WO2011029253A1 - Procédé d'équilibrage de charge web, serveur de grille correspondant et système - Google Patents

Procédé d'équilibrage de charge web, serveur de grille correspondant et système Download PDF

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Publication number
WO2011029253A1
WO2011029253A1 PCT/CN2009/075923 CN2009075923W WO2011029253A1 WO 2011029253 A1 WO2011029253 A1 WO 2011029253A1 CN 2009075923 W CN2009075923 W CN 2009075923W WO 2011029253 A1 WO2011029253 A1 WO 2011029253A1
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Prior art keywords
resource
grid
web
server
computing
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PCT/CN2009/075923
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English (en)
Chinese (zh)
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刘龙平
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中兴通讯股份有限公司
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Publication of WO2011029253A1 publication Critical patent/WO2011029253A1/fr

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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
    • H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
    • 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

Definitions

  • the present invention relates to a Web system, and more particularly to a Web load balancing method, and a grid server and a corresponding Web load balancing system for implementing Web load balancing. Background technique
  • a plurality of Web servers installed on the network side constitute a Web server cluster.
  • the user logs in to the Web server cluster through the client's web browser and accesses various functions provided by the Web system.
  • Existing Web server clusters can load balance between Web servers in a cluster. As the number of users logging in to the Web system increases significantly, each Web server in the cluster may reach its maximum load. In this case, the Web server is generally used to share the load to improve the load capacity of the entire Web system. . This method of increasing the load capacity of the system by adding a Web server undoubtedly increases the operating cost of the entire system.
  • the invention provides a web load balancing method, a grid server and a system, which solve the problem that the prior art needs to increase the web server to achieve web load balancing and improve the system load capacity.
  • the technical solution of the present invention is implemented as follows:
  • the present invention provides a Web load balancing method, including:
  • the current computing task is sent by the web server cluster to the grid server set on the network side;
  • the grid server selects one or more grid resources as the current computing resource in the online grid resource registered to the local, and allocates the computing task to the current computing resource; After the resource successfully completes the allocated computing task, the computing result is returned to the grid server;
  • the grid server transmits the calculation result to the web server cluster.
  • the computing task is that the user submits the system function web page provided by the web server cluster through the web browser of the client;
  • the method further includes: the web server cluster returns the calculation result to the client, and the client displays the result to the user .
  • the grid server selects one or more grid resources as the current computing resource in the online grid resource that is registered to the local network, and specifically includes:
  • the grid server queries a locally stored resource table, wherein the resource table is selected as a computing resource and successfully completes the historical cumulative number of allocated computing tasks; the grid server sorts the current online grid The number of corresponding historical accumulations recorded by the resource in the resource table, one or more in descending order of the corresponding historical cumulative number of times Grid resources as this computing resource;
  • the historical cumulative number of corresponding computing resources in the resource table is updated.
  • the resource table further records a corresponding selection time of the computing task that the grid resource was last selected and successfully completed the allocation
  • the grid resource whose selection time is closest to the current time is preferentially selected as the current calculation resource.
  • the grid server selects one or more grid resources as the current computing resource in the online grid resource that is registered to the local network, and specifically includes:
  • the grid server selects several grid resources as initial population individuals of the genetic algorithm in the online grid resource registered to the local;
  • the grid server invokes the genetic algorithm, and uses the grid resource output by the algorithm as the current computing resource.
  • selecting a plurality of grid resources as the initial population individuals of the genetic algorithm includes:
  • the grid server queries the locally stored resource fast table, and the resource quick table records the scheduling information weight value corresponding to each grid resource, and the scheduling information weight value and each grid resource are selected and successfully completed.
  • the number of historical calculation tasks is positively correlated;
  • the grid server determines, according to the ratio of the product of the scheduling information weight value corresponding to each grid resource and the inherent attribute value to the sum of the products of each grid resource, determining that each grid resource is selected as the genetic algorithm.
  • the probability of the initial population individual is selected as the initial population of the genetic algorithm according to the order of the probability from the largest to the smallest.
  • the method further includes: sending the current computing task to the grid server disposed on the network side only when the web server cluster determines that its load exceeds a set threshold.
