DNS resolution optimization method, device, equipment and storage medium for audio streaming media

Abstract

The present invention discloses a method, device, and storage medium for optimizing the DNS resolution of audio streaming media, including the following steps: receiving an audio stream request; generating a DNS resolution request according to the audio stream request, and sending the DNS resolution request to a DNS resolution engine in a DNS server cluster; obtaining the resolution performance data of the DNS resolution engine in real time, the resolution performance data including the resolution time and the resolution success rate; based on the resolution performance data, using a load balancing algorithm to dynamically assign resolution priorities to each DNS resolution engine in the DNS server cluster. By introducing a dynamic DNS resolution mechanism and an intelligent retry algorithm, the problem of audio loading delay caused by DNS resolution blocking is effectively avoided. The system monitors network performance in real time, optimizes the audio source loading speed by adaptively adjusting the DNS resolution path, and intelligently selects the best network path to ensure smooth playback of the audio stream.

Description

Technical Field

[0001] The present invention relates to the field of audio playback technology, and in particular to a DNS resolution optimization method, device, equipment and storage medium for audio streaming media. Background Art

[0002] With the popularization of streaming media technology, especially in the field of audio streaming, users have higher and higher requirements for sound quality and playback smoothness. However, network instability and excessive server load often lead to poor audio streaming playback experience, manifested in slow loading, playback interruptions and high latency. In existing technologies, many audio streaming services still rely on traditional DNS resolution and fixed path access, which is very vulnerable to changing network environments and service loads.

[0003] Traditional DNS resolution methods are usually static, that is, the preset DNS resolution path is used every time a user requests an audio resource. The problem with this method is that the DNS resolution process may become a bottleneck for audio streaming, resulting in loading delays. Especially in high-concurrency streaming media access, the delay in DNS resolution often seriously affects the audio loading speed and user experience. In addition, due to the instability of the network environment, timeouts, packet loss and other problems may occur during the DNS resolution process, resulting in the inability to load or play the audio stream smoothly.

[0004] In the existing technology, most streaming media applications rely on static DNS resolution and a single access path, lacking a dynamic adjustment mechanism. In addition, the existing solutions do not cover network performance monitoring enough, cannot intelligently respond to network anomalies and provide effective recovery measures, resulting in unstable services and poor user experience. Summary of the invention

[0005] The present invention provides a DNS resolution optimization method, device, equipment and storage medium for audio streaming media, which effectively avoids the problem of audio loading delay caused by DNS resolution blocking by introducing a dynamic DNS resolution mechanism and an intelligent retry algorithm. The system monitors network performance in real time, optimizes the audio source loading speed by adaptively adjusting the DNS resolution path, and intelligently selects the best network path to ensure smooth playback of the audio stream.

[0006] A first aspect of the present invention provides a DNS resolution optimization method for audio streaming media, which is applied to an audio streaming media server and comprises the following steps:

[0007] Receive audio stream request;

[0008] Generate a DNS resolution request according to the audio stream request, and send the DNS resolution request to a DNS resolution engine in a DNS server cluster;

[0009] Acquire the resolution performance data of the DNS resolution engine in real time, wherein the resolution performance data includes resolution time and resolution success rate;

[0010] Based on the resolution performance data, a load balancing algorithm is used to dynamically assign resolution priorities to each DNS resolution engine in the DNS server cluster.

[0011] Preferably, the method further comprises:

[0012] Real-time monitoring of network performance indicators of the audio stream, the network performance indicators including: network delay, packet loss rate and bandwidth fluctuation rate;

[0013] When any of the network performance indicators exceeds a preset threshold, a network switching mechanism is triggered.

[0014] Preferably, the network switching mechanism includes:

[0015] Switch to the preset backup DNS server;

[0016] Switch to the preset network acceleration node;

[0017] Try different network links in order of preset priority.

[0018] Preferably, the method further comprises:

[0019] Cache the audio stream data that has completed DNS resolution;

[0020] Updating the cache data according to a preset refresh cycle;

[0021] When a new audio stream request is received, a match is preferentially performed from the cache data.

