CN118689790A - A method for caching data in a network storage server - Google Patents

Abstract

The present invention discloses a method for caching data of a network storage server, which belongs to the field of computer network technology and includes the following steps: data classification, cache strategy formulation, cache update and cache capacity management. The cache strategy is formulated as follows: for hot data, a least recently used (LRU) strategy is used for caching; for warm data, a random replacement strategy is used for caching; for cold data, a first-in-first-out (FIFO) strategy is used for caching. The cache update is to move the hot data to the top of the cache pool when it is accessed. By means of data classification, cache strategy formulation, cache update and cache capacity management, the hit rate of cache data of the network storage server can be effectively improved, the update strategy of cache data can be optimized, and the capacity of cache data can be reasonably managed. These measures can not only improve the response speed and performance of the system, but also reduce the access pressure to the back-end storage, and improve the efficiency and stability of the overall system.

Description

A method for caching data in a network storage server

Technical Field

The present invention relates to the technical field of computer networks, and more specifically to a method for caching data in a network storage server.

Background Art

With the continuous development and popularization of the Internet, the amount of data on network storage servers is also increasing. Behind this trend is the growing demand for digital information and the popularity of various online services and applications. First, with the rise of platforms such as social media, e-commerce, and online entertainment, the amount of data generated by people on the Internet has exploded. Both individual users and corporate users are increasingly relying on the Internet to store and manage their data. Photos, videos, and chat records on social media platforms, transaction records and user reviews on e-commerce websites, and game progress and achievements on online entertainment platforms, all require a lot of storage space to support. Secondly, the rise of cloud computing technology has also provided strong support for the increase in the amount of data on network storage servers. Cloud computing concentrates computing resources and storage resources in the cloud, allowing users to access and manage their data anytime, anywhere. This flexibility and convenience has attracted more and more users and enterprises to adopt cloud computing technology, which has further promoted the increase in the amount of data on network storage servers. In addition, the advent of the big data era has also provided new opportunities for the increase in the amount of data on network storage servers. The application of big data technology allows people to analyze and mine massive amounts of data to obtain valuable information and insights. In order to support big data processing, a large amount of storage space is required to store and process these huge data sets.

However, how to effectively manage and cache this data has become an important issue. Existing network storage servers have some problems in caching data, which mainly include the following aspects: First, the low hit rate of cached data is a major problem. Due to the low hit rate of cached data, a large amount of data transmission and processing becomes necessary. This means that the server needs to frequently read data from the disk or other storage devices and load it into the cache. This not only increases the load on the server, but also causes delays and congestion in network transmission. Therefore, improving the hit rate of cached data is an important optimization direction. Secondly, the unreasonable update strategy of cached data is also an existing problem. When the data changes, the server needs to update the data in the cache in time to ensure data consistency. However, the current update strategy may have some problems, such as too frequent updates or inflexible update strategies. This may lead to inconsistency and errors in the data, causing inconvenience and distress to users. Therefore, designing a reasonable cache data update strategy is the key to solving this problem. Third, unreasonable capacity management of cached data is also a problem. Capacity management of cached data involves how to reasonably allocate and manage cache space to improve cache efficiency and performance. However, current capacity management methods may have some problems, such as waste of cache space or insufficient cache space. This may lead to the loss of cache data or the degradation of server performance. Therefore, optimizing the capacity management of cache data is one of the important measures to improve server performance.

Therefore, a method for caching data in a network storage server is proposed.

Summary of the invention

In view of the problems existing in the prior art, the object of the present invention is to provide a method for caching data in a network storage server.

To solve the above problems, the present invention adopts the following technical solutions.

A method for caching data in a network storage server comprises the following steps: data classification, cache strategy formulation, cache update and cache capacity management, wherein the data classification is to classify the data into three categories: hot data, warm data and cold data according to the importance and access frequency of the data, the cache strategy is to cache the hot data using a least recently used (LRU) strategy; cache the warm data using a random replacement strategy; and cache the cold data using a first-in-first-out (FIFO) strategy, the cache update is to move the hot data to the top of a cache pool when the hot data is accessed; and decide whether to move the warm data or the cold data to the top of the cache pool according to the access frequency and importance of the warm data or the cold data when the warm data or the cold data is accessed, and the cache capacity management is to dynamically adjust the size of the cache pool according to the actual cache demand and the resource situation of the server.