  • the invention also provides a grid server, comprising:
  • a grid resource management unit configured to receive a registration request of a grid resource, and record an online grid resource
  • a task receiving/feedback unit configured to receive a current computing task sent by the web server cluster; and receive a computing result returned after the computing resource successfully completes the allocated computing task, and send the result to the web server cluster;
  • a resource selection unit configured to select one or more online grid resources as the current computing resource
  • a task allocation unit configured to allocate the computing task received by the task receiving/feedback unit to the current computing resource selected by the resource selecting unit.
  • the Grid resource management unit stores a resource table, where the Grid resource is selected as a computing resource and successfully completes the historical accumulated number of the allocated computing tasks;
  • the resource selection unit is specifically configured to: sort the corresponding historical cumulative times recorded by the current online grid resource in the resource table, and select one or more grids according to a sequence of corresponding historical cumulative times from high to low.
  • the resource is used as the current computing resource; and when the task receiving/feedback unit receives the calculation result returned by the current computing resource, the historical cumulative number of corresponding computing resources in the resource table is updated.
  • a resource fast table is stored in the grid resource management unit, and a scheduling information weight value corresponding to each grid resource is recorded in the resource fast table, and the scheduling information weight value and each grid resource are selected. And the number of times of successful completion of the assigned computing task is positively correlated;
  • the resource selection unit is configured to determine, according to a ratio of a product of a scheduling information weight value corresponding to each grid resource and a value of the inherent attribute value, to a sum of the products of each grid resource, to determine that each grid resource is Selecting the probability of the initial population of the genetic algorithm, selecting several current network resources as the initial population of the genetic algorithm according to the order of the probability from large to small, and then The genetic algorithm is invoked, and the grid resource output by the algorithm is used as the current computing resource.
  • the invention also provides a web load balancing system, comprising: a web server cluster, a grid server and a grid resource;
  • the web server cluster is configured to send a current computing task to the grid server; and receive a calculation result transmitted by the grid server;
  • the grid server is configured to receive a registration request of a grid resource, record an online grid resource, and receive a computing task sent by the web server cluster, and select one or more grid resources in the registered online grid resource. As the current computing resource; and assigning the computing task to the current computing resource;
  • the grid resource is configured to be registered in the grid server, and after being selected as the current computing resource and successfully completing the allocated computing task, returning the calculation result to the grid server.
  • the system further includes a client, configured to submit the computing task by using a web browser to log in to a system function web page provided by the web server cluster; and receiving a calculation result returned by the web server cluster and displaying the result to the user.
  • each type of computer in the network is registered as a grid resource by registering with a grid server installed on the network side; the grid resource is managed by the grid server.
  • the grid server selects one or more grid resources as the current computing resource in the local online grid resource, and assigns the computing task to the current computing resource.
  • the computing resource completes the assigned computing task from the computing resource, and returns the calculated result to the grid server, and the grid server transmits the calculated result to the web server cluster.
  • FIG. 1 is a flowchart of a web load balancing method according to an embodiment of the present invention
  • 2 is a schematic structural diagram of a grid server according to an embodiment of the present invention
  • FIG. 3 is a schematic structural diagram of a web load balancing system according to an embodiment of the present invention. detailed description
  • the core idea of the Web load balancing solution of the present invention is as follows: All types of computers logged into the network can be used as grid resources, and the remaining computing power of each computer can be fully utilized to assist the Web server cluster to complete a part of computing tasks, thereby not only implementing Web load balancing, Moreover, the load capacity of the entire Web system can be improved, and the problem of increasing the operating cost of the entire system by increasing the Web server to achieve Web load balancing and increasing the system load capacity in the prior art is solved.
  • the invention adds a grid server on the network side, and each type of computer logged into the network can choose to register to the grid server to become a grid resource.
  • the grid server mainly implements the online and offline management of the grid resources, and interacts with the web server cluster, receives the computing tasks sent by the web server cluster, allocates them to the online grid resources for calculation, and transmits the calculation of the grid resource returns. The result is to the web server cluster.
  • Other specific functions of the grid server are described in detail later.
  • Step S101 A current server task is sent by a web server cluster to a grid server configured on a network side;
  • Step S102 The grid server selects one or more grid resources as the current computing resource in the online grid resource registered to the local, and allocates the computing task to the selected current computing resource;
  • Step S103 After the computing resource successfully completes the allocated computing task, the computing result is returned to the grid server.
