CN117499397A - Education cloud service platform based on big data analysis - Google Patents

Education cloud service platform based on big data analysis Download PDF

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Publication number
CN117499397A
CN117499397A CN202311847784.7A CN202311847784A CN117499397A CN 117499397 A CN117499397 A CN 117499397A CN 202311847784 A CN202311847784 A CN 202311847784A CN 117499397 A CN117499397 A CN 117499397A
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server
capacity
coordination
area
module
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CN117499397B (en
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黎国权
赵顺
朱晖
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Guangdong Xinjufeng Technology Co ltd
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Guangdong Xinjufeng Technology Co ltd
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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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0896—Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities
    • 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
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • 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
    • H04L67/50—Network services
    • H04L67/54—Presence management, e.g. monitoring or registration for receipt of user log-on information, or the connection status of the users
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to the technical field of education big data, in particular to an education cloud service platform based on big data analysis, which comprises a user management module, a capacity layout module, a capacity monitoring module, a capacity coordination module, a coordination server and a cross-cloud coordination module; according to the education cloud service platform provided by the invention, the cloud server layout is determined according to the IP addresses and the network environment of registered users, and then the capacities of each subordinate server, the secondary server and the regional server and the spare capacity in each subordinate server are determined according to the IP addresses and the online number of historical online users of the education cloud service platform; and determining a capacity adjustment strategy and a capacity cooperation strategy according to the actual use capacities of the subordinate servers and the secondary servers respectively, and using the cross-cloud server to meet the use requirements of online users when the cloud service platform area server is an overload area server.

Description

Education cloud service platform based on big data analysis
Technical Field
The invention relates to the technical field of education big data, in particular to an education cloud service platform based on big data analysis.
Background
The technical foundation behind the education cloud service platform comprises an advanced cloud computing architecture, a big data analysis and mining technology, an artificial intelligent algorithm and a safe and reliable network infrastructure, and the education cloud service platform utilizes cloud computing to realize the elastic allocation of resources, so that the education resources can be flexibly adjusted according to requirements. Big data techniques are used to collect, analyze, and mine student learning data to provide personalized learning support and real-time teaching feedback. The artificial intelligence algorithm is applied to an intelligent education recommendation system and provides personalized learning and teaching suggestions for students and teachers. In addition, the platform ensures the safety and stability of data transmission through an efficient network architecture, and provides a reliable online education experience for users. The organic combination of the technologies makes the education cloud service platform an efficient, intelligent and safe education ecological system.
The invention discloses a big data task scheduling method in an education cloud platform, which is characterized in that task scheduling is carried out according to total resources of the cloud platform, the priority of core big data processing tasks, the resource demand and the expected task running time of the cloud platform; if the task request is not a big data task, directly scheduling the task; if the task is a core task, predicting system resources and running time; adding corresponding task examples in a database according to various task parameters; adding tasks into a task queue according to a scheduling method, and acquiring the task with the highest operation priority; when the cloud platform resources meet the task resource requirements, distributing resources according to requirements, and distributing the tasks to big data clusters of corresponding trained persons if the currently used resources do not exceed the maximum available resources; otherwise, the task is added to the task queue again. The method can prevent excessive users from running big data tasks in high concurrency through the virtual machine clusters which are obtained and distributed, so that the supporting mechanism provides an effective big data experimental environment for each trained person by utilizing limited physical machine resources. Therefore, when the currently used resources exceed the maximum available resources, the trainee with low task operation priority can not obtain the required resources for a long time, so that the user experience and the task execution efficiency of the education cloud platform are negatively influenced.
Disclosure of Invention
Therefore, the invention provides an education cloud service platform based on big data analysis, which is used for solving the problems of poor stability of system performance, poor user experience and low task execution efficiency caused by unreasonable resource allocation strategy of the education cloud platform under the condition of high load in the prior art.
