Region-level remote disaster recovery method, system and device based on Paas cloud platform and medium

Abstract

The invention discloses a Region-level remote disaster recovery method, system and device based on a Paas cloud platform and a medium, in the Paas cloud platform, a WAN optimization technology is adopted, and a data compression algorithm, an acceleration algorithm and a cache algorithm are adopted, so that the data transmission time delay and network congestion are reduced, and the data synchronization efficiency and speed are improved; a remote multi-activity technology is introduced, so that the main data center and the backup data center simultaneously provide services to the outside, high availability and disaster tolerance of services are ensured, the main data center is automatically switched to the backup data center when the main data center breaks down, and rapid fault switching is realized; a load balancing mechanism and a fault switching mechanism are adopted, the flow is automatically switched to the backup data center when the main data center breaks down, and rapid fault switching is achieved. By adopting the WAN optimization technology, the time and delay of cross-geographic-position data synchronization are reduced, and the reliability and real-time performance of data synchronization are ensured.

Description

Technical Field

[0001] The present invention relates to the field of data security technology, and in particular to a region-level off-site disaster recovery method, system, equipment and medium based on a Paas cloud platform. Background Art

[0002] Region-level off-site disaster recovery technology is an important technical means to ensure the reliability and disaster recovery capabilities of business systems. With the rapid development of Internet technology, the business systems of enterprises and organizations are increasingly dependent on the Internet, but they are also facing risks such as natural disasters, man-made damage, and hardware failures. Once a failure or data loss occurs, it will cause serious losses to the enterprise. Therefore, it is becoming more and more important to ensure the reliability and disaster recovery capabilities of business systems.

[0003] Region-level off-site disaster recovery technology is mainly used to ensure high availability and disaster recovery capabilities of the business. It is suitable for enterprises and organizations that need to perform data backup and failover between multiple geographical locations, especially for industries such as finance and telecommunications that have extremely high requirements for reliability. Region-level off-site disaster recovery technology mainly ensures the backup and replication of business data by building backup data centers in different geographical locations. Once the primary data center fails, the backup data center will be able to quickly switch and provide services to achieve business continuity and high availability. This technology is mainly used for enterprises and organizations that need to perform data backup and failover between multiple geographical locations, especially for industries such as finance and telecommunications that have extremely high requirements for reliability.

[0004] The implementation of Region-level off-site disaster recovery technology involves multiple technologies, including data synchronization, network equipment, storage equipment, and fault switching. Among them, data synchronization is the key to the implementation of Region-level off-site disaster recovery technology. Data synchronization technology needs to achieve data backup and data consistency across geographical locations. Network equipment and storage equipment need to have high performance, high reliability, and high security to ensure the rapid response and reliability of data synchronization and fault switching. In terms of fault switching, it is necessary to use load balancing, DNS resolution and other technologies to achieve rapid traffic switching and business migration to ensure the continuity and high availability of the business system.

[0005] With the rapid development of new technologies such as cloud computing, big data, and the Internet of Things, regional-level off-site disaster recovery technology has gradually been widely used. In industries such as finance, telecommunications, and government, this technology has become an important means to ensure business security and reliability. It has also driven the rapid development of network equipment, storage equipment, data synchronization, and fault switching technologies, making regional-level off-site disaster recovery technology gradually mature and popular.

[0006] The current problems with region-level off-site disaster recovery technology are mainly that the network latency across geographical locations is large, which affects the speed of data synchronization and fault switching, and it is difficult to ensure data consistency, which is prone to data corruption or loss. In addition, the cost of region-level off-site disaster recovery technology is high, and it is necessary to build backup data centers in different geographical locations, and also purchase high-performance network equipment and storage equipment, which increases the investment cost of enterprises. Summary of the invention

[0007] The purpose of the present invention is to provide a region-level off-site disaster recovery method, system, device and medium based on Paas cloud platform, so as to reduce the time and delay of data synchronization across geographical locations and ensure the reliability and real-time performance of data synchronization.

