CN114124968B - Load balancing method, device, equipment and medium based on market data - Google Patents
Load balancing method, device, equipment and medium based on market data Download PDFInfo
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- CN114124968B CN114124968B CN202210097246.XA CN202210097246A CN114124968B CN 114124968 B CN114124968 B CN 114124968B CN 202210097246 A CN202210097246 A CN 202210097246A CN 114124968 B CN114124968 B CN 114124968B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1004—Server selection for load balancing
Abstract
The invention relates to the field of Internet, and provides a load balancing method, a device, equipment and a medium based on market data, which can calculate the sum of the current pre-occupied bandwidth of each server and the pre-requested bandwidth of a target client, obtaining a target pre-occupied bandwidth of each server, obtaining a pre-configured bandwidth threshold, obtaining the server with the target pre-occupied bandwidth smaller than the bandwidth threshold from each server as a candidate server, obtaining a client type corresponding to the target client, selecting a target server from the candidate servers according to the client type, accessing the target client to the target server, and further, load balancing based on market data is achieved from multiple dimensions of the server, the client and the client, service performance of the server cluster is improved, resource allocation is optimized, and robustness of the server cluster is guaranteed.
Description
Technical Field
The invention relates to the technical field of internet, in particular to a load balancing method, device, equipment and medium based on market data.
Background
In the field of financial market, a TCP (Transmission Control Protocol) long connection mode is usually adopted to issue market conditions subscribed by an internet client, and in order to ensure that data Transmission between a server and the client is more efficient and accurate, a load balancing technology is required to ensure that resource utilization is maximized in internet market condition subscription push service.
In the field of financial market, the number of subscribed market of different clients is dynamically changed and has a large amplitude, so that the change of occupied resources is large, and in the prior art, a general load balancing strategy is based on only one dimension or cannot be applied to a scene of dynamic market subscription, so that the load balancing effect is poor.
Disclosure of Invention
In view of the above, there is a need to provide a load balancing method, device, apparatus and medium based on market data, aiming at solving the problem of load balancing for market data.
A load balancing method based on market data comprises the following steps:
responding to an access request triggered by a target client, and calculating the pre-request bandwidth of the target client;
acquiring resource information of each server in a server cluster, and determining the current pre-occupied bandwidth of each server according to the resource information of each server;
respectively calculating the sum of the current pre-occupied bandwidth of each server and the pre-requested bandwidth of the target client to obtain the target pre-occupied bandwidth of each server;
acquiring a preset bandwidth threshold, and acquiring a server with the target pre-occupied bandwidth smaller than the bandwidth threshold from each server as a candidate server;
acquiring a client type corresponding to the target client, and selecting a target server from the candidate servers according to the client type;
and accessing the target client to the target server.
According to a preferred embodiment of the present invention, the calculating the pre-requested bandwidth of the target client includes:
acquiring bandwidth occupied by each snapshot quotation code subscribed by the target client, the number of the subscribed snapshot quotation codes, bandwidth occupied by each subscribed one-by-one consignation quotation code, the number of the subscribed one-by-one consignation quotation codes, bandwidth occupied by each subscribed one-by-one transaction quotation code, the number of the subscribed one-by-one transaction quotation codes, and a flow control value of the target client;
calculating the product of the bandwidth occupied by each snapshot market quotation code and the number of the snapshot market quotation codes to obtain a first numerical value;
calculating the product of the bandwidth occupied by each stroke-by-stroke entrustment market code and the number of the stroke-by-stroke entrustment market codes to obtain a second numerical value;
calculating the product of the bandwidth occupied by each transaction quotation code and the number of the transaction quotation codes to obtain a third numerical value;
calculating the sum of the first numerical value, the second numerical value and the third numerical value to obtain the pre-used bandwidth of the target client;
and comparing the flow control value with the size of the pre-used bandwidth, and determining the smaller value as the pre-requested bandwidth.
According to the preferred embodiment of the present invention, the determining the current pre-occupied bandwidth of each server according to the resource information of each server includes:
determining a client currently connected to each server according to the resource information of each server;
acquiring a pre-request bandwidth of each client;
and calculating the sum of the pre-requested bandwidths of the clients currently connected to each service end to obtain the current pre-occupied bandwidth of each service end.
According to a preferred embodiment of the present invention, the selecting a target server from the candidate servers according to the client type includes:
when the client type is a first type, sequencing each candidate server according to the target pre-occupied bandwidth of each candidate server from small to large to obtain a target sequence;
and extracting the candidate server ranked at the head from the target sequence as the target server.
According to a preferred embodiment of the present invention, the selecting a target server from the candidate servers according to the client type further includes:
when the client type is a second type, acquiring a client currently connected to each candidate server;
determining the client type of the client currently connected to each candidate server;
acquiring the client with the client type of the second type currently connected to the client of each candidate server, and taking the client as a first client corresponding to each candidate server;
respectively calculating the number of first clients corresponding to each candidate server to obtain the first number corresponding to each candidate server;
sequencing each candidate server according to the sequence of the first quantity from small to large;
and acquiring the candidate server ranked at the head as the target server.