  • each grid resource managed by the grid server participates in the task sharing, which reduces the load of the web server cluster and realizes web load balancing.
  • the current computing task sent by the web server cluster to the grid server may be a computing task initiated by the grid side or a computing task submitted by the user.
  • the computing task is submitted by the user, the user can log in to the system function web page provided by the web server cluster through the client's web browser to submit the task; the web server cluster sends the task submitted by the user to the grid server, and receives the grid server.
  • the calculated result of the calculation is calculated by the selected grid resource, and returned to the client submitting the corresponding task, which is displayed to the user by the client.
  • a simple method is to: arbitrarily select the current online grid resource. As this computing resource.
  • the advantage of this method is that the process cartridge is selected, but the computing power of the selected grid resource itself may be weak, which may result in the failure to successfully complete the calculation task of the assignment, or the delay of completing the assigned calculation task. long.
  • the embodiment of the present invention provides the following preferred selection grid resources as the implementation method of the current computing resource, but the present invention is not limited to the following examples.
  • the cumulative number of historical tasks that were selected as compute resources and successfully completed the assigned compute tasks before the grid resources were recorded in the grid server storage resource table.
  • the resource table length (that is, the number of records in the resource table) can be set to a larger value in advance, for example, 200 records are stored, or the grid server can be based on the current or segment.
  • the number of online grid resources in the time adjusts the length of the resource table.
  • resource table length rounding (number of online grid resources * length coefficient), the range of length coefficients is, for example, [0.2, 0.5].
  • the grid resources with more historical accumulation times are preferentially stored in the resource table.
  • set 5 records in the resource table as For example, the grid resources and their historical accumulation times are shown in Table 1 below:
  • the grid server After receiving the current computing task sent by the web server cluster, the grid server queries the locally stored resource table, sorts the current online grid resources in the resource table according to the historical cumulative number of times, and selects historical accumulation. One or more grid resources with a higher number of times are used as the current computing resource. Assume that the current online grid resource is grid resource 2, grid resource 3, and grid resource 4. According to the sorting result, the historical accumulation number of the grid resource 4 is the highest. When a grid resource is selected as the current computing resource, Select grid resource 4; When selecting two grid resources as the current computing resource, select grid resource 4 and grid resource 2. Assuming that Grid Resource 4 and Grid Resource 2 are selected as the current computing resources, the Grid Server will assign the current computing task to Network Resource 4 and Grid Resource 2.
  • the specific allocation method may be to divide the current computing task into two parts, or the grid server may determine the computing power of the grid resources 4 and 2 according to the respective intrinsic attributes of the registered grid resource 4 and the grid resource 2; Size, assigns most computing tasks to grid resources with large computing power.
  • the resource table since the resource table records the historical cumulative number of calculation tasks in which each grid resource is selected and successfully completed, the resource table needs to be dynamically updated, and the grid resource 4 and the grid resource 2 are successfully completed. After the calculation task, the calculation result is returned to the grid server. After receiving the returned calculation result, the grid server updates the historical accumulation times of the grid resource 4 and the grid resource 2 in the resource table, and the updated historical cumulative number of times. They are 21 and 6, respectively.
  • the above method of selecting a grid resource by using a resource table is based on the number of times the grid resource successfully completes the assigned computing task. a grid that has successfully completed a lot of calculation tasks Resources, which are considered to be highly reliable, are more likely to successfully complete this computing task.
  • the above selection method example 1 is only an example, and in practice, optimization can also be performed.
  • the resource table can also record the corresponding selection time of the computing task whose grid resource was last selected and successfully completed the allocation; for multiple grids whose historical cumulative times are the same, or the historical cumulative number of differences is within the set range
  • the resource is preferentially selected as the current computing resource corresponding to the grid resource whose selection time is closest to the current time.
  • Example 2 the genetic algorithm in the prior art is used to select a better grid resource to participate in the calculation. Specifically:
  • the grid server selects several grid resources as the initial population of the genetic algorithm in the online grid resource registered to the local; and then invokes the genetic algorithm to use the grid resource output by the algorithm as the computing resource.
