WO2015188504A1 - 对角加边模型分解协调计算的数据中心求解方法 - Google Patents
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/008—Reliability or availability analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45583—Memory management, e.g. access or allocation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45591—Monitoring or debugging support
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45595—Network integration; Enabling network access in virtual machine instances
Definitions
- the invention relates to a diagonal coordinate model decomposition and coordination calculation of a power system.
- it relates to a data center solving method for diagonal angled model decomposition coordination calculation.
- Cloud computing data center is a new type of Internet computing model.
- VM Virtual Machine
- enhancement technology provides ideas for complex computing in power systems. Data centers have more physical machines. The actual computation of a complex power system is broken down into multiple small tasks, the tasks are mapped to virtual machines, and the virtual machines are placed in the server for calculation. Through such calculations, the computation time of complex operations is greatly shortened.
- the technical problem to be solved by the present invention is to provide a packing model that establishes an energy efficiency priority in the solution process, and shortens the diagonalization and edge model decomposition of the power system diagonal addition model decomposition and coordination calculation time and the data center energy consumption reduction. Coordinated computing data center solution method.
- the technical solution adopted by the invention is: a data center solving method for diagonally-edge model decomposition and coordination calculation, comprising the following steps:
- the E server is the energy consumption of a single server in the data center
- the E switch is the energy consumption of a single switch in the data center
- N is the number of data center servers used
- T is the number of data center switches used
- the energy consumption model for a single server is:
- P baseline represents the power consumption of the server during no-load operation
- t max represents the continuous running time of the server
- P VM represents the power consumption of a virtual machine in the server
- t k represents the running time of a virtual machine in the server
- M Indicates the number of virtual machines on the server
- the energy consumption model for a single switch is:
- P switch represents the running power consumption of the switch
- t max represents the running time of the switch
- the collection of virtual machines is: with Represents the MIPS size and memory size of a single virtual machine.
- the capacity of a single server is with Represents the MIPS size and memory size of a single server.
- the total energy consumption model of the data center server is as follows:
- X j,i 1 means that virtual machine j is placed in server i
- X j,i 0 means that virtual machine j is not placed in server machine i
- H i 1 means that server i is used
- H i 0 means that server i is not being used
- step 7) loading the virtual machine set in step 3) into the server of the data center;
- Step 7) is boxed according to the constraints on the total energy consumption model of the data center server as described in step 5).
- Each task is loaded into a virtual machine in the data center server as described in step 8), using an indiscriminate order placement method or a binding method as described below:
- the data center solving method of the diagonal plus-edge model decomposition and coordination calculation of the invention can solve the diagonal-edge model decomposition and coordination algorithm widely applied to the power system through the data center, and the traditional single-machine multi-process and multi-machine Compared with the simple scheduling calculation mode, the invention can shorten the calculation time of the decomposition coordination algorithm and reduce the data center energy consumption, and the advantages of using the data center calculation become more obvious as the calculation scale and complexity of the power node network increases.
- 1 is a specific flow chart of a data center solving method for diagonal plus edge model decomposition coordination calculation
- Figure 3 is a workflow diagram of a diagonal-edge model decomposition coordination algorithm.
- the data center solving method for the diagonal plus-edge model decomposition coordination calculation of the present invention comprises the following steps:
- the power network can be partitioned by a node tearing method, a branch cutting method, or a unified network blocking method.
- the server's MIPS is set to 2580
- the memory is 512MB
- the virtual machine MIPS size is set to [700,900]
- the virtual memory is 128MB.
- the E server is the energy consumption of a single server in the data center
- the E switch is the energy consumption of a single switch in the data center
- N is the number of data center servers used
- T is the number of data center switches used
- the energy consumption model for a single server is:
- P baseline represents the power consumption of the server during no-load operation
- t max represents the continuous running time of the server
- P VM represents the power consumption of a virtual machine in the server
- t k represents the running time of a virtual machine in the server
- M Indicates the number of virtual machines on the server
- the energy consumption model for a single switch is:
- P switch represents the running power consumption of the switch
- t max represents the running time of the switch
- the energy consumption of the data center server accounts for about 80% of the total energy consumption of the IT equipment, according to the hierarchical sequence method, we can first calculate the energy consumption of the server, then calculate the energy consumption of the switch, and finally get the total IT equipment of the data center. Energy consumption can therefore minimize the energy consumption of the server and attribute the energy consumption model of the server to a packing model to reduce the total energy consumption.
- the server's energy consumption model is reduced to a boxing model, and the server's minimum energy model is given:
- the collection of virtual machines is: with Represents the MIPS size and memory size of a single virtual machine.
- the capacity of a single server is with Represents the MIPS size and memory size of a single server.
- the total energy consumption model of the data center server is as follows:
- X j,i 1 means that virtual machine j is placed in server i
- X j,i 0 means that virtual machine j is not placed in server machine i
- H i 1 means that server i is used
- H i 0 means that server i is not being used
- Best adaptation algorithm The best adaptation algorithm is to put the items into the box in order, first put the first item into the first box, then consider the second item, if the first box consumes the second Items, put the second item into the first box, if you can't put it down, reopen a box, in order, when the i-th item is put, put it in all the items that can hold the item and put In the box with the smallest remaining space after entering, when all the boxes do not meet the requirements, reopen a box.
