WO2018014566A1 - Procédé et appareil d'équilibrage de charge, support de stockage lisible par ordinateur et système associé - Google Patents

Procédé et appareil d'équilibrage de charge, support de stockage lisible par ordinateur et système associé Download PDF

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WO2018014566A1
WO2018014566A1 PCT/CN2017/076514 CN2017076514W WO2018014566A1 WO 2018014566 A1 WO2018014566 A1 WO 2018014566A1 CN 2017076514 W CN2017076514 W CN 2017076514W WO 2018014566 A1 WO2018014566 A1 WO 2018014566A1
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memory
cpu
physical machine
consumption type
usage rate
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PCT/CN2017/076514
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English (en)
Chinese (zh)
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何涛涛
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平安科技（深圳）有限公司
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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
    • 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
    • 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
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/485—Task life-cycle, e.g. stopping, restarting, resuming execution
    • G06F9/4856—Task life-cycle, e.g. stopping, restarting, resuming execution resumption being on a different machine, e.g. task migration, virtual machine migration
    • 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
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/503—Resource availability

Definitions

  • the present invention relates to the field of computer technologies, and in particular, to a load balancing method, apparatus, and computer readable storage medium and system.
  • the virtual machine allocation algorithm adopted by the existing cloud computing platform is a random algorithm, which easily leads to a large difference in the load of each physical machine. Some physical machines are at risk of downtime due to excessive load, or some physical machines are subject to some kind of physical machine. The load is too small and wastes resources.
  • Embodiments of the present invention provide a method and device for load balancing, which can implement load balancing of each physical machine.
  • an embodiment of the present invention provides a method for load balancing, which includes: if receiving a request of a virtual machine, acquiring a CPU core number and a memory size of the virtual machine; and according to the obtained CPU core number of the virtual machine
  • the memory size identifies the consumption type of the virtual machine; obtains the current CPU usage and memory usage of each physical machine; according to the consumption type of the virtual machine and the CPU usage and memory usage of each physical machine obtained, according to the preset
  • a rule calculates the equalization parameters of each physical machine; assigns the virtual machine to the physical machine with the largest equalization parameter.
  • an embodiment of the present invention provides a load balancing apparatus, where the apparatus includes an obtaining module, an identifying module, a first calculating module, and an allocating module, wherein the obtaining module is configured to acquire a virtual machine if receiving a request of the virtual machine.
  • the CPU core and the memory size are also used to obtain the current CPU usage and memory usage of each physical machine;
  • the identification module is configured to identify the consumption type of the virtual machine according to the obtained CPU core number and memory size of the virtual machine;
  • the calculation module is configured to calculate an equalization parameter of each physical machine according to a preset first rule according to a consumption type of the virtual machine and a CPU usage and a memory usage rate of each physical machine acquired;
  • the allocation module is configured to allocate the virtual machine Give the physical machine with the largest balance parameter.
  • an embodiment of the present invention provides a computer readable storage medium storing one or more programs, one or more programs executable by one or more processors to perform the following Operation:
  • the CPU core and the memory size of the virtual machine are obtained; the consumption type of the virtual machine is identified according to the CPU core number and the memory size of the obtained virtual machine; and the current CPU usage and memory of each physical machine are obtained.
  • the usage rate is calculated according to the consumption type of the virtual machine and the CPU usage and memory usage of each physical machine obtained, and the equalization parameter of each physical machine is calculated according to the preset first rule; the virtual machine is allocated to the maximum equalization parameter. Physical machine.
  • embodiments of the present invention provide a load balancing system including one or more processors and one or more memories coupled to one or more processors,
  • a memory for storing one or more programs for implementing load balancing; a processor for executing a program stored in the memory to perform the following operations:
  • the CPU core and the memory size of the virtual machine are obtained; the consumption type of the virtual machine is identified according to the CPU core number and the memory size of the obtained virtual machine; and the current CPU usage and memory of each physical machine are obtained.
  • the usage rate is calculated according to the consumption type of the virtual machine and the CPU usage and memory usage of each physical machine obtained, and the equalization parameter of each physical machine is calculated according to the preset first rule; the virtual machine is allocated to the maximum equalization parameter. Physical machine.
  • the equalization parameter of each physical machine is calculated, and then the physical machine that places the virtual machine is determined according to the equalization parameter, and the equalization parameter is related to the CPU usage of the physical machine, the memory usage rate, and the consumption type of the virtual machine, wherein
  • the CPU usage and memory usage of each physical machine are related to the balance of the physical machine cluster, and the consumption type of the virtual machine is related to the internal CPU and memory balance of each physical machine. Therefore, the method considers the overall balance of the physical machine cluster. Considering the load balancing of the internal resources of a single physical machine, the load balancing of the physical machine cluster is more effectively realized.
  • FIG. 1 is a schematic flowchart of a method for load balancing according to an embodiment of the present invention
  • FIG. 2 is a line diagram showing a load imbalance of a physical machine cluster
  • Figure 3 is a schematic diagram of the sub-flow of Figure 1;
  • Figure 4 is a schematic diagram of the sub-flow of Figure 1;
  • Figure 5 is a schematic diagram of the sub-flow of Figure 4.
  • FIG. 6 is a line diagram of load balancing of a physical machine cluster according to an embodiment of the present invention.
  • FIG. 7 is a scatter diagram of load balancing of a physical machine cluster according to an embodiment of the present invention.
  • FIG. 8 is a schematic diagram of a composition of a load balancing apparatus according to an embodiment of the present invention.
  • FIG. 9 is a schematic diagram showing the composition of a first computing unit according to an embodiment of the present invention.
  • FIG. 10 is a hardware structural diagram of a load balancing system according to an embodiment of the present invention.
  • a cluster composed of multiple hosts.
  • One cluster includes several physical machines, and the physical machine is a host for running virtual machines. Because the request from the virtual machine is continuously received, the physical machine needs to be allocated to place the virtual machine. Therefore, it is necessary to select a physical machine with a relatively small load from the physical machine cluster to run the virtual machine according to the load of the physical machine, thereby implementing load balancing of the physical machine cluster.
  • FIG. 1 a load balancing method according to an embodiment of the present invention is shown. As shown in the figure, a method for load balancing includes S100 to S106.
  • CPU central processing unit
  • CPU central processing unit
  • the number of CPU cores is used to indicate the number of CPU cores.
