CN111176792B - Resource scheduling method and device and related equipment - Google Patents
Resource scheduling method and device and related equipment Download PDFInfo
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- G06F9/45533—Hypervisors; Virtual machine monitors
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Abstract
The application provides a resource scheduling method, a 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 plurality of virtual machines according to the performance data to obtain a plurality of virtual machine clusters; determining service relationships among services in the plurality of virtual machines according to the attribute data; when the virtual machines in the resource pool are determined to be required to be scheduled according to the performance data of the plurality of virtual machine clusters, the virtual machines in the resource pool are scheduled according to the business relation among the businesses in the plurality of virtual machines. By the method, the resource is scheduled based on the relation between the services by determining the relation between the services running in the virtual machine, 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 method and apparatus for scheduling resources, and related devices.
Background
Dynamic resource scheduling (dynamic resource scheduler, DRS) refers to load balancing scheduling algorithm, which is used to periodically check the load condition of each physical machine in the same resource pool, and migrate Virtual Machines (VMs) between different physical machines, so as to achieve the purpose of load balancing between different physical machines in the same resource pool.
The virtual machine migration refers to that a virtual machine is migrated from one physical machine to another physical machine, and the current virtual machine migration mainly migrates the virtual machines among different physical machines according to the use condition 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 machines, so that the purposes of balancing loads among different physical machines in the same resource pool and improving the resource utilization rate are achieved. However, the current virtual machine migration only considers the physical resource utilization condition of the physical machines in the resource pool and the physical resource requirements of the virtual machines to migrate the virtual machines, and the distribution of the virtual machines after migration does not necessarily meet the requirements of actual services.
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 between services in the virtual machines in a resource pool, so that the scheduled virtual machines 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 plurality of virtual machines according to the performance data to obtain a plurality of virtual machine clusters;
determining business relations among businesses in the plurality of virtual machines according to the attribute data;
when the scheduling of the virtual machines in the resource pool is determined according to the performance data of the virtual machines in each virtual machine cluster, scheduling the resources in the resource pool according to the business relationship among the businesses in the plurality of 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, virtual machines in a resource pool are clustered according to the performance data, and the perception of the service borne in the virtual machines and the relation among the service borne by the virtual machines are perceived 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 are required to be scheduled according to the clustered performance data of the virtual machines of each category, and scheduling the resources in the resource pool according to the relation between services under the condition that the resources are required to be scheduled, so that when the resources in the resource pool are scheduled, the services in the virtual machines are perceived to determine the resources required by obligations, and the resources are scheduled according to the relation between the resources required by the services and 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, according to the performance data of the virtual machines in each virtual machine cluster, to schedule the virtual machines in the resource pool includes:
acquiring resource use data of each virtual machine in each virtual machine cluster, determining resource use indexes of each virtual machine cluster, and determining resource use indexes of the resource pool according to the resource use indexes of each virtual machine cluster;
acquiring resource allocation data of each virtual machine in each virtual machine cluster, determining resource performance indexes of each virtual machine cluster, and determining resource performance indexes of a resource pool according to the resource performance indexes of each virtual machine cluster;
and determining to schedule the resources in the resource pool according to the resource use index of the resource pool and the resource performance index of the resource pool.
In a possible embodiment, the determining to schedule the resources 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 resources in the resource pool under the condition that the ratio of the resource usage 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 determining to schedule the resources in the resource pool under the condition that the resource usage index of the resource pool is smaller than a third threshold value.
In a possible embodiment, the scheduling the virtual machines in the resource pool according to the service relationships between the services in the plurality of virtual machines includes:
the scheduling the resources in the resource pool according to the business relation among the businesses in the plurality of virtual machines includes:
under the condition that an association relationship exists between a first service in a first virtual machine and a second service in a second virtual machine, the first virtual machine and the second virtual machine are migrated to the same physical machine, or the first virtual machine is migrated to a first physical machine, the second virtual machine is migrated to a second physical machine, and the path cost between the first physical machine and the second physical machine is smaller than the path cost between a third physical machine and a fourth physical machine, wherein before virtual machine migration, 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 unidirectional relationship, a bidirectional relationship and a same family relationship.