- Descending optimal adaptation algorithm Before packing, sort the items in descending order according to their size, then put the first item into the first box, then consider the second item, if the first box can put down the first Two items, put the second item into the first box, if you can't put it down, reopen a box, in order, when the i-th item is placed, put it in all the items that can hold the item and In the box with the smallest remaining space after the insertion, when all the boxes do not meet the requirements, reopen a box;
- step 7) loading the virtual machine set in step 3) into the server of the data center, specifically according to the constraint on the total energy consumption model of the data center server described in step 5);
- Each task in step 2) is correspondingly loaded into a virtual machine in the data center server, and the task is mapped to the data center server, as shown in FIG. 2, and each task is loaded into a virtual machine.
- the workflow diagram can be obtained according to the calculation process of the decomposition and coordination algorithm of the diagonal plus edge model.
- the workflow diagram is shown in FIG. 3.
- the rectangle in the figure is a task, which represents a calculation step, and the ellipse is data, and the task is directed to the task.
- the input data of the task, the arrow indicated by the task indicates that the task calculates the output result, and since each data is connected to the arrow of one task and the arrow of the other task, it is the communication data between the two tasks.
- the system is divided into three sub-areas, the number of nodes of the three sub-areas is 35, 35, 48, respectively, and the number of nodes of the virtual border network is 7.
- the data center is a Fat-tree structure, which includes 54 servers and 45 6-port switches.
- Each server is configured as CPU: Pentium4 (2.8GHz), memory: 512Mb, baseline power consumption: 145w, load virtual machine operation power consumption: 10w, switch uses Huawei S3552F-EA three-layer switch, running power: 54w.
- the switch port rate fluctuates between [0,8]Mb;
- the total energy consumption of the data center IT equipment is obtained according to the total energy consumption calculation formula given in step 4).
- the virtual machine is put into the server using the best adaptive algorithm and the optimal ordering algorithm for descending order.
- the task is placed in the virtual machine and placed in the order of the box binding. Therefore, the combination generates four algorithms, namely, the indiscriminate order placement-optimal adaptation algorithm.
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Abstract
Description
Claims (3)
- 一种对角加边模型分解协调计算的数据中心求解方法,其特征在于,包括如下步骤:1)通过已有的网络分块法将电力网络进行划分得到电力网络的分块;2)将电力系统对角加边模型分解协调算法的计算流程中的每一个计算步骤设定为一个任务,确定整个计算过程的任务数;3)获得数据中心服务器的MIPS大小和内存大小,设定虚拟机的个数以及虚拟机中CPU的MIPS大小和内存大小;4)计算数据中心的IT设备总能耗Etotal:式中,Eserver为数据中心单台服务器的能耗,Eswitch为数据中心单个交换机的能耗,N为数据中心服务器的使用台数,T为数据中心交换机的使用个数,其中,单台服务器的能耗模型为:式中:Pbaseline表示服务器的空载运行时的功耗,tmax表示服务器的持续运行时间,PVM表示服务器中一个虚拟机的功耗,tk表示服务器中一个虚拟机的运行时间,M表示服务器上虚拟机的数量;单个交换机的能耗模型为:Eswitch=Pswitchtmax (3)式中:Pswitch表示交换机运行功耗,tmax表示交换机运行时间;5)将服务器的能耗模型归结为装箱模型,并给出服务器的最小能耗模型:对数据中心服务器的总能耗模型的约束如下:Xj,i=0or1 (7)Hi=0or1 (8)式中,Xj,i=1表示虚拟机j放置于服务器i中,Xj,i=0表示虚拟机j未放置于服务器机i中;Hi=1表示服务器i被使用,Hi=0表示服务器i未被使用;6)采用最佳适应算法或降序最佳适应算法对数据中心服务器的总能耗模型求解;7)将步骤3)中所设定的虚拟机装入数据中心的服务器中;8)将步骤2)中的每一个任务对应装入数据中心服务器中的一个虚拟机里。
- 根据权利要求1所述的对角加边模型分解协调计算的数据中心求解方法,其特征在于,步骤7)中是根据步骤5)中所述的对数据中心服务器的总能耗模型的约束进行的装箱的。
- 根据权利要求1所述的对角加边模型分解协调计算的数据中心求解方法,其特征在于,步骤8)中所述的将每一个任务对应装入数据中心服务器中的一个虚拟机里,是采用无差别顺序放置方法或者是采用如下所述的绑定方法进行:(1)根据对角加边模型的分块情况以及任务的指令长度,将计算协调量后的执行过程中每一个块的计算步骤所代表的任务进行绑定,从而实现不同块的并行计算,节省计算时间;(2)对于计算协调量前的各任务,将有输入输出关系的任务组放置在相邻虚拟机中,减少交互数据量,降低通信开销。
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CN110968953B (zh) * | 2019-11-29 | 2022-03-25 | 浙江大学 | 一种基于嵌套对角加边形式的电力系统暂态稳定仿真并行计算方法 |
CN110991034B (zh) * | 2019-11-29 | 2022-03-22 | 浙江大学 | 基于全并行嵌套bbdf的电力系统暂态稳定仿真并行计算方法 |
CN111488052B (zh) * | 2020-04-16 | 2022-03-08 | 中国工商银行股份有限公司 | 应用于物理机集群的容器启用方法和装置、计算机系统 |
CN112235859B (zh) * | 2020-09-22 | 2022-08-05 | 国家卫星气象中心(国家空间天气监测预警中心) | 一种基于多目标约束的动态能耗控制方法 |
CN115237241B (zh) * | 2022-09-26 | 2022-12-09 | 张北云联数据服务有限责任公司 | 一种数据中心节能调度方法及系统 |
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