CN111176792A - Resource scheduling method, device and related equipment - Google Patents
Resource scheduling method, device and related equipment Download PDFInfo
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- CN111176792A CN111176792A CN201911419644.3A CN201911419644A CN111176792A CN 111176792 A CN111176792 A CN 111176792A CN 201911419644 A CN201911419644 A CN 201911419644A CN 111176792 A CN111176792 A CN 111176792A
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Abstract
The application provides a resource scheduling method, a resource scheduling device and related equipment, wherein the method comprises the following steps: acquiring performance data and attribute data of each virtual machine in a resource pool, wherein the performance data comprises physical resource information of each virtual machine, and the attribute data comprises port number information and address information of a data packet; clustering the virtual machines according to the performance data to obtain a plurality of virtual machine clusters; determining the service relationship among the services in the virtual machines according to the attribute data; and when the virtual machines in the resource pool need to be scheduled according to the performance data of the virtual machine clusters, scheduling the virtual machines in the resource pool according to the service relationship among the services in the virtual machines. By the method, the relation between the services running in the virtual machine is determined, and the resources are scheduled based on the relation between the services, so that the scheduled virtual machine is more suitable for service requirements.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a resource scheduling method, an apparatus, and a related device.
Background
Dynamic Resource Scheduling (DRS) refers to migrating Virtual Machines (VMs) among different physical machines by periodically checking the load of each physical machine in the same resource pool by using a load balancing scheduling algorithm, so as to achieve the purpose of load balancing among different physical machines in the same resource pool.
The virtual machine migration refers to the migration of a virtual machine from one physical machine to another physical machine, and the current migration of the virtual machine is mainly to migrate the virtual machine among different physical machines according to the use conditions of physical resources (processors, memories and the like) of each physical machine in the same resource pool and the physical resource requirements of the virtual machine, so that the purposes of load balancing among different physical machines in the same resource pool and resource utilization rate improvement are achieved. However, in the current virtual machine migration, only the physical resource utilization condition of the physical machine in the resource pool and the physical resource demand of the virtual machine are considered to migrate the virtual machine, and the distribution of the migrated virtual machine does not necessarily meet the demand of the actual service.
Disclosure of Invention
The embodiment of the application discloses a resource scheduling method, a resource scheduling device and related equipment, which can dynamically schedule virtual machines according to the relation among services in the virtual machines in a resource pool, so that the scheduled virtual machines can better meet the requirements of actual services.
In a first aspect, an embodiment of the present application provides a resource scheduling method, including:
acquiring performance data of each virtual machine and attribute data of each virtual machine in a plurality of virtual machines in a resource pool, wherein the performance data comprises physical resource information of each virtual machine, and the attribute data comprises port number information and address information of a data packet;
clustering the virtual machines according to the performance data to obtain a plurality of virtual machine clusters;
determining service relationships among services in the virtual machines according to the attribute data;
and when the virtual machines in the resource pool are determined to be scheduled according to the performance data of the virtual machines in each virtual machine cluster, scheduling the resources in the resource pool according to the service relationship among the services in the virtual machines.
By implementing the method, the performance data of each virtual machine and the attribute data of the data packet received in each physical machine are obtained, the virtual machines in the resource pool are clustered according to the performance data, and the sensing of the service loaded in the virtual machines and the relation between the services loaded by the virtual machines are sensed according to the address information, the port number information and the like in the attribute data. And then determining whether the resources in the resource pool need to be scheduled according to the performance data of the clustered virtual machines of each category, and scheduling the resources in the resource pool according to the relation between the services under the condition that the resources need to be scheduled, so that when the resources in the resource pool are scheduled, sensing the services in the virtual machines is realized to determine the resources needed by obligations, and the resources are scheduled according to the resources needed by the services and the relation between the services, so that the requirements of actual services can be met after the resources in the resource pool are scheduled.
In a possible embodiment, the determining to schedule the virtual machines in the resource pool according to the performance data of the virtual machines in each virtual machine cluster includes:
acquiring resource use data of each virtual machine in each virtual machine cluster, determining a resource use index of each virtual machine cluster, and determining a resource use index of the resource pool according to the resource use index of each virtual machine cluster;
acquiring resource allocation data of each virtual machine in each virtual machine cluster, determining a resource performance index of each virtual machine cluster, and determining a resource performance index of a resource pool according to the resource performance index of each virtual machine cluster;
and determining to schedule the resources in the resource pool according to the resource utilization index of the resource pool and the resource performance index of the resource pool.
In a possible embodiment, the determining to schedule the resource in the resource pool according to the resource usage index of the resource pool and the resource performance index of the resource pool includes:
determining to schedule the resources in the resource pool under the condition that the ratio of the resource utilization index to the resource performance index of the resource pool is smaller than a first threshold; or,
determining to schedule the resources in the resource pool under the condition that the resource performance index of the resource pool is smaller than a second threshold value; or,
and under the condition that the resource utilization index of the resource pool is smaller than a third threshold value, determining to schedule the resources in the resource pool.