[0022] Preferably, the load balancing algorithm includes:

[0023] Calculate the load weight based on the resolution performance data of each DNS resolution engine;

[0024] Allocating DNS resolution requests in a weighted round-robin manner based on the load weight;

[0025] The load weight is updated periodically.

[0026] Preferably, the method further comprises:

[0027] Collect performance data of each DNS resolution engine in real time;

[0028] inputting the performance data into a pre-trained deep learning model;

[0029] Predicting the performance trend of each DNS resolution engine based on the output results of the deep learning model;

[0030] The allocation strategy of the DNS resolution requests is adjusted based on the performance trend.

[0031] Preferably, the method further comprises:

[0032] Set a retry mechanism, the retry mechanism includes:

[0033] When DNS resolution fails, the first retry is triggered;

[0034] If the first retry fails, an exponential backoff algorithm is used to determine the time interval for the next retry;

[0035] When the number of retries reaches the preset threshold, switch to the backup DNS server.

[0036] Preferably, the method further comprises:

[0037] Record the failure information of DNS resolution process;

[0038] Classifying and counting the fault information;

[0039] Generate a fault report including fault type, occurrence time, and impact scope;

[0040] Automatically adjust the DNS resolution strategy according to the fault report.

[0041] A second aspect of the present invention provides a DNS resolution optimization device for audio streaming media, characterized in that it includes:

[0042] A request receiving module, used for receiving an audio stream request;

[0043] A DNS resolution module, configured to generate a DNS resolution request according to the audio stream request, and send the DNS resolution request to a DNS resolution engine in a DNS server cluster;

[0044] A performance monitoring module, used to obtain the resolution performance data of the DNS resolution engine in real time, wherein the resolution performance data includes resolution time and resolution success rate;

[0045] The load balancing module is used to dynamically allocate resolution priorities to each DNS resolution engine in the DNS server cluster based on the resolution performance data using a load balancing algorithm.

[0046] A third aspect of the present invention provides a DNS resolution optimization device for audio streaming media, including a processor and a memory, wherein the memory stores a computer program, and when the processor executes the program, the steps of the DNS resolution optimization method for audio streaming media described in any one of the above items are implemented.

[0047] A fourth aspect of the invention provides a computer-readable storage medium having instructions stored thereon, and when the instructions are executed by a processor, the DNS resolution optimization method for audio streaming media described in any one of the above items is implemented.

[0048] In the technical solution provided by the present invention, the changes between domain names and IP addresses can be quickly responded to through dynamic DNS technology, ensuring that user requests always point to the optimal network node. This rapid response capability reduces the phenomenon of audio stream playback jamming or interruption caused by DNS resolution delays. Real-time monitoring of network performance indicators (such as bandwidth utilization, latency, packet loss rate, etc.) enables the system to automatically adjust the DNS query priority according to the current network conditions and select the best resolution path. This helps to avoid network bottlenecks and improve the stability and fluency of audio stream transmission. By caching DNS resolution results and periodically refreshing according to network changes, the need to re-resolve DNS for each request is reduced, thereby reducing resolution time and improving user access speed. The core advantage of this technical solution lies in its efficient dynamic DNS adjustment and network path optimization capabilities, which can automatically identify and switch to the optimal path to ensure stable transmission of audio streams. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a flow chart of a DNS resolution optimization method for audio streaming media of the present invention;

[0050] Figure 2 A schematic diagram of a process of adjusting a DNS resolution request using a deep learning model according to an embodiment;

[0051] Figure 3 A schematic diagram of a process for automatically adjusting a DNS resolution strategy according to an embodiment;

[0052] Figure 4 A schematic diagram of an embodiment of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] The present invention provides a DNS resolution optimization method, system and device for audio streaming media, which effectively avoids the problem of audio loading delay caused by DNS resolution blocking by introducing a dynamic DNS resolution mechanism and an intelligent retry algorithm. The system monitors network performance in real time, optimizes the audio source loading speed by adaptively adjusting the DNS resolution path, and intelligently selects the best network path to ensure smooth playback of the audio stream.

