WO2021139273A1

WO2021139273A1 - Cdn scheduling method and apparatus based on pareto algorithm, and computer device and storage medium

Info

Publication number: WO2021139273A1
Application number: PCT/CN2020/118921
Authority: WO
Inventors: 张安发
Original assignee: 平安科技（深圳）有限公司
Priority date: 2020-07-28
Filing date: 2020-09-29
Publication date: 2021-07-15
Also published as: CN111901425B; CN111901425A

Abstract

Disclosed are a CDN scheduling method and apparatus based on a Pareto algorithm, and a computer device and a storage medium, relating to the field of artificial intelligence. The method comprises: acquiring access quality data and usage cost data of each CDN manufacturer to be selected; acquiring a quality objective function value and a cost objective function value of each said CDN manufacturer; acquiring a Pareto optimal set according to a preset Pareto algorithm, the quality objective function value of each said CDN manufacturer and the cost objective function value of each said CDN manufacturer; determining whether a selected objective CDN manufacturer is in the Pareto optimal set; and if not, scheduling a CDN connection of a terminal to said CDN manufacturers in the Pareto optimal set.

Description

CDN scheduling method, device, computer equipment and storage medium based on Pareto algorithm

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office, the application number is 202010740112.6, and the invention title is "Pareto algorithm-based CDN scheduling method, device, computer equipment and storage medium" on July 28, 2020, all of which The content is incorporated in this application by reference.

Technical field

This application relates to the field of artificial intelligence technology, and in particular to a CDN scheduling method, device, computer equipment, and storage medium based on the Pareto algorithm.

Background technique

CDN (Content Delivery Network, Content Delivery Network) is a new type of network content service system, which is based on IP network and provides content distribution and services based on the efficiency requirements, quality requirements and content order of content access and application.

As the core part of the CDN system, the scheduling system, its scheduling strategy has always been the focus of people's research. As a converged CDN, it not only involves the scheduling between node lines within a single CDN vendor, but also the scheduling between multiple CDN vendors, which undoubtedly increases the complexity of scheduling. How to formulate high-quality and low-cost scheduling strategies has become the focus of various integrated CDN vendors.

The traditional converged CDN vendor scheduling strategy is generally determined in advance based on the service quality and service price of each vendor, and the subsequent manual adjustment of the strategy according to the service situation, but the network service is a fluctuating process. How to formulate a good scheduling strategy to achieve the most economical benefits is one of the main goals, and the service quality of various CDN vendors at different moments will also fluctuate. How to maximize the service quality under the economically optimal goal becomes another An important goal.

The inventor found that it is generally difficult for the current converged CDN scheduling system to dynamically and real-time adjust the scheduling strategy in order to take into account economy and service quality at the same time. At the same time, the frequent manual intervention of the scheduling system to adjust the scheduling strategy also greatly increases the manual maintenance cost and is inefficient.

Summary of the invention

The embodiments of the present application provide a CDN scheduling method, device, computer equipment, and storage medium based on the Pareto algorithm, aiming to solve the problem that the existing CDN scheduling method relies on manual scheduling and cannot balance economy and service quality.

In the first aspect, an embodiment of the present application provides a CDN scheduling method based on the Pareto algorithm, which includes:

Obtain access quality data and usage cost data of each candidate CDN vendor;

Respectively acquiring the quality objective function value of each candidate CDN manufacturer according to the preset quality objective function and the access quality data of each candidate CDN manufacturer;

Obtain the cost objective function value of each candidate CDN manufacturer according to the preset cost objective function and the cost data of each candidate CDN manufacturer;

Obtaining the Pareto optimal set according to the preset Pareto algorithm, the quality objective function value of each candidate CDN manufacturer and the cost objective function value of each candidate CDN manufacturer;

Judge whether the selected target CDN manufacturer is in the Pareto optimal concentration;

If the target CDN vendor is not in the Pareto optimal concentration, the CDN connection of the terminal is scheduled to the candidate CDN vendors in the Pareto optimal concentration.

In the second aspect, an embodiment of the present application also provides a CDN scheduling device based on the Pareto algorithm, which includes:

The first obtaining unit is used to obtain access quality data and usage cost data of each candidate CDN vendor;

The second acquiring unit is configured to respectively acquire the quality objective function value of each candidate CDN manufacturer according to the preset quality objective function and the access quality data of each candidate CDN manufacturer;

The third obtaining unit is configured to obtain the cost objective function value of each candidate CDN manufacturer according to the preset cost objective function and the cost data of each candidate CDN manufacturer;

The fourth obtaining unit is configured to obtain the Pareto optimal set according to the preset Pareto algorithm and the quality objective function value of each candidate CDN manufacturer and the cost objective function value of each candidate CDN manufacturer;

The first judgment unit is used to judge whether the selected target CDN manufacturer is in the Pareto optimal concentration;

The first scheduling unit is configured to schedule the CDN connection of the terminal to the candidate CDN vendors in the Pareto optimal set if the target CDN vendor is not in the Pareto optimal set.