  • the consumption type of the virtual machine includes a regular type, a CPU consumption type, and a memory consumption type.
  • the virtual machines of different consumption types occupy different resources, and the CPU resources occupied by the CPU consumption type are occupied by the memory consumption type and the regular type.
  • the CPU resources are large; the memory resources occupied by the memory consumption type have more memory resources than the CPU consumption type and the regular type.
  • the internal CPU and memory resources of some physical machines are not balanced. Therefore, it is necessary to consider the balance of internal resources of the physical machine and the consumption type of the virtual machine, and place the virtual machine on a better physical machine to achieve resource balance within the physical machine.
  • the specific implementation process is to determine the consumption type of the virtual machine according to the ratio of the acquired CPU core number and the memory size.
  • S104 Calculate an equalization parameter of each physical machine according to a preset first rule according to the consumption type of the virtual machine and the acquired CPU usage rate and memory usage rate of each physical machine.
  • the equalization parameter is represented by S2.
  • the equalization parameter S2 is used to indicate the selectivity of the physical machine when considering load balancing of the entire physical machine cluster and load balancing of the internal CPU and memory resources of the physical machine.
  • the preset first rule is used to indicate the consumption type of the virtual machine, the obtained CPU usage of each physical machine, and the relationship between the memory usage rate and the equalization parameter, and the equalization parameters and physical machines of the physical machine for different consumption types of virtual machines.
  • the relationship between CPU usage and memory usage is different. Specifically, if the consumption type of the virtual machine is CPU consumption or memory consumption, the equalization parameter of the physical machine is also related to the ratio of the CPU usage and the memory usage of the physical machine. If the consumption type of the virtual machine is a regular type, the physical type The equalization parameters of the machine are independent of the ratio of CPU usage and memory usage of the physical machine.
  • the virtual machine Assign the virtual machine to the physical machine with the largest equalization parameter. Since the equalization parameter S2 is used to indicate the load balance of the physical machine cluster as a whole and the load balancing of the internal CPU and memory resources of the physical machine, the selectivity of the physical machine. The larger the physical machine equalization parameter S2, the better it is to consider the physical machine to place the virtual machine. Therefore, the virtual machine is allocated to the physical machine with the largest balance parameter S2, so as to implement load balancing of the physical machine cluster. It should be understood that the load balancing of the physical machine cluster includes load balancing between physical machines and internal resources of a single physical machine. balanced.
  • L1 is the CPU usage rate
  • L2 is the memory usage rate.
  • the CPU usage is high and the memory usage is low. Larger, it will lead to the resource utilization of these physical machines is not high, at full load, it is easy to cause a large waste of CPU or memory, and easily lead to the balance of CPU and memory in the physical machine. Therefore, it is necessary to consider the load balancing of memory and CPU resources inside a single physical machine.
  • FIG. 3 a schematic diagram of the sub-flow of FIG. 1 is provided in the embodiment of the present invention.
  • S102 identifies the consumption type of the virtual machine according to the obtained CPU core number and memory size, including:
  • S302. Determine whether the ratio is in a preset first range or a preset second range or a preset third range.
  • the preset second range is greater than the preset first range and the preset third range, and the preset first range is greater than the preset third range.
  • the preset first range is preferably greater than 0.25 and less than 1, and the preset second range is greater than or equal to 1, and the preset third range is less than or equal to 0.25.
  • the consumption type of the identified virtual machine is a regular type.
  • the consumption type of the recognition virtual machine is a CPU consumption type.
  • the consumption type of the recognition virtual machine is a memory consumption type.
  • the consumption type is identified according to the CPU core number of the virtual machine and the memory size, and the identified consumption type is used to select the physical machine when considering the balance of the internal resources of the physical machine.
  • the equalization parameter S2 calculated in S104 is used to indicate the selectivity of the physical machine when considering load balancing of the entire physical machine cluster and load balancing of the internal CPU and memory resources of the physical machine.
  • the first equalization degree S1 is used to indicate the selectivity of the physical machine when considering the load balancing of the physical machine cluster as a whole; and the second equalization degree t is used to represent the load balancing of the internal CPU and memory resources of the physical machine, and the physical machine is used. The selectivity.
  • S104 calculates each physical machine according to a preset first rule according to the consumption type of the virtual machine and the acquired CPU usage rate and memory usage rate of each physical machine.
  • Equilibrium parameters including S401 ⁇ S405:
  • the first equalization degree S1 is used to indicate the selectivity of the physical machine when considering load balancing of the entire physical machine cluster. It should be understood that if only the load balancing of the entire cluster is considered, and the internal resource balancing of each physical machine is not considered, the physical machine with the largest first equalization S1 will be selected to place the virtual machine.
  • the virtual machine should be allocated to the physical machine with the lowest memory usage. Similarly, only the CPU is considered, and the memory is not considered. When the impact, the virtual machine should be requested to the physical machine with the lowest CPU usage. In this embodiment, the CPU and memory are considered at the same time. Therefore, the first equalization S1 is related to the CPU usage Ui and the memory usage rate Mi. A balance S1 is a function of CPU usage and memory usage.
  • the preset second rule is used to indicate the relationship between the CPU usage and the memory usage of each physical machine and the first equalization degree.
  • the preset second rule is specifically: if the CPU and the memory are paired with the first equalization degree.
  • the degree of influence is the same or the degree of influence between the two is negligible, the CPU usage and memory usage of one physical machine are greater than the CPU usage and memory usage of another physical machine, respectively, then the first physical machine
  • the equalization degree S1 is smaller than the first equalization degree S1 of the other physical machine; if the difference between the influence of the CPU and the memory on the first equalization degree is not negligible, the first equalization degree and the CPU usage rate, the memory usage rate, and the selection weight of the CPU relative memory
  • the selection weight W1 is used to indicate the difference in the degree of influence of the CPU and memory on the first balance.
  • S401 calculates the first equalization degree of each physical machine according to the preset second rule according to the CPU usage rate and the memory usage rate of each physical machine, including S501 to S504:
  • S501 Calculate a reciprocal of the current CPU usage of each physical machine and a reciprocal of the memory usage rate.
  • S502 sum the reciprocal of the CPU usage of all the physical machines to obtain the total weight of the CPU, and sum the reciprocal of the memory usage of all the physical machines to obtain the total memory weight.