[0008] To achieve the above purpose, the technical solution provided by the present invention is:

[0009] The first aspect of the present application provides a region-level off-site disaster recovery method based on a Paas cloud platform. In the Paas cloud platform, off-site multi-active technology and WAN optimization technology are used to provide services simultaneously through the off-site multi-active technology, and the time and delay of cross-geographical location data synchronization are reduced through the WAN optimization technology, thereby improving the reliability and real-time performance of data synchronization; the method comprises the following steps:

[0010] WAN optimization technology uses data compression algorithms, acceleration algorithms, and caching algorithms to reduce data transmission delays and network congestion, and improve data synchronization efficiency and speed;

[0011] Introducing remote multi-active technology, so that the primary and backup data centers can provide services to the outside world at the same time, ensuring high service availability and disaster recovery capabilities;

[0012] Use load balancing and fault switching mechanisms to automatically switch traffic to the backup data center when the primary data center fails;

[0013] Among them, a cloud platform data synchronization measurement model is established using WAN optimization technology, and the overall efficiency is improved through optimization algorithms. The cloud platform data synchronization measurement model includes: calculating bandwidth utilization to reflect the bandwidth demand reduced by data compression algorithms, acceleration algorithms and caching algorithms; then substituting the calculated bandwidth utilization into the data transmission time model to obtain the time required for data transmission; substituting the time required for data transmission into the data synchronization delay model to calculate the delay.

[0014] To optimize the above technical solutions, the specific measures taken also include:

[0015] The WAN optimization technology described above reduces the size of data during transmission and reduces the bandwidth and time required for transmission through a data compression algorithm, optimizes the path and method of data transmission through an acceleration algorithm to reduce network latency, and sets caches at the source and target ends of data transmission through a cache algorithm to reduce dependence on the network; the WAN optimization technology described above limits the amount of data transmitted and adjusts the priority of data transmission, and adjusts the priority of data transmission according to the importance and urgency of the data to optimize the efficiency and reliability of data transmission; the WAN optimization technology described above avoids excessive occupation of bandwidth resources by managing network bandwidth and controlling traffic.

[0016] Furthermore, in the cloud platform data synchronization measurement model, the reliability of data synchronization is measured by the ratio of the number of failures occurring during the synchronization process to the total number of failures. Real-time performance is the inverse of delay, reflecting the immediacy of data synchronization. In the cloud platform data synchronization measurement model, various indicators are comprehensively considered, including: data transmission time, data synchronization delay, synchronization reliability and synchronization real-time performance, to form an objective function. The indicators in the objective function are optimized through WAN optimization technology, and the weight coefficients in the objective function are adjusted according to the actual application scenario to ensure that the focus of optimization meets specific needs.

[0017] Furthermore, in the cloud platform data synchronization measurement model, the system stability is measured by the ratio of the number of system failures to the total operating time, and the system security is represented by the ratio of the number of security events to the total number of data synchronizations; by introducing monitoring and fault handling mechanisms, potential problems can be discovered and resolved in a timely manner.

[0018] The cloud platform data synchronization measurement model is represented by a cloud platform that comprehensively measures the time required for data transmission, bandwidth utilization, and latency; wherein the overall optimization goal of the cloud platform is expressed as follows:

[0019] Assuming that the overall optimization goal is to minimize the data transmission time T and data synchronization delay L, while maximizing the data synchronization reliability R and real-time performance P, the function of the overall optimization goal is:

[0020] F(T,L,R,P)=α*T+β*L-γ*R-δ*P

[0021] Among them, α, β, γ and δ are weight coefficients, which can be adjusted according to the needs of the actual application scenario. The value of each weight coefficient is between 0 and 1, and satisfies α+β+γ+δ=1.