[0054] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0055] Embodiment 1

[0056] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , provides a DNS resolution optimization method for audio streaming media, which is applied to an audio streaming media server and includes the following steps:

[0057] S100: receiving an audio stream request;

[0058] S200: Generate a DNS resolution request according to the audio stream request, and send the DNS resolution request to a DNS resolution engine in a DNS server cluster;

[0059] S300: Acquire the resolution performance data of the DNS resolution engine in real time, wherein the resolution performance data includes resolution time and resolution success rate;

[0060] S400: Based on the resolution performance data, a load balancing algorithm is used to dynamically assign resolution priorities to each DNS resolution engine in the DNS server cluster.

[0061] The technical solution of this embodiment is intended to solve the problems of common DNS resolution delay, slow loading, high first request timeout rate and insufficient network performance in audio streaming media access in the prior art. Through dynamic DNS technology, it is possible to quickly respond to changes between domain names and IP addresses to ensure that user requests always point to the optimal network node. This fast response capability reduces the phenomenon of audio streaming playback jamming or interruption caused by DNS resolution delay. Real-time monitoring of network performance indicators (such as bandwidth utilization, delay, packet loss rate, etc.) enables the system to automatically adjust the DNS query priority according to the current network conditions and select the best resolution path. This helps to avoid network bottlenecks and improve the stability and fluency of audio streaming transmission. By caching DNS resolution results and refreshing periodically according to network changes, the need to re-resolve DNS for each request is reduced, thereby reducing resolution time and improving user access speed. The core advantage of this technical solution lies in its efficient dynamic DNS adjustment and network path optimization capabilities, which can automatically identify and switch to the optimal path to ensure stable transmission of audio streams.

[0062] Preferably, the method further comprises:

[0063] Real-time monitoring of network performance indicators of the audio stream, the network performance indicators including: network delay, packet loss rate and bandwidth fluctuation rate;

[0064] When any of the network performance indicators exceeds a preset threshold, a network switching mechanism is triggered.

[0065] In this embodiment, by real-time monitoring of key performance indicators such as network latency, packet loss rate, and bandwidth fluctuation, the system can quickly identify network problems and take corresponding optimization measures. This helps to reduce the phenomenon of jamming and interruption in audio streaming, thereby significantly improving the user's auditory experience. In addition, real-time monitoring data provides the system with rich network performance information, which can be used to support smarter decision making. For example, the system can predict future network trends based on historical monitoring data, and make capacity planning and resource reservations in advance.

[0066] Specifically, when the network is abnormal based on the network delay, packet loss rate and bandwidth fluctuation of the audio stream, the system switches to the backup network path or accelerates the node to restore network communication. Furthermore, when the network is abnormal based on the network delay, packet loss rate and bandwidth fluctuation of the audio stream, the network performance data is fed back to the DNS resolution engine in real time to optimize the path selection of the next DNS resolution process; after multiple iterations, the system can more accurately select the best network path.

[0067] Based on real-time monitoring data, the system can intelligently adjust the allocation of network resources. For example, when the network is congested, the system can reduce the bandwidth usage of certain non-critical services to ensure the transmission quality of the audio stream. This dynamic resource management strategy helps to use network resources more efficiently. Real-time monitoring enables the system to detect and handle potential network problems in a timely manner, thereby preventing small problems from turning into major failures. This preventive maintenance strategy helps to improve the overall stability and reliability of the system and ensure that the audio streaming service can run continuously and stably. In addition, more importantly, once an anomaly is detected, the system will immediately feed back the relevant network performance data to the DNS resolution engine. This data includes key information such as network latency, packet loss rate, and bandwidth fluctuations, helping the DNS resolution engine to more accurately evaluate the performance of each network path.