In a possible embodiment, the scheduling, according to a service relationship between services in the plurality of virtual machines, the virtual machines in the resource pool includes:
the scheduling the resources in the resource pool according to the service relationship among the services in the plurality of virtual machines includes:
under the condition that an association relationship is determined between a first service in a first virtual machine and a second service in a second virtual machine, migrating the first virtual machine and the second virtual machine to the same physical machine, or migrating the first virtual machine to a first physical machine and migrating the second virtual machine to a second physical machine, wherein the path overhead between the first physical machine and the second physical machine is smaller than the path overhead between a third physical machine and a fourth physical machine, wherein before the virtual machine is migrated, the first virtual machine is located in the third physical machine, the second virtual machine is located in the fourth physical machine, and the association relationship comprises a dependency relationship, a one-way relationship, a two-way relationship and a family relationship.
In a second aspect, an embodiment of the present application provides an apparatus for scheduling resources, where the apparatus includes:
a communication unit to: acquiring performance data of each virtual machine in a plurality of virtual machines in a resource pool, wherein the performance data comprises physical resource information of each virtual machine;
acquiring attribute data of a data packet corresponding to each virtual machine in a plurality of virtual machines in a resource pool, wherein the attribute data comprises port number information and address information of the data packet;
a processing unit to: clustering the virtual machines according to the performance data to obtain a plurality of virtual machine clusters;
determining service relationships among services in the virtual machines according to the attribute data;
and when the virtual machines in the resource pool are determined to be scheduled according to the performance data of the virtual machines in each virtual machine cluster, scheduling the resources in the resource pool according to the service relationship among the services in the virtual machines.
In a possible embodiment, the processing unit is specifically configured to:
acquiring resource use data of each virtual machine in each virtual machine cluster, determining a resource use index of each virtual machine cluster, and determining a resource use index of the resource pool according to the resource use index of each virtual machine cluster;
acquiring resource allocation data of each virtual machine in each virtual machine cluster, determining a resource performance index of each virtual machine cluster, and determining a resource performance index of a resource pool according to the resource performance index of each virtual machine cluster;
and determining to schedule the resources in the resource pool according to the resource utilization index of the resource pool and the resource performance index of the resource pool.
In a possible embodiment, the processing unit is specifically configured to:
determining to schedule the resources in the resource pool under the condition that the ratio of the resource utilization index to the resource performance index of the resource pool is smaller than a first threshold; or,
determining to schedule the resources in the resource pool under the condition that the resource performance index of the resource pool is smaller than a second threshold value; or,
and under the condition that the resource utilization index of the resource pool is smaller than a third threshold value, determining to schedule the resources in the resource pool.
In a possible embodiment, the processing unit is specifically configured to: under the condition that an association relationship is determined between a first service in a first virtual machine and a second service in a second virtual machine, migrating the first virtual machine and the second virtual machine to the same physical machine, or migrating the first virtual machine to a first physical machine and migrating the second virtual machine to a second physical machine, wherein the path overhead between the first physical machine and the second physical machine is smaller than the path overhead between a third physical machine and a fourth physical machine, wherein before the virtual machine is migrated, the first virtual machine is located in the third physical machine, the second virtual machine is located in the fourth physical machine, and the association relationship comprises a dependency relationship, a one-way relationship, a two-way relationship and a family relationship.
In a third aspect, an embodiment of the present application provides a computing device, including a processor and a memory, where the memory is configured to store instructions, and the processor is configured to execute the instructions, and when the processor executes the instructions, the server performs the method according to the first aspect or any possible embodiment of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the processor performs the method as described in the first aspect or any possible embodiment of the first aspect.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of virtual machine migration according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a resource pool provided in an embodiment of the present application;
fig. 3 is a flowchart illustrating a resource scheduling method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a resource scheduling apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a computing device according to an embodiment of the present application.
Detailed Description
First, a part of words and related technologies referred to in the present application will be explained with reference to the accompanying drawings so that those skilled in the art can understand embodiments of the present application.
Dynamic Resource Scheduling (DRS) refers to dynamically scheduling resources in a resource pool by periodically checking the load conditions of different hosts in the same resource pool by using a load balancing scheduling algorithm, so as to achieve the purpose of load balancing between different physical machines in the same resource pool. In the embodiment of the present application, the virtual machine is taken as an example for explanation, and dynamic scheduling of other resources such as a container is similar to that of the virtual machine.
The dynamic scheduling of the resources includes migrating the virtual machine. Migrating a virtual machine refers to migrating a virtual machine from one physical machine to another physical machine to run. For example, as shown in fig. 1, the resource pool includes three physical machines, i.e., physical machine 1, physical machine 2, and physical machine 3, VM1, VM2, and VM3 run in physical machine 1, VM4 and VM5 run in physical machine 2, and VM5, VM7, and VM8 run in physical machine 3. Now migrating this VM2 to physical machine 2, then VM2 would run on physical machine 2, while VM2 would not be present on physical machine 1.
