CN116382892A - Load balancing method and device based on multi-cloud fusion and cloud service - Google Patents
Load balancing method and device based on multi-cloud fusion and cloud service Download PDFInfo
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Abstract
The invention is applicable to the technical field of cloud services, and provides a load balancing method and device based on multi-cloud fusion and cloud services and a load balancing server, wherein the load balancing method comprises the following steps: determining a processing difficulty index of the service request according to the request type; acquiring the selection probability and the resource data corresponding to each of the plurality of servers to be selected, and calculating the current processing capacity index corresponding to each of the plurality of servers to be selected according to the selection probability and the resource data; calculating the processing efficiency corresponding to each of the plurality of servers to be selected according to the processing difficulty index and the current processing capacity index; and screening target servers from the plurality of servers to be selected according to the processing efficiency, and processing the service request through the target servers. The invention comprehensively judges the processing efficiency of the server to be selected based on a plurality of dimensions (request type, selection probability and resource data), so that the balanced load is more fit with the actual situation, and the situations of overload and the like of the server can be well avoided.
Description
Technical Field
The invention belongs to the technical field of cloud services, and particularly relates to a load balancing method and device based on multi-cloud fusion and cloud services.
Background
SaaS is a short for Software-as-a-Service, and is a completely innovative Software application mode starting to emerge in the 21 st century with the development of internet technology and the maturation of application Software. The method is a mode of providing software through Internet, vendors uniformly deploy application software on own servers, customers can order required application software services to vendors through Internet according to actual demands of the customers, pay fees to the vendors according to the number and time of the ordered services, and obtain the services provided by the vendors through Internet.
The SaaS provides different services for clients through server clusters in different regions. In order to optimize the running situation of the server cluster, the service request of the client is often uniformly distributed to each service node in the cluster through a load balancing technology, and the server receiving the service request independently completes the service to the client. The purpose of server cluster load balancing is to distribute service requests to corresponding service nodes according to a certain algorithm according to the load condition and the running condition of each node in the cluster, so that the processing speed of the service requests is improved.
However, most of the current load scheduling algorithms are polling algorithms or minimum connection algorithms. The polling algorithm and the minimum connection algorithm cannot be well combined with the actual running condition of the server to carry out comprehensive judgment, so that overload and other conditions of the server are easy to occur.
Disclosure of Invention
In view of the above, the embodiments of the present invention provide a load balancing method, a load balancing device, a load balancing server and a computer readable storage medium based on multi-cloud fusion and cloud service, so as to solve the technical problem that the polling algorithm and the minimum connection algorithm cannot be well combined with the actual running situation of the server to perform comprehensive judgment, so that the server is easy to be overloaded.
A first aspect of an embodiment of the present invention provides a load balancing method based on multi-cloud fusion and cloud service, where the load balancing method includes:
in response to a received service request, identifying a request type of the service request, and determining a processing difficulty index of the service request according to the request type;
acquiring selection probability and resource data corresponding to each of a plurality of servers to be selected, and calculating current processing capacity indexes corresponding to each of the plurality of servers to be selected according to the selection probability and the resource data; the selection probability refers to the probability that the server to be selected is selected as a target server within preset times;
Calculating the processing efficiency corresponding to each of the plurality of servers to be selected according to the processing difficulty index and the current processing capacity index;
and screening target servers from a plurality of servers to be selected according to the processing efficiency, and processing the service request through the target servers.
Further, the step of identifying a request type of the service request in response to the received service request and determining a processing difficulty index of the service request according to the request type includes:
identifying a request type of a service request in response to the received service request; the request types comprise a dynamic request and a static request, wherein the dynamic request refers to a request for data processing by a server, and the static request refers to a request for data processing by a server;
if the request type is a dynamic request, determining that the service request is a first processing difficulty index;
and if the request type is a static request, determining that the service request is a second processing difficulty index.
Further, the resource data comprises the current request processing amount, the disk occupancy rate corresponding to each of the plurality of history periods, the processor utilization rate corresponding to each of the plurality of history periods, the request response time corresponding to each of the plurality of history periods and the current network utilization rate;
The step of obtaining the selection probability and the resource data corresponding to each of the plurality of to-be-selected servers, and calculating the current processing capacity index corresponding to each of the plurality of to-be-selected servers according to the selection probability and the resource data comprises the following steps:
substituting the selection probability, the current request processing amount, the disk occupancy rate corresponding to each of the plurality of history periods, the processor utilization rate corresponding to each of the plurality of history periods, the request response time corresponding to each of the plurality of history periods and the current network utilization rate into the following formula I to obtain the current processing capacity index;
equation one:
wherein M represents the current throughput index, N represents the current request throughput, p represents the selection probability, T i Representing the request response time, C, of each corresponding one of the plurality of history periods of the ith history request x Representing the disk occupancy rate corresponding to each of the plurality of history periods in the xth history period, S k And representing the processor utilization rate corresponding to each of the plurality of history periods in the kth history period, and F represents the current network utilization rate.