According to a preferred embodiment of the invention, the method further comprises:
when the client type is the second type and a server with the target pre-occupied bandwidth smaller than the bandwidth threshold value is not obtained from each server as the candidate server, determining the client type of the client currently connected to each server;
acquiring a client with the client type of the first type currently connected to each server, and taking the client as a second client corresponding to each server;
acquiring the occupied bandwidth of a second client corresponding to each server;
respectively calculating the sum of occupied bandwidths of the second client corresponding to each server, and taking the sum as the releasable bandwidth of each server;
selecting a server with the releasable bandwidth larger than or equal to the pre-requested bandwidth of the target client from each server, and using the server as at least one alternative server;
acquiring the current pre-occupied bandwidth of each alternative server in the at least one alternative server;
sequencing each alternative server according to the sequence that the current occupied bandwidth of each alternative server is from large to small;
acquiring an alternative server arranged at the head as a server to be processed;
controlling the to-be-processed server to disconnect the preset number of second clients and accessing the target client to the to-be-processed server;
the preset number is the minimum value of the number of the disconnected second clients when the sum of the occupied bandwidths of the disconnected second clients is larger than or equal to the pre-requested bandwidth of the target client.
According to a preferred embodiment of the invention, the method further comprises:
and when the server with the releasable bandwidth larger than or equal to the pre-requested bandwidth of the target client is not selected from each server, sending alarm information for prompting the full load of the server cluster and sending a capacity expansion request for the server cluster.
A load balancing apparatus based on market data, the load balancing apparatus based on market data comprising:
the computing unit is used for responding to an access request triggered by a target client and computing a pre-request bandwidth of the target client;
the determining unit is used for acquiring resource information of each server in the server cluster and determining the current pre-occupied bandwidth of each server according to the resource information of each server;
the computing unit is further configured to compute a sum of a current pre-occupied bandwidth of each server and a pre-requested bandwidth of the target client, respectively, to obtain a target pre-occupied bandwidth of each server;
the acquisition unit is used for acquiring a preset bandwidth threshold value and acquiring a server with the target pre-occupied bandwidth smaller than the bandwidth threshold value from each server as a candidate server;
the selection unit is used for acquiring the client type corresponding to the target client and selecting the target server from the candidate servers according to the client type;
and the access unit is used for accessing the target client to the target server.
A computer device, the computer device comprising:
a memory storing at least one instruction; and
and the processor executes the instructions stored in the memory to realize the load balancing method based on the market information.
A computer-readable storage medium having stored therein at least one instruction for execution by a processor in a computer device to implement the market data-based load balancing method.
It can be seen from the above technical solutions that the present invention can respond to an access request triggered by a target client, calculate a pre-request bandwidth of the target client, collect resource information of each server in a server cluster, determine a current pre-occupied bandwidth of each server according to the resource information of each server, calculate a sum of the current pre-occupied bandwidth of each server and the pre-request bandwidth of the target client, obtain the target pre-occupied bandwidth of each server, obtain a pre-configured bandwidth threshold, obtain a server with the target pre-occupied bandwidth being less than the bandwidth threshold from each server as a candidate server, obtain a client type corresponding to the target client, select a target server from the candidate servers according to the client type, and access the target client to the target server, and further, load balancing based on market data is achieved from multiple dimensions of the server, the client and the client, service performance of the server cluster is improved, resource allocation is optimized, and robustness of the server cluster is guaranteed.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of a load balancing method based on market data according to the present invention.
Fig. 2 is a functional block diagram of a preferred embodiment of the market data-based load balancing apparatus according to the present invention.
Fig. 3 is a schematic structural diagram of a computer device according to a preferred embodiment of the method for implementing load balancing based on market data according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flow chart of a preferred embodiment of the load balancing method based on market data according to the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
The load balancing method based on market data is applied to one or more computer devices, which are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware of the computer devices includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive web Television (IPTV), an intelligent wearable device, and the like.
The computer device may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network servers.
The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The Network in which the computer device is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
S10, responding to the access request triggered by the target client, calculating the pre-request bandwidth of the target client.
In at least one embodiment of the present invention, the target client may be a client of any user, and the target client requests to access the server to implement subscription to the financial market data.
In at least one embodiment of the present invention, the calculating the pre-requested bandwidth of the target client includes:
acquiring bandwidth occupied by each snapshot quotation code subscribed by the target client, the number of the subscribed snapshot quotation codes, bandwidth occupied by each subscribed one-by-one consignation quotation code, the number of the subscribed one-by-one consignation quotation codes, bandwidth occupied by each subscribed one-by-one transaction quotation code, the number of the subscribed one-by-one transaction quotation codes, and a flow control value of the target client;
calculating the product of the bandwidth occupied by each snapshot market quotation code and the number of the snapshot market quotation codes to obtain a first numerical value;
calculating the product of the bandwidth occupied by each stroke-by-stroke entrustment market code and the number of the stroke-by-stroke entrustment market codes to obtain a second numerical value;
calculating the product of the bandwidth occupied by each transaction quotation code and the number of the transaction quotation codes to obtain a third numerical value;
calculating the sum of the first numerical value, the second numerical value and the third numerical value to obtain the pre-used bandwidth of the target client;
and comparing the flow control value with the size of the pre-used bandwidth, and determining the smaller value as the pre-requested bandwidth.