The virtual machines exhibit different load characteristics according to different requirements, for example, the virtual machines are generally divided into three categories, including a computing type virtual machine, a storage type virtual machine, and a network type virtual machine. The computer virtual machine indicates that a processor (CPU) resource is mainly needed by the virtual machine, the storage virtual machine indicates that a memory resource is mainly needed by the virtual machine, and the network virtual machine indicates that a bandwidth resource is mainly needed by the virtual machine. If the virtual machines carried on the same physical machine are of the same type, for example, are computing virtual machines, each virtual machine will compete for CPU resources, while the utilization rate of other resources (such as memory, bandwidth, etc.) will be lower, and as each virtual machine competes for using CPU resources, the service quality will be reduced, and the overall resource utilization rate will be lower. Therefore, in order to avoid such a situation, it is necessary to dynamically schedule resources in the resource pool according to the physical resource usage of the physical machine and the physical resource demand of the virtual machine, so as to improve the overall resource utilization of the physical machine in the resource pool.
The current dynamic scheduling of the virtual machine mainly determines a dynamic scheduling policy of the virtual machine by periodically acquiring the use states of physical resources (such as a processor, a memory, a bandwidth and the like) of each physical machine in the same resource pool and combining the historical resource use information, the historical migration time length, the physical resource demand and the like of the physical machine. For example, when the occupancy rate of the processor and/or memory resources of the physical machine exceeds a preset threshold, the virtual machine within the physical machine is migrated. In fig. 1, the occupancy rates of the CPU and the memory of the physical machine 1 at the current time are both less than the preset threshold, but according to the historical resource usage data, the CPU occupancy rate of the physical machine 1 in the future 20 minutes exceeds the preset threshold and the duration of the time that the CPU occupancy rate exceeds the preset threshold is longer, while the occupancy rate of the CPU in the physical machine 2 is always below the preset threshold and the processor resource meets the requirement of any virtual machine in the physical machine 1, so that part of the virtual machines in the physical machine 1 can be migrated to the physical machine 2 in advance.
The current scheduling of resources in the resource pool is mainly to migrate virtual machines among different physical machines according to the use conditions of physical resources of each physical machine in the same virtual machine cluster and the physical resource requirements of the virtual machines, so that the purposes of load balancing among different physical machines in the same resource pool and resource utilization rate improvement are achieved. However, in the current migration of the virtual machine, only the physical resource utilization condition of the physical machine in the resource pool and the physical resource demand of the virtual machine are considered, and the service in the virtual machine is not sensed, and after the virtual machine is migrated from the original physical machine to the new physical machine, the new physical machine does not necessarily meet the demand of the service actually running in the virtual machine, and the running effect on the actual service is not good.
In view of the above problems, an embodiment of the present application provides a resource scheduling method, which clusters virtual machines according to performance data by periodically obtaining the performance data and attribute data of the virtual machines, and determines services carried in the virtual machines and relationships between different services according to the attribute data. After determining the category of the virtual machine and the relation between the services in the virtual machine, determining a resource scheduling strategy according to the category of the virtual machine and the relation between the services. The performance data is data of different performance indexes of the virtual machine, and includes CPU occupancy, memory occupancy, storage space occupancy, bandwidth occupancy, and the like, and the attribute data includes a source port number, a destination port number, a source Internet Protocol (IP) address, a destination IP address, and the like of a data packet received by the physical machine. The relation between the services refers to the relation between the services carried in different virtual machines, wherein the relation between the services includes a dependency relation, a bidirectional relation, a unidirectional relation, a family relation, a mutual exclusion relation and the like. The dependency relationship refers to data provided by the service B required by the operation of the service A; the bidirectional relationship refers to data interaction between the service A and the service B; the unidirectional relation means that the service A acquires data and provides the data to the service B; the family relation means that the service A and the service B both need to access the same resource; the mutual exclusion relationship means that the service A and the service B cannot run in the same virtual machine. For example, if the service a in the virtual machine VM14 is providing web service, the service b in the virtual machine VM21 is database service, and the service b provides data required by the web service for the service a, the relationship between the service a and the service b is a dependency relationship; service c in virtual machine VM23 is a data collection service, and the collected data is provided to database service d in virtual machine VM42, so that there is a unidirectional relationship between service c and service d.
As shown in fig. 2, fig. 2 is a schematic structural diagram of a resource pool provided in this embodiment, where the system includes a management node 10 and a plurality of physical machines 20, each of the physical machines includes a plurality of virtual machines running on the physical machine, a Virtual Machine Monitor (VMM) 210, and a data collection module 220. The data collection module 220 may be one module in the VMM210 or one module outside the VMM210, and in the embodiment of the present application, the data collection module 220 is explained as one module outside the VMM210 as an example. The data collection module 220 is configured to collect the performance data and the attribute data, and send the performance data and the attribute data to the management node 10. The management node 10 is configured to analyze the performance data and the attribute data fed back by each physical machine and generate a corresponding resource scheduling policy, the management node 10 includes a data analysis module 110 and a management module 120, the data analysis module 110 is configured to analyze the data acquired by the data acquisition module 220 and send an analysis result to the management module 120, and the management module 120 generates the resource scheduling policy according to the analysis result. It should be noted that the structure of each of the above physical machines is similar, but the number of virtual machines deployed in each physical machine and the types of physical resources that each physical machine can provide are different.