In order to achieve the above object, the present invention provides an educational cloud service platform based on big data analysis, comprising:
the user management module is used for registering and verifying the identity information of the user and determining the IP address and the network environment of each user according to the registration information and the verification information of the user;
the capacity layout module is connected with the user management module and used for determining cloud server layout of the education cloud service platform according to the IP address and the network environment;
the capacity monitoring module is connected with the capacity layout module and used for monitoring real-time online users and actual use capacities of all subordinate servers in real time and determining corresponding secondary server capacity adjustment strategies according to the number of the real-time online users and the actual use capacities;
the capacity coordination module is respectively connected with the capacity monitoring module and the capacity layout module and is used for determining a capacity coordination strategy according to the real-time capacity of each secondary server so as to meet the use requirement of a user using the corresponding secondary server;
the collaboration server is connected with the capacity layout module and used for increasing the service capacity of the server through collaborative work;
and the cross-cloud coordination module is respectively connected with the capacity layout module, the capacity monitoring module, the capacity coordination module and the coordination server and is used for determining whether to use the coordination server outside the cloud server layout to expand the capacity of the cloud server according to the real-time cloud server layout.
Further, the cloud server comprises a plurality of regional servers, each regional server comprises a plurality of secondary servers, and each secondary server comprises a plurality of subordinate servers.
Further, all secondary servers in any one regional server and all secondary servers in any one secondary server are connected with each other among the regional servers so as to increase the capacity of the secondary servers and the scalability of the capacity of the secondary servers.
Further, the lower server capacity includes a working capacity and a standby capacity;
wherein the working capacity multiplied by 15% of the individual lower servers is not less than the spare capacity is not more than the working capacity multiplied by 25%.
Further, the capacity layout module determines capacity distribution of each regional server according to the IP address when each user registers identity information, determines capacity distribution of all secondary servers in each regional server and capacity distribution of all subordinate servers in each secondary server according to the IP address of the historical online user, and determines spare capacity of each subordinate server according to the network environment of the historical online user.
Further, the capacity monitoring module determines whether the capacity of the single lower server needs to be adjusted according to the real-time online user number and the actual use capacity of the single lower server, including:
determining the relation between the real-time online user quantity of a single lower server and a user quantity reference value, and determining the relation between the actual use capacity of the single lower server and the capacity of the lower server;
if the real-time online user number of the single lower server is larger than the user number reference value and the actual use capacity of the single lower server is larger than or equal to the capacity of the single lower server, the single lower server needs to be adjusted.
Further, the capacity detection module determines a capacity adjustment policy of a lower server according to a secondary server corresponding to the lower server to be adjusted, including:
marking the lower server to be adjusted as a lower server to be adjusted;
determining a secondary server where the secondary server is located as a secondary server to be regulated according to the secondary server to be regulated;
determining whether an adjustable lower server exists in the secondary server to be adjusted, and reallocating the redundant capacity of the adjustable lower server to the lower server to be adjusted;
the adjustable lower server is a non-to-be-adjusted lower server in the to-be-adjusted secondary server, and the capacity of the adjustable lower server is more than the actual use capacity of the adjustable lower server by X (1+15%);
the redundant capacity = adjustable lower server capacity-actual used capacity of adjustable lower server x (1+15%).
Further, the capacity coordination module determines a capacity coordination policy according to the actual usage capacity of each secondary server, including:
determining a secondary server triggering to formulate a capacity coordination strategy as a coordination server;
the regional server where the coordination server is located is recorded as a coordination regional server;
determining an overflow area in a coordination area server, wherein the capacity coordination module automatically connects an online user with small load characteristics in the coordination server to the overflow area according to the load characteristics;
wherein the overflow area is a non-coordination server in the coordination area server, and the overflow area satisfies that the capacity of the overflow area is larger than the actual use capacity of the overflow area by x (1+5%);
the load characteristics include request frequency and resource requirements, and the load characteristics are small to satisfy the request frequency and/or the resource requirements are small.
Further, the capacity coordination module determines the coordination server according to the actual use capacity of any secondary server;
and if the single secondary server meets that the actual use capacity is larger than or equal to the secondary server capacity, the capacity coordination module judges that the single secondary server is a coordination server and triggers the capacity coordination strategy.
Further, the cross-cloud collaboration module determines an overload area server according to the real-time cloud server capacity distribution, and sends the required content to the collaboration server according to the use requirements of online users who are newly online after the overload area server is overloaded and/or online users with small load characteristics so as to meet the use requirements of the users;
and the overload area server meets the area server with the actual use capacity reaching the upper limit, and/or the corresponding area server which is received by the online user with small load characteristics in the coordination server and does not have an overflow area exists.