[0068] Preferably, the network switching mechanism includes:

[0069] Switch to the preset backup DNS server;

[0070] Switch to the preset network acceleration node;

[0071] Try different network links in order of preset priority.

[0072] Based on real-time network performance indicator data, the DNS resolution engine can more intelligently select the best network path for resolution, thereby improving the efficiency and accuracy of DNS resolution. By dynamically adjusting the DNS resolution path, the system can avoid service interruptions or performance degradation caused by network anomalies. Through multiple iterations and real-time feedback, the system is able to continuously learn and optimize the DNS resolution process. Over time, the system will accumulate more network performance data, allowing it to more accurately predict and select the best network path.

[0073] Preferably, the method further comprises:

[0074] Cache the audio stream data that has completed DNS resolution;

[0075] Updating the cache data according to a preset refresh cycle;

[0076] When a new audio stream request is received, a match is preferentially performed from the cache data.

[0077] DNS cache is performed on the resolved audio stream to avoid re-resolving DNS for each request. The cache is refreshed periodically according to network changes to ensure the real-time nature of the cached information. Prioritizing matching from the cached data can avoid a complete DNS resolution process for each request, thereby significantly reducing the response time of audio streaming requests. This is particularly important for audio streaming services with high real-time requirements, and can significantly improve the user experience.

[0078] Preferably, the load balancing algorithm includes:

[0079] Calculate the load weight based on the resolution performance data of each DNS resolution engine;

[0080] Allocating DNS resolution requests in a weighted round-robin manner based on the load weight;

[0081] The load weight is updated periodically.

[0082] By adopting a strategy based on weighted polling, the system can dynamically adjust the query priority according to the performance and load of each DNS server. This strategy helps to distribute DNS requests more evenly to nodes with the best network status, thereby reducing delays in the resolution process and improving overall DNS resolution efficiency. In addition, reducing delays in the resolution process means that users can obtain the required audio streaming content faster, which significantly improves the user experience. In high-concurrency scenarios, this optimization is particularly important because it ensures that the waiting time for each user is kept to a minimum even when a large number of users request at the same time.

[0083] Furthermore, the load balancing algorithm includes:

[0084] Calculate the load weight W based on the resolution performance data of each DNS resolution engine:

[0085] W=α*(1 / T)+β*(S / 100)+γ*(C / Cmax)

[0086] Wherein: T is the average resolution time of the DNS resolution engine, in milliseconds; S is the resolution success rate of the DNS resolution engine, ranging from 0 to 100; C is the current concurrency of the DNS resolution engine; Cmax is the maximum concurrent load of the DNS resolution engine; α, β, and γ are the time weight factor, success rate weight factor, and load weight factor, respectively, and α+β+γ=1.

[0087] Allocating DNS resolution requests in a weighted round-robin manner based on the load weight;

[0088] The load weight is updated at every preset time period.

[0089] For example:

[0090] Assume there are three DNS resolution engines, and their performance data are as follows:

[0091] DNS1: T=50ms, S=95%, C=1000, Cmax=5000

[0092] DNS2: T=80ms, S=98%, C=800, Cmax=5000

[0093] DNS3: T=100ms, S=99%, C=500, Cmax=5000

[0094] Set weight factors: α = 0.4, β = 0.4, γ = 0.2

[0095] The weight of each DNS resolution engine is calculated as:

[0096] DNS1:W1=0.4*(1 / 50)+0.4*(95 / 100)+0.2*(1-1000 / 5000)=0.008+0.38+0.16=0.548

[0097] DNS2:W2=0.4*(1 / 80)+0.4*(98 / 100)+0.2*(1-800 / 5000)=0.005+0.392+0.168=0.565

[0098] DNS3:W3=0.4*(1 / 100)+0.4*(99 / 100)+0.2*(1-500 / 5000)=0.004+0.396+0.18=0.580

[0099] After weight normalization, the final distribution ratio is obtained:

[0100] DNS1: 32.4%

[0101] DNS2: 33.4%

[0102] DNS3: 34.2%

[0103] The advantages of this algorithm are:

[0104] 1. Comprehensively consider the three key indicators of response time, success rate and load conditions.