The bandwidth occupied by each snapshot quotation code, the bandwidth occupied by each consignment quotation code and the bandwidth occupied by each transaction quotation code can be tested and evaluated according to actual conditions, and the method is not limited.
The flow control value of the target client can be configured according to an account corresponding to the target client.
Through the implementation mode, the pre-request bandwidth of the target client can be determined based on multiple dimensions, so that the determined pre-request bandwidth of the target client is more accurate.
S11, collecting the resource information of each server in the server cluster, and determining the current pre-occupied bandwidth of each server according to the resource information of each server.
In this embodiment, the server cluster includes at least one server, and is configured to provide market data to a connected client.
In at least one embodiment of the present invention, the resource information of each server may include, but is not limited to, one or more of the following information:
the client currently connected to each server, the client subscription condition connected to each server, and the number of client subscription limits.
In at least one embodiment of the present invention, the determining the current occupied bandwidth of each server according to the resource information of each server includes:
determining a client currently connected to each server according to the resource information of each server;
acquiring a pre-request bandwidth of each client;
and calculating the sum of the pre-requested bandwidths of the clients currently connected to each service end to obtain the current pre-occupied bandwidth of each service end.
The manner of calculating the bandwidth requested by each client is similar to the manner of calculating the bandwidth requested by the target client, and is not described herein again.
And S12, respectively calculating the sum of the current pre-occupied bandwidth of each server and the pre-requested bandwidth of the target client to obtain the target pre-occupied bandwidth of each server.
In the above embodiment, the sum of the pre-occupied bandwidth of each server and the pre-requested bandwidth of the target client is used as the target pre-occupied bandwidth of each server, and then this is used as a comparison basis to ensure that the corresponding server can satisfy the load after accessing the target client.
S13, obtaining a preset bandwidth threshold, and obtaining the server with the target pre-occupied bandwidth smaller than the bandwidth threshold from each server as a candidate server.
In this embodiment, the bandwidth threshold may be configured by self-defining according to actual requirements, which is not limited in the present invention.
Through the implementation mode, the candidate server can be selected by combining the resource occupation condition of the server and the resource occupation condition of the client at the same time.
And S14, acquiring the client type corresponding to the target client, and selecting the target server from the candidate servers according to the client type.
In at least one embodiment of the invention, the customer types may include, but are not limited to: VIP customers, non-VIP customers (premium customers, regular customers, free customers), etc.
In at least one embodiment of the present invention, the selecting a target server from the candidate servers according to the client type includes:
when the client type is a first type, sequencing each candidate server according to the target pre-occupied bandwidth of each candidate server from small to large to obtain a target sequence;
and extracting the candidate server ranked at the head from the target sequence as the target server.
Wherein the first type may comprise a non-VIP customer type.
In the above embodiment, for a non-key client, a server with low bandwidth occupation is selected as a target server for service.
In at least one embodiment of the present invention, the selecting a target server from the candidate servers according to the client type further includes:
when the client type is a second type, acquiring a client currently connected to each candidate server;
determining the client type of the client currently connected to each candidate server;
acquiring the client with the client type of the second type currently connected to the client of each candidate server, and taking the client as a first client corresponding to each candidate server;
respectively calculating the number of the first clients corresponding to each candidate server to obtain the first number corresponding to each candidate server;
sequencing each candidate server according to the sequence of the first quantity from small to large;
and acquiring the candidate server ranked at the head as the target server.
Wherein the second type may comprise a VIP client type.
In the above embodiment, for the key client, the server with the least number of connected key clients is selected as the target server, so as to provide better service to the key client.
Through the implementation mode, the load balance can be realized by further integrating the customer dimension on the basis of the server dimension and the client dimension, and further the attribute of market data can be met.
And S15, accessing the target client to the target server.
Through the implementation mode, the load balance aiming at the market data issuing service is realized by combining the server dimension, the client dimension and the client dimension, the service quality and the network data processing capacity are improved, the time delay of the client for receiving the data is reduced, the resources of the server cluster can be fully utilized, and the overall service performance is improved.