Preferably, the hot data is characterized by high access frequency and great impact on performance. The hot data processing method is to place the hot data in a high-speed cache to increase the access speed. The cold data is characterized by low access frequency and small impact on performance. The cold data processing method is to store the cold data in a low-speed cache or remote storage to reduce the access cost.

Preferably, the LRU (Least Recently Used) algorithm is to give priority to eliminating the least recently used data when the cache is full. The LRU (Least Recently Used) algorithm is simple to implement and is suitable for most scenarios. The LFU (Least Frequently Used) algorithm is to give priority to eliminating the least frequently used data when the cache is full. The LFU (Least Frequently Used) algorithm can better protect hot spot data and avoid long-term non-updates.

Preferably, the cache update is a scheduled update and an on-demand update. The scheduled update is to regularly update the data in the cache according to a set time interval. The scheduled update is simple and easy to implement, and is suitable for scenarios with low real-time requirements. The on-demand update is to immediately update the corresponding cache data when the data changes. The on-demand update can ensure the real-time nature of the data and reduce the impact of delays.

Preferably, the cache capacity management includes a fixed capacity strategy and an expandable capacity strategy. The fixed capacity strategy is to pre-set the total capacity of the cache and reject new requests when the cache is full. The fixed capacity strategy is simple to implement and easy to monitor and manage. The expandable capacity strategy is to dynamically adjust the cache capacity to cope with changing data access requirements. The expandable capacity strategy can better protect hot data and improve system performance.

Furthermore, the present invention also provides a method applicable to the above, comprising the following steps:

S1. Data classification: Data is classified into three categories: hot data, warm data, and cold data according to its importance and access frequency.

S2. Cache strategy formulation: For hot data, the least recently used (LRU) strategy is used for caching; for warm data, the random replacement strategy is used for caching; for cold data, the first-in-first-out (FIFO) strategy is used for caching;

S3, cache update: When hot data is accessed, it is moved to the top of the cache pool; when warm data or cold data is accessed, it is decided whether to move it to the top of the cache pool based on its access frequency and importance;

S4. Cache capacity management: Dynamically adjust the size of the cache pool based on actual cache requirements and server resource conditions.

Compared with the prior art, the present invention has the following beneficial effects:

(1) This solution can effectively improve the hit rate of cache data in the network storage server, optimize the cache data update strategy, and reasonably manage the cache data capacity through means such as data classification, cache strategy formulation, cache update, and cache capacity management. These measures can not only improve the system's response speed and performance, but also reduce the access pressure on the back-end storage and improve the efficiency and stability of the overall system.

(2) This solution provides a method for caching data in a network storage server, which improves the data processing efficiency of the network storage server by optimizing the storage and retrieval process of cached data. This method adopts an efficient cache strategy, a strategy for dynamically adjusting the cache size, and multi-threading technology, so that the network storage server can read and write data more quickly, thereby improving the response speed and throughput of the system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the overall process of the present invention;

FIG2 is a schematic diagram of a data classification process of the present invention;

FIG3 is a schematic diagram of a cache strategy formulation process of the present invention;

FIG4 is a schematic diagram of a buffer update process of the present invention;

FIG. 5 is a schematic diagram of a buffer capacity management process according to the present invention.

DETAILED DESCRIPTION

The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work are within the scope of protection of the present invention.