Further, the step of calculating the processing efficiency corresponding to each of the plurality of servers to be selected according to the processing difficulty index and the current processing capability index includes:
Dividing the processing difficulty index corresponding to each of the plurality of servers to be selected by the current processing capacity index to obtain the processing efficiency corresponding to each of the plurality of servers to be selected.
Further, the step of selecting a target server from a plurality of servers to be selected according to the processing efficiency, and processing the service request through the target server includes:
screening a first server to be selected, wherein the processing efficiency of the first server to be selected is greater than a first threshold value;
if the first server to be selected is not selected, the step of selecting the first server to be selected with the processing efficiency being greater than a threshold value and the subsequent steps are re-executed in the plurality of servers to be selected after waiting for a preset time period;
if a single first server to be selected is screened, the first server to be selected is used as the target server, and the service request is processed through the target server;
and if a plurality of first servers to be selected are screened, taking the first server to be selected corresponding to the maximum processing efficiency as the target server, and processing the service request through the target server.
Further, the step of selecting a target server from a plurality of servers to be selected according to the processing efficiency, and processing the service request through the target server includes:
calculating the balance degree among a plurality of the servers to be selected; the balance degree is used for evaluating the balance degree of the processing efficiency among the plurality of servers to be selected;
if the balance degree is greater than a second threshold value, a second server to be selected in a preset distance range is obtained, wherein the preset distance range refers to a distance range taking a request sending end as an origin;
screening a third server to be selected, of which the processing efficiency is greater than a first threshold value, from a plurality of second servers to be selected;
in the plurality of third to-be-selected servers, taking the third to-be-selected server corresponding to the maximum processing efficiency as the target server, and processing the service request through the target server;
if the balance degree is not greater than the second threshold value, calculating average processing efficiency corresponding to each of the plurality of server clusters;
and in the server cluster corresponding to the maximum average processing efficiency, taking a fourth server to be selected corresponding to the maximum processing efficiency as the target server, and processing the service request through the target server.
Further, before the step of identifying a request type of the service request in response to the received service request and determining a processing difficulty index of the service request according to the request type, the method further includes:
matching the load balancing frequency corresponding to the current time period; wherein the load balancing frequency is a frequency set based on the service request amount in the history period;
if the load balancing frequency is smaller than a third threshold value, minimum connection scheduling is adopted;
and if the load balancing frequency is not smaller than the third threshold value, executing the steps of responding to the received service request, identifying the request type of the service request, and determining the processing difficulty index of the service request according to the request type and the subsequent steps according to the load balancing frequency.
A second aspect of an embodiment of the present invention provides an apparatus for load balancing, including:
the identification unit is used for responding to the received service request, identifying the request type of the service request and determining the processing difficulty index of the service request according to the request type;
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring selection probability and resource data corresponding to each of a plurality of servers to be selected, and calculating current processing capacity indexes corresponding to each of the plurality of servers to be selected according to the selection probability and the resource data; the selection probability refers to the probability that the server to be selected is selected as a target server within preset times;
The computing unit is used for computing the processing efficiency corresponding to each of the plurality of servers to be selected according to the processing difficulty index and the current processing capacity index;
and the screening unit is used for screening target servers from a plurality of servers to be selected according to the processing efficiency, and processing the service request through the target servers.
A third aspect of an embodiment of the present invention provides a load balancing server comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of the method of the first aspect described above when said computer program is executed.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method of the first aspect.