In at least one embodiment of the invention, the method further comprises:
when the client type is the second type and a server with the target pre-occupied bandwidth smaller than the bandwidth threshold value is not obtained from each server as the candidate server, determining the client type of the client currently connected to each server;
acquiring a client with the client type of the first type currently connected to each server, and taking the client as a second client corresponding to each server;
acquiring the occupied bandwidth of a second client corresponding to each server;
respectively calculating the sum of occupied bandwidths of the second client corresponding to each server, and taking the sum as the releasable bandwidth of each server;
selecting a server with the releasable bandwidth larger than or equal to the pre-requested bandwidth of the target client from each server, and using the server as at least one alternative server;
acquiring the current pre-occupied bandwidth of each alternative server in the at least one alternative server;
sequencing each alternative server according to the sequence that the current occupied bandwidth of each alternative server is reduced from large to small;
acquiring an alternative server arranged at the head as a server to be processed;
controlling the to-be-processed server to disconnect the second clients with a preset number, and accessing the target client to the to-be-processed server;
the preset number is the minimum value of the number of the disconnected second clients when the sum of the occupied bandwidths of the disconnected second clients is larger than or equal to the pre-requested bandwidth of the target client.
Specifically, when the to-be-processed server is controlled to disconnect the preset number of second clients, a client with a longer connection time may be selected from the second clients to perform disconnection operation, a client with a higher resource occupation may also be selected from the second clients to perform disconnection operation, or a client may be arbitrarily selected from the second clients to perform disconnection operation, so that the bandwidth released after disconnection just meets the pre-requested bandwidth of the target client.
Through the implementation mode, under the condition of limited resources, the client side of the non-key client is disconnected, so that the key client can preferentially and stably use the resources of the server side, and flexible and differentiated load balancing is realized on the premise of ensuring reliable service.
In at least one embodiment of the invention, the method further comprises:
and when the server with the releasable bandwidth larger than or equal to the pre-requested bandwidth of the target client is not selected from each server, sending alarm information for prompting the full load of the server cluster and sending a capacity expansion request for the server cluster.
Through the embodiment, full-load warning can be sent out when no available resources exist in the server cluster, and capacity expansion is requested, so that normal service can be timely recovered by the server cluster, and service experience of customers is prevented from being influenced.
In this embodiment, when there is no target server capable of accessing, a notification of a request failure may be sent to the target client in time, or a reconnect prompt may also be sent to the target client at the same time, so as to avoid long-time waiting for a client.
It can be seen from the above technical solutions that the present invention can respond to an access request triggered by a target client, calculate a pre-request bandwidth of the target client, collect resource information of each server in a server cluster, determine a current pre-occupied bandwidth of each server according to the resource information of each server, calculate a sum of the current pre-occupied bandwidth of each server and the pre-request bandwidth of the target client, obtain the target pre-occupied bandwidth of each server, obtain a pre-configured bandwidth threshold, obtain a server with the target pre-occupied bandwidth being less than the bandwidth threshold from each server as a candidate server, obtain a client type corresponding to the target client, select a target server from the candidate servers according to the client type, and access the target client to the target server, and further, load balancing based on market data is achieved from multiple dimensions of the server, the client and the client, service performance of the server cluster is improved, resource allocation is optimized, and robustness of the server cluster is guaranteed.
Fig. 2 is a functional block diagram of a preferred embodiment of the load balancing apparatus based on market data according to the present invention. The load balancing device 11 based on market data comprises a calculating unit 110, a determining unit 111, an obtaining unit 112, a selecting unit 113 and an accessing unit 114. The module/unit referred to in the present invention refers to a series of computer program segments that can be executed by the processor 13 and that can perform a fixed function, and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.
The calculation unit 110 calculates a pre-requested bandwidth of a target client in response to an access request triggered by the target client.
In at least one embodiment of the present invention, the target client may be a client of any user, and the target client requests to access the server to implement subscription to the financial market data.
In at least one embodiment of the present invention, the calculating unit 110 calculates the pre-requested bandwidth of the target client includes:
acquiring bandwidth occupied by each snapshot quotation code subscribed by the target client, the number of the subscribed snapshot quotation codes, bandwidth occupied by each subscribed consignation quotation code, the number of the subscribed consignation quotation codes, bandwidth occupied by each subscribed transaction quotation code, and the number of the subscribed transaction quotation codes, and acquiring a flow control value of the target client;
calculating the product of the bandwidth occupied by each snapshot quotation code and the number of the snapshot quotation codes to obtain a first numerical value;
calculating the product of the bandwidth occupied by each stroke-by-stroke entrustment market code and the number of the stroke-by-stroke entrustment market codes to obtain a second numerical value;
calculating the product of the bandwidth occupied by each transaction quotation code and the number of the transaction quotation codes to obtain a third numerical value;
calculating the sum of the first numerical value, the second numerical value and the third numerical value to obtain the pre-used bandwidth of the target client;
and comparing the flow control value with the size of the pre-used bandwidth, and determining a smaller value as the pre-requested bandwidth.
The bandwidth occupied by each snapshot quotation code, the bandwidth occupied by each consignment quotation code and the bandwidth occupied by each transaction quotation code can be tested and evaluated according to actual conditions, and the method is not limited.
And the flow control value of the target client can be configured according to the account corresponding to the target client.