Embodiment 1:

Referring to FIG. 1 to FIG. 5 , a method for caching data in a network storage server includes the following steps: data classification, cache strategy formulation, cache update, and cache capacity management. The data classification is classified into three categories: hot data, warm data, and cold data according to the importance and access frequency of the data. Hot data refers to data that is frequently accessed and processed. These data are usually core data in a business system and are essential for the normal operation of the system and decision-making. Hot data has a very high access frequency and needs to be processed and responded to in real time or near real time. For example, transaction records of e-commerce websites and user behavior data of social media are all hot data. Secondly, warm data refers to data that has a relatively low access frequency but still has a certain importance. These data are usually used for statistical analysis, report generation, and other purposes. Compared with hot data, warm data has a lower access frequency, but still needs to be backed up and restored regularly to ensure the integrity and availability of the data. For example, the financial data of an enterprise, market research reports, etc. are all warm data. Finally, cold data refers to data that has a very low access frequency and is of low importance. These data are usually stored on low-cost storage media and are only accessed and used under specific circumstances. Cold data is accessed very infrequently and may only be backed up and restored once within a certain time interval. For example, historical transaction records, old versions of documents, etc. are all cold data. By classifying data, storage resources can be better managed and optimized. Hot data usually requires high-performance storage devices and fast access speeds to ensure system performance and response speed. Cold data can use low-cost storage media to reduce storage costs. In addition, adopting different backup strategies for different categories of data can also help improve backup efficiency and reduce backup costs;

The cache strategy is formulated as follows: for hot data, the least recently used (LRU) strategy is used for caching; for warm data, the random replacement strategy is used for caching; for cold data, the first-in-first-out (FIFO) strategy is used for caching. For hot data, we use the least recently used (LRU) strategy for caching. This means that when the cache space is limited, we will give priority to retaining the most recently accessed data and eliminate the data that has not been accessed for the longest time from the cache. This strategy ensures that the data that users frequently access is always in the cache, thereby improving the data reading speed and response time. For warm data, we use a random replacement strategy for caching. Under this strategy, when the cache space is insufficient, we will randomly select some data to eliminate instead of replacing them in a specific order. This ensures that the data in the cache is more evenly distributed and prevents some data from occupying the cache space for a long time and affecting the access of other data. For cold data, we use a first-in-first-out (FIFO) strategy for caching. This means that the data that enters the cache earliest will be eliminated from the cache first. This strategy is suitable for data that is less frequently accessed but still needs to be retained, because it ensures that the data will not survive in the cache for too long, thereby making room for new data;

The cache is updated to move hot data to the top of the cache pool when it is accessed; when warm data or cold data is accessed, it is decided whether to move it to the top of the cache pool based on its access frequency and importance. When hot data is accessed, the system will immediately move it to the top of the cache pool so that it can be obtained faster the next time it is accessed. For warm data or cold data, the system will decide whether to move it to the top of the cache pool based on its access frequency and importance. If a warm data or cold data is frequently accessed and its access importance is high, the system will move it to the top of the cache pool to increase its access speed. On the contrary, if a warm data or cold data is less frequently accessed or its access importance is not high, the system may choose to keep its position in the cache pool unchanged. Through this dynamic adjustment method, the system can better utilize cache resources and improve data access efficiency. At the same time, since hot data is always at the top of the cache pool, users can get faster response time when accessing this data, improving user experience;

The cache capacity management dynamically adjusts the size of the cache pool according to the actual cache demand and server resource situation. The cache capacity management system monitors the user's cache demand in real time. By analyzing indicators such as user behavior patterns, access frequency, and data usage, the system can accurately predict the cache capacity required at the current moment. In this way, the system can prepare in advance to ensure that there is enough space in the cache pool to store the data required by the user. Secondly, the cache capacity management system also considers the server's resource situation. It monitors key indicators such as the server's memory, disk space, and processor load to evaluate the server's available resources. If the server's resources are tight, the system will automatically reduce the size of the cache pool to avoid placing too much burden on the server. On the contrary, if the server's resources are sufficient, the system will increase the size of the cache pool to improve the data reading speed and response time. In addition, the cache capacity management system also has an intelligent scheduling function. It will dynamically adjust the priority of different data in the cache pool according to the actual situation. For frequently accessed data, the system will place it at the front end of the cache pool so that users can get the required data faster. For infrequently accessed data, the system will place it at the back end of the cache pool to save valuable cache space.