  • a simple method is to arbitrarily select the current online grid resource as the initial population of the genetic algorithm.
  • the advantage of this method is that the process cartridge is selected, but since the initial population is arbitrarily selected, it is possible that the final output grid resource is not the optimal grid resource.
  • the resource quick table is stored in the grid server, and the scheduling information weight value corresponding to each grid resource is recorded in the resource fast table; wherein the scheduling information weight value and each grid resource are selected and the history of the allocated computing task is successfully completed.
  • the number of times is positively correlated.
  • the scheduling information weight value can be determined in the following way:
  • r is used to represent the scheduling information weight value of the grid resource j, and the grid computing server assigns the initial value r, .( 0 ) to the scheduling information weight value of the grid resource j.
  • F in equation (1) MIPS (Million Instructions Per Second) for grid resource j, that is, the number of machine language instructions of millions of classes processed per second.
  • the grid server After receiving the current computing task sent by the web server cluster, the grid server queries the locally stored resource fast table, and obtains the scheduling information weight value corresponding to each stored grid resource, according to the scheduling information weight corresponding to each grid resource.
  • the ratio of the value, the product of its intrinsic property value to the sum of the corresponding products of each grid resource determines the probability that each grid resource is selected as the initial population of the genetic algorithm, and then selects the order according to the probability from large to small.
  • Corresponding several grid resources are used as the initial population of the genetic algorithm.
  • a specific implementation example is:
  • Q*m grid resources are selected as the initial population individuals according to the probability P] k from the largest to the smallest, and are divided into Q populations, each population containing m Individuals; then call the genetic algorithm to select the optimal grid resources. Since the specific calculation process of the genetic algorithm is prior art, it will not be described in detail herein.
  • the above example 2 can be further optimized.
  • the grid resources currently stored in the resource table (the grids) If the resource is selected and successfully completed, the calculated task has a large number of historical accumulations and is selected in a time period that is closer to the current time.
  • the corresponding probability may be appropriately increased to increase the corresponding grid resource selected as Possible examples of individuals in the initial population of genetic algorithms:
  • resource table resource coefficient if the grid resource j is in the resource table, for example, it can take 0.2, otherwise, ⁇ takes 0.
  • the above example 1 and the example 2 and the optimization method thereof disclose several methods for selecting the grid resource as the implementation method of the current computing resource by way of example. It is specifically stated that, in practical applications, other similar methods may be used to select network resources as the current computing resources.
  • the grid server allocates the current computing task sent by the web server cluster to the computing resource.
  • a specific distribution method is:
  • the grid server divides the current computing task into m based on the number m of computing resources.
  • the subtasks use the fitness function to sort the computing resources, and assign the subtasks with large tasks to the current computing resources with high fitness.
  • sub-tasks with large tasks are assigned to grid resources with high computing power and high task completion rate, which is beneficial to the completion of tasks.
  • S is the historical cumulative number of computation tasks that the grid resource j successfully completes
  • D is the total number of times the grid resource j is selected.
  • the web load balancing method disclosed in the above embodiment of the present invention can control a specific startup condition by a web server cluster. For example: When the web server cluster determines that its load exceeds the set threshold, the current computing task is sent to the grid server, and the grid resource server allocates the selected grid resource to the current computing task. In practical applications, even if the load of the Web server set does not exceed the set threshold, the Web load balancing method described above can be used for Web load balancing to reduce the computing workload of the Web server.
  • the grid server even if the grid server selects the current computing resource and allocates the current computing task, some of the computing resources may not successfully complete the allocated computing. Task, or refuse to accept the assigned computing task due to limited resources, in which case a rejection message can be returned to the grid server; the grid server transmits a rejection message to the web server cluster. After receiving the reject message, the web server cluster can determine which part of the computing task is not shared, and then each web server in the web server cluster undertakes the part of the computing task, and can also send the part of the computing task to the grid again. The server performs a task assignment again.
  • the embodiment of the present invention further provides a grid server having a corresponding function, and the structure diagram thereof is as shown in FIG. 2, including:
  • a grid resource management unit 21 configured to receive a registration request of a grid resource, and record an online network.