Based on the above description, an embodiment of the present application provides a resource scheduling method, referring to fig. 3, where fig. 3 is a schematic flowchart of the resource scheduling method provided in the embodiment of the present application, and the method includes:
s301, the physical machine acquires performance data and attribute data of each virtual machine.
In the embodiment of the present application, each physical machine has a respective physical machine identifier PMi, for example, the identifier of physical machine 1 is PM1, the identifier of physical machine 2 is PM2, and the identifier of physical machine m is PMm. The virtual machine in each physical machine has a virtual machine identifier VMij, wherein i represents the ith physical machine, j represents the jth virtual machine, and VMij represents the jth virtual machine in the ith physical machine. E.g., virtual machine VM23, represents the 3 rd virtual machine in physical machine 2.
The data collection module 220 in each physical machine includes a performance data collection module 2201 and an attribute data collection module 2202. The performance data collection module 2201 obtains performance data of each virtual machine in the physical machines, and the attribute data collection module 2202 collects attribute data of data packets received by each physical machine. The performance data includes CPU occupancy, memory occupancy, storage space occupancy, bandwidth occupancy, and the like of each virtual machine, and the attribute data includes a source port number, a destination port number, a source IP address, and a destination IP address of the data packet. The destination IP address is used for determining a destination virtual machine corresponding to the data packet, the destination port number is used for determining the service type borne in the destination virtual machine corresponding to the destination IP address, and the source IP address and the destination IP address are used for determining two virtual machines for data interaction. Illustratively, if the destination port number in the data packet is 110, it indicates that the service provided by the virtual machine receiving the data packet includes a mail service, and if the destination port number in the data packet is 80, it indicates that the service provided by the virtual machine receiving the data packet includes a web service.
And S302, the physical machine sends the acquired performance data and attribute data to a management node.
In this embodiment of the application, the data sent by the performance data collection module 2201 to the management node 10 includes an identifier of each virtual machine and performance data corresponding to each virtual machine. The data sent by the attribute data collection module 2202 to the management node 10 includes the physical machine identifier of each physical machine and the attribute data of the data packet received by the physical machine. In a possible embodiment, the attribute data collection module 2202 may further determine, according to the destination IP address in each data packet, a destination virtual machine to which each data packet is to reach, so as to send attribute data corresponding to each virtual machine to the management node 10.
S303, the management node receives the performance data and the attribute data of each virtual machine sent by the physical machine.
The data collection module 220 in each physical machine sends the collected performance data and attribute data to the management node 10 at a preset period, and the management node 10 receives the performance data and attribute data and sends the performance data and attribute data to the data analysis module 110 in the management node 10.
And S304, the management node analyzes according to the received performance data and the attribute data to obtain an analysis result.
In this embodiment of the present application, the analysis result includes a category of each virtual machine in the resource pool and a relationship between services running in the virtual machine. The data analysis module 110 includes a clustering module 1101 and a service analysis module 1102, where the clustering module 1101 clusters the virtual machines according to the performance data of each virtual machine in the resource pool, and classifies the virtual machines in the resource pool into multiple categories. For example, virtual machines are classified into compute type virtual machines, storage type virtual machines, network type virtual machines, compute and network type virtual machines, and storage and network type virtual machines. The service analysis module 1102 analyzes the relationship between the services running in the virtual machine according to the attribute data. After determining the type of the virtual machine and the relation between the services in the virtual machine, determining a resource scheduling strategy of a resource pool according to the type of the virtual machine and the relation between the services.
Specifically, after acquiring the virtual machine identifier of each virtual machine and the performance data corresponding to each virtual machine identifier, the clustering module 1101 clusters the virtual machines in the resource pool according to a clustering algorithm in combination with the performance data of each virtual machine, so as to obtain multiple categories of virtual machines. The clustering algorithm may be a K-means clustering algorithm, a mean shift clustering algorithm, a fuzzy clustering algorithm, etc., and the embodiments of the present application are not limited. For example, the clustering module 1101 may cluster the virtual machines according to the performance data corresponding to each of the virtual machines by using a fuzzy C-means clustering algorithm (FCM). The method for clustering the virtual machines by adopting the FCM algorithm comprises the following steps:
(1) and establishing a sample matrix according to the performance data of each virtual machine, and carrying out standardization processing on the data in the sample matrix to convert the performance data into data between [0, 1 ]. The sample matrix comprises M rows and N columns, wherein M is the number of samples, namely a resource pool comprises M virtual machines, and N is the number of indexes included in performance data of each virtual machine. Each row of data in the sample matrix represents N individual performance data corresponding to one virtual machine, and each column of data represents data of the same index of different virtual machines;
(2) randomly dividing the M virtual machines into 5 categories;
(3) calculating the clustering center of each category to obtain an initial clustering center Ci, wherein i belongs to {1, 2, 3, 4, 5 };
repeating the following operations (4) and (5) until the membership function values of the respective samples converge:
(4) calculating a membership function of each virtual machine by using the current clustering center;
(5) and (4) recalculating the center of each new cluster by using the membership function in the step (4), and clustering the virtual machines according to the new cluster center.