In a third aspect, an embodiment of the present application also provides a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and the processor is used to run the computer program to perform the following steps :

Obtain access quality data and usage cost data of each candidate CDN vendor;

Obtain the Pareto optimal set according to the preset Pareto algorithm, the quality objective function value of each candidate CDN manufacturer and the cost objective function value of each candidate CDN manufacturer;

In a fourth aspect, the embodiments of the present application also provide a computer-readable storage medium that stores a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps :

Obtain access quality data and usage cost data of each candidate CDN vendor;

Description of the drawings

In order to explain the technical solutions of the embodiments of the present application more clearly, the following will briefly introduce the drawings used in the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present application. Ordinary technicians can obtain other drawings based on these drawings without creative work.

FIG. 1 is a schematic diagram of an application scenario of a CDN scheduling method based on the Pareto algorithm provided by an embodiment of the application;

2 is a schematic flowchart of a CDN scheduling method based on the Pareto algorithm provided by an embodiment of the application;

FIG. 3 is a schematic diagram of a sub-flow of a CDN scheduling method based on the Pareto algorithm provided by an embodiment of the application;

4 is a schematic diagram of a sub-flow of a CDN scheduling method based on the Pareto algorithm provided by an embodiment of the application;

FIG. 5 is a schematic diagram of a sub-flow of a CDN scheduling method based on the Pareto algorithm according to an embodiment of the application;

6 is a schematic diagram of a sub-flow of a CDN scheduling method based on the Pareto algorithm provided by an embodiment of the application;

FIG. 7 is a schematic flowchart of a CDN scheduling method based on the Pareto algorithm according to another embodiment of the application;

FIG. 8 is a schematic block diagram of a CDN scheduling device based on the Pareto algorithm according to an embodiment of the application;

Fig. 9 is a schematic block diagram of a first obtaining unit of a CDN scheduling device based on the Pareto algorithm according to an embodiment of the application;

10 is a schematic block diagram of a first scheduling unit of a CDN scheduling device based on the Pareto algorithm according to an embodiment of the application;

11 is a schematic block diagram of a first scheduling unit and a second scheduling unit of a CDN scheduling device based on the Pareto algorithm according to an embodiment of the application;

12 is a schematic block diagram of a first scheduling unit and a third scheduling unit of a CDN scheduling device based on the Pareto algorithm according to an embodiment of the application;

FIG. 13 is a schematic block diagram of a CDN scheduling device based on the Pareto algorithm according to another embodiment of the application;

FIG. 14 is a schematic block diagram of a computer device provided by an embodiment of this application.

Detailed ways

The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.

It should be understood that when used in this specification and appended claims, the terms "including" and "including" indicate the existence of the described features, wholes, steps, operations, elements and/or components, but do not exclude one or The existence or addition of multiple other features, wholes, steps, operations, elements, components, and/or collections thereof.

It should also be understood that the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in the specification of this application and the appended claims, unless the context clearly indicates other circumstances, the singular forms "a", "an" and "the" are intended to include plural forms.

It should be further understood that the term "and/or" used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations .

As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "once" or "in response to determination" or "in response to detection" depending on the context . Similarly, the phrase "if determined" or "if detected [described condition or event]" can be interpreted as meaning "once determined" or "in response to determination" or "once detected [described condition or event]" depending on the context ]" or "in response to detection of [condition or event described]".

Please refer to FIG. 1 and FIG. 2. FIG. 1 is a schematic diagram of an application scenario of a CDN scheduling method based on the Pareto algorithm provided by an embodiment of the application. Fig. 2 is a schematic flowchart of a CDN scheduling method based on the Pareto algorithm provided by an embodiment of the application. The CDN scheduling method based on the Pareto algorithm proposed in this application is applied to the terminal 10. The terminal 10 selects the candidate CDN vendor 20 (CDN server) according to the preset Pareto algorithm, and schedules its CDN connection to the selected candidate CDN vendor 20 .

Fig. 2 is a schematic flowchart of a CDN scheduling method based on the Pareto algorithm provided by an embodiment of the present application. This application can be applied to smart government affairs/smart city management/smart communities/smart security/smart logistics/smart healthcare/smart education/smart environmental protection/smart transportation scenarios to promote the construction of smart cities. As shown in the figure, the method includes the following steps S1-S6.

S1. Obtain access quality data and usage cost data of each candidate CDN vendor.

At present, most CDN systems adopt a method of integrating multiple CDN vendors, that is, a CDN system contains multiple CDN vendors (CDN servers) for users to choose from. In the embodiment of this application, all CDN vendors of the CDN system are regarded as candidate CDN vendors.

In the specific implementation, the access quality data and usage cost data of each candidate CDN vendor are obtained.