  • the preset CPU relative memory selection weight w1 indicates that the CPU influence factor or the memory influence factor is prioritized.
  • w1 is equal to 0.5
  • the second rule is that the CPU usage and the memory usage rate of one physical machine are respectively greater than the CPU usage and the memory usage rate of another physical machine, and the first physical machine One equalization S1 is smaller than the first equalization S1 of the other physical machine.
  • the equalization parameter assigned to each physical machine is the first equalization degree of the corresponding physical machine.
  • the consumption type of the virtual machine is a regular type
  • the consumption type of the virtual machine is a regular type
  • the virtual machine is allocated to the physical machine, no additional imbalance is caused to the internal resources of the physical machine.
  • the consumption type of the virtual machine is CPU consumption type or memory consumption type
  • Uavg is used to indicate the average CPU usage of the physical machine cluster
  • Mavg is the average memory usage of the physical machine cluster. It should be noted that if the consumption type of the virtual machine is a CPU consumption type or a memory consumption type, the equalization parameter of the computing physical machine further includes S404 and S405.
  • the second equalization degree t represents the selectivity of the physical machine when considering load balancing of the internal CPU and memory resources of the physical machine.
  • the second equalization t is equal to the difference between the ratio of the CPU usage Ui of the physical machine and the memory usage rate Mi to the ratio of the average CPU usage Uavg and the average memory usage ratio Mavg, as shown below:
  • t>0 it means that the CPU usage of the physical machine is relatively higher than the memory usage, that is, the CPU consumption is relatively more memory consumption.
  • the larger t is, the more unbalanced the CPU and memory resources in the physical machine are; when t ⁇ 0 , indicating that the memory usage of the physical machine is relatively high compared to the CPU usage, that is, the memory consumption is relatively large compared to the CPU consumption, and the larger the absolute value of t, the more unbalanced the CPU and memory resources in the physical machine.
  • the first equalization S1 is used to indicate the selectivity of the physical machine when considering the load balancing of the physical machine cluster as a whole; and the second equalization t represents the selectivity of the physical machine when considering the load balancing of the internal CPU and memory resources of the physical machine.
  • the equalization parameter is related to the consumption type of the virtual machine, the first equalization degree, and the second equalization degree.
  • the equalization parameter S2 has:
  • n is an odd number
  • k is a positive number
  • k and n are set to equalize the overall balance of the physical machine cluster and the internal resource balance of the physical machine, even if the values of S1 and k ⁇ t n are of the same order of magnitude.
  • the consumption type of the virtual machine is CPU consumption type
  • the physical machine with high memory and high CPU consumption should be selected to run the virtual machine, thereby balancing the internal resources of the physical machine, and the second equalization degree of the selected physical machine. The value is less than zero.
  • the equalization parameter S2 has:
  • n is an odd number
  • k is a positive number
  • k and n are set to make the overall balance of the physical machine cluster and the internal resource balance of the physical machine equivalent, even if the values of S1 and k ⁇ t n are of the same order of magnitude. It should be understood that if the consumption type of the virtual machine is memory consumption type, the physical machine with high CPU memory consumption should be selected to run the virtual machine, thereby balancing the internal resources of the physical machine, and the second equilibrium degree t value of the selected physical machine. Greater than zero.
  • the calculation is performed by allocating 8C/8G and 4C/32G of the physical machine, and the range of Ui/Mi is (0.32, 2.56), so the range of the second equalization t is (-2.24, 2.24).
  • the equalization parameter S2 If the consumption type of the virtual machine is CPU consumption type, the equalization parameter S2:
  • the equalization parameter S2 has:
  • the second equalization degree t is processed such that the range of t/3 is (-1, 1), and after the third power, the influence of the CPU and the memory resources in the physical machine is unbalanced, and the influence on the equalization parameter S2 is performed.
  • the consumption type of the virtual machine is CPU consumption type
  • the first equalization degree of one physical machine is greater than the first equalization degree of another physical machine
  • the second equalization degree of the one physical machine is smaller than another physical medium.
  • the second equalization degree of the machine, the equalization parameter of the one physical machine is greater than the equalization parameter of the other physical machine; if the consumption type of the virtual machine is the memory consumption type, the first equalization degree and the second equalization degree of one physical machine are respectively greater than another
  • the first equalization degree and the second equalization degree of the physical machine, the equalization parameter of the one physical machine is greater than the equalization parameter of the other physical machine.
  • the above load balancing method is adopted, that is, the load balancing of the physical machine cluster as a whole is considered, and the load balancing of the CPU and memory resources of a single physical machine is considered, so that the CPU usage and memory of different physical machines are made by this method.
  • the usage rate is close, and the CPU usage and memory usage of a single physical machine are also close, which ensures the load balancing between the physical machines and the internal resources of a single physical machine, and also improves the resources in each physical machine. Utilization, when the physical machine is fully loaded, the number of virtual machines running increases. Referring to FIG. 6 and FIG. 7, the figure shows a line graph and a scatter plot of the CPU usage and memory usage of the physical machine after using the above load balancing method.
  • L1 is the CPU usage rate
  • L2 is Memory usage.
  • the load balancing method also includes:
  • the average CPU usage and average memory usage are calculated.
  • the integrated equalization Q is obtained according to the degree of dispersion of the CPU usage of each physical machine and the average usage of the CPU, and the degree of dispersion of the memory usage of each physical machine and the average memory usage.
  • the integrated equalization Q is used to represent the physical machine cluster. Load balancing. Specifically, the weighted calculation is performed by calculating the mean square error of the CPU usage rate and the memory usage rate of all the physical machines.
  • the CPU equalization Q1 of the physical machine cluster is a mean square error calculation for the CPU usage of all physical machines.
  • Mi represents the memory usage of the i-th physical machine
  • Mavg represents the average memory usage of the physical machine cluster.
  • the memory balance Q2 of the physical machine cluster is the mean square error calculation for the memory usage of all physical machines:
  • W2 represents the equalization weight of the CPU relative to the memory when considering the integrated equalization degree. If W2 is larger, it means that the influence of the CPU on the integrated equalization is preferably considered. This is because the impact of CPU and memory on the overall balance may be different. In this embodiment, the CPU and the memory are temporarily set to have similar effects on the integrated equalization, and the difference between them is ignored, and W2 is taken as 0.5.