Compared with the prior art, the invention has the beneficial effects that the user management module provided by the invention allows the user to efficiently register and verify the identity information, and determines the actual IP address and network environment of each user according to the registration information and verification result of the user, thereby providing personalized support for the subsequent service of the capacity layout module; the capacity layout module intelligently determines the layout of the cloud server by comprehensively considering the IP address and the network environment of the user, so that the optimal allocation of resources is realized; the capacity monitoring module monitors online users and actual use capacities of the lower servers in real time, and the education cloud service platform provided by the invention can accurately evaluate the real-time load conditions of the lower servers through big data analysis, so that corresponding secondary server capacity adjustment strategies are formulated according to the number of the real-time online users and the actual use capacities caused by the real-time online users, and the education cloud service platform can be guaranteed to provide high-efficiency and stable services in a high-load period.
Further, the capacity coordination module in the invention also enhances the elasticity and adaptability of the capacity of each server in the education cloud service platform provided by the invention, and establishes a capacity coordination strategy according to the real-time capacity of each secondary server, so as to ensure that the education cloud service platform can flexibly meet the use requirements of online users; the education cloud platform cooperates with the server module under the overload condition in a cooperative working mode, so that the use capacity of the whole server is effectively increased, and the overall performance of the system is improved.
Further, the cross-cloud coordination module introduces dynamic capacity expansion judgment to the cloud server layout external servers, and intelligently decides whether to use the external coordination servers to expand capacity through real-time cloud server layout information so as to cope with the resource application demands of the online users which are continuously increased; the educational cloud service platform has the advantages of being intelligent, efficient and flexible, and accordingly user experience and overall service quality are improved.
Drawings
FIG. 1 is a connection diagram of an educational cloud service platform based on big data analysis of the present invention;
fig. 2 is a schematic structural diagram of a cloud server according to an embodiment of the present invention;
FIG. 3 is a method for determining a capacity adjustment strategy according to an embodiment of the present invention;
fig. 4 is a method for determining a capacity coordination policy according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Fig. 1 shows a connection diagram of the educational cloud service platform based on big data analysis according to the present invention. The embodiment of the invention provides an education cloud service platform based on big data analysis, which comprises the following steps:
the user management module is used for registering and verifying the identity information of the user and determining the IP address and the network environment of each user according to the registration information and the verification information of the user;
the capacity layout module is connected with the user management module and used for determining cloud server layout of the education cloud service platform according to the IP address and the network environment;
the capacity monitoring module is connected with the capacity layout module and used for monitoring real-time online users and actual use capacities of all subordinate servers in real time and determining corresponding secondary server capacity adjustment strategies according to the number of the real-time online users and the actual use capacities;
the capacity coordination module is respectively connected with the capacity monitoring module and the capacity layout module and is used for determining a capacity coordination strategy according to the real-time capacity of each secondary server so as to meet the use requirement of a user using the corresponding secondary server;
the collaboration server is connected with the capacity layout module and used for increasing the service capacity of the server through collaborative work;
and the cross-cloud coordination module is respectively connected with the capacity layout module, the capacity monitoring module, the capacity coordination module and the coordination server and is used for determining whether to use the coordination server outside the cloud server layout to expand the capacity of the cloud server according to the real-time cloud server layout.
It is understood that server capacity refers to the upper limit of resources that a server is allocated or configured during the layout phase. Such resources include processor performance, memory capacity, storage space, bandwidth, etc.; the actual use capacity refers to the current use resource amount of the server, and the current load and resource utilization condition of the cloud server are monitored in real time when the education cloud service platform operates.
In implementation, the collaboration server is a server of other companies of the non-education cloud service platform, and is temporarily applied to and used by the other company servers when the server of the education cloud service platform is overloaded.
It is understood that a cloud server layout refers to each regional server including a number of secondary servers and each secondary server including a number of secondary servers, and coverage of each secondary server and each secondary server.
Specifically, the lower server capacity includes a working capacity and a standby capacity;
wherein the working capacity multiplied by 15% of the single subordinate server is less than or equal to the standby capacity is less than or equal to the working capacity multiplied by 25%.
In practice, if the capacity allocated by server No. one of the two north China areas is Q, then Q is divided into working capacities Q1 and Q2, wherein 0.15Q1.ltoreq.Q2.ltoreq. 0.25Q1, and Q1+Q2=Q.