The access quality data is used to characterize the service quality of the candidate CDN vendor. For example, the access quality data may be the access delay of the candidate CDN vendor.

The usage cost data is used to characterize the usage cost of the candidate CDN manufacturer. For example, the usage cost data may be the price of the candidate CDN manufacturer.

As shown in FIG. 3, in an embodiment, the above step S1 specifically includes the following steps S11-S14.

S11: Obtain respectively the access delays of each node of the candidate CDN vendor.

In specific implementation, each candidate CDN vendor includes multiple nodes. Obtain the access delay of each node of the candidate CDN vendor respectively. Specifically, the access delay of the node can be obtained by measuring the network speed of the node.

S12: Calculate the average value of the access delay of each node of the candidate CDN vendor.

In specific implementation, after obtaining the access delay of each node of the candidate CDN vendor, calculate the average value of the access delay of each node of the candidate CDN vendor, that is, the average value of the access delay of each node of the candidate CDN vendor Mean.

The average access delay can reflect the overall service quality of the candidate CDN vendors. In this embodiment, the access quality data is the average access delay.

S13: Send a price query request to the server of the candidate CDN manufacturer.

In specific implementation, a price query request is sent to the server of the candidate CDN manufacturer.

After receiving the price query request, the server of the candidate CDN manufacturer will return a price response message to the terminal, and the price response message contains the bandwidth unit price of the candidate CDN manufacturer.

S14: Receive a price response message returned by the server of the candidate CDN vendor, where the price response message includes the bandwidth unit price of the candidate CDN vendor.

In specific implementation, a price response message returned by the server of the candidate CDN vendor is received, where the price response message includes the bandwidth unit price of the candidate CDN vendor. The terminal obtains the bandwidth unit price of the candidate CDN manufacturer according to the price response message.

In this embodiment, the usage cost data is the bandwidth unit price.

It should be noted that the above steps S11-S12 and steps S13-S14 are not executed sequentially. Alternatively, steps S11-S12 and steps S13-S14 can be executed in parallel.

S2: Obtain the quality objective function value of each candidate CDN vendor according to the preset quality objective function and the access quality data of each candidate CDN vendor.

In a specific embodiment, the quality objective function value of each candidate CDN vendor is obtained according to the preset quality objective function and the access quality data of each candidate CDN vendor.

Specifically, the access quality data of the candidate CDN manufacturer is input into a preset quality objective function to obtain the quality objective function value of the candidate CDN manufacturer.

In one embodiment, the quality objective function is f _k (x)=dt, dt is the average access delay of the candidate CDN vendor, and x is the candidate CDN vendor.

S3: Obtain the cost objective function value of each candidate CDN vendor according to the preset cost objective function and the cost data of each candidate CDN vendor.

In a specific embodiment, the cost objective function value of each candidate CDN manufacturer is obtained according to the preset cost objective function and the cost data of each candidate CDN manufacturer.

Specifically, the cost data of the candidate CDN manufacturer is input into a preset cost objective function to obtain the cost objective function value of the candidate CDN manufacturer.

In one embodiment, the cost objective function is _fr (x)=pr*bw, where pr represents the bandwidth unit price of the candidate CDN vendor, bw represents the bandwidth selected by the user, and x represents the candidate CDN vendor.

It should be noted that the above steps S2 and S3 are not executed sequentially. Alternatively, step S2 and step S3 can be executed in parallel.

S4: Obtain the Pareto optimal set according to the preset Pareto algorithm, the quality objective function value of each candidate CDN manufacturer and the cost objective function value of each candidate CDN manufacturer.

Pareto algorithm, also known as Pareto optimal solution algorithm, is a resource optimization algorithm.

Among them, the core definition of Pareto algorithm is as follows:

Definition 1 (Pareto dominates): x1, x2 ∈ X, if

So as to satisfy _{_{f k (x1) ≤f k (}} x2), and

Satisfying f _r (x1)<f _r (x2), then it is said that x1 is Pareto dominant compared to x2, which can be described as

Definition 2 (non-inferior solution): x1, x2 ∈ X, if

r∈{1,2,...m}, satisfying f _k (x1)≥f _k (x2) and f _r (x1)≤f _r (x2), then x1 and x2 are called non-inferior solutions to each other, which can be described as x1||x2.

Definition 3 (Pareto optimal solution): For x1 ∈ X, if there is no solution in X that can be better than x1, then x1 is called the Pareto optimal solution.

Definition 4 (Pareto optimal set): The set composed of Pareto optimal solutions is called Pareto optimal set.

In specific implementation, x1, x2...xn respectively represent the candidate CDN vendors. f _k (x) is the quality objective function, and f _r (x) is the cost objective function. Then obtain the Pareto optimal set according to the above definition 1 to definition 4.

At the same time, the Pareto optimal set can be stored in the blockchain to ensure data security and non-tampering.