  • the load balancing situation of the physical machine cluster can be known according to the comprehensive equalization degree Q. If the integrated equalization degree is smaller, the representation is more balanced, so that the degree of load balancing of the physical machine cluster can be monitored in real time according to the integrated equalization degree Q, so as to be timely. Adjust the load balancing of the physical machine cluster or use the integrated equalization Q to monitor whether an abnormal virtual machine is allocated.
  • the first equalization degree and the second equalization degree calculation manner may be adjusted according to the integrated equalization degree Q or the relationship between the first equalization degree, the second equalization degree, and the equalization parameter may be adjusted according to the integrated equalization degree Q, so as to be physical.
  • the load of the cluster is more balanced.
  • a person skilled in the art may understand that all or part of the steps of implementing the above embodiments may be completed by hardware, or may be instructed by a program to execute related hardware, and the program may be stored in a computer readable storage medium.
  • the storage medium mentioned may be a read only memory, a magnetic disk or an optical disk or the like.
  • the invention also provides a computer readable storage medium having stored one or more programs, one or more programs executable by one or more processors to perform the operations:
  • the following operations are specifically performed:
  • the judgment ratio is whether the preset first range or the preset second range or the preset third range.
  • the preset second range is greater than the preset first range and the preset third range, and the preset first range is greater than the preset third range;
  • the consumption type of the identified virtual machine is a regular type
  • the consumption type of the recognition virtual machine is CPU consumption type
  • the consumption type of the recognition virtual machine is a memory consumption type, wherein the preset second range is greater than the preset first range and the preset third range, the preset A range is greater than the preset third range.
  • the preset first range is: greater than 0.25 and less than 1
  • preset second The range is: greater than or equal to 1
  • the preset third range is less than or equal to 0.25.
  • the one or more programs are executed by one or more processors, according to a consumption type of the virtual machine and a CPU usage and a memory usage rate of each physical machine acquired, according to a preset
  • the first rule calculates the equalization parameter of each physical machine, the following operations are specifically performed:
  • the equalization parameter assigned to each physical machine is the first equalization degree of the corresponding physical machine
  • the average CPU usage rate and the average memory usage rate are calculated according to the obtained CPU usage rate and memory usage rate of each physical machine;
  • the equalization parameters of each physical machine are calculated according to the consumption type of the virtual machine, the first equalization degree of each physical machine, and the second equalization degree.
  • the second equalization is equal to the difference between the ratio of the CPU usage and the memory usage of the physical machine to the ratio of the average CPU usage to the average memory usage.
  • the relationship is as follows:
  • t>0 it means that the CPU usage of the physical machine is relatively higher than the memory usage, that is, the CPU consumption is relatively more memory consumption.
  • the larger t is, the more unbalanced the CPU and memory resources in the physical machine are; when t ⁇ 0 , indicating that the memory usage of the physical machine is relatively high compared to the CPU usage, that is, the memory consumption is relatively large compared to the CPU consumption, and the larger the absolute value of t, the more unbalanced the CPU and memory resources in the physical machine.
  • the equalization parameter S2 has:
  • n is an odd number
  • k is a positive number
  • k and n are set to equalize the overall balance of the physical machine cluster and the internal resource balance of the physical machine, even if the values of S1 and k ⁇ t n are of the same order of magnitude.
  • the consumption type of the virtual machine is CPU consumption type
  • the physical machine with high memory and high CPU consumption should be selected to run the virtual machine, thereby balancing the internal resources of the physical machine, and the second equalization degree of the selected physical machine. The value is less than zero.
  • the equalization parameter S2 has:
  • n is an odd number
  • k is a positive number
  • k and n are set to make the overall balance of the physical machine cluster and the internal resource balance of the physical machine equivalent, even if the values of S1 and k ⁇ t n are of the same order of magnitude. It should be understood that if the consumption type of the virtual machine is memory consumption type, the physical machine with high CPU memory consumption should be selected to run the virtual machine, thereby balancing the internal resources of the physical machine, and the second equilibrium degree t value of the selected physical machine. Greater than zero.
  • the calculation is performed by allocating 8C/8G and 4C/32G of the physical machine, and the range of Ui/Mi is (0.32, 2.56), so the range of the second equalization t is (-2.24, 2.24).
  • the equalization parameter S2 If the consumption type of the virtual machine is CPU consumption type, the equalization parameter S2:
  • the equalization parameter S2 has:
  • the second equalization degree t is processed such that the range of t/3 is (-1, 1), and after the third power, the influence of the CPU and the memory resources in the physical machine is unbalanced, and the influence on the equalization parameter S2 is performed.
  • the consumption type of the virtual machine is a CPU consumption type
  • a first equalization degree of one physical machine is greater than a first equalization degree of another physical machine
  • a second equalization degree of the one physical machine is smaller than the other a second equalization degree of the physical machine
  • the equalization parameter of the one physical machine is greater than the equalization parameter of the another physical machine
  • the consumption type of the virtual machine is a memory consumption type
  • the first equalization degree of a physical machine The second equalization degree is respectively greater than the first equalization degree and the second equalization degree of the other physical machine, and the equalization parameter of the one physical machine is greater than the equalization parameter of the another physical machine.
  • the CPU usage and the memory usage rate of each physical machine to be acquired are calculated according to a preset second rule.
  • the following operations are performed:
  • the sum of the CPU usage of all physical machines is summed to obtain the total weight of the CPU, and the reciprocal of the memory usage of all physical machines is summed to obtain the total memory weight;
  • the first equalization degree S1 is calculated according to the calculated CPU ratio c of each physical machine and the memory ratio m and the selection weight w1 of the preset CPU relative memory.
  • the relationship between the first equalization degree S1 and the CPU ratio c, the memory ratio m, and the CPU relative memory selection weight w1 is as follows:
  • the preset CPU relative memory selection weight w1 indicates that the CPU influence factor or the memory influence factor is prioritized. The larger the w1 is, the more priority is given to the influence of the CPU factor on the overall load balancing of the physical machine cluster, and the smaller the w1 is, the memory factor is given priority. Impact on overall load balancing of physical machine clusters.
  • the one or more programs are also executable by one or more processors to perform the following operations:
  • the integrated equalization Q is obtained according to the degree of dispersion of the CPU usage of each physical machine and the average usage of the CPU, and the degree of dispersion of the memory usage of each physical machine and the average memory usage.
