CN116541178B - Dynamic load balancing method and device for Docker cloud platform - Google Patents
Dynamic load balancing method and device for Docker cloud platform Download PDFInfo
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- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
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Abstract
The invention provides a dynamic load balancing method and device for a Docker cloud platform, wherein the method comprises the following steps: load data of a plurality of nodes and containers are obtained; judging whether the load data meets a preset load condition or not; if the load data does not meet the preset load condition, expanding or shrinking the container; obtaining the load unbalance degree among the nodes according to the load data of all the nodes, and judging whether the load unbalance degree among the nodes exceeds a preset load balance threshold value or not; if the load unbalance degree among the nodes exceeds the preset load balance threshold, inputting the load data of the nodes and the containers into a load decision model to obtain a load balance strategy; and scheduling the container resources according to the load balancing strategy. The method and the device can solve the problems that in the prior art, when load balancing is realized, single index is considered, heuristic algorithm calculation with poor expandability is adopted, and the real network environment is difficult to deal with.
Description
Technical Field
The invention relates to the technical field of computer cloud computing, in particular to a dynamic load balancing method and device for a Docker cloud platform.
Background
With the rapid development and wide application of technologies such as big data and artificial intelligence, the core-virtualization technology of the cloud computing platform as the bottom support is more self-evident. Compared with the traditional virtualization technology based on an operating system, the Docker container technology has the advantages of light and flexible weight, high resource utilization rate, high starting speed, easiness in migration and the like, and becomes a core representative technology in the cloud computing field. At present, more and more enterprises take a Docker cluster as a main task execution environment, and a plurality of different application containers are deployed on each node in the Docker cluster to execute different tasks, so that load imbalance of a Docker cloud platform is easy to occur.
The unbalanced load of the Docker cloud platform not only greatly reduces the availability, the resource utilization rate and the stability of the system, but also causes the waste of energy and space. In addition, the load balancing strategy of the existing Docker cloud platform only considers singleness, only considers load balancing among nodes or elastic expansion of a container independently, does not well combine the singleness and the elastic expansion of the container and omits consideration of QoS (Quality of Service) of users, and the problems are that the index is single and the scalability is poor are adopted mostly when the load balancing is realized, so that the heuristic calculation is difficult to deal with the real network environment.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a dynamic load balancing method and device for a Docker cloud platform, aiming at the defects of the prior art, so as to at least solve the problems that in the prior art, when load balancing is realized, consideration indexes are single, heuristic algorithm calculation with poor expandability is adopted, and the real network environment is difficult to deal with.
In a first aspect, the present invention provides a dynamic load balancing method for a Docker cloud platform, including:
load data of a plurality of nodes and containers are obtained;
load data for each of the containers:
judging whether the load data meet a preset load condition according to the load data of the container;
if the load data does not meet the preset load condition, expanding or shrinking the container to meet the preset load condition;
when the load data of all the containers meet the preset load condition, obtaining the load unbalance degree among the nodes according to the load data of all the nodes, and judging whether the load unbalance degree among the nodes exceeds a preset load balance threshold value or not;
if the load unbalance degree among the nodes exceeds the preset load balance threshold, inputting load data of the nodes and the containers into a load decision model to obtain a load balance strategy, wherein the load decision model is a model constructed based on an asynchronous dominant action evaluation A3C algorithm network, and the optimization target of the load decision model is determined by the task average instruction response time ratio of the user quality of service QoS and the load unbalance degree among the nodes;
and scheduling the container resources according to the load balancing strategy.
Further, the judging, according to the load data, whether the load data meets a preset load condition specifically includes:
if the load data is larger than a preset overload threshold or smaller than a preset underload threshold, judging that the load data does not meet the preset load condition;
and if the load data is not greater than a preset overload threshold value and is not less than a preset underload threshold value, judging that the load data meets the preset load condition.
Further, if the load data does not meet the preset load condition, expanding or shrinking the container specifically includes:
if the load data is larger than a preset overload threshold, expanding the container;
and if the load data is smaller than a preset underload threshold value, the container is reduced.
Further, the obtaining the load imbalance degree between the nodes according to the load data of all the nodes specifically includes:
for all nodes at each of the T times, the following steps are performed:
acquiring the average utilization rate of CPU of the central processing units of all the nodes;
calculating the CPU utilization standard deviation of all the nodes according to the CPU average utilization;
and calculating the average value of the CPU utilization standard deviations of all the nodes at T moments, and taking the average value of the CPU utilization standard deviations of all the nodes as the load unbalance degree among the nodes.