In a second aspect, an embodiment of the present application provides a resource scheduling apparatus, including:
a communication unit configured 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 for: clustering the plurality of virtual machines according to the performance data to obtain a plurality of virtual machine clusters;
determining business relations among businesses in the plurality of 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 business relation among the businesses in the plurality of 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 resource use indexes of each virtual machine cluster, and determining resource use indexes of the resource pool according to the resource use indexes of each virtual machine cluster;
Acquiring resource allocation data of each virtual machine in each virtual machine cluster, determining resource performance indexes of each virtual machine cluster, and determining resource performance indexes of a resource pool according to the resource performance indexes of each virtual machine cluster;
and determining to schedule the resources in the resource pool according to the resource use 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 resources in the resource pool under the condition that the ratio of the resource usage 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 determining to schedule the resources in the resource pool under the condition that the resource usage index of the resource pool is smaller than a third threshold value.
In a possible embodiment, the processing unit is specifically configured to: under the condition that an association relationship exists between a first service in a first virtual machine and a second service in a second virtual machine, the first virtual machine and the second virtual machine are migrated to the same physical machine, or the first virtual machine is migrated to a first physical machine, the second virtual machine is migrated to a second physical machine, and the path cost between the first physical machine and the second physical machine is smaller than the path cost between a third physical machine and a fourth physical machine, wherein before virtual machine migration, 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 unidirectional relationship, a bidirectional relationship and a same family relationship.
In a third aspect, embodiments of the present application provide a computing device comprising a processor and a memory for storing instructions, the processor for executing the instructions, the server performing the method as described in the first aspect or any of the possible embodiments of the first aspect, when the processor executes the instructions.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, performs a method as described in the first aspect or any of the possible embodiments of the first aspect.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic migration diagram of a virtual machine according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a resource pool according to an embodiment of the present application;
Fig. 3 is a schematic flow chart of a resource scheduling method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a resource scheduling device 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, some of the expressions and related techniques involved in the present application are explained in conjunction with the drawings to facilitate understanding of the embodiments of the present application by those skilled in the art.
Dynamic resource scheduling (dynamic resource scheduler, DRS) refers to the use of a load balancing scheduling algorithm, and the load conditions of different hosts in the same resource pool are periodically checked to dynamically schedule the resources in the resource pool, so as to achieve the purpose of load balancing among different physical machines in the same resource pool. The above resources refer to examples such as a virtual machine or a container, and 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 machines. Migrating a virtual machine refers to migrating a virtual machine from one physical machine to another physical machine for operation. For example, as shown in fig. 1, the resource pool includes three physical machines, namely, 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. The VM2 is now migrated to physical machine 2, then VM2 will run on physical machine 2, and VM2 will not be present on physical machine 1.
Virtual machines are also different in load characteristics according to different demands, for example, virtual machines are generally classified into three types, including a computing type virtual machine, a storage type virtual machine and a network type virtual machine. The computing type virtual machine represents that the virtual machine mainly needs processor (central processing unit, CPU) resources, the storage type virtual machine represents that the virtual machine mainly needs memory resources, and the network type virtual machine represents that the virtual machine mainly needs bandwidth resources. If the virtual machines carried on the same physical machine are of the same type, such as computing virtual machines, the virtual machines compete for the CPU resources, and the utilization rate of other resources (such as memory, bandwidth, etc.) is low, and the service quality is reduced and the overall resource utilization rate is low due to the competition of the virtual machines for using the CPU resources. Therefore, in order to avoid such a situation, resources in the resource pool need to be dynamically scheduled according to the physical resource usage situation of the physical machine and the physical resource requirement of the virtual machine, so as to improve the overall resource utilization rate of the physical machine in the resource pool.