S5: Determine whether the selected target CDN manufacturer is in the Pareto optimal concentration.

In specific implementation, the CDN vendor currently selected by the terminal is taken as the target CDN vendor.

After obtaining the Pareto optimal set, whether the selected target CDN manufacturer is in the Pareto optimal set.

S6: If the target CDN vendor is not in the Pareto optimal set, schedule the terminal's CDN connection to the candidate CDN vendors in the Pareto optimal set.

In specific implementation, if the target CDN vendor is not in the Pareto optimal set, the CDN connection of the terminal is scheduled to the candidate CDN vendors in the Pareto optimal set.

Specifically, if there is only one candidate CDN vendor in the Pareto optimal set, it is scheduled to the candidate CDN vendor.

If there are multiple candidate CDN vendors in the Pareto optimal set, it is scheduled to one of the candidate CDN vendors in the Pareto optimal set.

As shown in FIG. 4, in an embodiment, the above step S6 specifically includes S61-S63.

S61: Acquire a scheduling mode preset by the user, where the scheduling mode includes a quality priority mode and a price priority mode.

In specific implementation, the scheduling mode preset by the user is obtained. In this application, there are two scheduling modes for users to choose, namely the quality priority mode and the price priority mode.

S62: If the scheduling mode preset by the user is the quality priority mode, acquire the candidate CDN vendor with the smallest value of the Pareto optimal centralized quality objective function as the first target CDN vendor, and schedule the CDN connection of the terminal to the first target CDN vendor. One of the target CDN vendors.

In specific implementation, if the user preset scheduling mode is the quality priority mode, the candidate CDN vendor with the smallest value of the Pareto optimal centralized quality objective function (that is, the candidate CDN vendor with the best Pareto optimal centralized network connection quality) is obtained ) As the first target CDN vendor, and schedule the terminal's CDN connection to the first target CDN vendor.

As shown in FIG. 5, in an embodiment, the above step S62 specifically includes S621-S622.

S621: Acquire the node with the smallest access delay from the first target CDN vendor as the first target node.

In specific implementation, the node with the smallest access delay from the first target CDN vendor is acquired as the first target node. The access delay of the first target node is the smallest, and the quality of service will be the best.

S622: Schedule the CDN connection of the terminal to the first target node.

In specific implementation, the CDN connection of the terminal is scheduled to the first target node to increase the network connection rate.

S63: If the scheduling mode preset by the user is the price priority mode, obtain the candidate CDN manufacturer with the smallest value of the Pareto optimal centralized cost objective function as the second target CDN manufacturer, and schedule the CDN connection of the terminal to the second target CDN manufacturer. 2. Among the target CDN vendors.

In specific implementation, if the scheduling mode preset by the user is the price priority mode, the candidate CDN vendor with the smallest value of the Pareto optimal centralized cost objective function (ie the candidate CDN vendor with the lowest Pareto optimal centralized cost) is obtained as The second target CDN manufacturer, and schedules the CDN connection of the terminal to the second target CDN manufacturer.

As shown in FIG. 6, in an embodiment, the above step S63 specifically includes S631-S632.

S631: Acquire the node with the smallest access delay from the second target CDN vendor as the second target node.

In specific implementation, the node with the smallest access delay from the second target CDN vendor is acquired as the second target node.

The access delay of the second target node is the smallest, and the quality of service will be the best.

S632 schedules the CDN connection of the terminal to the second target node.

In specific implementation, the CDN connection of the terminal is scheduled to the second target node to increase the network connection rate.

By applying the technical solutions of the embodiments of the present application, the access quality data and usage cost data of each candidate CDN manufacturer are obtained; each of the candidate CDN manufacturers is obtained according to the preset quality objective function and the access quality data of each candidate CDN manufacturer. Select the quality objective function value of the CDN manufacturer; obtain the cost objective function value of each candidate CDN manufacturer according to the preset cost objective function and the cost data of each candidate CDN manufacturer; According to the preset Pareto algorithm and each The quality objective function value of the candidate CDN vendor and the cost objective function value of each candidate CDN vendor obtain the Pareto optimal set; determine whether the target CDN vendor is in the Pareto optimal set; if the target CDN vendor is not In the Pareto optimal concentration, the CDN connection of the terminal is scheduled to the candidate CDN vendors in the Pareto optimal concentration, so that the CDN scheduling can be automatically realized without manual operation by the user, and the scheduling efficiency is high; at the same time, the Pareto optimal concentration The excellent concentration of candidate CDN vendors can take into account the economy and service quality of the candidate CDN vendors, which greatly improves the user experience.

FIG. 7 is a schematic flowchart of a CDN scheduling method based on the Pareto algorithm according to another embodiment of the present application. As shown in Figure 7, the CDN scheduling method based on the Pareto algorithm of this embodiment includes steps S71-S78.

S71: Determine whether the current time reaches a preset time node.

In specific implementation, it is determined whether the current time reaches the preset time node.