Further, the calculation formula of the average CPU utilization of all the nodes is as follows:
wherein ,the CPU average utilization rate of all nodes at the time T is represented by M, the number of containers, N, the number of nodes, T, the number of time and +.>CPU utilization at time t for container m, < >>The position relation between the container m and the node n at the moment t;
the calculation formula of the CPU utilization standard deviation of all the nodes is as follows:
wherein ,CPU utilization standard deviation of all nodes at t moment;
the calculation formula of the average value of the standard deviation of the CPU utilization rates of all the nodes at the T moments is as follows:
wherein ,the average value of the standard deviation of CPU utilization of all nodes at T moments.
Further, before the load data of the plurality of nodes and the container is input into the load decision model to obtain the load balancing policy, the method further includes:
for each task in all containers, the following steps are performed:
acquiring the transmission time of the task to the corresponding container;
calculating the waiting time of the task after reaching the corresponding container according to the transmission time;
acquiring the execution time of the task in the corresponding container;
and calculating the average instruction response time ratio of all tasks in all containers according to the transmission time, the waiting time and the execution time of each task.
Further, the calculation formula of the transmission time of the task to the corresponding container is as follows:
wherein ,for the data transmission time of task i to container m, < >>As the data transmission amount of the task i,for the network transmission speed to container m;
the calculation formula of the waiting time after the task reaches the corresponding container is as follows:
wherein ,for the waiting time after task i reaches container m, k=1, …, C is the number of tasks that container m has not performed, +.>The execution time of task k in container m;
the calculation formula of the execution time of the task in the corresponding container is as follows:
wherein ,for the execution time of task i in container m, < >>Instruction length for task i, +.>CPU utilization for container m, +.>Instruction execution speed for container m;
the calculation formula of the average instruction response time ratio of all tasks in all containers is as follows:
wherein ,the average instruction response time ratio for a total of I tasks within M containers, I being the number of tasks and M being the number of containers.
Further, the calculation formula of the optimization target of the load decision model is as follows:
wherein ,weight for task average instruction response time ratio, +.>The CPU is weighted by the average value of the standard deviation of the utilization rate.
In a second aspect, the present invention provides a dynamic load balancing device for a dock cloud platform, including:
the acquisition module is used for acquiring load data of the plurality of nodes and the container;
the first judging module is connected with the acquiring module and is used for judging whether the load data meet a preset load condition according to the load data of the container;
the expansion and reduction module is connected with the first judging module and is used for expanding or reducing the container to meet the preset load condition if the load data does not meet the preset load condition;
the second judging module is connected with the expansion and reduction module and is used for obtaining the load unbalance degree among the nodes according to the load data of all the nodes when the load data of all the containers meet the preset load condition and judging whether the load unbalance degree among the nodes exceeds a preset load balance threshold value or not;
the input model module is connected with the second judging module and is used for inputting the load data of the plurality of nodes and the containers into a load decision model to obtain a load balancing strategy if the load unbalance degree among the nodes exceeds the preset load balancing threshold;
and the scheduling module is connected with the input model module and used for scheduling the container resources according to the load balancing strategy.
In a third aspect, the present invention provides a dynamic load balancing device for a dock cloud platform, including a memory and a processor, where the memory stores a computer program, and the processor is configured to run the computer program to implement the dynamic load balancing method for a dock cloud platform in the first aspect.
According to the dynamic load balancing method and device for the Docker cloud platform, load data of a plurality of nodes and containers are obtained; load data for each of the containers is then: judging whether the load data meet a preset load condition according to the load data of the container; if the load data does not meet the preset load condition, expanding or shrinking the container to meet the preset load condition; when the load data of all the containers meet the preset load condition, obtaining the load unbalance degree among the nodes according to the load data of all the nodes, and judging whether the load unbalance degree among the nodes exceeds a preset load balance threshold value; if the load unbalance degree among the nodes exceeds the preset load balance threshold, inputting load data of the nodes and the containers into a load decision model to obtain a load balance strategy, wherein the load decision model is a model constructed based on an asynchronous dominant action evaluation A3C algorithm network, and the optimization target of the load decision model is determined by the task average instruction response time ratio of the user quality of service QoS and the load unbalance degree among the nodes; and finally, scheduling the container resources according to the load balancing strategy. The invention sets three thresholds, and utilizes the deep reinforcement learning algorithm which jointly forms an optimization target by the load unbalance degree among the nodes and the QoS of the user to carry out reasonable resource scheduling, and can timely carry out efficient resource scheduling according to the load change of the working system, thereby effectively aiming at the load unbalance condition in the real network environment, and solving the problems that the prior art realizes the load balancing by considering the heuristic algorithm calculation with single index and poor expandability, which causes difficulty in coping with the real network environment.