[0022] The remote multi-active technology provides a high availability and disaster recovery solution by running the main data center and the backup data center at the same time; in the remote multi-active technology, data synchronization methods include synchronous replication and asynchronous replication, and the appropriate data synchronization method is selected according to business requirements and the importance of data; the remote multi-active technology includes a load balancing mechanism and a fault switching mechanism, the load balancing mechanism is used to distribute requests to appropriate nodes in the main and backup data centers to achieve balanced utilization of resources, and the fault switching mechanism is used to detect faults in the main data center and automatically switch to the backup data center to provide services; in the fault switching mechanism, fault detection and automatic switching based on heartbeat detection are adopted, and DNS resolution technology is used to achieve traffic switching.

[0023] Furthermore, the remote multi-active technology comprehensively considers the DNS resolution technology in the load balancing mechanism and the failover mechanism to automatically switch the traffic to the backup data center when the primary data center fails, specifically including:

[0024] Configure the load balancer: In the Paas cloud platform, deploy the load balancer and add the servers of the primary data center and the backup data center to the backend pool of the load balancer. The load balancer distributes the traffic to different data centers according to certain rules based on the preset load balancing algorithm.

[0025] Set up health checks: The load balancer regularly performs health checks on the primary data center to detect faults. The health check detects the availability of the primary data center by sending heartbeats or timed requests. If the primary data center fails to respond or returns an error status code, the load balancer will identify it as a fault and automatically trigger a failover.

[0026] Configure DNS resolution: On the DNS server, configure the domain name resolution of the primary data center to point to the IP address of the primary data center. At the same time, configure the domain name resolution of the backup data center to point to the IP address of the backup data center. When the primary data center fails, modify the DNS record to point the domain name to the IP address of the backup data center to switch traffic.

[0027] Monitoring and automatic switching: The status of the primary data center is monitored in real time through the monitoring system, and the switching operation is automatically triggered when a failure of the primary data center is detected; the switching process includes updating the configuration of the load balancer to distribute traffic only to the backup data center, and updating the domain name resolution to point to the IP address of the backup data center, so that user requests will be automatically forwarded to the backup data center, realizing fast fault switching.

[0028] A second aspect of the present application provides a regional-level off-site disaster recovery system based on a Paas cloud platform, wherein the system adopts off-site multi-active technology and WAN optimization technology, realizes simultaneous service through off-site multi-active technology, reduces the time and delay of cross-geographical location data synchronization through WAN optimization technology, and improves the reliability and real-time performance of data synchronization;

[0029] The system uses WAN optimization technology to establish a cloud platform data synchronization measurement model and improves overall efficiency through optimization algorithms. The cloud platform data synchronization measurement model includes:

[0030] A bandwidth calculation module, used to calculate bandwidth utilization to reflect the bandwidth demand reduced by data compression algorithms, acceleration algorithms and caching algorithms;

[0031] The data transmission time calculation module is used to substitute the calculated bandwidth utilization into the data transmission time model to obtain the time required for data transmission;

[0032] The delay calculation module is used to substitute the time required for data transmission into the data synchronization delay model to calculate the delay.

[0033] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the Region-level off-site disaster recovery method based on the Paas cloud platform as described in the first aspect of the present application.

[0034] The fourth aspect of the present application provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the Region-level off-site disaster recovery method based on the Paas cloud platform as described in the first aspect of the present application.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention adopts the remote multi-active technology and WAN optimization technology based on the traditional region-level remote disaster recovery technology to solve the shortcomings of the traditional technology in data synchronization speed and reliability.

[0037] The present invention adopts the remote multi-active technology to provide services to the main data center and the backup data center at the same time, synchronizes data to multiple data centers, and ensures data consistency and availability; compared with traditional backup and fault switching technologies, the remote multi-active technology can provide services at the same time to ensure high availability and disaster recovery capabilities of the business.

[0038] The present invention reduces the time and delay of data synchronization across geographical locations by adopting WAN optimization technologies, such as compression, acceleration and caching algorithms, and ensures the reliability and real-time performance of data synchronization.

[0039] The present invention also introduces technologies such as load balancing and DNS resolution, and can automatically switch traffic to a backup data center when a primary data center fails, thereby achieving rapid fault switching.