Specifically, the cloud server provided by the invention comprises a plurality of regional servers, each regional server comprises a plurality of secondary servers, and each secondary server comprises a plurality of subordinate servers. Fig. 2 is a schematic structural diagram of a cloud server according to an embodiment of the present invention, in which the distribution of north China servers is shown, and the distribution of other regional servers is similar to the north China service area.
Specifically, the capacity layout module determines the capacity distribution of each regional server according to the IP address when each user registers identity information, determines the capacity distribution of all secondary servers in each regional server and the capacity distribution of all secondary servers in each secondary server according to the IP address of the historical online user, and determines the spare capacity of each secondary server according to the network environment of the historical online user.
In implementation, for example, the cloud server includes regional servers that are north China server, east China server, south China server, west China server, middle China server and foreign server, respectively; each regional server is provided with a plurality of secondary servers, for example, the secondary servers included in the North China server are provided with North China regional servers, north China three-regional servers and … … North China regional servers; each secondary server is further divided into a plurality of lower servers, such as a first area server of north China including a first area server of north China, a second area server of north China, a third area server of north China, … … and a first area x server of north China.
In implementation, setting the capacity of a foreign server to be 5% of that of a cloud server; the capacity ratio between the North China server, the east China server, the south China server, the West China server and the middle China server is equal to the number ratio of registered users in the coverage area of the five servers; therefore, when the cloud server capacity is known, the foreign server capacity, the North China server capacity, the east China server capacity, the south China server capacity, the West China server capacity and the middle China server capacity can be calculated.
For example, assume a cloud server capacity of 1000PB, north China server capacity: east China server capacity: south China server capacity: capacity of the warsier server: in-China server capacity = number of registered users in North China server: registering the number of users in the east China server: registering the number of users in the south China server: the number of registered users in the Huaxi server: registered user number in the chinese server=1.2: 1.26:1.34:1.08:1.35;
the foreign server capacity is 50PB;
north China server capacity=1.2≡ (1.2+1.26+1.34+1.08+1.35) =1.2≡6.23×950PB≡182PB;
east China server capacity=1.26≡6.23×950pb≡192PB;
south china server capacity = 1.34 ≡6.23×950PB ≡204PB;
warsier server capacity = 1.08 ≡6.23×950PB ≡164PB;
huazhong server capacity = 1.35 ≡6.23 x 950PB ≡205PB;
at this time, the cloud server also has a 3PB (1000 PB-50PB-192PB-192PB-204PB-164PB-205 PB) redundant capacity as the reserve capacity of the cloud server.
In the implementation, the secondary server sets a standard that the number of registered people in the secondary server is more than or equal to 50 ten thousand; the lower server sets an index that the number of registered persons in the lower server is more than or equal to 15 ten thousand persons.
It will be appreciated that the value of n is determined by the number of secondary administrative areas contained within the coverage of each regional server and the number of registrations in each region: (1) If the north China server covers the north China area and the northeast China area, secondary and directly administered levels of the north China server are Beijing, tianjin, hebei, shanxi, inner Mongolia, liaoning, jilin and Heilongjiang eight secondary administrative areas, and the north China server is primarily divided into a first north China area, a second north China area, … … and an eighth north China area; (2) Then determining whether the registered persons in the eight secondary servers meet the secondary server setting standard, combining the secondary servers which do not meet the secondary server setting standard with single secondary servers which do not meet the standard in the other 7 secondary servers until the secondary server setting standard is met, and combining the secondary server which does not meet the standard with the secondary server with the smallest registered person in the other seven secondary servers if only one of the eight secondary servers does not meet the standard: for example, in case (1), the registered numbers in the eight secondary servers are respectively: the registered number in the North China area is 35 ten thousand people, the North China area and the North China area are 60, 70, 64, 55, 53, 68 and 50 in sequence, and then the North China area which is initially set in the step (1) is canceled, the North China area and the North China area are combined into the same area and renamed into the North China area, and the North China server comprises the North China area and the North China area; in the case (2), the registered persons (unit: ten thousands of persons) in the first North China to the eighth North China are 60, 20, 64, 55, 53, 68, 50 and 20 respectively, and the second North China and the eighth North China are combined into the same area and renamed into the second North China, and the North