Please refer to FIG2 . The hot data is characterized by high access frequency and great impact on performance. The hot data processing method is to place the hot data in the cache to improve the access speed. The cold data is characterized by low access frequency and small impact on performance. The cold data processing method is to store the cold data in the low-speed cache or remote storage to reduce the access cost. The method of placing the hot data in the cache. By caching the frequently accessed data in the high-speed memory, the data reading time can be greatly reduced, thereby improving the overall performance. In contrast to the hot data, the cold data is characterized by low access frequency and small impact on performance. Since the access demand for cold data is less, storing it in the low-speed cache or remote storage is a more economical and efficient way. By migrating the cold data to the low-speed cache or remote storage, the occupancy of the high-speed memory can be reduced and the access cost can be reduced. At the same time, this also helps to improve the overall performance of the system, because the low-speed memory and remote storage usually have lower costs and higher scalability.

Please refer to Figure 3. The LRU (Least Recently Used) algorithm is to prioritize the least recently used data when the cache is full. The LRU (Least Recently Used) algorithm is simple to implement and is suitable for most scenarios. The LFU (Least Frequently Used) algorithm is to prioritize the least frequently used data when the cache is full. The LFU (Least Frequently Used) algorithm can better protect hot data and avoid long-term non-updates.

Please refer to Figure 4. The cache update is scheduled update and on-demand update. The scheduled update is to regularly update the data in the cache according to the set time interval. The scheduled update is simple and easy to implement, and is suitable for scenarios with low real-time requirements. The on-demand update is to immediately update the corresponding cache data when the data changes. The on-demand update can ensure the real-time nature of the data and reduce the impact of delay. The cache update mechanism includes two modes: scheduled update and on-demand update. Scheduled update is to regularly update the data in the cache according to the set time interval. This method is simple and easy to implement and is suitable for scenarios with low real-time requirements. By setting a suitable time interval, it can be ensured that the cached data is updated within a certain period of time, thereby maintaining the freshness of the data. On-demand update is to immediately update the corresponding cached data when the data changes. This method can ensure the real-time nature of the data and reduce the impact of delay. When the data changes, the system will immediately perceive and update the corresponding cached data to ensure that the user obtains the latest data results. This method is suitable for scenarios with high real-time requirements, such as financial transactions, real-time monitoring, etc.

Please refer to Figure 5. The cache capacity management includes a fixed capacity strategy and an expandable capacity strategy. The fixed capacity strategy is to pre-set the total capacity of the cache. When the cache is full, new requests are rejected. The fixed capacity strategy is simple to implement and easy to monitor and manage. The expandable capacity strategy is to dynamically adjust the cache capacity to cope with the ever-changing data access needs. The expandable capacity strategy can better protect hot data and improve system performance. The cache capacity management strategy includes a fixed capacity strategy and an expandable capacity strategy. The fixed capacity strategy refers to pre-setting the total capacity of the cache. When the cache is full, new requests are rejected. This strategy is simple to implement and easy to monitor and manage. The expandable capacity strategy is to dynamically adjust the cache capacity to cope with the ever-changing data access needs. This strategy can better protect hot data and improve system performance. It can automatically increase or decrease the cache capacity according to actual needs to adapt to different data access situations.

The present invention also provides a method applicable to the above, comprising the following steps:

S1. Data classification: Data is classified into three categories: hot data, warm data, and cold data according to its importance and access frequency.

S2. Cache strategy formulation: For hot data, the least recently used (LRU) strategy is used for caching; for warm data, the random replacement strategy is used for caching; for cold data, the first-in-first-out (FIFO) strategy is used for caching;

S3, cache update: When hot data is accessed, it is moved to the top of the cache pool; when warm data or cold data is accessed, it is decided whether to move it to the top of the cache pool based on its access frequency and importance;

S4. Cache capacity management: Dynamically adjust the size of the cache pool based on actual cache requirements and server resource conditions.