[0105] 2. Flexible strategy configuration is achieved through adjustable weight factors.

[0106] 3. The calculation method is simple and efficient, suitable for real-time calculation.

[0107] 4. The weight results can intuitively reflect the comprehensive performance of the DNS server.

[0108] Preferably, the method further comprises:

[0109] S201: Collecting performance data of each DNS resolution engine in real time;

[0110] S202: Inputting the performance data into a pre-trained deep learning model;

[0111] S203: Predicting the performance trend of each DNS resolution engine according to the output result of the deep learning model;

[0112] S204: Adjusting the allocation strategy of the DNS resolution request based on the performance trend.

[0113] The deep learning model can learn and adapt to different network environments and DNS resolution request patterns, so that it can make the best resolution engine selection in various situations. This adaptive capability improves the overall stability and reliability of the system and reduces service interruptions caused by network fluctuations or resolution failures. At the same time, the deep learning model can predict the performance trends of each DNS resolution engine, allowing the system to make adjustments in advance to avoid service interruptions caused by single engine failures, thereby enhancing the robustness and reliability of the system. Dynamic adjustment strategies based on performance trends can more effectively allocate DNS resolution requests, ensuring that high-performance engines are fully utilized while reducing the load on inefficient or faulty engines. This optimized resource utilization method can improve the efficiency and cost-effectiveness of the overall system.

[0114] Preferably, the method further comprises:

[0115] Set a retry mechanism, the retry mechanism includes:

[0116] When DNS resolution fails, the first retry is triggered;

[0117] If the first retry fails, an exponential backoff algorithm is used to determine the time interval for the next retry;

[0118] When the number of retries reaches the preset threshold, switch to the backup DNS server.

[0119] When the audio stream fails to load, the system automatically triggers the retry mechanism and selects different DNS servers or network paths for attempts based on the intelligent retry algorithm. This mechanism increases the chances of successful audio stream loading, especially when the network environment is unstable or the DNS server response is unstable. Through the intelligent retry algorithm, the system is able to try as many different loading paths as possible without increasing the user's waiting time until a viable solution is found. This helps reduce the trouble users encounter due to loading failures and improves the overall user experience.

[0120] When retrying, a gradual acceleration strategy is used. Each retry will select a more optimized parsing path or acceleration node and gradually reduce the retry interval to resume audio playback in the shortest possible time.

[0121] When a playback interruption caused by a network anomaly is detected, the system can intelligently identify and resume audio stream playback. The recovery strategies include: switching to an alternative DNS server; switching to a different acceleration node; and retrying to load audio data.

[0122] By using a gradual acceleration strategy, the system can select a more optimized resolution path or acceleration node at each retry and gradually reduce the retry interval. This strategy helps to resume audio playback in the shortest time, reduce user waiting time, and improve user experience. When a playback interruption caused by a network anomaly is detected, the system can intelligently identify and adopt multiple recovery strategies, including switching to an alternate DNS server, switching to a different acceleration node, and retrying to load audio data. This multi-strategy recovery mechanism enhances the system's fault tolerance and ensures that audio streaming playback can be restored as soon as possible under various network anomalies. By dynamically adjusting the retry path and acceleration node, the system can better adapt to changes in the network environment and reduce service interruptions caused by network fluctuations or failures. This adaptive capability improves the overall stability and reliability of the system. At the same time, the gradual acceleration strategy and intelligent recovery strategy help to use network resources more efficiently. By selecting a more optimized resolution path and acceleration node, the system can reduce unnecessary network congestion and delay and improve overall network efficiency.

[0123] Preferably, the method further comprises:

[0124] S301: Recording the fault information of the DNS resolution process;

[0125] S302: Classify and count the fault information;

[0126] S303: Generate a fault report including fault type, occurrence time, and impact scope;

[0127] S304: Automatically adjust the DNS resolution strategy according to the fault report.