It should be noted that the time node is set by those skilled in the art based on experience. For example, set to every hour of the day. Since the network conditions of the candidate CDN vendors change over time, it is necessary to obtain the access quality data of the candidate CDN vendors regularly.

If the current time has not reached the preset time node, continue to determine whether the current time has reached the preset time node, and so on, until the current time reaches the preset time node.

S72: If the current time reaches a preset time node, obtain access quality data and usage cost data of each CDN vendor to be selected.

At present, most of the CDN system adopts the method of integrating multiple CDN vendors, that is, a CDN system contains multiple CDN vendors for users to choose from. In the embodiment of this application, all CDN vendors of the CDN system are regarded as candidate CDN vendors.

S73: Acquire the quality objective function value of each candidate CDN vendor according to the preset quality objective function and the access quality data of each candidate CDN vendor.

S74: Obtain the cost objective function value of each candidate CDN manufacturer according to the preset cost objective function and the cost data of each candidate CDN manufacturer.

S75: Obtain the Pareto optimal set according to the preset Pareto algorithm, the quality objective function value of each candidate CDN manufacturer, and the cost objective function value of each candidate CDN manufacturer.

Among them, the core definition of Pareto algorithm is as follows:

Definition 1 (Pareto dominates): x1, x2 ∈ X, if

So as to satisfy _{_{f k (x1) ≤f k (}} x2), and

Definition 2 (non-inferior solution): x1, x2 ∈ X, if

S76: Determine whether the selected target CDN vendor is in the Pareto optimal concentration.

After obtaining the Pareto optimal set, it is judged whether the target CDN manufacturer is in the Pareto optimal set.

S77: If the target CDN vendor is not in the Pareto optimal set, schedule the terminal's CDN connection to the candidate CDN vendors in the Pareto optimal set.

S78: If the target CDN vendor is in the Pareto optimal concentration, keep the CDN connection of the terminal in the target CDN vendor.

In specific implementation, if the target CDN manufacturer is in the Pareto optimal concentration, there is no need to schedule CDN connections, and the CDN connection of the terminal is kept in the target CDN manufacturer to avoid frequent scheduling of CDN connections to cause network fluctuations to users.

FIG. 8 is a schematic block diagram of a CDN scheduling device 70 based on the Pareto algorithm according to an embodiment of the present application. As shown in FIG. 8, corresponding to the above CDN scheduling method based on the Pareto algorithm, the present application also provides a CDN scheduling device 70 based on the Pareto algorithm. The Pareto algorithm-based CDN scheduling device 70 includes a unit for executing the above-mentioned Pareto algorithm-based CDN scheduling method. The Pareto algorithm-based CDN scheduling device 70 can be configured in a desktop computer, a tablet computer, a laptop computer, and other terminals. Specifically, the CDN system includes multiple candidate CDN vendors. Please refer to FIG. 8. The CDN scheduling device 70 based on the Pareto algorithm includes a first obtaining unit 71, a second obtaining unit 72, a third obtaining unit 73, and a fourth obtaining unit. The unit 74, the first judging unit 75, and the first scheduling unit 76.

The first obtaining unit 71 is configured to obtain access quality data and usage cost data of each candidate CDN vendor.

The second obtaining unit 72 is configured to obtain the quality objective function value of each candidate CDN vendor according to the preset quality objective function and the access quality data of each candidate CDN vendor.

The third obtaining unit 73 is configured to obtain the cost objective function value of each candidate CDN manufacturer according to the preset cost objective function and the cost data of each candidate CDN manufacturer.

The fourth obtaining unit 74 is configured to obtain the Pareto optimal set according to the preset Pareto algorithm, the quality objective function value of each candidate CDN manufacturer, and the cost objective function value of each candidate CDN manufacturer.

The first judging unit 75 is used to judge whether the selected target CDN manufacturer is in the Pareto optimal concentration.

The first scheduling unit 76 is configured to, if the target CDN vendor is not in the Pareto optimal set, schedule the CDN connection of the terminal to the candidate CDN vendors in the Pareto optimal set.

In an embodiment, as shown in FIG. 9, the first obtaining unit 71 includes a fifth obtaining unit 711, a calculating unit 712, a sending unit 713 and a receiving unit 714.

The fifth obtaining unit 711 is configured to obtain the access delay of each node of the candidate CDN vendor respectively.

The calculation unit 712 is configured to calculate the average value of the access delay of each node of the candidate CDN vendor.

The sending unit 713 is configured to send a price query request to the server of the candidate CDN manufacturer.

The receiving unit 714 is configured to receive a price response message returned by the server of the candidate CDN manufacturer, where the price response message includes the bandwidth unit price of the candidate CDN manufacturer.

In an embodiment, as shown in FIG. 10, the first scheduling unit 76 includes a sixth obtaining unit 761, a second scheduling unit 762, and a third scheduling unit 763.