And (5) when the membership function value in the step (4) is not changed any more or the change value is smaller than a preset threshold value, determining that the clustering result meets the requirement, and taking the current clustering as a final virtual machine clustering result. It is understood that the class of a virtual machine may indicate the type of traffic carried within the virtual machine, e.g., if virtual machine VM12 belongs to a computer type virtual machine, indicating that CPU resources are primarily required by the virtual machine, then CPU resources are also required by the traffic carried within the virtual machine.
The service analysis module 1102 includes a feature configuration library and a corresponding relationship between an IP address and a virtual machine identifier, where the feature configuration library indicates a relationship between a port number and a service, and the service analysis module 1102 determines a service to which a data packet belongs by using a destination port number in the data packet, and determines a virtual machine corresponding to the data packet according to the destination IP address in the data packet, thereby determining a virtual machine corresponding to the service and obtaining a service borne by the virtual machine. For example, if the destination port number in one data packet collected by the attribute data collection module 2202 in the physical machine 1 is 80, it indicates that a virtual machine in the physical machine provides a web service. Then, according to the destination IP address in the data packet, it is determined that the virtual machine identifier of the virtual machine corresponding to the destination IP address is VM12, which indicates that the virtual machine VM12 provides web service services. By the method, the service borne by each virtual machine can be obtained.
Further, the data received by the service analysis module 1102 includes an identifier of each physical machine and attribute data of a data packet received by each physical machine, and the service analysis module 1102 determines two virtual machines having an interaction relationship according to a source IP address and a destination IP address in the attribute data, and determines a physical machine to which the two virtual machines having the interaction relationship belong according to a physical machine identifier corresponding to the data packet. The attribute data further includes a source port number and a destination port number of the data packet, and after the service analysis module 1102 determines the two virtual machines having an interaction relationship, the service analysis module may further determine the services loaded in each virtual machine respectively according to the destination port numbers in the data packets sent by the two virtual machines. For example, if the source IP address received by the physical machine 1 in the first packet is the address of the VM24, the destination IP address is the address of the VM13 in the physical machine 1, the source port number in the first packet is a, and the destination port number in the first packet is b. The source IP address in the second packet received in the physical machine 2 is the address of VM13, the destination IP address is the address of VM24 in the physical machine 2, the source port number in the first packet is b, the destination port number is a, VM13 in physical machine 1 has an interactive relationship with VM24 in physical machine 2, service 1 in physical machine 1 has an association with service 2 in physical machine 2, if the difference between the data amount of the first packet and the data amount of the second packet is less than the preset difference threshold, it means that service 1 and service 2 are in a bidirectional relationship, if the difference between the data amount of the first data packet and the data amount of the second data packet is greater than or equal to the preset difference threshold, for example the amount of data of the first data packet is larger than the amount of data of the second data packet and the difference is larger than or equal to a preset difference threshold, it means that service 1 mainly provides data to service 2, i.e. service 1 and service 2 are dependent.
S305, the management node schedules the virtual machine according to the analysis result.
After the data analysis module 110 classifies the virtual machines in the resource pool to obtain multiple classes of virtual machines, the management module 120 determines the resource usage index Pu and the resource performance index Ru of the resource pool according to the resource allocation data and the resource usage data of the multiple virtual machines of each class. The resource allocation data refers to the amount of resources allocated to each virtual machine, and comprises CPU resources, memory resources, bandwidth resources and storage resources; the resource usage data refers to resources used by each virtual machine, and includes CPU occupancy, memory occupancy, storage space occupancy, and bandwidth occupancy, and the resource usage data may be obtained by calculation according to performance data corresponding to each virtual machine and the resource allocation data, or may be obtained by acquisition by the performance data acquisition module 2201 and sent to the management node as a part of the performance data.
After determining the resource usage index Pu and the resource performance index Ru of the resource pool, determining whether to schedule the virtual machine according to preset conditions. If the comprehensive utilization rate of the resource pool is increased to serve as a resource scheduling target, and the value of the comprehensive utilization rate of the resource pool is U ═ Pu/Ru, the virtual machine is not scheduled when the value of U is greater than or equal to the preset comprehensive utilization rate threshold, and the virtual machine is scheduled when the value of U is less than the preset comprehensive utilization rate threshold. If the performance priority of the resource pool is taken as the target of resource scheduling, the virtual machine is not scheduled when the Ru is greater than or equal to the threshold value of the resource performance index, and the virtual machine is scheduled when the Ru is less than the threshold value of the resource performance index. If the resource utilization rate of the resource pool is preferentially taken as a target of resource scheduling, the virtual machine is not scheduled when the value of Pu is greater than or equal to the resource utilization index threshold, and the virtual machine is scheduled when the value of Pu is less than the resource utilization index threshold.