The current dynamic scheduling of the virtual machine mainly determines the dynamic scheduling strategy of the virtual machine by periodically acquiring the use states of physical resources (processors, memories, bandwidths and the like) of each physical machine in the same resource pool and combining the historical resource use information, the historical migration duration, the physical resource requirement of the virtual machine and the like of the physical machine. For example, when the occupancy rate of the processor and/or the memory resource of the physical machine exceeds a preset threshold, the virtual machine in 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 moment are smaller than the preset threshold, but according to the historical resource usage data, the occupancy rate of the CPU of the physical machine 1 exceeds the preset threshold within 20 minutes in the future and the duration of exceeding the preset threshold is longer, while the occupancy rate of the CPU of 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 scheduling of the resources in the current resource pool mainly carries out migration on the virtual machines among different physical machines according to the use condition of the physical resources of all physical machines in the same virtual machine cluster and the physical resource requirements of the virtual machines, thereby realizing the purposes of balancing the loads among different physical machines in the same resource pool and improving the utilization rate of the resources. However, the current virtual machine migration only considers the physical resource utilization condition of the physical machine in the resource pool and the physical resource requirement of the virtual machine, does not sense the service in the virtual machine, 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 requirement of the service actually operated in the virtual machine, and has poor operation effect on the actual service.
In view of the above problems, an embodiment of the present application provides a resource scheduling method, which periodically acquires performance data and attribute data of a virtual machine, clusters the virtual machine according to the performance data, and determines a service carried in the virtual machine and a relationship between different services according to the attribute data. After determining the relationship between the category to which the virtual machine belongs and the service in the virtual machine, determining a resource scheduling policy according to the relationship between the category to which the virtual machine belongs and the service. The performance data are data of different performance indexes of the virtual machine, including CPU occupancy rate, memory occupancy rate, storage space occupancy rate, bandwidth occupancy rate and the like, and the attribute data include source port numbers, destination port numbers, source Internet protocol (internet protocol, IP) addresses, destination IP addresses and the like of data packets received by the physical machine. The relation between services refers to the relation between services carried in different virtual machines, wherein the relation between services comprises a dependency relation, a bidirectional relation, a unidirectional relation, a same 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 means that data interaction exists between the service A and the service B; the unidirectional relation means that the service A collects data and provides the data to the service B; the same family relationship means that both the service A and the service B need to access the same resource; the mutual exclusion relationship means that service a and service B cannot run in the same virtual machine. For example, the service a in the VM14 is a web service, the service b in the VM21 is a database service, the service b is data required by the web service for the service a, and the relationship between the service a and the service b is a dependency relationship; service c in the virtual machine VM23 is a data collection service, and the collected data is provided to the database service d in the virtual machine VM42, so that the service c and the service d are in a unidirectional relationship.
As shown in fig. 2, fig. 2 is a schematic diagram of a resource pool according to an embodiment of the present application, where the system includes a management node 10 and a plurality of physical machines 20, each of which includes a plurality of virtual machines running on the physical machines, a virtual machine monitor (virtual machine monitor, VMM) 210, and a data acquisition module 220. The data collection module 220 may be a module in the VMM210, or may be a module outside the VMM210, which is illustrated in the embodiment of the present application by taking the data collection module 220 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 performance data and 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 data collected by the data collection module 220, 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 physical machine is similar, except that the number of virtual machines deployed in each physical machine and the various 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, fig. 3 is a schematic flow chart of the resource scheduling method provided by the embodiment of the present application, where 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 the physical machine 1 is PM1, the identifier of the physical machine 2 is PM2, and the identifier of the physical machine m is PMm. The virtual machines in each physical machine have a virtual machine identification VMij, where i represents an ith physical machine, j represents a jth virtual machine, and VMij represents a jth virtual machine in the ith physical machine. Such as 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 acquisition module 2201 acquires performance data of each virtual machine in the physical machines, and the attribute data acquisition module 2202 acquires attribute data of a data packet received by each physical machine. The performance data comprise CPU occupancy rate, memory occupancy rate, storage space occupancy rate, bandwidth occupancy rate and the like of each virtual machine, and the attribute data comprise source port numbers, destination port numbers, source IP addresses and destination IP addresses of data packets. 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 a 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, the destination port number in the data packet is 110, which indicates that the service provided by the virtual machine receiving the data packet includes mail service, and if the destination port number in the data packet is 80, which indicates that the service provided by the virtual machine receiving the data packet includes web service.
S302, the physical machine sends the acquired performance data and attribute data to the management node.