The sixth acquiring unit 761 is configured to acquire a scheduling mode preset by the user, and the scheduling mode includes a quality priority mode and a price priority mode.

The second scheduling unit 762 is configured to, if the user preset scheduling mode is the quality priority mode, obtain the candidate CDN vendor with the smallest value of the Pareto optimal centralized quality objective function as the first target CDN vendor, and set the CDN of the terminal The connection is scheduled to the first target CDN vendor.

The third scheduling unit 763 is configured to, if the scheduling mode preset by the user is the price priority mode, obtain the candidate CDN manufacturer with the smallest value of the Pareto optimal centralized cost objective function as the second target CDN manufacturer, and set the CDN of the terminal The connection is scheduled to the second target CDN vendor.

In an embodiment, as shown in FIG. 11, the second scheduling unit 762 includes a seventh obtaining unit 7621 and a fourth scheduling unit 7622.

The seventh acquiring unit 7621 is configured to acquire the node with the smallest access delay among the first target CDN vendors as the first target node.

The fourth scheduling unit 7622 is configured to schedule the CDN connection of the terminal to the first target node.

In an embodiment, as shown in FIG. 12, the third scheduling unit 763 includes an eighth obtaining unit 7631 and a fifth scheduling unit 7632.

The eighth obtaining unit 7631 is configured to obtain the node with the smallest access delay among the second target CDN vendors as the second target node.

The fifth scheduling unit 7632 is configured to schedule the CDN connection of the terminal to the second target node.

FIG. 13 is a schematic block diagram of a CDN scheduling device 70 based on the Pareto algorithm according to another embodiment of the present application. As shown in FIG. 13, the CDN scheduling apparatus 70 based on the Pareto algorithm of this embodiment adds a second judgment unit 77 and a holding unit 78 on the basis of the foregoing embodiment.

The second judging unit 77 is used to judge whether the current time has reached a preset time node.

The first obtaining unit 71 is specifically configured to perform the steps of obtaining access quality data and usage cost data of each candidate CDN vendor if the current time reaches a preset time node.

The maintaining unit 78 is configured to maintain the CDN connection of the terminal in the target CDN manufacturer if the target CDN manufacturer is in the Pareto optimal concentration.

It should be noted that those skilled in the art can clearly understand that the above-mentioned CDN scheduling device 70 based on the Pareto algorithm and the specific implementation process of each unit can refer to the corresponding description in the foregoing method embodiment, for the convenience and conciseness of the description. , I won’t repeat it here.

The above-mentioned CDN scheduling apparatus based on the Pareto algorithm may be implemented in the form of a computer program, and the computer program may run on the computer device as shown in FIG. 14.

Please refer to FIG. 14, which is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 is a terminal, and the terminal may be an electronic device with communication functions such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device.

Referring to FIG. 14, the computer device 500 includes a processor 502, a memory, and a network interface 505 connected through a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.

The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, the processor 502 can execute a CDN scheduling method based on the Pareto algorithm.

The processor 502 is used to provide calculation and control capabilities to support the operation of the entire computer device 500.

The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can make the processor 502 execute a CDN scheduling method based on the Pareto algorithm.

The network interface 505 is used for network communication with other devices. Those skilled in the art can understand that the structure shown in FIG. 14 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

Wherein, the processor 502 is configured to run a computer program 5032 stored in a memory to implement the CDN scheduling method based on the Pareto algorithm of the present application.

It should be understood that in this embodiment of the application, the processor 502 may be a central processing unit (Central Processing Unit, CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSPs), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments may be implemented by computer programs instructing relevant hardware. The computer program may be stored in a storage medium, and the storage medium is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the foregoing method embodiment.

Therefore, this application also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program. When the computer program is executed by the processor, the processor executes the CDN scheduling method based on the Pareto algorithm of the present application.

The storage medium is a physical, non-transitory storage medium, such as a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a magnetic disk, or an optical disk, etc., which can store program codes. medium. The computer-readable storage medium may be non-volatile or volatile.

A person of ordinary skill in the art may be aware that the units and algorithm steps of the examples described in the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both, in order to clearly illustrate the hardware and software Interchangeability, in the above description, the composition and steps of each example have been generally described in accordance with the function. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.

In the several embodiments provided in this application, it should be understood that the disclosed device and method may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be omitted or not implemented.

The steps in the method in the embodiment of the present application can be adjusted, merged, and deleted in order according to actual needs. The units in the devices in the embodiments of the present application may be combined, divided, and deleted according to actual needs. In addition, the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of this application is essentially or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium. It includes several instructions to make a computer device (which may be a personal computer, a terminal, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application.

In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in an embodiment, reference may be made to related descriptions of other embodiments.

Obviously, those skilled in the art can make various changes and modifications to the application without departing from the spirit and scope of the application. In this way, even if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application also intends to include these modifications and variations.