China server comprises the first North China to the seventh North China; in the case (3), the registered persons (unit: ten thousands of persons) in the first-north-China-eight-China are 60, 40, 64, 15, 53, 68, 50 and 20, respectively, and the first-north-China-two-China-area and the first-north-China-four-area are combined into the same first-north-China-area and renamed into the first-north-China-second-area, the registered persons in the first-north-China-second-area become 55 to meet the setting standard of the secondary server, and the first-north-China server comprises the first-north-China-area, the second-north-China-area (new), the third-north-China-area, the fourth-north-China-area (original five-north-China-area), the fifth-north-China-area (original six-north-China-area), the sixth-north-China-area (original-north-seven-China-area) and the seventh-north-China-area (original-north-China-eight-area); secondly, combining the North China seven area into the North China six area to become a new North China six area with the registered user being 70, wherein the North China server comprises a North China one area, a North China two area (new), a North China three area, a North China four area (original North China five area), a North China five area (original North China six area) and a North China six area (original North China seven area+original North China eight area); in case (4), the number of registrations in the first to eighth north China (unit: ten thousands of people) are 60, 20, 64, 15, 53, 68, 50 and 20, respectively, then the second north China area, the fourth north China area and the eighth north China area are combined into a new second north China area, and the second north China server comprises a first north China area, a second north China area (the first second north China area+the first fourth north China area+the first north China area), a third north China area, a fourth north China area (the first fifth north China area), a fifth north China area (the first sixth north China area) and a sixth north China area (the first seventh north China area).
It will be appreciated that the value of x is determined by the number of registered users in each secondary server, for example 35 ten thousand registered users in the second north China area, then x=35 ten thousand/15 ten thousand/2.33 ≡2 (the approximate method directly adopts the number after the decimal point is omitted), then the second north China area is divided into two parts according to the latitude, namely a first north China area server and a second north China area server from north to south, and the registered number of any one server in the two lower servers is greater than or equal to 15 ten thousand.
In implementation, when the north China server capacity is 182PB, the cloud server capacity is distributed and updated once every preset time (half month, one month, two months, one quarter and half year … …), the preset time is set to be one month in the embodiment, the IP address of the historical online population of the last 6 months (including the present month) of the cloud service platform is determined according to the IP address when the last day of each month, the historical online population ratio of each subordinate server is determined according to the IP address, and then the historical online population ratio of each subordinate server can be known, and then the capacity ratio of each subordinate server and the capacity ratio of each subordinate server of the next month can be reset; knowing the capacity ratio of each secondary server and the capacity of each area, the capacity of each secondary server can be calculated, and the capacity of each secondary server can be further calculated.
It can be understood that the capacity allocation manner of the regional server is the same as follows: (1) the capacity of each secondary server in any regional server is distributed by adopting the same approximate principle, and if the remaining capacity exists, the remaining capacity is set as the reserve capacity of the regional server; (2) the capacity of each lower server in any secondary server is allocated by the same approximate principle, and if the remaining capacity exists, the remaining capacity is set as the reserve capacity of the secondary server.
In practice, the working capacity and the standby capacity of the lower server are determined according to the capacity of each lower server and the historical network environment of the historical online user in the lower server: the backup capacity of the lower server is determined based on the historical network environment score of the lower server for the online user, the network environment score W being jointly determined by the bandwidth B and the stability S and represented using a percentile.
The calculation formula of the spare capacity Q2 is: q2= (W/100×0.1+0.15) ×q1
For example, the percentage score W of the network environment is represented by a weighted average of bandwidth and stability, assuming that the bandwidth score of all the historical online users in the lower server is B, the stability score of all the historical online users in the lower server is S, the bandwidth weight is W1, and the stability weight is W2, where values of B and S are numbers between 1 and 100 (including 1 and 100, where a larger value of the bandwidth score B indicates a larger bandwidth and a larger value of the stability score S indicates a higher stability), the bandwidth weight and the stability weight satisfy w1+w2=1, and the formula for calculating the weighted average score of the network environment is: w=w1×b+w2×s.
In this embodiment, the bandwidth weight is w1=0.6, and the stability weight w2=0.4.