Drawings
Fig. 1 is a flowchart of a dynamic load balancing method of a Docker cloud platform according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of a dynamic load balancing architecture according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an Actor network model according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a Critical network model according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a dynamic load balancing device for a dock cloud platform according to embodiment 2 of the present invention;
fig. 6 is a schematic structural diagram of a dynamic load balancing device for a dock cloud platform according to embodiment 3 of the present invention.
Detailed Description
In order to make the technical scheme of the present invention better understood by those skilled in the art, the following detailed description of the embodiments of the present invention will be given with reference to the accompanying drawings.
It is to be understood that the specific embodiments and figures described herein are merely illustrative of the invention, and are not limiting of the invention.
It is to be understood that the various embodiments of the invention and the features of the embodiments may be combined with each other without conflict.
It is to be understood that only the portions relevant to the present invention are shown in the drawings for convenience of description, and the portions irrelevant to the present invention are not shown in the drawings.
It should be understood that each unit and module in the embodiments of the present invention may correspond to only one physical structure, may be formed by a plurality of physical structures, or may be integrated into one physical structure.
It will be appreciated that, without conflict, the functions and steps noted in the flowcharts and block diagrams of the present invention may occur out of the order noted in the figures.
It is to be understood that the flowcharts and block diagrams of the present invention illustrate the architecture, functionality, and operation of possible implementations of systems, apparatuses, devices, methods according to various embodiments of the present invention. Where each block in the flowchart or block diagrams may represent a unit, module, segment, code, or the like, which comprises executable instructions for implementing the specified functions. Moreover, each block or combination of blocks in the block diagrams and flowchart illustrations can be implemented by hardware-based systems that perform the specified functions, or by combinations of hardware and computer instructions.
It should be understood that the units and modules related in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, for example, the units and modules may be located in a processor.
Example 1:
the embodiment provides a dynamic load balancing method for a Docker cloud platform, as shown in fig. 1, which comprises the following steps:
step S101: load data of a plurality of nodes and containers is acquired.
It should be noted that, as shown in fig. 2, the dynamic load balancing method for the Docker cloud platform provided by the present embodiment is applied to the dynamic load balancing architecture provided by the present embodiment, and the whole structure is formed by two parts of Docker clusters and logic layers together.
Specifically, the logic layer is a core part of the realization of the whole dynamic load strategy, and comprises a container resource acquisition module, a container data storage module and a container resource scheduling module. The container resource collection module is mainly responsible for counting and transmitting the usage of various resources (i.e. load data of a plurality of nodes and containers) of the physical host and the dock container, wherein the usage includes a CPU (Central Processing Unit ), a memory, a network, an IO (Input/Output), and the like. The container data storage module is responsible for storing the acquired data into a database, sorting the data into a specified format and sending the data to the container resource scheduling module. The container resource scheduling module is the core of the whole logic layer and is mainly responsible for the expansion and contraction of the container and the realization of load balancing among nodes.
It should be noted that, aiming at the heuristic algorithm which is commonly used at present, that is, the parameters of the heuristic algorithm need to be readjusted and complex calculation is executed again along with the change of the network state, and the problem that the local optimal solution is easy to fall in exists, the container resource scheduling module integrates the deep reinforcement learning algorithm, and can directly obtain the optimal strategy of container resource scheduling, without complex calculation again, and finally, the optimal allocation of the container resources is realized.
Specifically, load data of a host, a plurality of nodes and a container are obtained through a container resource acquisition module, wherein the load data of the host is mainly used for judging whether the container can be created.
Step S102: load data for each of the containers:
and judging whether the load data meets a preset load condition according to the load data of the container.
Specifically, if the load data is greater than a preset overload threshold or less than a preset underload threshold, judging that the load data does not meet the preset load condition, and if the load data is not greater than the preset overload threshold and not less than the preset underload threshold, judging that the load data meets the preset load condition.
Step S103: and if the load data does not meet the preset load condition, expanding or shrinking the container to meet the preset load condition.
Specifically, if the load data is greater than a preset overload threshold, the container is expanded, and if the load data is less than a preset underload threshold, the container is contracted.