The above are only specific implementations of this application, but the protection scope of this application is not limited to this. Anyone familiar with the technical field can easily think of various equivalents within the technical scope disclosed in this application. Modifications or replacements, these modifications or replacements shall be covered within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

A CDN scheduling method based on the Pareto algorithm. The CDN system includes multiple CDN vendors to be selected. The CDN scheduling method based on the Pareto algorithm includes:

Obtain access quality data and usage cost data of each candidate CDN vendor;

Respectively acquiring the quality objective function value of each candidate CDN manufacturer according to the preset quality objective function and the access quality data of each candidate CDN manufacturer;

Obtain the cost objective function value of each candidate CDN manufacturer according to the preset cost objective function and the cost data of each candidate CDN manufacturer;

Obtaining the Pareto optimal set according to the preset Pareto algorithm, the quality objective function value of each candidate CDN manufacturer and the cost objective function value of each candidate CDN manufacturer;

Judge whether the selected target CDN manufacturer is in the Pareto optimal concentration;

If the target CDN vendor is not in the Pareto optimal concentration, the CDN connection of the terminal is scheduled to the candidate CDN vendors in the Pareto optimal concentration.
The CDN scheduling method based on the Pareto algorithm according to claim 1, wherein said obtaining access quality data and usage cost data of each candidate CDN vendor includes:

Respectively acquiring the access delays of each node of the candidate CDN vendor;

Calculate the average value of the access delay of each node of the candidate CDN vendor.
The CDN scheduling method based on the Pareto algorithm according to claim 2, wherein said obtaining access quality data and usage cost data of each candidate CDN vendor further comprises:

Sending a price query request to the server of the candidate CDN manufacturer;

Receive a price response message returned by the server of the candidate CDN vendor, where the price response message includes the bandwidth unit price of the candidate CDN vendor.
The CDN scheduling method based on the Pareto algorithm according to claim 1, wherein the scheduling the CDN connection of the terminal to the candidate CDN vendors in the Pareto optimal set comprises:

Acquiring a scheduling mode preset by the user, the scheduling mode including a quality priority mode and a price priority mode;

If the scheduling mode preset by the user is the quality priority mode, the candidate CDN vendor with the smallest value of the Pareto optimal centralized quality objective function is obtained as the first target CDN vendor, and the CDN connection of the terminal is scheduled to the first target Among CDN vendors;

If the scheduling mode preset by the user is the price priority mode, the candidate CDN vendor with the smallest value of the Pareto optimal centralized cost objective function is obtained as the second target CDN vendor, and the CDN connection of the terminal is scheduled to the second target Among CDN vendors.
The CDN scheduling method based on the Pareto algorithm according to claim 4, wherein the scheduling the CDN connection of the terminal to the first target CDN vendor comprises:

Acquiring the node with the smallest access delay in the first target CDN vendor as the first target node;

Scheduling the CDN connection of the terminal to the first target node;

The scheduling the CDN connection of the terminal to the second target CDN vendor includes:

Acquiring the node with the smallest access delay in the second target CDN vendor as the second target node;

The CDN connection of the terminal is scheduled to the second target node.
The CDN scheduling method based on the Pareto algorithm according to claim 1, wherein before said obtaining access quality data and usage cost data of each candidate CDN vendor, the method further comprises:

Determine whether the current time has reached the preset time node;

If the current time reaches the preset time node, the steps of obtaining access quality data and usage cost data of each candidate CDN vendor are executed.
The CDN scheduling method based on the Pareto algorithm according to claim 1, wherein after the determining whether the selected target CDN vendor is in the Pareto optimal concentration, the method further comprises:

If the target CDN vendor is in the Pareto optimal concentration, keep the CDN connection of the terminal in the target CDN vendor.
The CDN scheduling method based on the Pareto algorithm according to claim 1, wherein the quality objective function is f k (x)=dt, dt is the average access delay of the CDN vendor to be selected, and x is the CDN vendor to be selected.
The CDN scheduling method based on the Pareto algorithm according to claim 1, wherein the cost objective function is fr (x)=pr*bw, where pr represents the bandwidth unit price of the candidate CDN manufacturer, and bw represents the bandwidth selected by the user The size of, x represents the candidate CDN vendor.
A CDN scheduling device based on the Pareto algorithm. The CDN system includes multiple candidate CDN vendors. The CDN scheduling device based on the Pareto algorithm includes:

The first obtaining unit is used to obtain access quality data and usage cost data of each candidate CDN vendor;

The second acquiring unit is configured to respectively acquire the quality objective function value of each candidate CDN manufacturer according to the preset quality objective function and the access quality data of each candidate CDN manufacturer;

The third obtaining unit is configured to obtain the cost objective function value of each candidate CDN manufacturer according to the preset cost objective function and the cost data of each candidate CDN manufacturer;