Through the implementation mode, the pre-request bandwidth of the target client can be determined based on multiple dimensions, so that the determined pre-request bandwidth of the target client is more accurate.
The determining unit 111 collects resource information of each server in the server cluster, and determines a current pre-occupied bandwidth of each server according to the resource information of each server.
In this embodiment, the server cluster includes at least one server, and is configured to provide market data to a connected client.
In at least one embodiment of the present invention, the resource information of each server may include, but is not limited to, one or more of the following information:
the client currently connected to each server, the client subscription condition connected to each server, and the number of client subscription limits.
In at least one embodiment of the present invention, the determining unit 111 determines the current occupied bandwidth of each server according to the resource information of each server, including:
determining a client currently connected to each server according to the resource information of each server;
acquiring a pre-request bandwidth of each client;
and calculating the sum of the pre-requested bandwidths of the clients currently connected to each service end to obtain the current pre-occupied bandwidth of each service end.
The manner of calculating the bandwidth requested by each client is similar to the manner of calculating the bandwidth requested by the target client, and is not described herein again.
The calculating unit 110 calculates the sum of the current pre-occupied bandwidth of each server and the pre-requested bandwidth of the target client, respectively, to obtain the target pre-occupied bandwidth of each server.
In the above embodiment, the sum of the pre-occupied bandwidth of each server and the pre-requested bandwidth of the target client is used as the target pre-occupied bandwidth of each server, and then this is used as a comparison basis to ensure that the corresponding server can satisfy the load after accessing the target client.
The obtaining unit 112 obtains a bandwidth threshold configured in advance, and obtains a server with the target pre-occupied bandwidth smaller than the bandwidth threshold from each server as a candidate server.
In this embodiment, the bandwidth threshold may be configured by self-defining according to actual requirements, which is not limited in the present invention.
Through the implementation mode, the candidate server can be selected by combining the resource occupation condition of the server and the resource occupation condition of the client at the same time.
The selecting unit 113 obtains a client type corresponding to the target client, and selects a target server from the candidate servers according to the client type.
In at least one embodiment of the invention, the customer types may include, but are not limited to: VIP customers, non-VIP customers (premium customers, regular customers, free customers), etc.
In at least one embodiment of the present invention, the selecting unit 113 selects a target server from the candidate servers according to the client type includes:
when the client type is a first type, sequencing each candidate server according to the target pre-occupied bandwidth of each candidate server from small to large to obtain a target sequence;
and extracting the candidate server ranked at the head from the target sequence as the target server.
Wherein the first type may comprise a non-VIP customer type.
In the above embodiment, for a non-key client, a server with low bandwidth occupation is selected as a target server for service.
In at least one embodiment of the present invention, the selecting unit 113 further includes, according to the customer type, selecting a target server from the candidate servers:
when the client type is a second type, acquiring a client currently connected to each candidate server;
determining the client type of the client currently connected to each candidate server;
acquiring the client with the client type of the second type currently connected to the client of each candidate server, and taking the client as a first client corresponding to each candidate server;
respectively calculating the number of first clients corresponding to each candidate server to obtain the first number corresponding to each candidate server;
sequencing each candidate server according to the sequence of the first number from small to large;
and acquiring the candidate server ranked at the head as the target server.
Wherein the second type may comprise a VIP client type.
In the above embodiment, for the key client, the server with the least number of connected key clients is selected as the target server, so as to provide better service to the key client.
Through the implementation mode, the load balance can be realized by further integrating the customer dimension on the basis of the server dimension and the client dimension, and further the attribute of market data can be met.
The access unit 114 accesses the target client to the target server.
Through the implementation mode, the load balance aiming at market data issuing service is realized by combining the server dimension, the client dimension and the client dimension, the service quality and the network data processing capacity are improved, the time delay of the client for receiving data is reduced, meanwhile, the resources of the server cluster can be fully utilized, and the overall service performance is improved.
In at least one embodiment of the present invention, when the client type is the second type and a server with the target pre-occupied bandwidth smaller than the bandwidth threshold is not obtained from each server as the candidate server, determining the client type of the client currently connected to each server;
acquiring a client with the client type of the first type currently connected to each server, and taking the client as a second client corresponding to each server;
acquiring the occupied bandwidth of a second client corresponding to each server;
respectively calculating the sum of the occupied bandwidths of the second client corresponding to each server, and taking the sum as the releasable bandwidth of each server;
selecting a server with the releasable bandwidth larger than or equal to the pre-requested bandwidth of the target client from each server, and using the server as at least one alternative server;
acquiring the current pre-occupied bandwidth of each alternative server in the at least one alternative server;
sequencing each alternative server according to the sequence that the current occupied bandwidth of each alternative server is reduced from large to small;
acquiring an alternative server arranged at the head as a server to be processed;
controlling the to-be-processed server to disconnect the preset number of second clients and accessing the target client to the to-be-processed server;
the preset number is the minimum value of the number of the disconnected second clients when the sum of the occupied bandwidths of the disconnected second clients is larger than or equal to the pre-requested bandwidth of the target client.