Step S104: when the load data of all the containers meet the preset load condition, obtaining the load unbalance degree among the nodes according to the load data of all the nodes, and judging whether the load unbalance degree among the nodes exceeds a preset load balance threshold value.
Specifically, when all the container load data are not greater than a preset overload threshold and not less than a preset underload threshold, calculating to obtain the load imbalance degree (namely the load imbalance degree among the nodes) of the Docker container cluster according to the load data of all the nodes, and judging whether the load imbalance degree of the Docker container cluster exceeds the preset load balance threshold.
In an optional embodiment, the obtaining the load imbalance degree between the nodes according to the load data of all the nodes specifically includes:
for all nodes at each of the T times, the following steps are performed:
acquiring the average utilization rate of CPU of the central processing units of all the nodes;
calculating the CPU utilization standard deviation of all the nodes according to the CPU average utilization;
and calculating the average value of the CPU utilization standard deviations of all the nodes at T moments, and taking the average value of the CPU utilization standard deviations of all the nodes as the load unbalance degree among the nodes.
Specifically, the calculation formula of the average CPU utilization of all the nodes is as follows:
wherein ,the CPU average utilization rate of all nodes at the time T is represented by M, the number of containers, N, the number of nodes, T, the number of time and +.>CPU utilization at time t for container m, < >>The positional relationship between the container m and the node n at the time t is that when the container m is positioned at the node n, the value is1, otherwise 0;
the calculation formula of the CPU utilization standard deviation of all the nodes is as follows:
wherein ,CPU utilization standard deviation of all nodes at t moment;
the calculation formula of the average value of the standard deviation of the CPU utilization rates of all the nodes at the T moments is as follows:
wherein ,the average value of the standard deviation of CPU utilization of all nodes at T moments.
Step S105: if the load unbalance degree among the nodes exceeds the preset load balance threshold, inputting the load data of the nodes and the containers into a load decision model to obtain a load balance strategy, wherein the load decision model is a model constructed based on an asynchronous dominant action evaluation A3C algorithm network, and the optimization target of the load decision model is determined by the task average instruction response time ratio of the user quality of service QoS and the load unbalance degree among the nodes.
Specifically, if the load imbalance degree of the Docker container cluster exceeds a preset load balancing threshold, inputting network states corresponding to load data of a plurality of nodes and containers into a trained load decision model, and outputting an optimal strategy (namely a load balancing strategy) of load balancing by the load decision model.
It should be noted that, the A3C (Asynchronous Advantage Actor-Critic, asynchronous dominant motion evaluation) algorithm is mainly responsible for learning the decision model with load energy-gathering decision knowledge (i.e. the above-mentionedLoad decision model). The model comprehensively considers the load unbalance degree among the nodes and the QoS of users, the number of known containers is M, the number of the nodes is N, the state space of the load decision model (namely the network state) can be represented by an MxN two-dimensional matrix A, and when the container M is positioned at the node N, the A is mn =1, otherwise a mn =0, the action space of the load decision model (i.e. the best strategy for load balancing described above) represents the allocation of containers to virtual machines, then action mn On behalf of container m migrate to node n:
the value of the reward function of the load decision model is related to the container cluster state, when a certain action is executed, the current container cluster state is good, the positive reward is given, otherwise, the negative reward is given:
wherein ,for rewarding function-> and />Is of different prize values, and +.>>/>>0，Is the network state after the container has migrated.
Specifically, the A3C algorithm actually performs synchronous training by placing the Actor-Critic in a plurality of threads, where an Actor network model is designed as shown in fig. 3, and the Actor network model specifically includes: the input of the Actor network model is a two-dimensional matrix of a state space, and the output is the probability corresponding to each action in the action space in the current state. Critic network model design As shown in FIG. 4, the Critic network model specifically includes: the input layer, the first convolution layer, the second convolution layer, the flame layer, and the full connection layer are sequentially connected, in the Critic network model, the input is still a two-dimensional matrix, and the output is a value evaluation for performing a specified action, so that the Critic network structure is approximately the same as the Actor network structure, except that only one neuron is output.
In an optional embodiment, before the load data of the plurality of nodes and the container is input into the load decision model to obtain the load balancing policy, the method further includes:
for each task in all containers, the following steps are performed:
acquiring the transmission time of the task to the corresponding container;
calculating the waiting time of the task after reaching the corresponding container according to the transmission time;
acquiring the execution time of the task in the corresponding container;
and calculating the average instruction response time ratio of all tasks in all containers according to the transmission time, the waiting time and the execution time of each task.