Assume that: the capacity allocated by the first server in the second North China is Q, the bandwidth score is 80, and the stability score is 90; then the percent rating of the network environment w=0.6x80+0.4x90=48+36=84, i.e. the network environment takes a value of 84; q2= (84++100×0.1+0.15) ×q1= 0.234Q1, therefore q=q1+q2= 1.234Q1, i.e. working capacity and standby capacity in the second-northbound server No. one can be known.
Specifically, each area server is connected to each other between all secondary servers in any one area server and to each other between all secondary servers in any one secondary server, so as to increase the scalability of the secondary server capacity and the secondary server capacity.
For example, the n secondary servers included in the north China server are connected with each other, so that the user in one or more overloaded secondary servers can be transferred to other non-overloaded secondary servers; the x lower servers contained in the north China regional server are also connected with each other, so that the user in one or more overloaded lower servers can be transferred to other non-overloaded lower servers.
Specifically, the capacity monitoring module determines whether the capacity of a single lower server needs to be adjusted according to the real-time online user number and the actual use capacity of the single lower server, and includes:
determining the relation between the real-time online user quantity of a single lower server and a user quantity reference value, determining the relation between the actual use capacity of the single lower server and the capacity of the single lower server, and judging whether the capacity of the lower server needs to be adjusted according to the determined two relations;
if the real-time online user number of the single lower server is larger than the user number reference value, and the actual use capacity of the single lower server is larger than or equal to the capacity of the single lower server, the single lower server needs to be adjusted.
It will be appreciated that the user number reference value is determined by the historical online population of the subordinate server, and the user number reference value P is determined by the following formula: p=1.3P (-), where P (-) is the average number of historical online people for the subordinate server.
In the implementation, when the actual use capacity of a certain lower server is more than or equal to the working capacity of the lower server, the capacity monitoring module automatically starts the spare capacity of the lower server;
when the real-time online user quantity is more than the user quantity reference value, the capacity monitoring module marks the subordinate server as a key server needing key monitoring;
the actual use capacity of a certain lower server is larger than or equal to the capacity (working capacity + standby capacity) of the lower server, and when the real-time online user quantity is larger than the user quantity reference value, the capacity of the lower server is judged to need to be adjusted.
As shown in fig. 3, a method for determining a capacity adjustment policy according to an embodiment of the present invention is shown. The capacity detection module of the embodiment of the invention determines the capacity adjustment strategy of a lower server according to the secondary server corresponding to the lower server needing capacity adjustment, and comprises the following steps:
marking the lower server to be adjusted as a lower server to be adjusted;
determining a secondary server where the secondary server is located as a secondary server to be regulated according to the secondary server to be regulated;
determining whether an adjustable lower server exists in the to-be-adjusted secondary server, and reallocating the redundant capacity of the adjustable lower server to the to-be-adjusted lower server;
the adjustable lower server is a non-to-be-adjusted lower server in the to-be-adjusted secondary server, and the adjustable lower server meets the requirement that the capacity of the adjustable lower server is larger than the actual use capacity of the adjustable lower server by X (1+15%);
redundant capacity = adjustable lower server capacity-actual used capacity of adjustable lower server x (1+15%).
In an implementation, the step of determining the capacity adjustment policy includes:
step S01, determining a lower server (a lower server to be adjusted) to be adjusted;
step S02, a secondary server (secondary server to be tuned) corresponding to the lower server;
and S03, after the adjustable lower-level server in the to-be-adjusted secondary server is determined, the redundant capacity in the adjustable lower-level server is distributed to the to-be-adjusted lower-level server.
For example, the first north China area server is marked as a lower server to be regulated, the first north China area server is a secondary server to be regulated, whether the first north China area server is the first north China area server or not is determined, if yes, whether the redundant capacity of the lower server meets the requirement of the lower server to be regulated is judged, if yes, only one lower server is needed, and if not, other lower servers are continuously searched.
It will be appreciated that a to-be-tuned lower level server may have one or more tunable lower level servers.