When the virtual machine needs to be scheduled according to the method, for example, the comprehensive utilization rate of the resource pool is increased to be used as a resource scheduling target, when the value of U is smaller than a preset comprehensive utilization rate threshold, a policy for scheduling the virtual machine in the resource pool is determined according to the relationship among the services, the class to which the virtual machine belongs and the requirement of the virtual machine on the resource, which is determined according to the class of the virtual machine, after the virtual machine is scheduled according to the scheduling policy, the steps in S301 to S305 are executed again, and Pu and Ru after scheduling are calculated until the value of U is greater than or equal to the preset comprehensive utilization rate threshold. The policy includes migrating virtual machines where two services with a unidirectional relationship, a bidirectional relationship, or a dependency relationship are located to the same physical machine, or migrating virtual machines where two services with a unidirectional relationship, a bidirectional relationship, or a dependency relationship are located to two similar physical machines, where the similarity means that the path overhead of data transmitted from one physical machine to another physical machine is small. For example, if the service a runs in the virtual machine VM11, the service b runs in the virtual machine VM54, and there is a bidirectional relationship between the service a and the service b, the virtual machine VM54 can be migrated from the physical machine 5 to the physical machine 1, so that the virtual machine VM11 and the virtual machine VM54 are located in the same physical machine. If the service b needs CPU resources, the CPU occupancy of the physical machine 1 is high, the path overhead between the physical machine 1 and the physical machine 2 is smaller than the path overhead between the physical machine 1 and the physical machine 5, and the CPU occupancy of the physical machine 2 is low, the VM54 is migrated to the physical machine 2.
In a possible implementation manner, the management module 120 calculates the resource usage index of each category of virtual machine by obtaining the resource occupancy data of each virtual machine in each category of virtual machines, and further calculates the resource usage index of the resource pool. The resource occupation data comprises CPU occupation amount, memory occupation amount, storage space occupation amount and bandwidth occupation amount. For example, after the management module 120 obtains the resource occupation data of each virtual machine in the ith category of virtual machines, it calculates an average value of each category of data to obtain the resource occupation data P corresponding to the ith category of virtual machinesi＝{picpu，pimem，pisto，pbwIn which p isicpuMean CPU occupancy, p, for a virtual machine of the ith classimemRepresents the average memory occupancy of the virtual machines of the ith category, pistoRepresenting the average occupancy of storage space, r, of virtual machines of the ith classibwRepresenting the average occupied bandwidth of the virtual machine of the ith category, the resource utilization index P of the virtual machine of the ith categoryiCan be expressed as:
wherein the ratio of a, b,c and d respectively represent coefficients of corresponding physical resources. Resource usage index P of resource pooluComprises the following steps:
wherein n is the number of classes of the virtual machine.
The management module 120 calculates the resource performance index of each category of virtual machine by obtaining the resource allocation data of each virtual machine in each category of virtual machine, and further calculates the resource performance index of the resource pool. After obtaining the resource allocation data of each virtual machine in the ith category of virtual machines, the management module 120 calculates an average value of each category of data to obtain resource allocation data, R, corresponding to the ith category of virtual machinesi＝{ricpu，rimem，risto、ribwWherein r isicpuMean value of CPU assigned to virtual machine representing ith class, rimemMean value of memory allocated to virtual machines representing the ith class, ristoMean value, r, of the storage space allocated by the virtual machine representing the ith classibwThe average value of the bandwidth allocated by the virtual machine of the ith category is represented, and then the resource performance index R of the virtual machine of the ith category is obtainediCan be expressed as:
wherein, x, y, z, w are coefficients of corresponding physical resources respectively. Resource performance index R of resource pooluComprises the following steps:
by implementing the embodiment, each physical machine in the resource pool periodically acquires the performance data of each virtual machine and the attribute data of the data packet received in each physical machine, and sends the acquired performance data and the attribute data to the management node, the management node clusters the virtual machines in the resource pool according to the performance data, and senses the sensing of the service loaded inside the virtual machine and the relationship between the services loaded by the virtual machine according to the address information, the port number information and the like in the attribute data. The management node determines whether the resources in the resource pool need to be scheduled according to the performance data of the clustered virtual machines of each category, determines the resources needed by the services in the virtual machines according to the categories to which the virtual machines belong under the condition that the resources need to be scheduled, and schedules the resources in the resource pool according to the resources and the relation between the services, so that when the resources in the resource pool are scheduled, the services in the virtual machines are sensed to determine the resources needed by the services, and the resources are scheduled according to the resources needed by obligation and the relation between the services, so that the requirements of actual services can be met after the resources in the resource pool are scheduled.
It should be noted that, for simplicity of description, the above method embodiments are described as a series of action combinations, but those skilled in the art should understand that the present invention is not limited by the described action sequences, and those skilled in the art should understand that the embodiments described in the description belong to the preferred embodiments, and the actions involved are not necessarily required by the present invention.
Other reasonable combinations of steps that can be conceived by one skilled in the art from the above description are also within the scope of the invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
The resource scheduling method provided by the embodiment of the present application is described in detail above with reference to fig. 1 to fig. 3, and related apparatuses and devices for implementing resource scheduling provided by the embodiment of the present application are described below with reference to fig. 4 and fig. 5. Fig. 4 is a schematic structural diagram of a resource scheduling apparatus provided in an embodiment of the present application, where the resource scheduling apparatus 400 is deployed in the management node 10 to implement the function of the management node 10. The resource scheduling apparatus 400 comprises a communication unit 410 and a processing unit 420, wherein,
a communication unit 410, configured to receive performance data and attribute data sent by each physical machine in the resource pool, where the performance data includes physical resource information of each virtual machine in the resource pool, and the attribute data includes a port number and address information of a received data packet. Specifically, the communication unit 410 performs the receiving and sending actions of the management node 10, such as the receiving performance data and the attribute data described in S303 in fig. 3, which are not described herein again.