[0128] DNS resolution latency and error rate data are collected regularly, and the DNS resolution path is optimized based on the DNS resolution latency and error rate data; during the resolution process, if a DNS server fails to resolve or responds too slowly, the system will intelligently switch to the backup DNS path to avoid affecting the loading of the audio stream due to a single node problem, where the backup DNS path uses the current optimal DNS resolution engine output by the deep learning model. By regularly collecting DNS resolution latency and error rate data, the system can more accurately evaluate the performance of each DNS resolution engine. Based on these data, the system can optimize the DNS resolution path and select a more efficient and reliable resolution engine, thereby improving the overall resolution efficiency and accuracy. During the resolution process, if a DNS server fails to resolve or responds too slowly, the system will intelligently switch to the backup DNS path to ensure that even if a node has a problem, it will not affect the loading of the audio stream, thereby enhancing the system's fault tolerance. By reducing the latency of DNS resolution and avoiding resolution failures, users can access audio streaming content faster and more stably. This seamless playback experience significantly improves the overall user satisfaction and loyalty.

[0129] Preferably, the system performance is evaluated regularly and optimized based on the evaluation results. The optimization directions include improving DNS resolution speed, reducing audio source loading time, and reducing network abnormality recovery time. Strict performance testing is carried out after each optimization, and the optimization effect is verified through A / B testing to continuously improve the loading speed and playback stability of the audio stream.

[0130] Through regular performance evaluation, the system can identify bottlenecks that affect DNS resolution speed and perform targeted optimization. This may include improving resolution algorithms, adjusting cache strategies, or upgrading hardware facilities. These measures help reduce the latency of DNS resolution and thus increase the loading speed of audio streams.

[0131] Second embodiment

[0132] Based on the same inventive concept, the second aspect of the present invention provides a DNS resolution optimization device for audio streaming media, comprising:

[0133] A request receiving module, used for receiving an audio stream request;

[0134] A DNS resolution module, configured to generate a DNS resolution request according to the audio stream request, and send the DNS resolution request to a DNS resolution engine in a DNS server cluster;

[0135] A performance monitoring module, used to obtain the resolution performance data of the DNS resolution engine in real time, wherein the resolution performance data includes resolution time and resolution success rate;

[0136] The load balancing module is used to dynamically allocate resolution priorities to each DNS resolution engine in the DNS server cluster based on the resolution performance data using a load balancing algorithm.

[0137] The technical solution of this embodiment is intended to solve the problems of common DNS resolution delay, slow loading, high first request timeout rate and insufficient network performance in audio streaming media access in the prior art. Through dynamic DNS technology, it is possible to quickly respond to changes between domain names and IP addresses to ensure that user requests always point to the optimal network node. This fast response capability reduces the phenomenon of audio streaming playback jamming or interruption caused by DNS resolution delay. Real-time monitoring of network performance indicators (such as bandwidth utilization, delay, packet loss rate, etc.) enables the system to automatically adjust the DNS query priority according to the current network conditions and select the best resolution path. This helps to avoid network bottlenecks and improve the stability and fluency of audio streaming transmission. By caching DNS resolution results and refreshing periodically according to network changes, the need to re-resolve DNS for each request is reduced, thereby reducing resolution time and improving user access speed. The core advantage of this technical solution lies in its efficient dynamic DNS adjustment and network path optimization capabilities, which can automatically identify and switch to the optimal path to ensure stable transmission of audio streams.

[0138] Based on the same inventive concept, the third aspect of the present invention provides a DNS resolution optimization device for audio streaming media, including a processor and a memory, wherein the memory stores a computer program, and when the processor executes the program, the steps of the DNS resolution optimization method for audio streaming media described in any one of the above items are implemented.

[0139] The following describes in detail the DNS resolution optimization computer device for audio streaming media in this embodiment from the perspective of hardware processing.