  • the integrated equalization Q is used to represent the physical machine cluster. Load balancing. Specifically, the weighted calculation is performed by calculating the mean square error of the CPU usage rate and the memory usage rate of all the physical machines.
  • a load balancing device is provided in an embodiment of the present invention.
  • the device 100 is configured to allocate a physical machine to place and operate a virtual machine.
  • the device 100 communicates with a physical machine and a virtual machine respectively, as shown in the figure.
  • the device includes a receiving module 81, an obtaining module 82, an identifying module 83, a first calculating module 84, and an assigning module 85.
  • the receiving module 81 is configured to receive a request of the virtual machine.
  • the obtaining module 82 is configured to acquire the CPU core number and the memory size of the virtual machine and also obtain the current CPU usage and memory usage rate of each physical machine.
  • the CPU central processing unit
  • the CPU central processing unit
  • the number of CPU cores is used to indicate the number of CPU cores.
  • the identification module 83 is configured to identify the consumption type of the virtual machine according to the acquired CPU core number and memory size of the virtual machine.
  • the first calculating module 84 calculates the equalization parameter of each physical machine according to the preset first rule according to the consumption type of the virtual machine and the acquired CPU usage rate and memory usage rate of each physical machine.
  • the allocation module 85 is configured to allocate the virtual machine to the physical machine with the largest equalization parameter.
  • the identification module 83 identifies the consumption type of the virtual machine
  • the consumption type of the virtual machine includes a regular type, a CPU consumption type, and a memory consumption type, and different virtual types of virtual machines occupy different resources, and the CPU
  • the CPU resources occupied by the consumption type are more CPU resources than the memory consumption type and the conventional type; the memory resources occupied by the memory consumption type have more memory resources than the CPU consumption type and the regular type.
  • the identification module 83 further includes a first operation unit 831, a determination unit 832, and a processing unit 833.
  • the first operation unit 831 is configured to calculate a ratio of the obtained CPU core number to the memory size.
  • the determining unit 832 is configured to determine whether the ratio is in the preset first range or the preset second range or the preset third range.
  • the preset second range is greater than the preset first range and the preset third range, and the preset first range is greater than the preset third range.
  • the preset first range is preferably greater than 0.25 and less than 1, and the preset second range is greater than or equal to 1, and the preset third range is less than or equal to 0.25.
  • the processing unit 833 is configured to: if the determining unit 832 determines that the ratio is in the preset first range, identify that the consumption type of the virtual machine is a regular type; determine that the ratio is in the preset second range, and identify that the consumption type of the virtual machine is a CPU consumption type and It is determined that the ratio ratio is in the preset third range, and the consumption type of the recognition virtual machine is a memory consumption type.
  • the first calculation module 84 calculates the equalization parameter of each physical machine, which is the selectivity of the physical machine when comprehensively considering the load balancing of the physical machine cluster as a whole and the load balancing of the internal CPU and memory resources of the physical machine.
  • the first equalization degree S1 is used to indicate the selectivity of the physical machine when considering the load balancing of the physical machine cluster as a whole; and the second equalization degree t is used to represent the load balancing of the internal CPU and memory resources of the physical machine, and the physical machine is used.
  • the first calculation module 84 includes a first calculation unit 841, a setting unit 842, a second calculation unit 843, and a third calculation unit 844 and a fourth calculation unit 845.
  • the first calculating unit 841 is configured to calculate the first equalization degree of each physical machine according to a preset second rule by using the obtained CPU usage and memory usage of each physical machine.
  • the first equalization degree S1 is used to indicate the selectivity of the physical machine when considering load balancing of the entire physical machine cluster. It should be understood that if only the load balancing of the entire cluster is considered, regardless of the individual The internal resources of the machine are balanced, and the virtual machine with the largest current balance S1 is selected to place the virtual machine. It should be understood that if only memory is considered, regardless of the influence factor of the CPU, in order to load balance the physical machine cluster, the virtual machine should be allocated to the physical machine with the lowest memory usage. Similarly, only the CPU is considered, and the memory is not considered. When the impact, the virtual machine should be requested to the physical machine with the lowest CPU usage. In this embodiment, the CPU and memory are considered at the same time. Therefore, the first equalization S1 is related to the CPU usage Ui and the memory usage rate Mi. A balance S1 is a function of CPU usage and memory usage.
  • the first calculation unit 841 includes a second operation unit 8411, a summation unit 8412, a third operation unit 8413, and a fourth operation unit 8414.
  • the second operation unit 8411 is configured to calculate a reciprocal of the current CPU usage of each physical machine and a reciprocal of the memory usage rate.
  • the summation unit 8412 is configured to sum the reciprocal of the CPU usage of all the physical machines to obtain the total CPU weight, and sum the reciprocal of the memory usage of all the physical machines to obtain the total memory weight.
  • the third operation unit 8413 is configured to calculate a ratio of the reciprocal of the CPU usage of each physical machine to the total weight of the CPU, and obtain a ratio of the CPU ratio c and a reciprocal of the memory usage of each physical machine to the total memory weight to obtain a memory ratio. m.
  • the fourth operation unit 8414 is configured to calculate the first equalization degree S1 according to the calculated CPU ratio c of each physical machine and the memory ratio m and the selection weight w1 of the preset CPU relative memory.
  • the relationship between the first equalization degree S1 and the CPU ratio c, the memory ratio m, and the CPU relative memory selection weight w1 is as follows:
  • the preset CPU relative memory selection weight w1 indicates that the CPU influence factor or the memory influence factor is prioritized.
  • the difference between the CPU factor and the memory factor is temporarily ignored, and w1 is set to 0.5.
  • w1 can be set to other values according to the influence degree of the CPU factor or the memory factor.
  • the setting unit 842 is configured to: if the consumption type of the virtual machine is a regular type, give each physical machine The balance parameter is the first equalization of the corresponding physical machine.
  • the second calculating unit 843 is configured to calculate the average CPU usage rate and the average memory usage rate according to the acquired CPU usage rate and memory usage rate of each physical machine if the consumption type of the virtual machine is a CPU consumption type or a memory consumption type.
  • Uavg is used to indicate the average CPU usage of the physical machine cluster
  • Mavg is the average memory usage of the physical machine cluster.