Specifically, the calculation formula of the transmission time of the task to the corresponding container is:
wherein ,for the data transfer time of task i to container m,/>as the data transmission amount of the task i,for the network transmission speed to container m;
the calculation formula of the waiting time after the task reaches the corresponding container is as follows:
wherein ,for the waiting time after task i reaches container m, k=1, …, C is the number of tasks that container m has not performed, +.>The execution time of task k in container m;
the calculation formula of the execution time of the task in the corresponding container is as follows:
wherein ,for the execution time of task i in container m, < >>Instruction length for task i, +.>CPU utilization for container m, +.>Instruction execution speed for container m;
the calculation formula of the average instruction response time ratio of all tasks in all containers is as follows:
wherein ,the average instruction response time ratio for a total of I tasks within M containers, I being the number of tasks and M being the number of containers.
In an alternative embodiment, the calculation formula of the optimization target of the load decision model is:
wherein ,weight for task average instruction response time ratio, +.>The CPU is weighted by the average value of the standard deviation of the utilization rate.
Step S106: and scheduling the container resources according to the load balancing strategy.
Specifically, according to a load balancing strategy, a container to be scheduled and a target node are selected, and the container to be scheduled is migrated and scheduled to the target node.
The dynamic load balancing method of the Docker cloud platform provided by the embodiment of the invention comprises the steps of firstly obtaining load data of a plurality of nodes and containers; load data for each of the containers is then: judging whether the load data meet a preset load condition according to the load data of the container; if the load data does not meet the preset load condition, expanding or shrinking the container to meet the preset load condition; when the load data of all the containers meet the preset load condition, obtaining the load unbalance degree among the nodes according to the load data of all the nodes, and judging whether the load unbalance degree among the nodes exceeds a preset load balance threshold value; if the load unbalance degree among the nodes exceeds the preset load balance threshold, inputting load data of the nodes and the containers into a load decision model to obtain a load balance strategy, wherein the load decision model is a model constructed based on an asynchronous dominant action evaluation A3C algorithm network, and the optimization target of the load decision model is determined by the task average instruction response time ratio of the user quality of service QoS and the load unbalance degree among the nodes; and finally, scheduling the container resources according to the load balancing strategy. The invention sets three thresholds, and utilizes the deep reinforcement learning algorithm which jointly forms an optimization target by the load unbalance degree among the nodes and the QoS of the user to carry out reasonable resource scheduling, and can timely carry out efficient resource scheduling according to the load change of the working system, thereby effectively aiming at the load unbalance condition in the real network environment, and solving the problems that the prior art realizes the load balancing by considering the heuristic algorithm calculation with single index and poor expandability, which causes difficulty in coping with the real network environment.
Example 2:
as shown in fig. 5, this embodiment provides a dynamic load balancing device for a dock cloud platform, configured to execute the above dynamic load balancing method for a dock cloud platform, including:
an acquisition module 11 for acquiring load data of a plurality of nodes and containers;
a first judging module 12, connected to the acquiring module 11, for judging whether the load data meets a preset load condition according to the load data of the container;
an expansion and contraction module 13, connected to the first judging module 12, configured to expand or contract a container to meet a preset load condition if the load data does not meet the preset load condition;
the second judging module 14 is connected with the expansion and contraction module 13, and is configured to obtain a load imbalance degree between nodes according to the load data of all the nodes when the load data of all the containers meet the preset load condition, and judge whether the load imbalance degree between the nodes exceeds a preset load balance threshold;
the input model module 15 is connected with the second judging module 14, and is configured to input the load data of the plurality of nodes and the container into a load decision model if the load imbalance degree between the nodes exceeds the preset load balancing threshold value, so as to obtain a load balancing strategy;
and the scheduling module 16 is connected with the input model module 15 and is used for scheduling the container resources according to the load balancing strategy.
Further, the first judging module 12 specifically includes:
the first judging unit is used for judging that the load data does not meet the preset load condition if the load data is larger than a preset overload threshold or smaller than a preset underload threshold;
and the second judging unit is used for judging that the load data meets the preset load condition if the load data is not greater than a preset overload threshold value and not less than a preset underload threshold value.
Further, the expansion and reduction module 13 specifically includes:
the expansion unit is used for expanding the container if the load data is larger than a preset overload threshold value;
and the reduction unit is used for reducing the container if the load data is smaller than a preset underload threshold value.