In the embodiment of the present application, the data sent to the management node 10 by the performance data collection module 2201 includes an identifier of each virtual machine and performance data corresponding to each virtual machine. The data sent by the attribute data acquisition 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 arrives, 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 in 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.
S304, the management node analyzes according to the received performance data and attribute data to obtain an analysis result.
In the embodiment of the present application, the analysis result includes a relationship between a class of each virtual machine in the resource pool and a service running in the virtual machine. The data analysis module 110 includes a clustering module 1101 and a traffic analysis module 1102, where the clustering module 1101 clusters virtual machines according to performance data of each virtual machine in the resource pool, and classifies the virtual machines in the resource pool into a plurality of categories. For example, virtual machines are classified into computing-type virtual machines, storage-type virtual machines, network-type virtual machines, computing-and-network-type virtual machines, and storage-and-network-type virtual machines. The service analysis module 1102 analyzes and obtains the relation 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 the resource scheduling strategy of the resource pool according to the type of the virtual machine and the relation between the services.
Specifically, after the virtual machine identifier of each virtual machine and the performance data corresponding to each virtual machine identifier are obtained, the clustering module 1101 clusters the virtual machines in the resource pool according to a clustering algorithm by combining the performance data of each virtual machine, so as to obtain multiple types 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 embodiment of the application is not limited. Illustratively, the clustering module 1101 may cluster the virtual machines according to the performance data corresponding to each virtual machine by a fuzzy C-means algorism (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 the resource pool comprises M virtual machines, N is the number of indexes included in the performance data of each virtual machine, and in the embodiment of the application, the indexes of the performance data of each virtual machine comprise CPU occupancy rate, memory occupancy rate, storage space occupancy rate and bandwidth occupancy rate. Each row of data in the sample matrix represents N performance data corresponding to one virtual machine, and each column is data of the same index of different virtual machines;
(2) Randomly classifying the M virtual machines into 5 categories;
(3) Calculating the cluster center of each category to obtain an initial cluster center Ci, i epsilon {1,2,3,4,5};
repeating the operations in the following (4) and (5) until the membership function value of each sample converges:
(4) Calculating a membership function of each virtual machine by using the current clustering center;
(5) And (3) recalculating the centers of the clusters again by using the membership function in the step (4), and clustering the virtual machines according to the new cluster centers.
And (3) when the membership function value in the step (4) is not changed or the change value is smaller than a preset threshold value, determining that the clustering result meets the requirement, and taking the clustering at the moment as a final virtual machine clustering result. It will be appreciated that the class of the virtual machine may represent the type of traffic carried within the virtual machine, e.g., if the virtual machine VM12 belongs to a computing virtual machine, indicating that CPU resources are primarily needed for the virtual machine, then the traffic carried within the virtual machine also requires CPU resources.
The service analysis module 1102 includes a feature configuration library and a correspondence between an IP address and a virtual machine identifier, where the feature configuration library indicates a relationship between a port number and a service, the service analysis module 1102 determines, through a destination port number in a data packet, the service to which the data packet belongs, and determines, according to a destination IP address in the data packet, a virtual machine corresponding to 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. And then, according to the destination IP address in the data packet, determining that the virtual machine identifier of the virtual machine corresponding to the destination IP address is VM12, and indicating that the VM12 provides the webpage service. 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 the data packet received by each physical machine, and the service analysis module 1102 determines, according to a source IP address and a destination IP address in the attribute data, two virtual machines having an interaction relationship, and determines, according to the physical machine identifier corresponding to the data packet, the physical machines to which the two virtual machines having the interaction relationship belong. 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 two virtual machines with an interactive relationship, the service analysis module may further determine the service carried in each virtual machine according to the destination port number in the data packet sent by the two virtual machines. For example, if the source IP address in the first packet received by the physical machine 1 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 is b. The source IP address in the second data packet received in the physical machine 2 is the address of the VM13, the destination IP address is the address of the VM24 in the physical machine 2, the source port number in the first data packet is b, and the destination port number is a, so that the VM13 in the physical machine 1 has an interactive relationship with the VM24 in the physical machine 2, the service 1 in the physical machine 1 has a relationship with the service 2 in the physical machine 2, if the difference between the data volume of the first data packet and the data volume of the second data packet is less than the preset difference threshold, it indicates that the service 1 and the service 2 are in a bidirectional relationship, and if the difference between the data volume of the first data packet and the data volume of the second data packet is greater than or equal to the preset difference threshold, for example, the data volume of the first data packet is greater than the data volume of the second data packet and the difference is greater than or equal to the preset difference threshold, and it indicates that the service 1 mainly provides data to the service 2, that is in a dependent relationship between the service 1 and the service 2.