Compared with the prior art, the embodiment of the invention has the beneficial effects that: the invention identifies the request type of the service request by responding to the received service request, and determines the processing difficulty index of the service request according to the request type; acquiring selection probability and resource data corresponding to each of a plurality of servers to be selected, and calculating current processing capacity indexes corresponding to each of the plurality of servers to be selected according to the selection probability and the resource data; the selection probability refers to the probability that the server to be selected is selected as a target server within preset times; calculating the processing efficiency corresponding to each of the plurality of servers to be selected according to the processing difficulty index and the current processing capacity index; and screening target servers from a plurality of servers to be selected according to the processing efficiency, and processing the service request through the target servers. According to the scheme, the processing difficulty index of the service request is determined according to the service request type, the current processing capacity index is determined according to the selection probability of the server to be selected and the resource data, and the processing efficiency of each server to be selected is determined according to the processing difficulty index and the current processing capacity index, so that the target server is selected according to the processing efficiency. The invention comprehensively judges the processing efficiency of the server to be selected based on a plurality of dimensions (request type, selection probability and resource data), so that the balanced load is more fit with the actual situation, and the situations of overload and the like of the server can be well avoided.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
Fig. 1 shows a schematic diagram of a device architecture based on multi-cloud fusion and load balancing of cloud services according to the present invention;
FIG. 2 shows a schematic flow chart of a load balancing method based on multi-cloud fusion and cloud services provided by the invention;
fig. 3 shows a specific schematic flowchart of step 201 in a load balancing method based on multi-cloud fusion and cloud service provided by the present invention;
FIG. 4 is a specific schematic flowchart of step 204 in a load balancing method based on multi-cloud fusion and cloud services according to the present invention;
FIG. 5 is a specific schematic flowchart of step 204 in a load balancing method based on multi-cloud fusion and cloud services according to the present invention;
FIG. 6 shows a specific schematic flow chart of another load balancing method based on multi-cloud fusion and cloud services provided by the invention;
Fig. 7 is a schematic diagram of a load balancing device based on multi-cloud fusion and cloud service according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a load balancing server according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
For better understanding of the technical solution of the present invention, a supplementary explanation is made here with respect to the background art:
in the digital economic era, financial institutions face industry challenges such as high cost of acquiring market information, high cost of developing, operating and maintaining, long online period and the like. Mainstream market and information providers still provide financial data services which are simply processed to customers through special lines, file Transfer Protocol (FTP) or hypertext transfer protocol (HTTP) and the like, but the digital conversion requirements of financial institutions cannot be met.
Under the background, the quotation SaaS cloud service based on the multi-cloud fusion technology can provide a quotation information solution with low cost, quick online and easy expansion, meets the requirements of instant use and agile development, provides rich services such as extremely fast quotation, SDK service, quick customization and the like for financial institutions, and helps the financial institutions to complete digital infrastructure quickly, efficiently and at low cost.
Referring to fig. 1, fig. 1 shows a schematic diagram of a device architecture for load balancing provided by the present invention. As shown in fig. 1, the SaaS cloud service adopts a redundant line and is configured into a main line and a standby line, and when the main line fails, the redundant line is switched to the standby line, so that the data flow is ensured not to be interrupted, and the seamless switching effect is achieved. Here, fig. 1 is merely an example, and there is no limitation on the number of devices, the connection relationship of devices, and the kind of devices in fig. 1.
When the global traffic manager receives the service request, the service request is sent to a load balancing server (where the load balancing server may be an nmginx server). The load balancing server executes the load balancing method provided by the invention, determines the target server, and requests corresponding service from the target server through the API gateway.
Alternatively, for a candidate server, the access system operates in a manner of multiple independent working processes. Any working process is abnormal, and normal service of other working processes is not influenced. Any working process crashes and can be immediately switched to other standby working processes in the cluster, so that a certain standby process becomes a working process. And the daemon corresponding to the original working process can be restarted automatically, so that the daemon enters a standby state.
The embodiment of the invention provides a load balancing method, a load balancing device, a load balancing server and a computer readable storage medium based on cloud service, which are used for solving the technical problems that a polling algorithm and a minimum connection algorithm cannot be well combined with the actual running condition of the server to carry out comprehensive judgment, so that overload and the like of the server are easy to occur.
Firstly, the invention provides a load balancing method based on multi-cloud fusion and cloud service. The execution main body of the load balancing method based on the multi-cloud fusion and the cloud service is a load balancing server. Referring to fig. 2, fig. 2 shows a schematic flow chart of a load balancing method based on multi-cloud fusion and cloud service provided by the invention. As shown in fig. 2, the load balancing method based on the multi-cloud fusion and the cloud service may include the following steps:
Step 201: and in response to the received service request, identifying the request type of the service request, and determining the processing difficulty index of the service request according to the request type.
Request types include dynamic requests, static requests, and the like. Dynamic requests refer to requests requiring data processing, requiring more software and hardware resources of the target server, for example: trend analysis requests, etc. A static request refers to a request that does not require data processing, requiring less hardware and software resources, such as: access requests, etc.
Specifically, step 201 specifically includes steps 2011 to 2013. As shown in fig. 3, fig. 3 shows a specific schematic flowchart of step 201 in a load balancing method based on multi-cloud fusion and cloud service provided by the present invention.