Further, the second judging module 14 specifically includes:
the acquisition unit is used for acquiring the average utilization rate of the CPU of all the nodes;
the first calculation unit is used for calculating the CPU utilization standard deviation of all the nodes according to the CPU average utilization;
and the second calculation unit is used for calculating the average value of the CPU utilization standard deviations of all the nodes at T moments and taking the average value of the CPU utilization standard deviations of all the nodes as the load unbalance degree among the nodes.
Further, the calculation formula of the average CPU utilization of all the nodes is as follows:
wherein ,the CPU average utilization rate of all nodes at the time T is represented by M, the number of containers, N, the number of nodes, T, the number of time and +.>CPU utilization at time t for container m, < >>The position relation between the container m and the node n at the moment t;
the calculation formula of the CPU utilization standard deviation of all the nodes is as follows:
wherein ,CPU utilization standard deviation of all nodes at t moment;
the calculation formula of the average value of the standard deviation of the CPU utilization rates of all the nodes at the T moments is as follows:
wherein ,the average value of the standard deviation of CPU utilization of all nodes at T moments.
Further, the apparatus further comprises:
the transmission time acquisition module is connected with the input model module 15 and is used for acquiring the transmission time of the task to the corresponding container;
the waiting time calculation module is connected with the transmission time acquisition module and is used for calculating the waiting time of the task after reaching the corresponding container according to the transmission time;
the execution time acquisition module is connected with the waiting time calculation module and is used for acquiring the execution time of the task in the corresponding container;
and the third calculation unit is connected with the execution time acquisition module and is used for calculating the average instruction response time ratio of all tasks in all containers according to the transmission time, the waiting time and the execution time.
Further, the calculation formula of the transmission time of the task to the corresponding container is as follows:
wherein ,for the data transmission time of task i to container m, < >>As the data transmission amount of the task i,for the network transmission speed to container m;
the calculation formula of the waiting time after the task reaches the corresponding container is as follows:
wherein ,for the waiting time after task i reaches container m, k=1, …, C is the number of tasks that container m has not performed, +.>The execution time of task k in container m;
the calculation formula of the execution time of the task in the corresponding container is as follows:
wherein ,for the execution time of task i in container m, < >>Instruction length for task i, +.>CPU utilization for container m, +.>Instruction execution speed for container m;
the calculation formula of the average instruction response time ratio of all tasks in all containers is as follows:
wherein ,the average instruction response time ratio for a total of I tasks within M containers, I being the number of tasks and M being the number of containers.
Further, the calculation formula of the optimization target of the load decision model is as follows:
wherein ,weight for task average instruction response time ratio, +.>The CPU is weighted by the average value of the standard deviation of the utilization rate.
Example 3:
referring to fig. 6, the present embodiment provides a dynamic load balancing apparatus for a dock cloud platform, including a memory 21 and a processor 22, where the memory 21 stores a computer program, and the processor 22 is configured to run the computer program to execute the dynamic load balancing method for the dock cloud platform in embodiment 1.
The memory 21 is connected to the processor 22, the memory 21 may be a flash memory, a read-only memory, or other memories, and the processor 22 may be a central processing unit or a single chip microcomputer.
The dynamic load balancing method and device for the dock cloud platform provided in embodiments 2 to 3 firstly acquire load data of a plurality of nodes and containers; load data for each of the containers is then: judging whether the load data meet a preset load condition according to the load data of the container; if the load data does not meet the preset load condition, expanding or shrinking the container to meet the preset load condition; when the load data of all the containers meet the preset load condition, obtaining the load unbalance degree among the nodes according to the load data of all the nodes, and judging whether the load unbalance degree among the nodes exceeds a preset load balance threshold value; if the load unbalance degree among the nodes exceeds the preset load balance threshold, inputting load data of the nodes and the containers into a load decision model to obtain a load balance strategy, wherein the load decision model is a model constructed based on an asynchronous dominant action evaluation A3C algorithm network, and the optimization target of the load decision model is determined by the task average instruction response time ratio of the user quality of service QoS and the load unbalance degree among the nodes; and finally, scheduling the container resources according to the load balancing strategy. The invention sets three thresholds, and utilizes the deep reinforcement learning algorithm which jointly forms an optimization target by the load unbalance degree among the nodes and the QoS of the user to carry out reasonable resource scheduling, and can timely carry out efficient resource scheduling according to the load change of the working system, thereby effectively aiming at the load unbalance condition in the real network environment, and solving the problems that the prior art realizes the load balancing by considering the heuristic algorithm calculation with single index and poor expandability, which causes difficulty in coping with the real network environment.