And 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 a resource usage index Pu and a resource performance index Ru of the resource pool according to the resource allocation data and the resource usage data of the multiple virtual machines in 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, including a CPU occupation amount, a memory occupation amount, a storage space occupation amount, and a bandwidth occupation amount, where the resource usage data may be calculated according to performance data corresponding to each virtual machine and the resource allocation data, or may be acquired by the performance data acquisition module 2201 and then 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 a preset condition. If the comprehensive utilization rate of the resource pool is used as a target of resource scheduling, 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 a preset comprehensive utilization rate threshold value, and the virtual machine is scheduled when the value of U is less than the preset comprehensive utilization rate threshold value. If the performance priority of the resource pool is used as a resource scheduling target, the virtual machine is not scheduled when Ru is greater than or equal to a resource performance index threshold, and is scheduled when Ru is less than the resource performance index threshold. If the resource utilization rate of the resource pool is preferentially used 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 value, and the virtual machine is scheduled when the value of Pu is less than the resource utilization index threshold value.
When it is determined that the virtual machine needs to be scheduled according to the method, for example, the comprehensive utilization rate of the resource pool is taken as a target of resource scheduling, when the value of U is smaller than a preset comprehensive utilization rate threshold, determining a strategy for scheduling the virtual machine in the resource pool according to the relationship among the services, the category of the virtual machine and the requirement of the virtual machine for resources determined according to the category of the virtual machine, and after the virtual machine is scheduled according to the scheduling strategy, executing the steps in S301-S305 again, and calculating the scheduled Pu and Ru until the value of U is greater than or equal to the preset comprehensive utilization rate threshold. The policy includes that the virtual machines with the unidirectional relation, the bidirectional relation or the dependent relation are migrated to the same physical machine, or the virtual machines with the unidirectional relation, the bidirectional relation or the dependent relation are migrated to two similar physical machines, wherein the similar refers to that the path cost of data transmitted from one physical machine to the other 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 the service a and the service b have a bidirectional relationship, the virtual machine VM54 may 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 requires CPU resources, the CPU occupancy rate of the physical machine 1 is higher, 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 rate of the physical machine 2 is lower, the VM54 is migrated to the physical machine 2.
In one possible implementation, the management module 120 communicates withAnd acquiring the resource occupation data of each virtual machine in the virtual machines of each category, and calculating the resource use index of the virtual machine of each category, thereby calculating the resource use index of the resource pool. The resource occupation data comprise CPU occupation amount, memory occupation amount, storage space occupation amount and bandwidth occupation amount. Exemplary, the management module 120 calculates the average value of each type of data after obtaining the resource occupation data of each virtual machine in the virtual machines of the ith class, to obtain the resource occupation data P corresponding to the virtual machines of the ith class i ＝{p icpu ，p imem ，p isto ，p bw P is }, where icpu CPU average occupation amount, p, of virtual machine representing ith class imem Representing the average memory occupation amount, p, of virtual machines in the ith category isto Representing the average occupation amount of storage space of virtual machines in the ith category, r ibw Representing the average bandwidth occupation amount of the virtual machines in the ith category, the resource usage index P of the virtual machines in the ith category i Can be expressed as:
wherein a, b, c, d represent coefficients of corresponding physical resources, respectively. The resource usage index P of the resource pool u The method comprises the following steps:
where n is the number of classes of virtual machines.