Step 2011: identifying a request type of a service request in response to the received service request; the request types comprise a dynamic request and a static request, wherein the dynamic request refers to a request requiring a server to process data, and the static request refers to a request requiring no server to process data.
Step 2012: and if the request type is a dynamic request, determining the service request as a first processing difficulty index.
Step 2013: and if the request type is a static request, determining that the service request is a second processing difficulty index.
The invention calculates the processing efficiency of the server to be selected by combining the request types so as to better adapt to the actual load condition of the server to be selected because of the large difference of server resources required by different request types and in order to improve the load balancing effect.
Different request types correspond to different processing difficulty indexes, and a higher processing difficulty coefficient indicates that more server resources are required for servicing the request. The processing difficulty index is obtained in advance based on statistical data or priori knowledge, and further different request types are corresponding to different processing difficulty indexes.
According to the embodiment, the processing difficulty index of the service request is judged according to the request type of the service request, so that whether the service request can be processed by the server to be selected is better determined later, overload and other conditions of the server to be selected are prevented, and the load balancing effect is improved.
Step 202: acquiring selection probability and resource data corresponding to each of a plurality of servers to be selected, and calculating current processing capacity indexes corresponding to each of the plurality of servers to be selected according to the selection probability and the resource data; the selection probability refers to the probability that the server to be selected is selected as the target server within the preset times.
The resource data refers to a software resource or a hardware resource of the server to be selected, and includes, but is not limited to, a combination of one or more parameters, such as a current request processing amount, disk occupancy rates corresponding to each of a plurality of history periods, processor usage rates corresponding to each of the plurality of history periods, request response time corresponding to each of the plurality of history periods, and a current network usage rate.
The current request processing amount refers to the number of service requests in a current waiting queue of the waiting server. Disk occupancy refers to the average of occupancy over a historical period. Processor usage refers to average usage over a historical period of time. The request response time refers to the average response time over the history period. The current network usage is the network usage at the current time (it can be understood that, since the network usage uncertainty factor is large, only the network usage at the current time is used as the reference value).
The resource data can well reflect the actual load condition of the server to be selected, so the current processing capacity index of the server to be selected can be calculated based on the resource data. The selection probability refers to the probability that the server to be selected is selected as the target server in the history balancing load process at a preset number of times (the number of times refers to the number of times of performing balancing load, and can also be understood as the number of processing service requests). In the process of screening target servers in the server cluster, assuming that the preset number of times is 100, and the server to be selected is selected as the target server 5 times in the process of balancing load of 100 times, the selection probability of the server to be selected is 0.05. The preset times can be the times of executing load balancing in any time period. The selection probability can reflect the historical processing capacity of the server to be selected to a certain extent, so that the evaluation of the processing capacity of the server to be selected can be corrected based on the selection probability, and load balancing can be performed more reasonably.
Specifically, step 202 specifically includes: substituting the selection probability, the current request processing amount, the disk occupancy rate corresponding to each of the plurality of history periods, the processor utilization rate corresponding to each of the plurality of history periods, the request response time corresponding to each of the plurality of history periods and the current network utilization rate into the following formula I to obtain the current processing capacity index;
equation one:
wherein M represents the current throughput index, N represents the current request throughput, p represents the selection probability, T i Representing the request response time, C, of each corresponding one of the plurality of history periods of the ith history request x Representing the disk occupancy rate corresponding to each of the plurality of history periods in the xth history period, S k And representing the processor utilization rate corresponding to each of the plurality of history periods in the kth history period, and F represents the current network utilization rate.
Step 203: and calculating the processing efficiency corresponding to each of the plurality of servers to be selected according to the processing difficulty index and the current processing capacity index.
The processing efficiency is used for representing the capability of the server to be selected to process the service request. When the processing efficiency is higher than the first threshold, the server to be selected is currently provided with the capability of processing the service request. When the processing efficiency is lower than the first threshold, the server to be selected has no capability of processing the service request for a while.
Specifically, step 203 specifically includes: dividing the processing difficulty index corresponding to each of the plurality of servers to be selected by the current processing capacity index to obtain the processing efficiency corresponding to each of the plurality of servers to be selected.
Step 204: and screening target servers from a plurality of servers to be selected according to the processing efficiency, and processing the service request through the target servers.
It can be understood that if the target server to be selected is directly selected according to the current processing capability index, whether the server to be selected has the capability of processing the service request cannot be well estimated, and the condition of overload of the server may occur, so that the embodiment estimates whether the server to be selected has the capability of processing the service request through the processing efficiency, so as to prevent the overload of the server.