It is to be understood that the above embodiments are merely illustrative of the application of the principles of the present invention, but not in limitation thereof. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the invention, and are also considered to be within the scope of the invention.
Claims (9)
1. A dynamic load balancing method for a Docker cloud platform, the method comprising:
load data of a plurality of nodes and containers are obtained;
load data for each of the containers:
judging whether the load data meet a preset load condition according to the load data of the container;
if the load data does not meet the preset load condition, expanding or shrinking the container to meet the preset load condition;
when the load data of all the containers meet the preset load condition, obtaining the load unbalance degree among the nodes according to the load data of all the nodes, and judging whether the load unbalance degree among the nodes exceeds a preset load balance threshold value or not;
if the load unbalance degree among the nodes exceeds the preset load balance threshold, inputting load data of the nodes and the containers into a load decision model to obtain a load balance strategy, wherein the load decision model is a model constructed based on an asynchronous dominant action evaluation A3C algorithm network, and the optimization target of the load decision model is determined by the task average instruction response time ratio of the user quality of service QoS and the load unbalance degree among the nodes;
scheduling the container resources according to the load balancing strategy;
the calculation formula of the optimization target of the load decision model is as follows:
，
wherein ,weight for task average instruction response time ratio, +.>The weight of the average value of the standard deviation of CPU utilization,instruction length for task i, +.>For the data transmission quantity of task i, +.>For the network transmission speed to container m, k=1, …, C is the number of tasks that container m has not performed, +.>For the execution time of task k in container m,CPU utilization for container m, +.>Instruction execution speed for container m, +.>CPU utilization at time t for container m, < >>For the position relation of the container m and the node n at time t, < >>The CPU average utilization rate of all nodes at the time T is calculated, wherein I is the number of tasks, M is the number of containers, N is the number of nodes, and T is the number of times.
2. The method according to claim 1, wherein the determining, according to the load data, whether the load data meets a preset load condition specifically includes:
if the load data is larger than a preset overload threshold or smaller than a preset underload threshold, judging that the load data does not meet the preset load condition;
and if the load data is not greater than a preset overload threshold value and is not less than a preset underload threshold value, judging that the load data meets the preset load condition.
3. The method according to claim 2, wherein expanding or contracting the container if the load data does not meet a preset load condition, specifically comprises:
if the load data is larger than a preset overload threshold, expanding the container;
and if the load data is smaller than a preset underload threshold value, the container is reduced.
4. The method according to claim 1, wherein the obtaining the load imbalance degree between the nodes according to the load data of all the nodes specifically includes:
for all nodes at each of the T times, the following steps are performed:
acquiring the average utilization rate of CPU of the central processing units of all the nodes;
calculating the CPU utilization standard deviation of all the nodes according to the CPU average utilization;
and calculating the average value of the CPU utilization standard deviations of all the nodes at T moments, and taking the average value of the CPU utilization standard deviations of all the nodes as the load unbalance degree among the nodes.
5. The method of claim 4, wherein the calculation formula of the average CPU utilization of all nodes is:
，
wherein ,the CPU average utilization rate of all nodes at the time T is represented by M, the number of containers, N, the number of nodes, T, the number of time and +.>CPU utilization at time t for container m, < >>The position relation between the container m and the node n at the moment t;
the calculation formula of the CPU utilization standard deviation of all the nodes is as follows:
，
wherein ,CPU utilization standard deviation of all nodes at t moment;
the calculation formula of the average value of the standard deviation of the CPU utilization rates of all the nodes at the T moments is as follows:
，
wherein ,the average value of the standard deviation of CPU utilization of all nodes at T moments.
6. The method of claim 1, wherein before inputting the load data of the plurality of nodes and containers into the load decision model to derive the load balancing policy, the method further comprises:
for each task in all containers, the following steps are performed:
acquiring the transmission time of the task to the corresponding container;
calculating the waiting time of the task after reaching the corresponding container according to the transmission time;
acquiring the execution time of the task in the corresponding container;
and calculating the average instruction response time ratio of all tasks in all containers according to the transmission time, the waiting time and the execution time of each task.