Specifically, when the to-be-processed server is controlled to disconnect the preset number of second clients, a client with a longer connection time may be selected from the second clients to perform disconnection operation, a client with a higher resource occupation may also be selected from the second clients to perform disconnection operation, or a client may be arbitrarily selected from the second clients to perform disconnection operation, so that the bandwidth released after disconnection just meets the pre-requested bandwidth of the target client.
Through the implementation mode, under the condition of limited resources, the client side of the non-key client is disconnected, so that the key client can preferentially and stably use the resources of the server side, and flexible and differentiated load balancing is realized on the premise of ensuring reliable service.
In at least one embodiment of the present invention, when a server whose releasable bandwidth is greater than or equal to the pre-requested bandwidth of the target client is not selected from each server, an alarm message for prompting that the server cluster is fully loaded is sent, and a capacity expansion request for the server cluster is sent.
Through the implementation mode, full-load warning can be sent out when no available resources exist in the server cluster, capacity expansion is requested, normal service can be timely restored in the server cluster, and service experience of customers is prevented from being influenced.
In this embodiment, when there is no target server capable of accessing, a notification of a request failure may be sent to the target client in time, or a reconnect prompt may also be sent to the target client at the same time, so as to avoid long-time waiting for a client.
It can be seen from the above technical solutions that the present invention can respond to an access request triggered by a target client, calculate a pre-request bandwidth of the target client, collect resource information of each server in a server cluster, determine a current pre-occupied bandwidth of each server according to the resource information of each server, calculate a sum of the current pre-occupied bandwidth of each server and the pre-request bandwidth of the target client, obtain the target pre-occupied bandwidth of each server, obtain a pre-configured bandwidth threshold, obtain a server with the target pre-occupied bandwidth being less than the bandwidth threshold from each server as a candidate server, obtain a client type corresponding to the target client, select a target server from the candidate servers according to the client type, and access the target client to the target server, and further, load balancing based on market data is achieved from multiple dimensions of the server, the client and the client, service performance of the server cluster is improved, resource allocation is optimized, and robustness of the server cluster is guaranteed.
Fig. 3 is a schematic structural diagram of a computer device according to a preferred embodiment of the method for implementing load balancing based on market data according to the present invention.
The computer device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program, such as a load balancing program based on market data, stored in the memory 12 and executable on the processor 13.
It will be understood by those skilled in the art that the schematic diagram is merely an example of the computer device 1, and does not constitute a limitation to the computer device 1, the computer device 1 may have a bus-type structure or a star-shaped structure, the computer device 1 may further include more or less other hardware or software than those shown, or different component arrangements, for example, the computer device 1 may further include an input and output device, a network access device, etc.
It should be noted that the computer device 1 is only an example, and other electronic products that are currently available or may come into existence in the future, such as electronic products that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.
The memory 12 includes at least one type of readable storage medium, which includes flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 12 may in some embodiments be an internal storage unit of the computer device 1, e.g. a removable hard disk of the computer device 1. The memory 12 may also be an external storage device of the computer device 1 in other embodiments, such as a plug-in removable hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the computer device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the computer device 1. The memory 12 can be used not only for storing application software installed in the computer apparatus 1 and various types of data, such as codes of a load balancing program based on market data, etc., but also for temporarily storing data that has been output or is to be output.
The processor 13 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 13 is a Control Unit (Control Unit) of the computer device 1, connects various components of the entire computer device 1 by using various interfaces and lines, and executes various functions and processes data of the computer device 1 by running or executing programs or modules (for example, executing a load balancing program based on market data, and the like) stored in the memory 12 and calling data stored in the memory 12.
The processor 13 executes the operating system of the computer device 1 and various installed application programs. The processor 13 executes the application program to implement the steps in each embodiment of the market data-based load balancing method described above, such as the steps shown in fig. 1.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 12 and executed by the processor 13 to accomplish the present invention. The one or more modules/units may be a series of computer readable instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the computer device 1. For example, the computer program may be divided into a calculation unit 110, a determination unit 111, an acquisition unit 112, a selection unit 113, an access unit 114.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a computer device, or a network device) or a processor (processor) to execute parts of the market data-based load balancing method according to the embodiments of the present invention.
The integrated modules/units of the computer device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented.
Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), random-access Memory, or the like.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one line is shown in FIG. 3, but this does not mean only one bus or one type of bus. The bus is arranged to enable connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the computer device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 13 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The computer device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the computer device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the computer device 1 and other computer devices.