The processing unit 420 is configured to cluster the multiple virtual machines in the resource pool according to the performance data to obtain multiple virtual machine clusters; and determining the service relationship among the services in the virtual machines according to the attribute data. The method for clustering the virtual machines in the resource pool by the processing unit 420 may refer to the method executed by the clustering module 1101 in S304, and the method for determining the relationship between the services carried in the virtual machines by the processing unit 420 according to the attribute data may refer to the method executed by the service analysis module 1102 in S304, which is not described in detail in this embodiment.
The processing unit 420 is further configured to determine to schedule resources in the resource pool according to the performance data of each virtual machine cluster in the plurality of virtual machine clusters, and then schedule the resources in the resource pool according to the service relationship among the services in the plurality of virtual machines. The team member scheduling includes migrating a portion of the virtual machine, super-match ratio scheduling, NUMA aggregation, and the like. Specifically, the processing unit 420 determines whether to schedule the resource in the virtual machine, as referred to in S305 above, the management module 120 determines that the resource is scheduled according to both the resource usage index and the resource performance index. In the case that the processing unit 420 determines to schedule the resource in the resource pool, the policy for scheduling the resource by the processing unit 420 may refer to the policy for scheduling the resource generated by the management module 120 according to the relationship between the services in S305, and is not described herein again.
The communication unit 420 is further configured to, after the processing unit 420 determines to schedule resources in the resource pool and generates a resource scheduling policy according to the relationship between the services, send the resource scheduling policy to a physical machine in the resource pool, so that the physical machine receiving the resource scheduling policy performs resource scheduling according to the resource scheduling side rate.
Specifically, the operation of the resource scheduling apparatus 300 to implement resource scheduling may refer to the operation performed by the management node 10 in the foregoing method embodiment, and is not described herein again.
Fig. 5 is a schematic structural diagram of a computing device according to an embodiment of the present application, where the computing device 500 implements the functions of the management node 10 in the foregoing embodiments, and executes the resource scheduling method in the foregoing embodiments. The resource scheduling apparatus 500 includes at least: a processor 510, a communication interface 520, and a memory 530. Optionally, the processor 510, the communication interface 520, and the memory 530 are interconnected by a bus 540, wherein,
the processor 510 is configured to implement the operations performed by the data analysis module 110 and the management module 120, and specific implementation of the processor 510 to perform various operations may refer to specific operations performed by the management node 10 as an execution subject in the foregoing method embodiments. For example, the processor 510 is configured to perform the operations of the management node 10 in S304 and S305 in fig. 3, which are not described herein again.
The processor 510 may be implemented in various ways, for example, the processor 510 may be a Central Processing Unit (CPU) or a Graphics Processing Unit (GPU), and the processor 510 may also be a single-core processor or a multi-core processor. The processor 510 may be a combination of a CPU and a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof. The processor 510 may also be implemented using logic devices with built-in processing logic, such as an FPGA or a Digital Signal Processor (DSP).
The communication interface 520 may be a wired interface, such as ethernet interface, Local Interconnect Network (LIN), etc., or a wireless interface, such as a cellular network interface or a wireless lan interface, for communicating with other modules or devices.
In this embodiment of the application, the communication interface 520 performs the receiving and sending operations of the management node 10, for example, the communication interface may be configured to perform the receiving of the performance data and the attribute data of the virtual machine sent by the user terminal in S303, and details are not described herein again.
The memory 530 may be a non-volatile memory, such as a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. Memory 530 may also be volatile memory, which may be Random Access Memory (RAM), that acts as external cache memory.
The bus 540 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 540 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
Optionally, the server 500 may further include an input/output interface 550, and the input/output interface 550 is connected with an input/output device for receiving input information and outputting an operation result.
Specifically, the specific implementation of the coordinating server 500 to execute various operations may refer to the specific operations executed by the management node in the foregoing method embodiment, and details are not described herein again.
The embodiments of the present application further provide a non-transitory computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program runs on a processor, the method steps executed by the management node in the foregoing method embodiments may be implemented, and specific implementation of the processor of the computer-readable storage medium in executing the method steps may refer to specific operations of the management node in the foregoing method embodiments, which is not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a Solid State Drive (SSD).
The steps in the method of the embodiment of the application can be sequentially scheduled, combined or deleted according to actual needs; the modules in the device of the embodiment of the application can be divided, combined or deleted according to actual needs.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method for scheduling resources, comprising:
acquiring performance data of each virtual machine and attribute data of each virtual machine in a plurality of virtual machines in a resource pool, wherein the performance data comprises physical resource information of each virtual machine, and the attribute data comprises port number information and address information of a data packet;
clustering the virtual machines according to the performance data to obtain a plurality of virtual machine clusters;
determining service relationships among services in the virtual machines according to the attribute data;
and when the virtual machines in the resource pool are determined to be scheduled according to the performance data of the virtual machines in each virtual machine cluster, scheduling the virtual machines in the resource pool according to the service relationship among the services in the virtual machines.
