on the host in the cloud environment, the cloud computing task assignment allocates the virtual machines to the cloud computing tasks, and the cloud task assignment model can be constructed by establishing the task virtual machine pairing process. .
- the ant colony optimization unit 22 is configured to perform, by using a cloud task assignment model and a preset ant colony algorithm, a first preset number of times of allocation of the cloud computing tasks to generate a first preset number of intermediate allocation paths.
- the pheromone between the paired nodes of each task virtual machine may be initialized first, and the pheromone matrix in the ant colony algorithm is generated, and then the ants in the ant colony algorithm are out in the virtual pairing node of the task.
- the path is set to the intermediate optimal path in the path, and according to the running time required by the cloud computing task on the virtual machine, the pheromone on the path that each ant passes may be updated to pass the information. Guided by the subsequent ant colony optimization process.
- each ant in the ant colony algorithm selects the next node to be visited in the task virtual machine pairing node by using a preset probability formula when the ant is out of the corresponding path in the task virtual pairing node, thereby improving the path of the ant colony algorithm. Search ability.
- the ant colony algorithm simulates the ant colony phenomenon in the natural environment, so the pheromone update includes the pheromone left by the ants, and the natural volatilization of the pheromone.
- the update formula of the pheromone matrix is expressed as:
- rho is the default volatilization factor
- Delta is the pheromone left by the ant
- Delta Q/max(costVm)
- Q is the preset weight parameter
- costVm[1...M] is the path of the ant.
- the time consumption of each virtual machine, max(costVm) is the longest time consumption of the time consumption of each virtual machine on the path through which the ant passes, so that the running time of the cloud computing task on the virtual machine is on the corresponding path of each ant.
- the pheromone is updated to effectively improve the accuracy of the pheromone update.
- each ant passing the local optimal path releases the pheromone again on the local optimal path according to a preset optimal path pheromone update formula, thereby indirectly Increasing the pheromone of the global path optimal path is beneficial to quickly find the global optimal path.
- determining whether the optimization number of the ant colony algorithm reaches the first preset number is to complete the optimization process of the ant colony algorithm, and obtain a first preset number of intermediate allocation paths, otherwise continue to pass the ant colony algorithm.
- the ants go out of the corresponding path in the task virtual pairing node.
- the genetic evolution unit 23 is configured to perform a second predetermined number of evolutions on the intermediate distribution path by using a preset genetic algorithm to generate an optimal distribution path of the cloud computing task.
- the second preset number is the maximum number of iterations (or the maximum number of evolutions) of the genetic algorithm.
- each generation of the ant colony algorithm takes a long time, and the genetic algorithm costs each generation. The time is shorter, so the second preset number is set to be greater than the first preset number, thereby improving the effect of the cloud computing task allocation while reducing the time cost of the cloud computing task allocation.
- the intermediate allocation path obtained by the ant colony algorithm may be first encoded to obtain the current population of the genetic algorithm, and then the current population is evolved according to a preset fitness value function to generate a next generation population, and the genetics is determined. Whether the number of evolutions of the algorithm reaches the second preset number is to set the optimal individual in the next generation population as the optimal distribution path of the cloud computing task allocation, otherwise the next generation population is set as the current population, and the current population continues to be performed. evolution.
- the task virtual machine paired nodes in each intermediate allocation path are sorted, and a virtual machine sequence of each intermediate allocation path is generated, and the virtual machine sequences are Set as the individual population of the current population in the genetic algorithm, so as to realize the combination of ant colony algorithm and genetic algorithm in cloud computing task allocation.
- the fitness function used is:
- peNum is the number of hosts of a single virtual machine
- costPe is the cost of a single host
- costPerMem is the cost of a single host
- costPerStorage and costPerBw are respectively the memory cost, memory cost and bandwidth cost in the cloud computing environment
- ram size and bw are respectively cloud The amount of memory, memory size, and bandwidth in the computing environment.
- the task assignment unit 24 is configured to allocate the cloud computing task to the virtual machine in the cloud environment according to the optimal allocation path.
- the cloud computing experiment can be simulated in the cloud computing simulation tool and the distributed system simulator of the cloud computing environment (such as CloudSim tool) to reduce the cloud computing task allocation research. Test thresholds and costs.
- the ant colony optimization unit 22 includes:
- a pheromone initialization unit 321 configured to initialize a pheromone between each task virtual machine paired node in the task assignment model to generate a pheromone matrix of the ant colony algorithm
- the path generating unit 322 is configured to generate a corresponding path in the task virtual machine pairing node by the ant of the ant colony algorithm, and set the local optimal path in the corresponding path of each ant as the intermediate allocation path;
- a pheromone updating unit 323, configured to update a pheromone on a corresponding path of each ant according to a running time of the cloud computing task in the virtual environment of the cloud environment in the task allocation model;
- the ant colony optimization determining unit 324 is configured to determine whether the optimization number of the ant colony algorithm reaches the first preset number, and completes the optimization process of the ant colony algorithm, otherwise the trigger path generating unit 322 performs the ant in the task virtual through the ant colony algorithm. The operation of generating the corresponding path in the machine pairing node.