[0040] This application uses Region-level off-site disaster recovery technology based on the PaaS cloud platform to realize the sharing of virtualized resources, reduce enterprise investment costs, and at the same time improve the scalability and flexibility of the system, achieving the effect of virtualization cost reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 : Schematic diagram of the Region-level off-site disaster recovery method based on PaaS cloud platform of the present invention.

[0042] Figure 2 : Schematic diagram of the cloud platform data synchronization measurement model of the present invention. DETAILED DESCRIPTION

[0043] The above contents of the present invention are further described in detail below in the form of embodiments, but this should not be understood as the scope of the above subject matter of the present invention being limited to the following embodiments, and all technologies realized based on the above contents of the present invention belong to the scope of the present invention.

[0044] This application provides a regional-level off-site disaster recovery method based on a Paas cloud platform. In the Paas cloud platform, off-site multi-active technology and WAN optimization technology are used to provide services simultaneously through off-site multi-active technology, and WAN optimization technology is used to reduce the time and delay of cross-geographical data synchronization, thereby improving the reliability and real-time performance of data synchronization; Figure 1 As shown, the following steps are included:

[0045] WAN optimization technology uses data compression algorithms, acceleration algorithms, and caching algorithms to reduce data transmission delays and network congestion, and improve data synchronization efficiency and speed;

[0046] The introduction of remote multi-active technology enables the primary and backup data centers to provide services to the outside world at the same time, ensuring high service availability and disaster recovery capabilities. When the primary data center fails, it automatically switches to the backup data center to achieve fast fault switching.

[0047] Use load balancing and fault switching mechanisms to automatically switch traffic to the backup data center when the primary data center fails, achieving fast fault switching.

[0048] Among them, WAN optimization technology is used to establish a cloud platform data synchronization measurement model, and the overall efficiency is improved by optimizing the algorithm. The cloud platform data synchronization measurement model is as follows: Figure 2 As shown, it includes: calculating the bandwidth utilization to reflect the bandwidth demand reduced by the data compression algorithm, the acceleration algorithm and the cache algorithm; then substituting the calculated bandwidth utilization into the data transmission time model to obtain the time required for data transmission; substituting the time required for data transmission into the data synchronization delay model to calculate the delay.

[0049] A cloud platform data synchronization measurement model is established using WAN optimization technology, and the overall efficiency is improved through optimization algorithms. The cloud platform data synchronization measurement model includes: calculating bandwidth utilization to reflect the bandwidth demand reduced by data compression algorithms, acceleration algorithms, and caching algorithms; then substituting the calculated bandwidth utilization into the data transmission time model to obtain the time required for data transmission; substituting the time required for data transmission into the data synchronization delay model to calculate the delay.

[0050] In order to ensure the consistency of data synchronization, this application combines remote multi-active technology and WAN optimization technology to ensure fast synchronization and consistency of data synchronization by optimizing data synchronization delay, improving data synchronization reliability and real-time performance and other key indicators.

[0051] This application adopts Region-level off-site disaster recovery technology based on the PaaS cloud platform to realize the sharing of virtualized resources, reduce enterprise investment costs, and at the same time improve the scalability and flexibility of the system, thereby achieving the effect of virtualization cost reduction.

[0052] The following is a further detailed explanation, mainly including WAN optimization to reduce latency, multi-site live-activity to ensure availability, load balancing to quickly fail, data synchronization to ensure consistency, and virtualization to reduce costs:

[0053] (1) WAN optimization to reduce latency

[0054] This application uses WAN optimization technologies, such as compression, acceleration, and caching algorithms, to reduce the time and latency of data synchronization across geographic locations and improve the reliability and real-time performance of data synchronization.

[0055] WAN optimization technology is a technology that improves the efficiency and performance of data transmission in a wide area network (WAN) through compression, data retransmission control, traffic management, protocol optimization, and caching. Its purpose is to reduce data transmission time, improve bandwidth utilization, reduce latency, and improve the reliability and stability of the overall network, thereby optimizing remote data access and application performance.