Optionally, the computer device 1 may further comprise a user interface, which may be a Display (Display), an input unit, such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the computer device 1 and for displaying a visualized user interface.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
Fig. 3 shows only the computer device 1 with the components 12-13, and it will be understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the computer device 1 and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
With reference to fig. 1, the memory 12 of the computer device 1 stores a plurality of instructions to implement a method of market data-based load balancing, and the processor 13 can execute the plurality of instructions to implement:
responding to an access request triggered by a target client, and calculating the pre-request bandwidth of the target client;
acquiring resource information of each server in a server cluster, and determining the current pre-occupied bandwidth of each server according to the resource information of each server;
respectively calculating the sum of the current pre-occupied bandwidth of each server and the pre-requested bandwidth of the target client to obtain the target pre-occupied bandwidth of each server;
acquiring a preset bandwidth threshold, and acquiring a server with the target pre-occupied bandwidth smaller than the bandwidth threshold from each server as a candidate server;
obtaining a client type corresponding to the target client, and selecting a target server from the candidate servers according to the client type;
and accessing the target client to the target server.
Specifically, the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the instruction, which is not described herein again.
It should be noted that all the data involved in the present application are legally acquired.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The invention is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the present invention may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (8)
1. A load balancing method based on market data is characterized in that the load balancing method based on the market data comprises the following steps:
responding to an access request triggered by a target client, and calculating a pre-request bandwidth of the target client, wherein the calculating the pre-request bandwidth of the target client comprises: acquiring bandwidth occupied by each snapshot quotation code subscribed by the target client, the number of the subscribed snapshot quotation codes, bandwidth occupied by each subscribed one-by-one consignation quotation code, the number of the subscribed one-by-one consignation quotation codes, bandwidth occupied by each subscribed one-by-one transaction quotation code, the number of the subscribed one-by-one transaction quotation codes, and a flow control value of the target client; calculating the product of the bandwidth occupied by each snapshot market quotation code and the number of the snapshot market quotation codes to obtain a first numerical value; calculating the product of the bandwidth occupied by each stroke-by-stroke entrustment market code and the number of the stroke-by-stroke entrustment market codes to obtain a second numerical value; calculating the product of the bandwidth occupied by each transaction quotation code and the number of the transaction quotation codes to obtain a third numerical value; calculating the sum of the first numerical value, the second numerical value and the third numerical value to obtain the pre-used bandwidth of the target client; comparing the flow control value with the size of the pre-used bandwidth, and determining a smaller value as the pre-requested bandwidth;
acquiring resource information of each server in a server cluster, and determining the current pre-occupied bandwidth of each server according to the resource information of each server;
respectively calculating the sum of the current pre-occupied bandwidth of each server and the pre-requested bandwidth of the target client to obtain the target pre-occupied bandwidth of each server;
acquiring a preset bandwidth threshold, and acquiring a server with the target pre-occupied bandwidth smaller than the bandwidth threshold from each server as a candidate server;
obtaining a client type corresponding to the target client, and selecting a target server from the candidate servers according to the client type, wherein the selecting the target server from the candidate servers according to the client type comprises: when the client type is a first type, sequencing each candidate server according to the target pre-occupied bandwidth of each candidate server from small to large to obtain a target sequence; extracting candidate servers ranked at the head from the target sequence to serve as the target servers;
and accessing the target client to the target server.
2. The method for market data-based load balancing according to claim 1, wherein the determining the current pre-occupied bandwidth of each server according to the resource information of each server comprises:
determining a client currently connected to each server according to the resource information of each server;
acquiring a pre-request bandwidth of each client;
and calculating the sum of the pre-requested bandwidths of the clients currently connected to each service end to obtain the current pre-occupied bandwidth of each service end.
3. The method for market data-based load balancing according to claim 1, wherein said selecting a target server from said candidate servers based on said customer type further comprises:
when the client type is a second type, acquiring a client currently connected to each candidate server;
determining the client type of the client currently connected to each candidate server;
acquiring the client with the client type of the second type currently connected to the client of each candidate server, and taking the client as a first client corresponding to each candidate server;
respectively calculating the number of first clients corresponding to each candidate server to obtain the first number corresponding to each candidate server;
sequencing each candidate server according to the sequence of the first number from small to large;
and acquiring the candidate server ranked at the head as the target server.
4. The method for market data-based load balancing according to claim 3, wherein the method further comprises:
when the client type is the second type and a server with the target pre-occupied bandwidth smaller than the bandwidth threshold value is not obtained from each server as the candidate server, determining the client type of the client currently connected to each server;
acquiring a client with the client type of the first type currently connected to each server, and taking the client as a second client corresponding to each server;
acquiring the occupied bandwidth of a second client corresponding to each server;
respectively calculating the sum of occupied bandwidths of the second client corresponding to each server, and taking the sum as the releasable bandwidth of each server;
selecting a server with the releasable bandwidth larger than or equal to the pre-requested bandwidth of the target client from each server, and using the server as at least one alternative server;
acquiring the current pre-occupied bandwidth of each alternative server in the at least one alternative server;
sequencing each alternative server according to the sequence that the current occupied bandwidth of each alternative server is reduced from large to small;
acquiring an alternative server arranged at the head as a server to be processed;
controlling the to-be-processed server to disconnect the preset number of second clients and accessing the target client to the to-be-processed server;
the preset number is the minimum value of the number of the disconnected second clients when the sum of the occupied bandwidths of the disconnected second clients is larger than or equal to the pre-requested bandwidth of the target client.