For the screening mechanism of the target server, the invention provides two screening mechanisms, which are respectively as follows:
first screening mechanism: specifically, step 204 specifically includes steps A1 to A4. As shown in fig. 4, fig. 4 is a specific schematic flowchart of step 204 in a load balancing method based on multi-cloud fusion and cloud service provided by the present invention.
Step A1: and screening the first server to be selected, wherein the processing efficiency of the first server to be selected is greater than a first threshold value.
When the processing efficiency of the server to be selected is greater than the first threshold, the server to be selected is indicated to have the capability of processing the service request, so that the first server to be selected is screened from a plurality of servers to be selected based on the first threshold, and further the server to be selected which cannot process the service request is filtered.
Step A2: and if the first server to be selected is not selected, the step of selecting the first server to be selected with the processing efficiency greater than a threshold value and the subsequent steps are re-executed after waiting for a preset time period.
When the first server to be selected is not selected, the current server to be selected does not have the capability of processing the service request, and the first server to be selected meeting the requirements is re-selected after waiting for a certain period of time. Therefore, after the first server to be selected is not selected, the method and the device of the invention re-execute the steps A1 to A4 after waiting for the preset time. By the method, the situation that equipment breakdown is caused by too high load of the server to be selected is prevented.
Step A3: and if a single first server to be selected is screened, taking the first server to be selected as the target server, and processing the service request through the target server.
Step A4: and if a plurality of first servers to be selected are screened, taking the first server to be selected corresponding to the maximum processing efficiency as the target server, and processing the service request through the target server.
According to the method and the system for processing the service requests, the target server is screened from the plurality of servers to be selected based on the processing efficiency, so that the service requests can be processed in time by the appropriate servers to be selected, and the load balancing effect is improved.
Second screening mechanism: specifically, step 204 specifically includes steps B1 to B6. As shown in fig. 5, fig. 5 shows a specific schematic flowchart of step 204 in a load balancing method based on multi-cloud fusion and cloud service provided by the present invention.
Step B1: calculating the balance degree among a plurality of the servers to be selected; the balance degree is used for evaluating the balance degree of the processing efficiency among the plurality of the servers to be selected.
The calculation mode of the balance degree is as follows:
formula II:
wherein D is 0 Representing the average processing efficiency of all the servers to be selected, D i Representing the processing efficiency of the i-th candidate server.
Step B2: and if the balance degree is greater than a second threshold value, acquiring a second server to be selected in a preset distance range, wherein the preset distance range is a distance range taking a request sending end as an origin.
When the balance degree is larger than the second threshold value, the load balance effect among the plurality of the servers to be selected is good, so that the servers to be selected with a smaller distance can be preferentially considered to process the service request, and the transmission delay is reduced.
Step B3: and screening a third server to be selected, wherein the processing efficiency of the third server to be selected is greater than a first threshold value, from the plurality of second servers to be selected.
When the processing efficiency of the server to be selected is greater than the first threshold, the server to be selected is indicated to have the capability of processing the service request, so that the embodiment screens a third server to be selected from a plurality of servers to be selected based on the first threshold, and further filters the server to be selected which cannot process the service request.
Step B4: and in the plurality of third to-be-selected servers, taking the third to-be-selected server corresponding to the maximum processing efficiency as the target server, and processing the service request through the target server.
Step B5: and if the balance degree is not greater than the second threshold value, calculating the average processing efficiency corresponding to each of the plurality of server clusters.
When the balancing degree is not greater than the second threshold, the load balancing effect among the plurality of the servers to be selected is poor, so that the load among the plurality of the servers to be selected needs to be further optimized.
Step B6: and in the server cluster corresponding to the maximum average processing efficiency, taking a fourth server to be selected corresponding to the maximum processing efficiency as the target server, and processing the service request through the target server.
The load balancing is carried out according to different levels. First, server clusters corresponding to the maximum average processing efficiency are screened according to the average processing efficiency on the cluster level. And secondly, taking the fourth server to be selected corresponding to the maximum processing efficiency as a target server in the cluster level, and processing the service request through the target server. According to the embodiment, different server screening strategies are adopted based on the balancing degree, so that load balancing is performed by combining actual conditions better, and the load balancing effect is improved.