For example: (1) the actual usage of the first server in the first North China is 50% of the first server in the first North China, namely the first server in the first North China is larger than the first server in the first North China by 50% x (1+0.15), the first server in the first North China is an adjustable lower server, and the corresponding redundant capacity of the first server in the first North China is equal to the first server in the first North China by 50% x (1+0.15) =0.425 first North China; (2) the actual use of the first server in the first North China is 88% of the first server in the first North China, namely the first server in the first North China is less than 88% x (1+0.15) of the first server in the first North China, and the first server in the first North China is not a tunable lower server. When the step (1) is established, if the redundant capacity is smaller than the overload capacity of the to-be-adjusted lower server (the difference between the actual use capacity of the to-be-adjusted lower server and the capacity of the to-be-adjusted lower server), continuously determining whether the first-area third server … …, the first-area x server and the second-area third server are the adjustable lower servers and the redundant capacities thereof until the redundant capacities of all the adjustable lower servers are larger than or equal to the overload capacity of the to-be-adjusted lower server.
It can be understood that the redundancy capacity of one adjustable lower server can be allocated to a plurality of lower servers to be adjusted, namely when the first server in the first north China and the second server in the first north China are both lower servers to be adjusted, if the redundancy capacity of the third server in the first north China is equal to or greater than the overload capacity of the first server in the first north China and the second server in the first north China, the redundancy capacity of the third server in the first north China can be distributed to the first server in the first north China and the second server in the first north China as required.
As shown in fig. 4, a method for determining a capacity coordination policy according to an embodiment of the present invention is shown. The capacity coordination module of the embodiment of the invention determines a capacity coordination strategy according to the actual use capacity of each secondary server, and comprises the following steps:
determining a secondary server triggering to formulate a capacity coordination strategy as a coordination server;
the regional server where the coordination server is located is recorded as a coordination regional server;
determining an overflow area in the coordination area server, and automatically connecting an online user with small load characteristics in the coordination server to the overflow area by a capacity coordination module according to the load characteristics;
wherein the overflow area is a non-coordination server in the coordination area server, and the overflow area satisfies that the capacity of the overflow area is larger than the actual use capacity of the overflow area by X (1+5%);
the load characteristics include request frequency and resource requirements, and the load characteristics are small to satisfy the request frequency and/or the resource requirements are small.
In an implementation, the step of determining the capacity coordination policy includes:
a01, determining a coordination server and online users with small load characteristics in the coordination server;
step A02, determining a coordination area server;
step A03, determining an overflow area;
and step A04, automatically connecting the online users with small load characteristics in the coordination server to the overflow area.
Specifically, a capacity coordination module determines a coordination server according to the actual use capacity of any secondary server;
and if the single secondary server meets that the actual use capacity is larger than or equal to the secondary server capacity, the capacity coordination module judges that the single secondary server is a coordination server and triggers the capacity coordination strategy.
In practice, the actual usage capacity of any secondary server is less than the secondary server capacity, but when there is no overflow area available for receiving online users with small load characteristics in the coordination server, a collaborative server is needed for the online users with small load characteristics.
Specifically, the cross-cloud collaboration module determines an overload area server according to the real-time cloud server capacity distribution, and sends the required content to the collaboration server according to the use requirements of online users who are newly online after the overload area server is overloaded and/or online users with small load characteristics so as to meet the use requirements of the users;
and the overload area server meets the area server with the actual use capacity reaching the upper limit, and/or the corresponding area server which is received by the online user with small load characteristics in the coordination server and does not have an overflow area exists.
It can be understood that the overload area server classifies content files of online demands of online users with small load characteristics in the coordination server, which are received by the newly online users and the non-overflow area in the coverage area of the overload area server, and then sends the content files to different positions in the cross-cloud server, and encrypts the content files and then sends passwords to the online users receiving the files.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. An educational cloud service platform based on big data analysis, comprising:
the user management module is used for registering and verifying the identity information of the user and determining the IP address and the network environment of each user according to the registration information and the verification information of the user;
the capacity layout module is connected with the user management module and used for determining cloud server layout of the education cloud service platform according to the IP address and the network environment;
the capacity monitoring module is connected with the capacity layout module and used for monitoring real-time online users and actual use capacities of all subordinate servers in real time and determining corresponding secondary server capacity adjustment strategies according to the number of the real-time online users and the actual use capacities;
the capacity coordination module is respectively connected with the capacity monitoring module and the capacity layout module and is used for determining a capacity coordination strategy according to the real-time capacity of each secondary server so as to meet the use requirement of a user using the corresponding secondary server;
the collaboration server is connected with the capacity layout module and used for increasing the service capacity of the server through collaborative work;
and the cross-cloud coordination module is respectively connected with the capacity layout module, the capacity monitoring module, the capacity coordination module and the coordination server and is used for determining whether to use the coordination server outside the cloud server layout to expand the capacity of the cloud server according to the real-time cloud server layout.