[0056] Region-level off-site disaster recovery technology is an important IT infrastructure that can provide enterprises with high availability and disaster recovery capabilities to ensure the normal operation of the business; however, due to network latency and bandwidth limitations between off-site data centers, the time and latency of cross-geographical data synchronization is high, and the reliability and real-time performance of data synchronization are difficult to guarantee, which affects the effect of Region-level off-site disaster recovery technology. In order to solve this problem, this application proposes to use WAN optimization technology to reduce the time and latency of data synchronization and improve the reliability and real-time performance of data synchronization through compression, acceleration and caching algorithms.

[0057] WAN optimization technology can also optimize the efficiency and reliability of data transmission by limiting the amount of data transmitted and adjusting the priority of data transmission. It can adjust the priority of the transmission of important data and give priority to the transmission of important data, thereby reducing the time and delay of data transmission and improving the efficiency and reliability of data transmission. At the same time, it can also limit the transmission of non-important data to avoid network congestion and data transmission interruption.

[0058] WAN optimization technology can use a variety of algorithms and methods to optimize the efficiency and reliability of data synchronization across geographical locations.

[0059] In some embodiments, the WAN method is improved as follows:

[0060] Using data compression algorithms can reduce the size of data during transmission, thereby reducing the bandwidth and time required for transmission; compression algorithms can select different compression methods according to the characteristics of the data, such as lossless compression or lossy compression; by compressing data, the amount of data transmission can be reduced and the transmission efficiency can be improved;

[0061] Acceleration algorithms can reduce network latency by optimizing the path and method of data transmission. Accelerator devices or software can be used to optimize the transmission path of data packets and reduce the number of network transfers, thereby speeding up data transmission. In addition, technologies such as parallel transmission, data slicing, and data pre-fetching can also be used to improve the concurrency and efficiency of data transmission.

[0062] By setting up caches at the source and target ends of data transmission, you can reduce dependence on the network and improve the real-time and reliability of data synchronization. The cache can store copies of recently accessed data and quickly respond to data requests when needed, reducing visits to remote data centers. This can reduce the amount of data transmitted, reduce latency, and improve the efficiency of data synchronization.

[0063] Adjust the data transmission priority according to the importance and urgency of the data; set a higher transmission priority for important data to ensure that it is transmitted first, thereby reducing the time and delay of data synchronization; set a lower transmission priority or limit the transmission volume for non-important data to avoid network congestion and interruption; by setting the transmission priority reasonably, the efficiency and reliability of data transmission can be improved;

[0064] By managing network bandwidth and controlling traffic, we can avoid excessive usage of bandwidth resources and ensure the stability and reliability of data transmission. We can use bandwidth scheduling algorithms to reasonably allocate bandwidth resources and dynamically adjust data transmission according to real-time needs to ensure that the transmission needs of critical data are met.

[0065] Compared with the original technology, this application introduces data compression and transmission priority adjustment. Data compression reduces the amount of data transmitted, thereby reducing bandwidth and time requirements; transmission priority adjustment ensures the timely transmission of high-priority data. These improvement measures improve the efficiency and real-time performance of data transmission, bringing better performance, stability and security to the Paas cloud platform.

[0066] For example, an enterprise with off-site data centers in two different geographical locations for implementing regional-level off-site disaster recovery for business, due to network latency and bandwidth limitations between data centers, the time and latency of data synchronization are high, which has a certain impact on the normal operation of the enterprise's business. The WAN optimization technology of this application is used to improve the efficiency and reliability of data synchronization:

[0067] First, compression algorithms are used to compress data to reduce its size during data transmission, compressing large files into smaller files, thereby reducing the bandwidth and time required for transmission.