In the embodiment, a request type of a service request is identified by responding to the received service request, and a processing difficulty index of the service request is determined according to the request type; acquiring selection probability and resource data corresponding to each of a plurality of servers to be selected, and calculating current processing capacity indexes corresponding to each of the plurality of servers to be selected according to the selection probability and the resource data; the selection probability refers to the probability that the server to be selected is selected as a target server within preset times; calculating the processing efficiency corresponding to each of the plurality of servers to be selected according to the processing difficulty index and the current processing capacity index; and screening target servers from a plurality of servers to be selected according to the processing efficiency, and processing the service request through the target servers. According to the scheme, the processing difficulty index of the service request is determined according to the service request type, the current processing capacity index is determined according to the selection probability of the server to be selected and the resource data, and the processing efficiency of each server to be selected is determined according to the processing difficulty index and the current processing capacity index, so that the target server is selected according to the processing efficiency. The invention comprehensively judges the processing efficiency of the server to be selected based on a plurality of dimensions (request type, selection probability and resource data), so that the balanced load is more fit with the actual situation, and the situations of overload and the like of the server can be well avoided.
Optionally, step 201 is preceded by steps C1 to C3. As shown in fig. 6, fig. 6 shows a specific schematic flowchart of another load balancing method based on multi-cloud fusion and cloud service provided by the present invention.
Step C1: matching the load balancing frequency corresponding to the current time period; wherein the load balancing frequency is a frequency set based on the service request amount in the history period.
In order to reduce the calculation amount of the load balancing server, the invention adaptively adjusts the load balancing frequency. Firstly, counting service request amounts in different historical time periods, and setting load balancing frequencies corresponding to different time periods according to the request amounts, namely respectively corresponding to the load balancing frequencies in a plurality of time periods in one day. And then matching the corresponding load balancing frequency according to the current time period.
Step C2: and if the load balancing frequency is smaller than a third threshold value, adopting minimum connection scheduling.
Since the load balancing method corresponding to steps 201 to 204 requires more calculation, the embodiment adopts the minimum connection scheduling when the load balancing frequency is smaller than the third threshold value, so as to reduce unnecessary calculation. Wherein the minimum connection schedule is by assigning new service requests to the server with the smallest number of current connections.
Step C3: and if the load balancing frequency is not smaller than the third threshold value, executing the steps of responding to the received service request, identifying the request type of the service request, and determining the processing difficulty index of the service request according to the request type and the subsequent steps according to the load balancing frequency.
And if the load balancing frequency is not smaller than the third threshold value, executing steps 201 to 204 according to the load balancing frequency.
In this embodiment, different load balancing strategies are adopted according to load balancing frequencies corresponding to different periods, so that the calculated amount of the load balancing server is reduced under the condition that the load balancing effect is ensured.
Referring to fig. 7, fig. 7 shows a schematic diagram of a load balancing device based on multi-cloud fusion and cloud service, and fig. 7 shows a load balancing device based on multi-cloud fusion and cloud service, including:
an identifying unit 71, configured to identify a request type of a service request in response to the received service request, and determine a processing difficulty index of the service request according to the request type;
An obtaining unit 72, configured to obtain selection probabilities and resource data corresponding to each of a plurality of to-be-selected servers, and calculate current processing capability indexes corresponding to each of the plurality of to-be-selected servers according to the selection probabilities and the resource data; the selection probability refers to the probability that the server to be selected is selected as a target server within preset times;
a calculating unit 73, configured to calculate processing efficiencies corresponding to the plurality of servers to be selected according to the processing difficulty index and the current processing capability index;
and a screening unit 74, configured to screen a target server from multiple servers to be selected according to the processing efficiency, and process the service request through the target server.
The invention provides a load balancing device based on multi-cloud fusion and cloud service, which is characterized by identifying a request type of a service request by responding to the received service request and determining a processing difficulty index of the service request according to the request type; acquiring selection probability and resource data corresponding to each of a plurality of servers to be selected, and calculating current processing capacity indexes corresponding to each of the plurality of servers to be selected according to the selection probability and the resource data; the selection probability refers to the probability that the server to be selected is selected as a target server within preset times; calculating the processing efficiency corresponding to each of the plurality of servers to be selected according to the processing difficulty index and the current processing capacity index; and screening target servers from a plurality of servers to be selected according to the processing efficiency, and processing the service request through the target servers. According to the scheme, the processing difficulty index of the service request is determined according to the service request type, the current processing capacity index is determined according to the selection probability of the server to be selected and the resource data, and the processing efficiency of each server to be selected is determined according to the processing difficulty index and the current processing capacity index, so that the target server is selected according to the processing efficiency. The invention comprehensively judges the processing efficiency of the server to be selected based on a plurality of dimensions (request type, selection probability and resource data), so that the balanced load is more fit with the actual situation, and the situations of overload and the like of the server can be well avoided.