5. The method for market data-based load balancing according to claim 4, wherein the method further comprises:
and when the server with the releasable bandwidth larger than or equal to the pre-requested bandwidth of the target client is not selected from each server, sending alarm information for prompting the full load of the server cluster and sending a capacity expansion request for the server cluster.
6. A load balancing device based on market data is characterized in that the load balancing device based on market data comprises:
a calculating unit, configured to calculate a pre-requested bandwidth of a target client in response to an access request triggered by the target client, where the calculating the pre-requested bandwidth of the target client includes: acquiring bandwidth occupied by each snapshot quotation code subscribed by the target client, the number of the subscribed snapshot quotation codes, bandwidth occupied by each subscribed one-by-one consignation quotation code, the number of the subscribed one-by-one consignation quotation codes, bandwidth occupied by each subscribed one-by-one transaction quotation code, the number of the subscribed one-by-one transaction quotation codes, and a flow control value of the target client; calculating the product of the bandwidth occupied by each snapshot market quotation code and the number of the snapshot market quotation codes to obtain a first numerical value; calculating the product of the bandwidth occupied by each stroke-by-stroke entrustment market code and the number of the stroke-by-stroke entrustment market codes to obtain a second numerical value; calculating the product of the bandwidth occupied by each transaction quotation code and the number of the transaction quotation codes to obtain a third numerical value; calculating the sum of the first numerical value, the second numerical value and the third numerical value to obtain the pre-used bandwidth of the target client; comparing the flow control value with the size of the pre-used bandwidth, and determining a smaller value as the pre-requested bandwidth;
the determining unit is used for acquiring resource information of each server in the server cluster and determining the current pre-occupied bandwidth of each server according to the resource information of each server;
the computing unit is further configured to compute a sum of a current pre-occupied bandwidth of each server and a pre-requested bandwidth of the target client, respectively, to obtain a target pre-occupied bandwidth of each server;
the acquisition unit is used for acquiring a preset bandwidth threshold value and acquiring a server with the target pre-occupied bandwidth smaller than the bandwidth threshold value from each server as a candidate server;
a selecting unit, configured to obtain a client type corresponding to the target client, and select a target server from the candidate servers according to the client type, where the selecting a target server from the candidate servers according to the client type includes: when the client type is a first type, sequencing each candidate server according to the target pre-occupied bandwidth of each candidate server from small to large to obtain a target sequence; extracting candidate servers ranked at the head from the target sequence to serve as the target servers;
and the access unit is used for accessing the target client to the target server.
7. A computer device, characterized in that the computer device comprises:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the market data-based load balancing method according to any one of claims 1 to 5.
8. A computer-readable storage medium characterized by: the computer-readable storage medium has stored therein at least one instruction that is executable by a processor in a computer device to implement the market data-based load balancing method according to any one of claims 1 to 5.
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| US8949410B2 (en) * | 2010-09-10 | 2015-02-03 | Cisco Technology, Inc. | Server load balancer scaling for virtual servers |
| CN103795788B (en) * | 2014-01-20 | 2017-11-03 | 中国建设银行股份有限公司 | The load-balancing method and load-balanced server of instant communication server |
| CN108572871B (en) * | 2017-12-13 | 2021-03-26 | 北京金山云网络技术有限公司 | Resource allocation method and device, electronic equipment and storage medium |
| CN110109953B (en) * | 2018-01-19 | 2023-12-19 | 阿里巴巴集团控股有限公司 | Data query method, device and equipment |
| CN109976917B (en) * | 2019-04-08 | 2020-09-11 | 科大讯飞股份有限公司 | Load scheduling method, device, load scheduler, storage medium and system |
| CN110740164B (en) * | 2019-09-04 | 2021-01-08 | 华云数据控股集团有限公司 | Server determination method, regulation and control method, device, equipment and storage medium |
| CN111225069B (en) * | 2020-03-13 | 2023-06-20 | 浙江书香荷马文化有限公司 | Distributed market data processing system and method |
| CN112633867A (en) * | 2020-12-30 | 2021-04-09 | 中国农业银行股份有限公司 | Transaction request processing method and device |
| CN112860384A (en) * | 2021-03-15 | 2021-05-28 | 南京邮电大学 | VNF multiplexing and migrating method oriented to multi-dimensional resource load balancing |
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Address after: Room 2301, building 5, Shenzhen new generation industrial park, 136 Zhongkang Road, Meidu community, Meilin street, Futian District, Shenzhen, Guangdong 518000 Patentee after: Shenzhen Huarui Distributed Technology Co.,Ltd. Address before: Room 2301, building 5, Shenzhen new generation industrial park, 136 Zhongkang Road, Meidu community, Meilin street, Futian District, Shenzhen, Guangdong 518000 Patentee before: SHENZHEN ARCHFORCE FINANCIAL TECHNOLOGY Co.,Ltd. |