The management module 120 calculates the resource performance index of each class of virtual machines by acquiring the resource allocation data of each virtual machine in each class of virtual machines, thereby calculating the resource performance index of the resource pool. After the management module 120 obtains the resource allocation data of each virtual machine in the ith class of virtual machines, respectively calculating the average value of each class of data to obtain the resource allocation data corresponding to the ith class of virtual machines, and R i ＝{r icpu ，r imem ，r isto 、r ibw -where r icpu Mean value of CPU representing virtual machine allocation of ith class, r imem Mean value of memory allocated by virtual machine representing ith class, r isto Representing an average value of storage space allocated by virtual machines of an ith class, r ibw Representing the average value of the bandwidth allocated by the virtual machine of the ith class, the resource performance index R of the virtual machine of the ith class i Can be expressed as:
wherein x, y, z and w are coefficients corresponding to the physical resources respectively. The resource performance index R of the resource pool u The method comprises the following steps:
by implementing the above embodiment, each physical machine in the resource pool periodically acquires performance data of each virtual machine and attribute data of a data packet received in each physical machine, and sends the acquired performance data and attribute data to the management node, and the management node clusters the virtual machines in the resource pool according to the performance data, perceives services carried in the virtual machines and relations between services carried by the virtual machines according to address information, port number information and the like in the attribute data. The management node determines whether to schedule the resources in the resource pool according to the clustered performance data of each class of virtual machine, determines the resources required by the service in the virtual machine according to the class to which the virtual machine belongs under the condition that the resources are required 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 service in the virtual machine is perceived to determine the resources required by the service, and the scheduling of the resources is performed according to the relation between the resources required by obligations and the services, so that the requirements of actual service 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 all described as a series of combinations of actions, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, and further, that the embodiments described in the specification belong to preferred embodiments, and that the actions are not necessarily required by the present application.
Other reasonable combinations of steps that can be conceived by those skilled in the art from the foregoing description are also within the scope of the application. Furthermore, those skilled in the art will be familiar with the preferred embodiments, and the description of the preferred embodiments does not necessarily require the application.
The resource scheduling method provided by the embodiment of the present application is described in detail above with reference to fig. 1 to 3, and the related device and apparatus for implementing resource scheduling provided by the embodiment of the present application are described below with reference to fig. 4 and 5. Fig. 4 is a schematic structural diagram of a resource scheduling device according to an embodiment of the present application, where the resource scheduling device 400 is deployed in the management node 10 to implement the functions of the management node 10. The resource scheduling device 400 comprises a communication unit 410 and a processing unit 420, wherein,
And 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 port number and address information of a received data packet. Specifically, the communication unit 410 performs the actions of receiving and transmitting 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.
The processing unit 420 is configured to cluster a plurality of virtual machines in the resource pool according to the performance data, so as to obtain a plurality of virtual machine clusters; and determining service relationships among services in the plurality of 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, by the processing unit 420, the relationship between the services carried in the virtual machines according to the attribute data may refer to the method executed by the service analysis module 1102 in S304, which is not repeated in the embodiments of the present application.
The processing unit 420 is further configured to determine to schedule the resources in the resource pool according to the performance data of each of the plurality of virtual machine clusters, and then schedule the resources in the resource pool according to the business relationship between the businesses in the plurality of virtual machines. The panelist scheduling includes migration of a portion of the virtual machines, superscalar scheduling, NUMA aggregation, and the like. Specifically, the method for determining whether to schedule the resources in the virtual machine by the processing unit 420 may refer to the above-mentioned step S305, where the management module 120 determines that the scheduling is performed on the resources according to the resource usage index and the resource performance index. In the case that the processing unit 420 determines to schedule the resources in the resource pool, the policy of scheduling the resources by the processing unit 420 may refer to the above-mentioned policy of scheduling the resources generated by the management module 120 according to the relationship between services in S305, which is not described herein.
The communication unit 420 is further configured to, after determining that the processing unit 420 schedules the resources in the resource pool and generates a resource scheduling policy according to the relationship between services, send the resource scheduling policy to the physical machines in the resource pool, so that the physical machine that receives the resource scheduling policy performs resource scheduling according to the resource scheduling side rate.
Specifically, the operation of the resource scheduling device 300 for implementing resource scheduling may refer to the operation performed by the management node 10 in the above-mentioned method embodiment, which is not described herein.
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 embodiment, and performs the resource scheduling method in the foregoing embodiment. The resource scheduling apparatus 500 includes at least: processor 510, communication interface 520, and 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 implementations of the operations performed by the processor 510 may refer to specific operations performed by the management node 10 as a main body in the above-described 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, and the like, which are not described herein.