- the genetic evolution unit 23 comprises:
- a population coding unit 331, configured to encode an intermediate allocation path to generate a current population of the genetic algorithm
- a population evolution unit 332 configured to evolve a current population according to a preset fitness function function to generate a next generation population
- the genetic evolution determining unit 333 is configured to determine whether the number of evolutions of the genetic algorithm reaches a second preset number, and the optimal individual of the next generation population is set as the optimal allocation path, otherwise the next generation population is set as the current population, and the trigger is
- the population evolution unit 332 performs the step of evolving the current population according to a preset fitness value function.
- the ant colony algorithm optimizes the allocation of the cloud computing task, generates a first preset number of intermediate allocation paths, and performs a second predetermined number of evolutions on the intermediate allocation path by the genetic algorithm to generate a cloud computing.
- the optimal allocation path of the task, the hybrid group intelligent algorithm combined by ant colony and genetic algorithm combines the good robustness and solution search ability of the ant colony algorithm, and the global space search ability and parallelism of the genetic algorithm, effectively improving the efficiency.
- the performance of the cloud computing task allocation ensures the short response time and completion time of the cloud computing task, thereby improving the service quality and user experience of the cloud computing platform.
- each unit of the cloud computing task allocation device may be implemented by a corresponding hardware or software unit, and each unit may be an independent software and hardware unit, or may be integrated into a soft and hardware unit. To limit the invention.
- Embodiment 3 is a diagrammatic representation of Embodiment 3
- FIG. 4 shows a structure of a cloud computing device according to Embodiment 3 of the present invention. For convenience of description, only parts related to the embodiment of the present invention are shown.
- the cloud computing device 4 of an embodiment of the present invention includes a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40.
- the processor 40 when executing the computer program 42, implements the steps in the above-described method embodiments, such as steps S101 through S104 shown in FIG.
- processor 40 when executing computer program 42, implements the functions of the various units of the apparatus embodiments described above, such as the functions of units 21 through 24 of FIG.
- the ant colony algorithm optimizes the allocation of the cloud computing task, generates a first preset number of intermediate allocation paths, and performs a second predetermined number of evolutions on the intermediate allocation path by the genetic algorithm to generate a cloud computing.
- the optimal allocation path of the task, the hybrid group intelligent algorithm combined by ant colony and genetic algorithm combines the good robustness and solution search ability of the ant colony algorithm, and the global space search ability and parallelism of the genetic algorithm, effectively improving the efficiency.
- the performance of the cloud computing task allocation ensures the short response time and completion time of the cloud computing task, thereby improving the service quality and user experience of the cloud computing platform.
- Embodiment 4 is a diagrammatic representation of Embodiment 4:
- a computer readable storage medium storing a computer program, which when executed by a processor, implements the steps in the foregoing method embodiments, for example, FIG. Steps S101 to S104 are shown.
- the computer program when executed by the processor, implements the functions of the various units of the apparatus embodiments described above, such as the functions of units 21 through 24 shown in FIG.
- the ant colony algorithm optimizes the allocation of the cloud computing task, generates a first preset number of intermediate allocation paths, and performs a second predetermined number of evolutions on the intermediate allocation path by the genetic algorithm to generate a cloud computing.
- the optimal allocation path of the task, the hybrid group intelligent algorithm combined by ant colony and genetic algorithm combines the good robustness and solution search ability of the ant colony algorithm, and the global space search ability and parallelism of the genetic algorithm, effectively improving the efficiency.
- the performance of the cloud computing task allocation ensures the short response time and completion time of the cloud computing task, thereby improving the service quality and user experience of the cloud computing platform.
- the computer readable storage medium of the embodiments of the present invention may include any entity or device capable of carrying computer program code, a recording medium such as a ROM/RAM, a magnetic disk, an optical disk, a flash memory, or the like.