2. The big data analysis based educational cloud service platform according to claim 1, wherein the cloud server comprises a plurality of regional servers, each of the regional servers comprises a plurality of secondary servers, each of the secondary servers comprises a plurality of subordinate servers.
3. The educational cloud service platform based on big data analysis according to claim 2, wherein all secondary servers in any one regional server and all secondary servers in any secondary server are connected to each other between each regional server to increase the scalability of the secondary server capacity and the secondary server capacity.
4. The big data analysis based educational cloud service platform according to claim 1, wherein the subordinate server capacity comprises a working capacity and a reserve capacity;
wherein the working capacity multiplied by 15% of the individual lower servers is not less than the spare capacity is not more than the working capacity multiplied by 25%.
5. The educational cloud service platform based on big data analysis according to claim 4, wherein the capacity layout module determines the capacity distribution of each regional server according to the IP address when each user registers identity information, determines the capacity distribution of all secondary servers in each regional server and the capacity distribution of all secondary servers in each secondary server according to the IP address of the historical online user, and determines the spare capacity of each secondary server according to the network environment of the historical online user.
6. The big data analysis based educational cloud service platform according to claim 5, wherein said capacity monitoring module determines whether the capacity of a single subordinate server needs to be adjusted according to the real-time online user number and actual usage capacity of the single subordinate server, comprising:
determining the relation between the real-time online user quantity of a single lower server and a user quantity reference value, and determining the relation between the actual use capacity of the single lower server and the capacity of the lower server;
if the real-time online user number of the single lower server is larger than the user number reference value and the actual use capacity of the single lower server is larger than or equal to the capacity of the single lower server, the single lower server needs to be adjusted.
7. The educational cloud service platform based on big data analysis according to claim 6, wherein the capacity detection module determines a capacity adjustment policy of a lower server according to a secondary server corresponding to the lower server whose capacity is to be adjusted, comprising:
marking the lower server to be adjusted as a lower server to be adjusted;
determining a secondary server where the secondary server is located as a secondary server to be regulated according to the secondary server to be regulated;
determining whether an adjustable lower server exists in the secondary server to be adjusted, and reallocating the redundant capacity of the adjustable lower server to the lower server to be adjusted;
the adjustable lower server is a non-to-be-adjusted lower server in the to-be-adjusted secondary server, and the capacity of the adjustable lower server is more than the actual use capacity of the adjustable lower server by X (1+15%);
the redundant capacity = adjustable lower server capacity-actual used capacity of adjustable lower server x (1+15%).
8. The big data analysis based educational cloud service platform according to claim 1, wherein the capacity coordination module determines a capacity coordination policy according to actual usage capacity of each secondary server, comprising:
determining a secondary server triggering a capacity coordination strategy as a coordination server;
the regional server where the coordination server is located is recorded as a coordination regional server;
determining an overflow area in a coordination area server, wherein the capacity coordination module automatically connects an online user with small load characteristics in the coordination server to the overflow area according to the load characteristics;
wherein the overflow area is a non-coordination server in the coordination area server, and the overflow area satisfies that the capacity of the overflow area is larger than the actual use capacity of the overflow area by x (1+5%);
the load characteristics include request frequency and resource requirements, and the load characteristics are small to satisfy the request frequency and/or the resource requirements are small.
9. The big data analysis based educational cloud service platform according to claim 8, wherein the capacity coordination module determines the coordination server according to the actual usage capacity of any secondary server;
and if the single secondary server meets that the actual use capacity is larger than or equal to the secondary server capacity, the capacity coordination module judges that the single secondary server is a coordination server and triggers the capacity coordination strategy.
10. The educational cloud service platform based on big data analysis according to claim 1, wherein the cross-cloud collaboration module determines an overload area server according to the real-time cloud server capacity distribution, and sends the required content to the collaboration server according to the use requirement of the online user who is newly online after the overload area server is overloaded and/or the online user with small load characteristics so as to meet the use requirement of the user;
and the overload area server meets the area server with the actual use capacity reaching the upper limit, and/or the corresponding area server which is received by the online user with small load characteristics in the coordination server and does not have an overflow area exists.
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