2. The method of claim 1, wherein the determining to schedule the virtual machines in the resource pool according to the performance data of the virtual machines in each virtual machine cluster comprises:
acquiring resource use data of each virtual machine in each virtual machine cluster, determining a resource use index of each virtual machine cluster, and determining a resource use index of the resource pool according to the resource use index of each virtual machine cluster;
acquiring resource allocation data of each virtual machine in each virtual machine cluster, determining a resource performance index of each virtual machine cluster, and determining a resource performance index of a resource pool according to the resource performance index of each virtual machine cluster;
and determining to schedule the resources in the resource pool according to the resource utilization index of the resource pool and the resource performance index of the resource pool.
3. The method of claim 2, wherein the determining to schedule the resource in the resource pool according to the resource usage index of the resource pool and the resource performance index of the resource pool comprises:
determining to schedule the resources in the resource pool under the condition that the ratio of the resource utilization index to the resource performance index of the resource pool is smaller than a first threshold; or,
determining to schedule the resources in the resource pool under the condition that the resource performance index of the resource pool is smaller than a second threshold value; or,
and under the condition that the resource utilization index of the resource pool is smaller than a third threshold value, determining to schedule the resources in the resource pool.
4. The method according to any one of claims 1 to 3, wherein the scheduling the resources in the resource pool according to the service relationship between the services in the plurality of virtual machines comprises:
under the condition that an association relationship is determined between a first service in a first virtual machine and a second service in a second virtual machine, migrating the first virtual machine and the second virtual machine to the same physical machine, or migrating the first virtual machine to a first physical machine and migrating the second virtual machine to a second physical machine, wherein the path overhead between the first physical machine and the second physical machine is smaller than the path overhead between a third physical machine and a fourth physical machine, wherein before the virtual machine is migrated, the first virtual machine is located in the third physical machine, the second virtual machine is located in the fourth physical machine, and the association relationship comprises a dependency relationship, a one-way relationship, a two-way relationship and a family relationship.
5. An apparatus for scheduling resources, the apparatus comprising:
a communication unit to: acquiring performance data of each virtual machine in a plurality of virtual machines in a resource pool, wherein the performance data comprises physical resource information of each virtual machine;
acquiring attribute data of a data packet corresponding to each virtual machine in a plurality of virtual machines in a resource pool, wherein the attribute data comprises port number information and address information of the data packet;
a processing unit to: clustering the virtual machines according to the performance data to obtain a plurality of virtual machine clusters;
determining service relationships among services in the virtual machines according to the attribute data;
and when the virtual machines in the resource pool are determined to be scheduled according to the performance data of the virtual machines in each virtual machine cluster, scheduling the resources in the resource pool according to the service relationship among the services in the virtual machines.
6. The apparatus according to claim 5, wherein the processing unit is specifically configured to:
acquiring resource use data of each virtual machine in each virtual machine cluster, determining a resource use index of each virtual machine cluster, and determining a resource use index of the resource pool according to the resource use index of each virtual machine cluster;
acquiring resource allocation data of each virtual machine in each virtual machine cluster, determining a resource performance index of each virtual machine cluster, and determining a resource performance index of a resource pool according to the resource performance index of each virtual machine cluster;
and determining to schedule the resources in the resource pool according to the resource utilization index of the resource pool and the resource performance index of the resource pool.
7. The apparatus according to claim 6, wherein the processing unit is specifically configured to:
determining to schedule the resources in the resource pool under the condition that the ratio of the resource utilization index to the resource performance index of the resource pool is smaller than a first threshold; or,
determining to schedule the resources in the resource pool under the condition that the resource performance index of the resource pool is smaller than a second threshold value; or,
and under the condition that the resource utilization index of the resource pool is smaller than a third threshold value, determining to schedule the resources in the resource pool.
8. The apparatus according to any one of claims 5 to 7, wherein the processing unit is specifically configured to:
under the condition that an association relationship is determined between a first service in a first virtual machine and a second service in a second virtual machine, migrating the first virtual machine and the second virtual machine to the same physical machine, or migrating the first virtual machine to a first physical machine and migrating the second virtual machine to a second physical machine, wherein the path overhead between the first physical machine and the second physical machine is smaller than the path overhead between a third physical machine and a fourth physical machine, wherein before the virtual machine is migrated, the first virtual machine is located in the third physical machine, the second virtual machine is located in the fourth physical machine, and the association relationship comprises a dependency relationship, a one-way relationship, a two-way relationship and a family relationship.
9. A computing device comprising a processor and a memory, the memory to store instructions, the processor to execute the instructions, the computing device to perform the method of any of claims 1 to 4 when the processor executes the instructions.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, performs the method of any one of claims 1 to 4.
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| CN111176792B (en) | 2023-11-17 |
| WO2021136137A1 (en) | 2021-07-08 |
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