7. The method of claim 1, wherein the calculation formula of the transmission time of the task to the corresponding container is:
，
wherein ,for the data transmission time of task i to container m, < >>For the data transmission quantity of task i, +.>For the network transmission speed to container m;
the calculation formula of the waiting time after the task reaches the corresponding container is as follows:
，
wherein ,for the waiting time after task i reaches container m, k=1, …, C is the number of tasks that container m has not performed, +.>The execution time of task k in container m;
the calculation formula of the execution time of the task in the corresponding container is as follows:
，
wherein ,for the execution time of task i in container m, < >>Instruction length for task i, +.>CPU utilization for container m, +.>Instruction execution speed for container m;
the calculation formula of the average instruction response time ratio of all tasks in all containers is as follows:
，
wherein ,the average instruction response time ratio for a total of I tasks within M containers, I being the number of tasks and M being the number of containers.
8. The utility model provides a Docker cloud platform dynamic load balancing device which characterized in that includes:
the acquisition module is used for acquiring load data of the plurality of nodes and the container;
the first judging module is connected with the acquiring module and is used for judging whether the load data meet a preset load condition according to the load data of the container;
the expansion and reduction module is connected with the first judging module and is used for expanding or reducing the container to meet the preset load condition if the load data does not meet the preset load condition;
the second judging module is connected with the expansion and reduction module and is used for obtaining the load unbalance degree among the nodes according to the load data of all the nodes when the load data of all the containers meet the preset load condition and judging whether the load unbalance degree among the nodes exceeds a preset load balance threshold value or not;
the input model module is connected with the second judging module and is used for inputting the load data of the plurality of nodes and the containers into a load decision model to obtain a load balancing strategy if the load unbalance degree among the nodes exceeds the preset load balancing threshold;
the scheduling module is connected with the input model module and used for scheduling the container resources according to the load balancing strategy;
the calculation formula of the optimization target of the load decision model is as follows:
，
wherein ,weight for task average instruction response time ratio, +.>The weight of the average value of the standard deviation of CPU utilization,instruction length for task i, +.>For the data transmission quantity of task i, +.>For the network transmission speed to container m, k=1, …, C is the number of tasks that container m has not performed, +.>For the execution time of task k in container m,CPU utilization for container m, +.>Instruction execution speed for container m, +.>CPU utilization at time t for container m, < >>For the position relation of the container m and the node n at time t, < >>The CPU average utilization rate of all nodes at the moment t is calculated, and I is the number of tasksThe quantity M is the number of containers, N is the number of nodes, and T is the number of moments.
9. A dock cloud platform dynamic load balancing device, comprising a memory and a processor, the memory having stored therein a computer program, the processor being arranged to run the computer program to implement the dock cloud platform dynamic load balancing method of any one of claims 1 to 7.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102857577A (en) * | 2012-09-24 | 2013-01-02 | 北京联创信安科技有限公司 | System and method for automatic load balancing of cluster storage |
| CN104202388A (en) * | 2014-08-27 | 2014-12-10 | 福建富士通信息软件有限公司 | Automatic load balancing system based on cloud platform |
| CN106998303A (en) * | 2017-03-24 | 2017-08-01 | 中国联合网络通信集团有限公司 | The load-balancing method and SiteServer LBS of routing node |
| CN114816723A (en) * | 2021-01-29 | 2022-07-29 | 中移(苏州)软件技术有限公司 | Load balancing system, method and computer readable storage medium |
| CN115987995A (en) * | 2022-08-31 | 2023-04-18 | 兴业银行股份有限公司 | Method and system for improving node resource utilization rate balance load of kubernets |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10432709B2 (en) * | 2016-03-28 | 2019-10-01 | Industrial Technology Research Institute | Load balancing method, load balancing system, load balancing device and topology reduction method |
-
2023
- 2023-07-06 CN CN202310821239.4A patent/CN116541178B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102857577A (en) * | 2012-09-24 | 2013-01-02 | 北京联创信安科技有限公司 | System and method for automatic load balancing of cluster storage |
| CN104202388A (en) * | 2014-08-27 | 2014-12-10 | 福建富士通信息软件有限公司 | Automatic load balancing system based on cloud platform |
| CN106998303A (en) * | 2017-03-24 | 2017-08-01 | 中国联合网络通信集团有限公司 | The load-balancing method and SiteServer LBS of routing node |
| CN114816723A (en) * | 2021-01-29 | 2022-07-29 | 中移(苏州)软件技术有限公司 | Load balancing system, method and computer readable storage medium |
| CN115987995A (en) * | 2022-08-31 | 2023-04-18 | 兴业银行股份有限公司 | Method and system for improving node resource utilization rate balance load of kubernets |
Non-Patent Citations (1)
| Title |
|---|
| 基于云计算的弹性负载均衡服务研究;梅荣;;中国公共安全(01);全文 * |
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