Processor 510 may have a variety of specific implementations, for example, processor 510 may be a central processing unit (central processing unit, CPU) or an image processor (graphics processing unit, GPU), and processor 510 may also be a single-core processor or a multi-core processor. 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 (programmable logic device, PLD), or a combination thereof. The PLD may be a complex programmable logic device (complex programmable logic device, CPLD), a field-programmable gate array (field-programmable gate array, FPGA), general-purpose array logic (generic array logic, GAL), or any combination thereof. The processor 510 may also be implemented solely with logic devices incorporating processing logic, such as an FPGA or digital signal processor (digital signal processor, DSP) or the like.
The communication interface 520 may be a wired interface, which may be an ethernet interface, a local area network (local interconnect network, LIN), etc., or a wireless interface, which may be a cellular network interface, or use a wireless local area network interface, etc., for communicating with other modules or devices.
In the embodiment of the present application, the communication interface 520 performs the operations of receiving and sending the management node 10, for example, the performance data and the attribute data of the virtual machine that may be used to receive the user terminal sent in the step S303 are not described herein.
The memory 530 may be a nonvolatile memory such as a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Memory 530 may also be volatile memory, which may be random access memory (random access memory, RAM) used as external cache.
Memory 530 may also be used to store instructions and data such that processor 510 invokes the instructions stored in memory 530 to perform the operations described above as being performed by data analysis module 110 and management module 120, such as clustering virtual machines in the method embodiments described above. Moreover, computing device 500 may contain more or fewer components than shown in FIG. 5, or may have a different configuration of components.
Bus 540 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus 540 may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
Optionally, the server 500 may further include an input/output interface 550, where the input/output interface 550 is connected to an input/output device, for receiving input information and outputting an operation result.
Specifically, the specific implementation of the above-mentioned coordination server 500 to perform various operations may refer to the specific operation performed by the management node in the above-mentioned method embodiment, which is not described herein.
The embodiment of the present application further provides a non-transitory computer readable storage medium, where a computer program is stored, where when the computer program runs on a processor, the method steps executed by the management node in the foregoing method embodiment may be implemented, and the specific implementation of the processor of the computer storage medium in executing the foregoing method steps may refer to the specific operation of the management node in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. 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, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may 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 sets 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 disk (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 has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (12)
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;
determining business relations among businesses in the plurality of virtual machines according to the attribute data, wherein the business relations comprise a dependency relation, a unidirectional relation, a bidirectional relation, a family relation and a mutual exclusion relation;
And when the scheduling of the virtual machines in the resource pool is determined according to the performance data of each virtual machine, scheduling the virtual machines in the resource pool according to the business relation among the businesses in the plurality of virtual machines.
2. The method according to claim 1, wherein the method further comprises:
clustering the plurality of virtual machines according to the performance data to obtain a plurality of virtual machine clusters;
the determining, according to the performance data of each virtual machine, to schedule the virtual machines in the resource pool includes:
and determining to schedule the virtual machines in the resource pool according to the performance data of the virtual machines in each virtual machine cluster.
3. The method of claim 2, wherein determining to schedule the virtual machines in the resource pool based on 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 resource use indexes of each virtual machine cluster, and determining resource use indexes of the resource pool according to the resource use indexes of each virtual machine cluster;
Acquiring resource allocation data of each virtual machine in each virtual machine cluster, determining resource performance indexes of each virtual machine cluster, and determining resource performance indexes of a resource pool according to the resource performance indexes of each virtual machine cluster;
and determining to schedule the resources in the resource pool according to the resource use index of the resource pool and the resource performance index of the resource pool.
4. A method according to claim 3, wherein said determining to schedule resources in said resource pool based on a resource usage index of said resource pool and a resource performance index of said resource pool comprises:
determining to schedule resources in the resource pool under the condition that the ratio of the resource usage 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 determining to schedule the resources in the resource pool under the condition that the resource usage index of the resource pool is smaller than a third threshold value.