  • the third calculating unit 844 is configured to calculate a second equalization degree of each physical machine according to the acquired CPU usage rate and memory usage rate of each physical machine and the average CPU usage rate and the average memory usage rate.
  • the second equalization degree t represents the selectivity of the physical machine when considering load balancing of the internal CPU and memory resources of the physical machine.
  • the second equalization t is equal to the difference between the ratio of the CPU usage Ui of the physical machine and the memory usage rate Mi to the ratio of the average CPU usage Uavg and the average memory usage ratio Mavg, as shown below:
  • t>0 it means that the CPU usage of the physical machine is relatively higher than the memory usage, that is, the CPU consumption is relatively more memory consumption.
  • the larger t is, the more unbalanced the CPU and memory resources in the physical machine are; when t ⁇ 0 , indicating that the memory usage of the physical machine is relatively high compared to the CPU usage, that is, the memory consumption is relatively large compared to the CPU consumption, and the larger the absolute value of t, the more unbalanced the CPU and memory resources in the physical machine.
  • the fourth calculating unit 845 is configured to calculate an equalization parameter of each physical machine according to the consumption type of the virtual machine, the first equalization degree of each physical machine, and the second equalization degree.
  • the first equalization S1 is used to indicate the selectivity of the physical machine when considering the load balancing of the physical machine cluster as a whole; and the second equalization t represents the selectivity of the physical machine when considering the load balancing of the internal CPU and memory resources of the physical machine.
  • the equalization parameter is related to the consumption type of the virtual machine, the first equalization degree, and the second equalization degree.
  • the equalization parameter S2 has:
  • S2 S1-k ⁇ t n where n is an odd number and k is a positive number.
  • the setting of k and n is to make the overall balance of the physical machine cluster and the internal resource balance of the physical machine equivalent, even if S1 and k ⁇ t n
  • the values are on the same order of magnitude. It should be understood that if the consumption type of the virtual machine is CPU consumption type, the physical machine with high memory and high CPU consumption should be selected to run the virtual machine, thereby balancing the internal resources of the physical machine, and the second equalization degree of the selected physical machine. The value is less than zero.
  • the equalization parameter S2 has:
  • n is an odd number
  • k is a positive number
  • k and n are set to make the overall balance of the physical machine cluster and the internal resource balance of the physical machine equivalent, even if S1 and k ⁇ t
  • the values of n are on the same order of magnitude. It should be understood that if the consumption type of the virtual machine is memory consumption type, the physical machine with high CPU memory consumption should be selected to run the virtual machine, thereby balancing the internal resources of the physical machine, and the second equilibrium degree t value of the selected physical machine. Greater than zero.
  • the physical machine is all allocated 8C/8G and 4C/32G for calculation.
  • the range of Ui/Mi is (0.32, 2.56), so the range of t is (-2.24, 2.24).
  • the equalization parameter S2 If the consumption type of the virtual machine is CPU consumption type, the equalization parameter S2:
  • the equalization parameter S2 has:
  • the second equalization degree t is processed such that the range of t/3 is (-1, 1), and after the third power, the influence of the CPU and the memory resources in the physical machine is unbalanced, and the influence on the equalization parameter S2 is performed.
  • the consumption type of the virtual machine is CPU consumption type
  • the first equalization degree of one physical machine is greater than the first equalization degree of another physical machine
  • the second equalization degree of the one physical machine is smaller than another physical medium.
  • the second equalization degree of the machine, the equalization parameter of the one physical machine is greater than the equalization parameter of the other physical machine; if the consumption type of the virtual machine is the memory consumption type, the first equalization degree and the second equalization degree of one physical machine are respectively greater than another
  • the first equalization degree and the second equalization degree of the physical machine, the equalization parameter of the one physical machine is greater than the equalization parameter of the other physical machine.
  • the load balancing device 100 further includes a second computing module 86 and a third computing module 87.
  • the second calculating module 86 is configured to calculate the average CPU usage and the average memory usage rate according to the current CPU usage and memory usage of each physical machine.
  • the third calculating module 87 is configured to obtain the comprehensive equalization degree Q according to the degree of dispersion of the CPU usage rate of each physical machine and the average usage rate of the CPU, and the degree of dispersion of the memory usage rate of each physical machine and the average memory usage rate.
  • Q is used to represent load balancing of physical machine clusters. Specifically, the weighted calculation is performed by calculating the mean square error of the CPU usage rate and the memory usage rate of all the physical machines.
  • the CPU equalization Q1 of the physical machine cluster is a mean square error calculation for the CPU usage of all physical machines.
  • Mi represents the memory usage of the i-th physical machine
  • Mavg represents the average memory usage of the physical machine cluster.
  • the memory balance Q2 of the physical machine cluster is the mean square error calculation for the memory usage of all physical machines:
  • W2 represents the equalization weight of the CPU relative to the memory when considering the integrated equalization degree. If W2 is larger, it means that the influence of the CPU on the integrated equalization is preferably considered. This is because the impact of CPU and memory on the overall balance may be different. It should be understood that the load balancing situation of the physical machine cluster can be known according to the integrated equalization degree Q. If the integrated equalization degree is smaller, the representation is more balanced.
  • the above load balancing method and device consider the load balancing of the physical machine cluster as a whole, and consider the load balancing of the internal CPU and memory resources of a single physical machine. Therefore, the CPU usage and memory usage of different physical machines are determined by the method. Close, while the CPU usage and memory usage of a single physical machine are also close, which ensures load balancing, and also improves the utilization of resources in each physical machine, so that when the physical machine is fully loaded, the number of running virtual machines increases. If the physical machine is increased in size, the load balancing method can quickly integrate the newly added physical machine into the physical machine cluster, so that the CPU usage and memory usage are close to the average value, so that the physical machine cluster is restored. To equilibrium.
  • the units in the apparatus of the embodiment of the present invention may be combined, divided, and deleted according to actual needs.
  • the above receiving module 81, the obtaining module 82, the identifying module 83, and the like may be embedded in or independent of a load balancing system in hardware, or may be stored in a load balancing manner in software.
  • the processor performs the operations corresponding to the above units.
  • the processor can be a central processing unit (CPU), a microprocessor, a microcontroller, or the like.
  • FIG. 10 is a hardware structural diagram of a load balancing system according to an embodiment of the present invention.