How to use: First, divide the data into different cache levels according to its importance and access frequency. Specifically, evaluate the importance of the data and divide the data with high importance into high-priority cache; analyze the access frequency of the data and divide the data with high access frequency into high-frequency cache. Then, according to the different cache levels, adopt different cache strategies to store and retrieve data. Specifically, for high-priority cache data, adopt pre-reading and pre-loading strategies for storage and retrieval; for high-frequency cache data, adopt fast search and fast reading strategies for storage and retrieval. Finally, ensure the accuracy and timeliness of cache data through regular data cleaning and updating. In this way, not only can the data processing efficiency of the network storage server be improved, but also the real-time and accuracy of the data can be guaranteed.

The above is only a preferred specific implementation of the present invention; however, the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and its improved conception within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims (6)

1. A method for caching data in a network storage server, characterized in that it includes the following steps: data classification, cache strategy formulation, cache update and cache capacity management, wherein the data classification is to divide the data into three categories: hot data, warm data and cold data according to the importance and access frequency of the data, the cache strategy is to cache the hot data using the least recently used (LRU) strategy; the warm data is to cache using the random replacement strategy; the cold data is to cache using the first-in-first-out (FIFO) strategy, the cache update is to move the hot data to the top of the cache pool when the hot data is accessed; when the warm data or the cold data is accessed, it is decided whether to move it to the top of the cache pool according to its access frequency and importance, and the cache capacity management is to dynamically adjust the size of the cache pool according to the actual cache demand and the resource situation of the server. 2. According to claim 1, a method for caching data in a network storage server is characterized in that: the hot data is characterized by high access frequency and great impact on performance, and the hot data processing method is to place the hot data in a high-speed cache to increase the access speed; the cold data is characterized by low access frequency and small impact on performance, and the cold data processing method is to store the cold data in a low-speed cache or remote storage to reduce the access cost. 3. According to a method for caching data in a network storage server as described in claim 1, it is characterized in that: the LRU (Least Recently Used) algorithm is to give priority to eliminating the least recently used data when the cache is full. The LRU (Least Recently Used) algorithm is simple to implement and is applicable to most scenarios. The LFU (Least Frequently Used) algorithm is to give priority to eliminating the data with the least number of uses when the cache is full. The LFU (Least Frequently Used) algorithm can better protect hot spot data and avoid it from not being updated for a long time. 4. According to claim 1, a method for caching data in a network storage server is characterized in that: the cache update is a scheduled update and an on-demand update. The scheduled update is to regularly update the data in the cache according to a set time interval. The scheduled update is simple and easy to implement, and is suitable for scenarios with low real-time requirements. The on-demand update is to immediately update the corresponding cache data when the data changes. The on-demand update can ensure the real-time nature of the data and reduce the impact of delays. 5. According to claim 1, a method for caching data in a network storage server is characterized in that: the cache capacity management is a fixed capacity strategy and an expandable capacity strategy. The fixed capacity strategy is to pre-set the total capacity of the cache and reject new requests when the cache is full. The fixed capacity strategy is simple to implement and easy to monitor and manage. The expandable capacity strategy is to dynamically adjust the cache capacity to cope with changing data access requirements. The expandable capacity strategy can better protect hot data and improve system performance. 6. A method for caching data in a network storage server according to any one of claims 1 to 5, characterized in that it comprises the following steps: S1. Data classification: Data is classified into three categories: hot data, warm data, and cold data according to its importance and access frequency. S2. Cache strategy formulation: For hot data, the least recently used (LRU) strategy is used for caching; for warm data, the random replacement strategy is used for caching; for cold data, the first-in-first-out (FIFO) strategy is used for caching; S3, cache update: When hot data is accessed, it is moved to the top of the cache pool; when warm data or cold data is accessed, it is decided whether to move it to the top of the cache pool based on its access frequency and importance; S4. Cache capacity management: Dynamically adjust the size of the cache pool based on actual cache requirements and server resource conditions.