Fig. 8 is a schematic diagram of a load balancing server according to an embodiment of the present invention. As shown in fig. 8, a load balancing server 8 of this embodiment includes: a processor 80, a memory 81 and a computer program 82 stored in the memory 81 and executable on the processor 80, for example a program based on cloudiness fusion and load balancing of cloud services. The processor 80, when executing the computer program 82, implements the steps of the embodiments of the load balancing method based on the multi-cloud fusion and the cloud service, for example, the steps 201 to 204 shown in fig. 2. Alternatively, the processor 80, when executing the computer program 82, performs the functions of the units in the above-described device embodiments, for example the functions of the units 71 to 74 shown in fig. 7.
By way of example, the computer program 82 may be divided into one or more units, which are stored in the memory 81 and executed by the processor 80 to complete the present invention. The one or more elements may be a series of computer program instruction segments capable of performing a specific function describing the execution of the computer program 82 in the one load balancing server 8. For example, the specific functions of the computer program 82 that may be partitioned into units are as follows:
The identification unit is used for responding to the received service request, identifying the request type of the service request and determining the processing difficulty index of the service request according to the request type;
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring selection probability and resource data corresponding to each of a plurality of servers to be selected, and calculating current processing capacity indexes corresponding to each of the plurality of servers to be selected according to the selection probability and the resource data; the selection probability refers to the probability that the server to be selected is selected as a target server within preset times;
the computing unit is used for computing the processing efficiency corresponding to each of the plurality of servers to be selected according to the processing difficulty index and the current processing capacity index;
and the screening unit is used for screening target servers from a plurality of servers to be selected according to the processing efficiency, and processing the service request through the target servers.
Including but not limited to a processor 80 and a memory 81. It will be appreciated by those skilled in the art that fig. 8 is merely an example of one load balancing server 8 and is not meant to be limiting as one load balancing server 8 may include more or fewer components than shown, or may combine certain components, or different components, e.g., one load balancing server may further include input and output devices, network access devices, buses, etc.
The processor 80 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 81 may be an internal storage unit of the load balancing server 8, such as a hard disk or a memory of the load balancing server 8. The memory 81 may also be an external storage device of the load balancing server 8, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the load balancing server 8. Further, the memory 81 may also include both an internal storage unit and an external storage device of the load balancing server 8. The memory 81 is used for storing the computer program and other programs and data required for the one roaming control device. The memory 81 may also be used to temporarily store data that has been output or is to be output.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
It should be noted that, because the content of information interaction and execution process between the above device units is based on the same concept as the method embodiment of the present invention, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Embodiments of the present invention also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements steps for implementing the various method embodiments described above.
Embodiments of the present invention provide a computer program product which, when run on a mobile terminal, causes the mobile terminal to perform steps that enable the implementation of the method embodiments described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/load balancing server, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus network device and method may be implemented in other manners. For example, the above-described apparatus network device embodiments are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to a detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is monitored" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon monitoring a [ described condition or event ]" or "in response to monitoring a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the invention. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (10)
1. The load balancing method based on the multi-cloud fusion and the cloud service is characterized by comprising the following steps of:
in response to a received service request, identifying a request type of the service request, and determining a processing difficulty index of the service request according to the request type;
acquiring selection probability and resource data corresponding to each of a plurality of servers to be selected, and calculating current processing capacity indexes corresponding to each of the plurality of servers to be selected according to the selection probability and the resource data; the selection probability refers to the probability that the server to be selected is selected as a target server within preset times;
calculating the processing efficiency corresponding to each of the plurality of servers to be selected according to the processing difficulty index and the current processing capacity index;
and screening target servers from a plurality of servers to be selected according to the processing efficiency, and processing the service request through the target servers.
2. The cloud convergence and cloud service based load-balancing method as claimed in claim 1, wherein said step of identifying a request type of said service request in response to a received service request and determining a processing difficulty index of said service request based on said request type comprises:
Identifying a request type of a service request in response to the received service request; the request types comprise a dynamic request and a static request, wherein the dynamic request refers to a request for data processing by a server, and the static request refers to a request for data processing by a server;
if the request type is a dynamic request, determining that the service request is a first processing difficulty index;
and if the request type is a static request, determining that the service request is a second processing difficulty index.