[0140] See also Figure 4 As shown, the computer device includes a processor 400 and a memory 401, wherein the memory 401 stores machine executable instructions that can be executed by the processor 400, and the processor 400 executes the machine executable instructions to implement the above-mentioned DNS resolution optimization method for audio streaming media.

[0141] further, Figure 4 The computer device shown further includes a bus 402 and a communication interface 403 , and the processor 400 , the communication interface 403 and the memory 401 are connected via the bus 402 .

[0142] The memory 401 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), for example, at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 403 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 402 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0143] The processor 400 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 400. The above processor 400 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 401 , and the processor 400 reads the information in the memory 401 and completes the method steps of the above-mentioned embodiment in combination with its hardware.

[0144] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the DNS resolution optimization method for audio streaming media.

[0145] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0146] 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 computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0147] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A DNS resolution optimization method for audio streaming media, applied to an audio streaming media server, characterized in that: The steps include: Receive audio stream request; Generate a DNS resolution request according to the audio stream request, and send the DNS resolution request to a DNS resolution engine in a DNS server cluster; Acquire the resolution performance data of the DNS resolution engine in real time, wherein the resolution performance data includes resolution time and resolution success rate; Based on the resolution performance data, a load balancing algorithm is used to dynamically assign resolution priorities to each DNS resolution engine in the DNS server cluster.

2. The method according to claim 1, characterized in that: The method further comprises: Real-time monitoring of network performance indicators of the audio stream, the network performance indicators including: network delay, packet loss rate and bandwidth fluctuation rate; When any of the network performance indicators exceeds a preset threshold, a network switching mechanism is triggered.

3. The method according to claim 2, characterized in that: The network switching mechanism includes: Switch to the preset backup DNS server; Switch to the preset network acceleration node; Try different network links in order of preset priority.

4. The method according to claim 3, characterized in that: The method further comprises: Cache the audio stream data that has completed DNS resolution; Updating the cache data according to a preset refresh cycle; When a new audio stream request is received, a match is preferentially performed from the cache data.

5. The method according to claim 1, characterized in that: The load balancing algorithm includes: Calculate the load weight based on the resolution performance data of each DNS resolution engine; Allocating DNS resolution requests in a weighted round-robin manner based on the load weight; The load weight is updated periodically.

6. The method according to claim 5, characterized in that: The method further comprises: Collect performance data of each DNS resolution engine in real time; inputting the performance data into a pre-trained deep learning model; Predicting the performance trend of each DNS resolution engine based on the output results of the deep learning model; The allocation strategy of the DNS resolution requests is adjusted based on the performance trend.

7. The method according to claim 1, characterized in that: The method further comprises: Set a retry mechanism, the retry mechanism includes: When DNS resolution fails, the first retry is triggered; If the first retry fails, an exponential backoff algorithm is used to determine the time interval for the next retry; When the number of retries reaches the preset threshold, switch to the backup DNS server.

8. The method according to claim 1, characterized in that: The method further comprises: Record the failure information of DNS resolution process; Classifying and counting the fault information; Generate a fault report including fault type, occurrence time, and impact scope; Automatically adjust the DNS resolution strategy according to the fault report.

9. A DNS resolution optimization device for audio streaming media, characterized in that: include: A request receiving module, used for receiving an audio stream request; A DNS resolution module, configured to generate a DNS resolution request according to the audio stream request, and send the DNS resolution request to a DNS resolution engine in a DNS server cluster; A performance monitoring module, used to obtain the resolution performance data of the DNS resolution engine in real time, wherein the resolution performance data includes resolution time and resolution success rate; The load balancing module is used to dynamically allocate resolution priorities to each DNS resolution engine in the DNS server cluster based on the resolution performance data using a load balancing algorithm.

10. A DNS resolution optimization device for audio streaming media, comprising a processor and a memory, wherein a computer program is stored in the memory, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the DNS resolution optimization method for audio streaming media as described in any one of claims 1-8 is implemented.

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