The fourth obtaining unit is configured to obtain the Pareto optimal set according to the preset Pareto algorithm, the quality objective function value of each candidate CDN manufacturer, and the cost objective function value of each candidate CDN manufacturer;

The first judgment unit is used to judge whether the selected target CDN manufacturer is in the Pareto optimal concentration;

The first scheduling unit is configured to schedule the CDN connection of the terminal to the candidate CDN vendors in the Pareto optimal set if the target CDN vendor is not in the Pareto optimal set.
A computer device includes a memory and a processor, the memory stores a computer program, and the processor is used to run the computer program to perform the following steps:

Obtain access quality data and usage cost data of each candidate CDN vendor;

Respectively acquiring the quality objective function value of each candidate CDN manufacturer according to the preset quality objective function and the access quality data of each candidate CDN manufacturer;

Obtain the cost objective function value of each candidate CDN manufacturer according to the preset cost objective function and the cost data of each candidate CDN manufacturer;

Obtaining the Pareto optimal set according to the preset Pareto algorithm, the quality objective function value of each candidate CDN manufacturer and the cost objective function value of each candidate CDN manufacturer;

Judge whether the selected target CDN manufacturer is in the Pareto optimal concentration;

If the target CDN vendor is not in the Pareto optimal concentration, the CDN connection of the terminal is scheduled to the candidate CDN vendors in the Pareto optimal concentration.
11. The computer device according to claim 11, wherein the step of obtaining access quality data and usage cost data of each candidate CDN vendor comprises:

Respectively acquiring the access delays of each node of the candidate CDN vendor;

Calculate the average value of the access delay of each node of the candidate CDN vendor.
The computer device according to claim 12, wherein the step of obtaining access quality data and usage cost data of each candidate CDN vendor further comprises:

Sending a price query request to the server of the candidate CDN manufacturer;

Receive a price response message returned by the server of the candidate CDN vendor, where the price response message includes the bandwidth unit price of the candidate CDN vendor.
The computer device according to claim 11, wherein the step of scheduling the CDN connection of the terminal to the candidate CDN vendor in the Pareto optimal set comprises:

Acquiring a scheduling mode preset by the user, the scheduling mode including a quality priority mode and a price priority mode;

If the scheduling mode preset by the user is the quality priority mode, the candidate CDN vendor with the smallest value of the Pareto optimal centralized quality objective function is obtained as the first target CDN vendor, and the CDN connection of the terminal is scheduled to the first target Among CDN vendors;

If the scheduling mode preset by the user is the price priority mode, the candidate CDN vendor with the smallest value of the Pareto optimal centralized cost objective function is obtained as the second target CDN vendor, and the CDN connection of the terminal is scheduled to the second target Among CDN vendors.
The computer device according to claim 14, wherein the step of scheduling the CDN connection of the terminal to the first target CDN vendor comprises:

Acquiring the node with the smallest access delay in the first target CDN vendor as the first target node;

Scheduling the CDN connection of the terminal to the first target node;

The scheduling the CDN connection of the terminal to the second target CDN vendor includes:

Acquiring the node with the smallest access delay in the second target CDN vendor as the second target node;

The CDN connection of the terminal is scheduled to the second target node.
11. The computer device according to claim 11, wherein before said acquiring access quality data and usage cost data of each candidate CDN vendor, said processor further executes the following steps:

Determine whether the current time has reached the preset time node;

If the current time reaches the preset time node, the steps of obtaining access quality data and usage cost data of each candidate CDN vendor are executed.
3. The computer device according to claim 1, wherein after said determining whether the selected target CDN vendor is in the Pareto optimal concentration, the processor further executes the following steps:

If the target CDN vendor is in the Pareto optimal concentration, keep the CDN connection of the terminal in the target CDN vendor.
11. The computer device according to claim 11, wherein the quality objective function is f k (x) = dt, dt is the average access delay of the candidate CDN vendor, and x is the candidate CDN vendor.
The computer device according to claim 11, wherein the cost objective function is fr (x)=pr*bw, where pr represents the bandwidth unit price of the candidate CDN manufacturer, bw represents the bandwidth selected by the user, and x represents Candidate CDN vendors.
A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps:

Obtain access quality data and usage cost data of each candidate CDN vendor;

Respectively acquiring the quality objective function value of each candidate CDN manufacturer according to the preset quality objective function and the access quality data of each candidate CDN manufacturer;

Obtain the cost objective function value of each candidate CDN manufacturer according to the preset cost objective function and the cost data of each candidate CDN manufacturer;

Obtaining the Pareto optimal set according to the preset Pareto algorithm, the quality objective function value of each candidate CDN manufacturer and the cost objective function value of each candidate CDN manufacturer;

Judge whether the selected target CDN manufacturer is in the Pareto optimal concentration;

If the target CDN vendor is not in the Pareto optimal concentration, the CDN connection of the terminal is scheduled to the candidate CDN vendors in the Pareto optimal concentration.