5. The method according to any one of claims 1-4, wherein the scheduling the resources in the resource pool according to the traffic relationships between the traffic in the plurality of virtual machines comprises:
Under the condition that an association relationship exists between a first service in a first virtual machine and a second service in a second virtual machine, the first virtual machine and the second virtual machine are migrated to the same physical machine, or the first virtual machine is migrated to a first physical machine, the second virtual machine is migrated to a second physical machine, and the path cost between the first physical machine and the second physical machine is smaller than the path cost between a third physical machine and a fourth physical machine, wherein before virtual machine migration, 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 unidirectional relationship, a bidirectional relationship and a same family relationship.
6. A resource scheduling apparatus, the apparatus comprising:
a communication unit configured 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 for: determining business relations among businesses in the plurality of virtual machines according to the attribute data, wherein the business relations comprise a dependency relation, a unidirectional relation, a bidirectional relation, a family relation and a mutual exclusion relation;
and when the scheduling of the virtual machines in the resource pool is determined according to the performance data of each virtual machine, scheduling the resources in the resource pool according to the business relation among the businesses in the plurality of virtual machines.
7. The apparatus of claim 6, wherein the processing unit is further configured to:
clustering the plurality of virtual machines according to the performance data to obtain a plurality of virtual machine clusters;
the processing unit is specifically configured to:
and determining to schedule the virtual machines in the resource pool according to the performance data of the virtual machines in each virtual machine cluster.
8. The apparatus according to claim 7, wherein the processing unit is specifically configured to:
acquiring resource use data of each virtual machine in each virtual machine cluster, determining resource use indexes of each virtual machine cluster, and determining resource use indexes of the resource pool according to the resource use indexes of each virtual machine cluster;
Acquiring resource allocation data of each virtual machine in each virtual machine cluster, determining resource performance indexes of each virtual machine cluster, and determining resource performance indexes of a resource pool according to the resource performance indexes of each virtual machine cluster;
and determining to schedule the resources in the resource pool according to the resource use index of the resource pool and the resource performance index of the resource pool.
9. The apparatus according to claim 8, wherein the processing unit is specifically configured to:
determining to schedule resources in the resource pool under the condition that the ratio of the resource usage 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 determining to schedule the resources in the resource pool under the condition that the resource usage index of the resource pool is smaller than a third threshold value.
10. The device according to any one of claims 6 to 9, wherein the processing unit is specifically configured to:
under the condition that an association relationship exists between a first service in a first virtual machine and a second service in a second virtual machine, the first virtual machine and the second virtual machine are migrated to the same physical machine, or the first virtual machine is migrated to a first physical machine, the second virtual machine is migrated to a second physical machine, and the path cost between the first physical machine and the second physical machine is smaller than the path cost between a third physical machine and a fourth physical machine, wherein before virtual machine migration, 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 unidirectional relationship, a bidirectional relationship and a same family relationship.
11. A computing device comprising a processor and a memory, the memory for storing instructions, the processor for executing the instructions, the computing device performing the method of any of claims 1-5 when the processor executes the instructions.
12. 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 according to any of claims 1 to 5.
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| CN112667999B (en) * | 2020-12-31 | 2024-06-07 | 中电长城网际安全技术研究院(北京)有限公司 | Method and device for adjusting execution time of virtual machine |
| CN115202803A (en) * | 2021-04-13 | 2022-10-18 | 超聚变数字技术有限公司 | Fault processing method and device |
| CN113923215B (en) * | 2021-09-09 | 2023-12-29 | 深信服科技股份有限公司 | Virtual machine scheduling method, electronic equipment and storage medium |
| CN114546603B (en) * | 2022-04-24 | 2022-07-29 | 睿至科技集团有限公司 | Data processing method and system applied to Internet of things |
| CN115277693A (en) * | 2022-06-21 | 2022-11-01 | 中国电信股份有限公司 | Cloud resource scheduling method and system and computer readable storage medium |
| CN115567537B (en) * | 2022-09-20 | 2024-06-21 | 中国联合网络通信集团有限公司 | Resource scheduling method and equipment |
| CN116820766A (en) * | 2023-06-29 | 2023-09-29 | 黑龙江起速网络科技有限公司 | Computer resource distribution system and method based on big data technology |
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