  • the task receiving/feeding unit 22 is configured to receive a current computing task sent by the web server cluster, and receive a computing result returned after the computing resource successfully completes the allocated computing task, and transmit the result to the web server cluster;
  • a resource selection unit 23 configured to select one or more online grid resources as the current computing resource
  • the task assignment unit 24 is configured to allocate the calculation task received by the task receiving/feedback unit 22 to the current computing resource selected by the resource selection unit 23.
  • the grid resource management unit 21 stores a resource table in which the history accumulated number of computing tasks that were previously selected as computing resources and successfully completed the allocation of the grid resources.
  • the resource selection unit 23 is specifically configured to: sort the corresponding historical cumulative times recorded by the current online grid resource in the resource table, and select one or more grid resources as the basis according to the corresponding historical cumulative number of times from high to low.
  • the second computing resource and when the task receiving/feedback unit 22 receives the calculation result returned by the current computing resource, updates the historical cumulative number of corresponding computing resources in the resource table.
  • the grid resource management unit 21 stores a resource fast table, and the resource fast table records the scheduling information weight value corresponding to each grid resource, and the scheduling information weight value and each grid resource are selected and successfully completed.
  • the number of history of assigned computing tasks is positively correlated.
  • the resource selecting unit 23 is specifically configured to: according to the ratio of the product of the scheduling information weight value corresponding to each grid resource and the inherent attribute value to the sum of the products of each grid resource, determine that each grid resource is selected as the genetic
  • the probability of the initial population of the algorithm is selected according to the probability from large to small as the initial population of the genetic algorithm, and then the genetic algorithm is called, and the grid resource output by the algorithm is used as the computing resource.
  • the embodiment of the present invention further provides a web load balancing system, and a schematic structural diagram thereof is shown in FIG. 3 .
  • the system includes: a web server cluster 31, a grid server 32, and a grid resource 33.
  • a web server cluster 31 configured to send a current computing task to the grid server 32; and receive a calculation result transmitted by the grid server 32;
  • the grid server 32 is configured to receive a registration request of the grid resource 33, record the online grid resource, and receive the computing task sent by the web server cluster 31, and select one or more grids in the registered online grid resource. Resources as the current computing resources; and assign computing tasks to the computing resources;
  • the grid resource 33 is used for registering into the grid server, and after being selected as the current computing resource and successfully completing the assigned computing task, returns the calculation result to the grid server 32.
  • the client 34 is further configured to: use a web browser to log in to the web server provided by the web server cluster 31 to submit a computing task; and receive the computing result returned by the web server cluster and display the result to the user.
  • each type of computer in the network is registered as a grid resource by registering to a grid server disposed on the network side; the current computing task is sent to the grid server by the web server cluster, and the grid server is registered to the local online In the grid resource, one or more grid resources are selected as the current computing resource, and the computing task is allocated to the computing resource, and the computing task is completed by the computing resource, and the calculation result is returned to the grid server.
  • the grid server then transmits the results of the calculation to the web server cluster.

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Abstract

La présente invention se rapporte à un procédé d'équilibrage de charge Web comprenant les étapes suivantes : une grappe de serveurs Web envoie la tâche de calcul actuelle à un serveur de grille situé sur le côté réseau; le serveur de grille choisit une ressource de grille ou plus parmi les ressources de grille en ligne enregistrées localement comme ressource de calcul pour cette fois et il affecte la tâche de calcul à la ressource de calcul choisie pour cette fois; après l'exécution réussie de la tâche de calcul affectée, la ressource de calcul choisie pour cette fois retourne le résultat du calcul au serveur de grille; le serveur de grille transporte le résultat du calcul à la grappe de serveurs Web. La présente invention se rapporte également au serveur de grille correspondant et à un système d'équilibrage de charge Web. La solution technique de la présente invention permet d'utiliser à plein tous les types d'ordinateurs dans le réseau et de les utiliser comme des ressources de grille aptes à participer à la tâche de calcul. Elle permet en outre de réduire la charge de chaque serveur Web dans la grappe de serveurs Web. Il est donc ainsi possible d'équilibrer la charge Web et d'améliorer la capacité de charge du système Web dans son ensemble sans ajouter de serveurs Web.
PCT/CN2009/075923 2009-09-08 2009-12-24 Procédé d'équilibrage de charge web, serveur de grille correspondant et système WO2011029253A1 (fr)

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