3. The load balancing method based on the multi-cloud fusion and the cloud service according to claim 1, wherein the resource data comprises a current request processing amount, disk occupancy rates corresponding to each of a plurality of history periods, processor usage rates corresponding to each of the plurality of history periods, request response time corresponding to each of the plurality of history periods, and a current network usage rate;
the step of obtaining the selection probability and the resource data corresponding to each of the plurality of to-be-selected servers, and calculating the current processing capacity index corresponding to each of the plurality of to-be-selected servers according to the selection probability and the resource data comprises the following steps:
substituting the selection probability, the current request processing amount, the disk occupancy rate corresponding to each of the plurality of history periods, the processor utilization rate corresponding to each of the plurality of history periods, the request response time corresponding to each of the plurality of history periods and the current network utilization rate into the following formula I to obtain the current processing capacity index;
Equation one:
wherein M represents the current throughput index, N represents the current request throughput, p represents the selection probability, T i A request response time, C, representing the ith history request x Representing disk occupancy in the xth history period, S k Representing processor usage during a kth historical period, F representing the current network usage.
4. The load balancing method based on multi-cloud fusion and cloud service according to claim 1, wherein the step of calculating the processing efficiency corresponding to each of the plurality of the servers to be selected according to the processing difficulty index and the current processing capacity index comprises:
dividing the processing difficulty index corresponding to each of the plurality of servers to be selected by the current processing capacity index to obtain the processing efficiency corresponding to each of the plurality of servers to be selected.
5. The load balancing method based on multi-cloud fusion and cloud service as claimed in claim 1, wherein the step of selecting a target server among a plurality of servers to be selected according to the processing efficiency and processing the service request through the target server comprises:
screening a first server to be selected, wherein the processing efficiency of the first server to be selected is greater than a first threshold value;
If the first server to be selected is not selected, the step of selecting the first server to be selected with the processing efficiency being greater than a threshold value and the subsequent steps are re-executed in the plurality of servers to be selected after waiting for a preset time period;
if a single first server to be selected is screened, the first server to be selected is used as the target server, and the service request is processed through the target server;
and if a plurality of first servers to be selected are screened, taking the first server to be selected corresponding to the maximum processing efficiency as the target server, and processing the service request through the target server.
6. The load balancing method based on multi-cloud fusion and cloud service as claimed in claim 1, wherein the step of selecting a target server among a plurality of servers to be selected according to the processing efficiency and processing the service request through the target server comprises:
calculating the balance degree among a plurality of the servers to be selected; the balance degree is used for evaluating the balance degree of the processing efficiency among the plurality of servers to be selected;
if the balance degree is greater than a second threshold value, a second server to be selected in a preset distance range is obtained, wherein the preset distance range refers to a distance range taking a request sending end as an origin;
Screening a third server to be selected, of which the processing efficiency is greater than a first threshold value, from a plurality of second servers to be selected;
in the plurality of third to-be-selected servers, taking the third to-be-selected server corresponding to the maximum processing efficiency as the target server, and processing the service request through the target server;
if the balance degree is not greater than the second threshold value, calculating average processing efficiency corresponding to each of the plurality of server clusters;
and in the server cluster corresponding to the maximum average processing efficiency, taking a fourth server to be selected corresponding to the maximum processing efficiency as the target server, and processing the service request through the target server.
7. The cloud convergence and cloud service based load-balancing method of claim 1, wherein prior to said step of identifying a request type of said service request in response to a received service request and determining a process difficulty index of said service request based on said request type, further comprises:
matching the load balancing frequency corresponding to the current time period; wherein the load balancing frequency is a frequency set based on the service request amount in the history period;
If the load balancing frequency is smaller than a third threshold value, minimum connection scheduling is adopted;
and if the load balancing frequency is not smaller than the third threshold value, executing the steps of responding to the received service request, identifying the request type of the service request, and determining the processing difficulty index of the service request according to the request type and the subsequent steps according to the load balancing frequency.
8. The utility model provides a load balancing device based on many cloud integration and cloud service which characterized in that, load balancing device based on cloud service includes:
the identification unit is used for responding to the received service request, identifying the request type of the service request and determining the processing difficulty index of the service request according to the request type;
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring selection probability and resource data corresponding to each of a plurality of servers to be selected, and calculating current processing capacity indexes corresponding to each of the plurality of servers to be selected according to the selection probability and the resource data; the selection probability refers to the probability that the server to be selected is selected as a target server within preset times;
the computing unit is used for computing the processing efficiency corresponding to each of the plurality of servers to be selected according to the processing difficulty index and the current processing capacity index;
And the screening unit is used for screening target servers from a plurality of servers to be selected according to the processing efficiency, and processing the service request through the target servers.
9. A load balancing server comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when the computer program is executed by the processor.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 7.
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