  • the load balancing system includes one or more processors 1001 and a memory 1002.
  • the processor 1001 and the memory 1002 described above are connected by a bus.
  • the memory 1002 is configured to store one or more programs for implementing load balancing.
  • the memory 1002 of the embodiment of the present invention may be a system memory such as volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.), or a combination of the two.
  • the memory 1002 of the embodiment of the present invention may also be an external memory outside the system, such as a magnetic disk, an optical disk, a magnetic tape, or the like.
  • the processor 1001 is configured to execute a program stored in the memory 1002 to perform the following operations:
  • the processor 1001 executes the program stored in the memory 1002 to perform the process of identifying the consumption type of the virtual machine according to the acquired CPU core number and the memory size, the following operations are specifically performed:
  • the judgment ratio is whether the preset first range or the preset second range or the preset third range.
  • the preset second range is greater than the preset first range and the preset third range, and the preset first range is greater than the preset third range;
  • the consumption type of the identified virtual machine is a regular type
  • the consumption type of the recognition virtual machine is CPU consumption type
  • the consumption type of the recognition virtual machine is a memory consumption type
  • the preset second range is greater than the preset first range and the preset third range
  • the preset A range is greater than the preset third range.
  • the preset first range is: greater than 0.25 and less than 1
  • the preset second range is: greater than or equal to 1
  • the preset third range is less than or equal to 0.25.
  • the processor 1001 executes the program stored in the memory 1002, the processor calculates the location according to the preset first rule according to the consumption type of the virtual machine and the acquired CPU usage rate and memory usage rate of each physical machine.
  • the processor calculates the location according to the preset first rule according to the consumption type of the virtual machine and the acquired CPU usage rate and memory usage rate of each physical machine.
  • the equalization parameter assigned to each physical machine is the first equalization degree of the corresponding physical machine
  • the average CPU usage rate and the average memory usage rate are calculated according to the obtained CPU usage rate and memory usage rate of each physical machine;
  • the equalization parameters of each physical machine are calculated according to the consumption type of the virtual machine, the first equalization degree of each physical machine, and the second equalization degree.
  • the second equalization is equal to the difference between the ratio of the CPU usage and the memory usage of the physical machine to the ratio of the average CPU usage to the average memory usage.
  • the relationship is as follows:
  • t>0 it means that the CPU usage of the physical machine is relatively higher than the memory usage, that is, the CPU consumption is relatively more memory consumption.
  • the larger t is, the more unbalanced the CPU and memory resources in the physical machine are; when t ⁇ 0 , indicating that the memory usage of the physical machine is relatively high compared to the CPU usage, that is, the memory consumption is relatively large compared to the CPU consumption, and the larger the absolute value of t, the more unbalanced the CPU and memory resources in the physical machine.
  • the equalization parameter S2 has:
  • S2 S1-k ⁇ t n where n is an odd number and k is a positive number.
  • the setting of k and n is to make the overall balance of the physical machine cluster and the internal resource balance of the physical machine equivalent, even if S1 and k ⁇ t n
  • the values are on the same order of magnitude. It should be understood that if the consumption type of the virtual machine is CPU consumption type, the physical machine with high memory and high CPU consumption should be selected to run the virtual machine, thereby balancing the internal resources of the physical machine, and the second equalization degree of the selected physical machine. The value is less than zero.
  • the equalization parameter S2 has:
  • n is an odd number
  • k is a positive number
  • k and n are set to make the overall balance of the physical machine cluster and the internal resource balance of the physical machine equivalent, even if S1 and k ⁇ t
  • the values of n are on the same order of magnitude. It should be understood that if the consumption type of the virtual machine is memory consumption type, the physical machine with high CPU memory consumption should be selected to run the virtual machine, thereby balancing the internal resources of the physical machine, and the second equilibrium degree t value of the selected physical machine. Greater than zero.
  • the calculation is performed by allocating 8C/8G and 4C/32G of the physical machine, and the range of Ui/Mi is (0.32, 2.56), so the range of the second equalization t is (-2.24, 2.24).
  • the equalization parameter S2 If the consumption type of the virtual machine is CPU consumption type, the equalization parameter S2:
  • the equalization parameter S2 has:
  • the second equalization degree t is processed such that the range of t/3 is (-1, 1), and after the third power, the influence of the CPU and the memory resources in the physical machine is unbalanced, and the influence on the equalization parameter S2 is performed.
  • the consumption type of the virtual machine is a CPU consumption type
  • a first equalization degree of one physical machine is greater than a first equalization degree of another physical machine
  • a second equalization degree of the one physical machine is smaller than the other a second equalization degree of the physical machine
  • the equalization parameter of the one physical machine is greater than the equalization parameter of the another physical machine
  • the consumption type of the virtual machine is a memory consumption type
  • the first equalization degree of a physical machine The second equalization degree is respectively greater than the first equalization degree and the second equalization degree of the other physical machine, and the equalization parameter of the one physical machine is greater than the equalization parameter of the another physical machine.
  • the processor 1001 executes the program stored in the memory 1002, when the CPU usage rate and the memory usage rate of each physical machine to be acquired are calculated according to a preset second rule, the first equalization degree of each physical machine is calculated. To do the following:
  • the sum of the CPU usage of all physical machines is summed to obtain the total weight of the CPU, and the reciprocal of the memory usage of all physical machines is summed to obtain the total memory weight;
  • the first equalization degree S1 is calculated according to the calculated CPU ratio c of each physical machine and the memory ratio m and the selection weight w1 of the preset CPU relative memory.
  • the relationship between the first equalization degree S1 and the CPU ratio c, the memory ratio m, and the CPU relative memory selection weight w1 is as follows:
  • the preset CPU relative memory selection weight w1 indicates that the CPU influence factor or the memory influence factor is prioritized. The larger the w1 is, the more priority is given to the influence of the CPU factor on the overall load balancing of the physical machine cluster, and the smaller the w1 is, the memory factor is given priority. Impact on overall load balancing of physical machine clusters.
  • processor 1001 executes the program stored in the memory 1002, the following operations are also performed:
  • the integrated equalization Q is obtained according to the degree of dispersion of the CPU usage of each physical machine and the average usage of the CPU, and the degree of dispersion of the memory usage of each physical machine and the average memory usage.