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Abstract
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Claims (10)
- 一种云计算任务分配方法,其特征在于,所述方法包括下述步骤:当接收到用户的云任务分配请求时,根据所述云任务分配请求中待分配的云计算任务,构建云任务分配模型;通过所述云任务分配模型和预设的蚁群算法,对所述云计算任务的分配进行第一预设数目次优化,生成所述第一预设数目个中间分配路径;通过预设的遗传算法对所述中间分配路径进行第二预设数目次进化,生成所述云计算任务的最优分配路径;将所述云计算任务按照所述最优分配路径分配给云环境中的虚拟机。
- 如权利要求1所述的方法,其特征在于,通过所述云任务分配模型和预设的蚁群算法,对所述云计算任务的分配进行第一预设数目次优化,生成所述第一预设数目个中间分配路径的步骤,包括:对所述任务分配模型中每个任务虚拟机配对节点间的信息素进行初始化,生成所述蚁群算法的信息素矩阵;通过所述蚁群算法的蚂蚁在所述任务虚拟机配对节点中生成相应路径,将所述每只蚂蚁相应路径中的局部最优路径设置为所述中间分配路径;根据所述任务分配模型中所述云计算任务在所述云环境的虚拟机上的运行时间,对所述每只蚂蚁相应路径上的信息素进行更新;判断所述蚁群算法的优化次数是否达到所述第一预设数目,是则完成所述蚁群算法的优化过程,否则跳转至通过所述蚁群算法的蚂蚁在所述任务虚拟机配对节点中生成相应路径的步骤。
- 如权利要求2所述的方法,其特征在于,对所述每只蚂蚁相应路径上的信息素进行更新的步骤之后,判断所述蚁群算法的优化次数是否达到所述第一预设数目的步骤之前,所述方法还包括:按照预设的最优路径信息素更新公式,通过经过所述局部最优路径的每只蚂蚁对所述局部最优路径上的信息素进行更新。
- 如权利要求1所述的方法,其特征在于,通过预设的遗传算法对所述中间分配路径进行第二预设数目次进化,生成所述云计算任务的最优分配路径的步骤,包括:对所述中间分配路径进行编码,以生成所述遗传算法的当前种群;根据预设的适应值函数对所述当前种群进行进化,生成下一代种群;判断所述遗传算法的进化次数是否达到所述第二预设数目,是则将所述下一代种群的最优个体设置为所述最优分配路径,否则将所述下一代种群设置为所述当前种群,跳转至根据预设的适应值函数对所述当前种群进行进化的步骤。
- 如权利要求4所述的方法,其特征在于,对所述中间分配路径进行编码的步骤,包括:对所述中间分配路径中的任务虚拟机配对节点进行排序,生成所述每个中间分配路径的虚拟机序列;将所述虚拟机序列设置为所述遗传算法中的种群个体,由所述种群个体构成所述当前种群。
- 一种云计算任务分配装置,其特征在于,所述装置包括:模型构建单元,用于当接收到用户的云任务分配请求时,根据所述云任务分配请求中待分配的云计算任务,构建云任务分配模型;蚁群优化单元,用于通过所述云任务分配模型和预设的蚁群算法,对所述云计算任务的分配进行第一预设数目次优化,生成所述第一预设数目个中间分配路径;遗传进化单元,用于通过预设的遗传算法对所述中间分配路径进行第二预设数目次进化,生成所述云计算任务的最优分配路径;以及任务分配单元,用于将所述云计算任务按照所述最优分配路径分配给云环境中的虚拟机。
- 如权利要求6所述的装置,其特征在于,所述蚁群优化单元包括:信息素初始化单元,用于对所述任务分配模型中每个任务虚拟机配对节点 间的信息素进行初始化,生成所述蚁群算法的信息素矩阵;路径生成单元,用于通过所述蚁群算法的蚂蚁在所述任务虚拟机配对节点中生成相应路径,将所述每只蚂蚁相应路径中的局部最优路径设置为所述中间分配路径;信息素更新单元,用于根据所述任务分配模型中所述云计算任务在所述云环境的虚拟机上的运行时间,对所述每只蚂蚁相应路径上的信息素进行更新;以及蚁群优化判断单元,用于判断所述蚁群算法的优化次数是否达到所述第一预设数目,是则完成所述蚁群算法的优化过程,否则触发所述路径生成单元执行通过所述蚁群算法的蚂蚁在所述任务虚拟机配对节点中生成相应路径的操作。
- 如权利要求6所述的装置,其特征在于,所述遗传进化单元包括:种群编码单元,用于对所述中间分配路径进行编码,以生成所述遗传算法的当前种群;种群进化单元,用于根据预设的适应值函数对所述当前种群进行进化,生成下一代种群;以及遗传进化判断单元,用于判断所述遗传算法的进化次数是否达到所述第二预设数目,是则将所述下一代种群的最优个体设置为所述最优分配路径,否则将所述下一代种群设置为所述当前种群,触发所述种群进化单元执行根据预设的适应值函数对所述当前种群进行进化的步骤。
- 一种云计算设备,包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序,其特征在于,所述处理器执行所述计算机程序时实现如权利要求1至5任一项所述方法的步骤。
- 一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,其特征在于,所述计算机程序被处理器执行时实现如权利要求1至5任一项所述方法的步骤。
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CN108776612A (zh) | 2018-11-09 |
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