  • the integrated equalization Q is used to represent the physical machine cluster. Load balancing. Specifically, the weighted calculation is performed by calculating the mean square error of the CPU usage rate and the memory usage rate of all the physical machines.
  • the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be another general-purpose processor, a digital signal processor (DSP), and an application specific integrated circuit (Application). Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the disclosed apparatus and method can be In other ways.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, or an electrical, mechanical or other form of connection.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the embodiments of the present invention.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
  • the technical solution of the present invention contributes in essence or to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium.
  • a number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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Abstract

L'invention concerne un procédé et un appareil d'équilibrage de charge, un support de stockage lisible par ordinateur et un système associé. Le procédé comprend les étapes consistant à : lors d'une réception d'une demande d'une machine virtuelle, obtenir le nombre de cœurs de la CPU et une taille de la mémoire de la machine virtuelle (S101) ; reconnaître le type de consommation de la machine virtuelle d'après le nombre de cœurs de la CPU et la taille de la mémoire de la machine virtuelle obtenus (S102) ; obtenir un taux d'utilisation actuel de la CPU et un taux d'utilisation de la mémoire de chaque machine physique (S103) ; en fonction du type de consommation de la machine virtuelle, du taux d'utilisation de la CPU et du taux d'utilisation de la mémoire de chaque machine physique obtenus, calculer des paramètres d'équilibrage de chaque machine physique en fonction d'une première règle prédéfinie (S104) ; et affecter la machine virtuelle à une machine physique avec le paramètre d'équilibrage maximum (S105). Selon le procédé, le calcul des paramètres d'équilibrage permet d'affecter une machine physique de telle sorte qu'une machine virtuelle fonctionne, ce qui assure un équilibrage de charge de la machine physique.
PCT/CN2017/076514 2016-07-22 2017-03-14 Procédé et appareil d'équilibrage de charge, support de stockage lisible par ordinateur et système associé WO2018014566A1 (fr)

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CN116232781A (zh) * 2022-12-08 2023-06-06 中国联合网络通信集团有限公司 节能方法、装置及存储介质

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Publication number Priority date Publication date Assignee Title
CN106445629B (zh) * 2016-07-22 2019-05-21 平安科技（深圳）有限公司 一种负载均衡的方法及其装置
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CN107707612B (zh) * 2017-08-10 2020-11-13 北京奇艺世纪科技有限公司 一种负载均衡集群的资源利用率的评估方法及装置
CN107526640B (zh) * 2017-08-17 2020-03-27 Oppo广东移动通信有限公司 资源管理方法、装置、移动终端及计算机可读存储介质
CN108170517A (zh) * 2018-01-08 2018-06-15 武汉斗鱼网络科技有限公司 一种容器分配方法、装置、服务器及介质
CN111338745B (zh) * 2019-04-25 2023-09-19 中国移动通信集团湖南有限公司 一种虚拟机的部署方法、装置及智能设备

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101504620A (zh) * 2009-03-03 2009-08-12 华为技术有限公司 一种虚拟化集群系统负载平衡方法、装置及系统
CN102185779A (zh) * 2011-05-11 2011-09-14 田文洪 与综合配置能力成比例的数据中心资源负载均衡的方法及装置
CN102232282A (zh) * 2010-10-29 2011-11-02 华为技术有限公司 一种实现数据中心资源负载均衡的方法及装置
CN103514046A (zh) * 2013-09-24 2014-01-15 华为技术有限公司 一种虚拟机放置方法及集群管理服务器
CN106445629A (zh) * 2016-07-22 2017-02-22 平安科技（深圳）有限公司 一种负载均衡的方法及其装置

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8849971B2 (en) * 2008-05-28 2014-09-30 Red Hat, Inc. Load balancing in cloud-based networks
CN103164283B (zh) * 2012-05-10 2018-08-10 上海兆民云计算科技有限公司 一种虚拟桌面系统中虚拟化资源动态调度管理方法及系统
CN102866915B (zh) * 2012-08-21 2015-08-26 华为技术有限公司 虚拟化集群整合方法、装置及虚拟化集群系统
CN104065745A (zh) * 2014-07-07 2014-09-24 电子科技大学 云计算动态资源调度系统和方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101504620A (zh) * 2009-03-03 2009-08-12 华为技术有限公司 一种虚拟化集群系统负载平衡方法、装置及系统
CN102232282A (zh) * 2010-10-29 2011-11-02 华为技术有限公司 一种实现数据中心资源负载均衡的方法及装置
CN102185779A (zh) * 2011-05-11 2011-09-14 田文洪 与综合配置能力成比例的数据中心资源负载均衡的方法及装置
CN103514046A (zh) * 2013-09-24 2014-01-15 华为技术有限公司 一种虚拟机放置方法及集群管理服务器
CN106445629A (zh) * 2016-07-22 2017-02-22 平安科技（深圳）有限公司 一种负载均衡的方法及其装置

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109408227A (zh) * 2018-09-19 2019-03-01 平安科技（深圳）有限公司 负载均衡方法、装置及存储介质
CN111240577A (zh) * 2018-11-28 2020-06-05 中国移动通信集团山东有限公司 基于mpp数据库的数据多分片存储方法及装置
CN111240577B (zh) * 2018-11-28 2023-08-15 中国移动通信集团山东有限公司 基于mpp数据库的数据多分片存储方法及装置
CN110287017A (zh) * 2019-07-01 2019-09-27 北京首都在线科技股份有限公司 一种任务调度方法及任务调度装置
CN113128673A (zh) * 2019-12-31 2021-07-16 Oppo广东移动通信有限公司 数据处理方法、存储介质、神经网络处理器及电子设备
CN113128673B (zh) * 2019-12-31 2023-08-11 Oppo广东移动通信有限公司 数据处理方法、存储介质、神经网络处理器及电子设备
CN111367646A (zh) * 2020-03-31 2020-07-03 中国工商银行股份有限公司 容器转移方法及系统
CN111367646B (zh) * 2020-03-31 2023-08-22 中国工商银行股份有限公司 容器转移方法及系统
CN116232781A (zh) * 2022-12-08 2023-06-06 中国联合网络通信集团有限公司 节能方法、装置及存储介质
CN116232781B (zh) * 2022-12-08 2024-04-16 中国联合网络通信集团有限公司 节能方法、装置及存储介质

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