[0068] Enterprises introduce acceleration algorithms to reduce network latency; deploy specialized accelerator devices to optimize the transmission path of data packets, reduce the number of network transfers, and thus speed up data transmission; in this way, data can be transmitted faster from the source to the target, improving the real-time nature of data synchronization;

[0069] To further improve the efficiency of data synchronization, enterprises can also use cache technology. Cache is set up at both the source and target ends of data transmission to store recently accessed copies of data. When data synchronization is required, if the data already exists in the cache, the request can be responded to directly, avoiding access to the remote data center, thereby reducing data transmission volume and latency.

[0070] Enterprises can adjust the priority of data transmission according to the importance and urgency of the data; important data is set to high priority to ensure its priority transmission; key business data, transaction data, etc. can be set to high priority to ensure their synchronization and real-time; non-important data can be set to a lower priority or the transmission volume can be limited to avoid network congestion and interruption;

[0071] Enterprises can implement bandwidth management and traffic control strategies; use bandwidth scheduling algorithms to reasonably allocate available bandwidth resources; dynamically adjust bandwidth allocation for data transmission based on real-time needs to ensure that transmission needs for critical data are met and avoid excessive use of bandwidth resources;

[0072] By adopting WAN optimization technology, this multinational enterprise has successfully optimized the process of data synchronization across geographical locations; algorithms such as compression, acceleration, and caching have reduced the time and delay of data synchronization, and improved the reliability and real-time performance of data synchronization; at the same time, by optimizing the priority of data transmission, limiting the transmission of non-important data, and performing bandwidth management and flow control, the efficiency and reliability of data transmission have been further improved; in this way, the enterprise's region-level off-site disaster recovery technology has been effectively enhanced to ensure the normal operation of the business.

[0073] In order to solve the problems based on WAN optimization technology, the technology of the present application can abstract factors such as data transmission time, bandwidth utilization, delay, synchronization reliability and synchronization real-time into a set of models. The purpose of these models is to measure the performance of a cloud platform when performing data synchronization and improve the overall efficiency through optimization algorithms. The specific process includes: calculating bandwidth utilization to reflect the bandwidth demand reduced by algorithms such as compression, acceleration and caching, and then substituting the bandwidth utilization into the data transmission time model to obtain the time required for data transmission, and then substituting the transmission time into the data synchronization delay model to calculate the delay. The reliability of data synchronization is measured by the ratio of the number of failures occurring during the synchronization process to the total number of failures, and the real-time performance is the inverse of the delay, reflecting the immediacy of data synchronization.

[0074] In order to form a comprehensive optimization goal, an objective function is defined, which comprehensively considers data transmission time, data synchronization delay, synchronization reliability and synchronization real-time performance. By adjusting algorithms such as compression, acceleration and caching, these indicators are optimized to improve data transmission efficiency, reduce delays, and improve synchronization reliability and real-time performance. The weight coefficient in the objective function is adjusted according to the actual application scenario to ensure that the focus of optimization meets specific needs.

[0075] Taking the stability and security of the system into further consideration, the two indicators of system stability and system security are also incorporated into the optimization model. System stability is measured by the ratio of the number of system failures to the total running time, while security is represented by the ratio of the number of security events to the total number of data synchronizations. The new optimization objective function incorporates system stability and security, ensuring that while optimizing data synchronization performance, it also pays attention to system reliability and security.

[0076] The entire optimization process may include the following steps: first, calculate the bandwidth utilization; second, substitute the bandwidth utilization into the data transmission time model to calculate the data transmission time; then, substitute the transmission time into the delay model to obtain the delay; at the same time, calculate the synchronization reliability and real-time performance; finally, substitute these calculation results into the objective function and solve the optimal solution through numerical calculation.

[0077] This process aims to find the best compression, acceleration and caching strategies to optimize the cloud platform's data synchronization effect while improving the stability and security of the system. This comprehensive optimization method helps ensure the efficiency, reliability and security of data transmission, thereby improving the overall system performance and user experience. Through the above mathematical derivation and optimization process, it can effectively guide the WAN optimization strategy in practical applications and provide a better solution for cloud platform data synchronization.

[0078] In specific implementation, it is necessary to select appropriate compression algorithms, acceleration technologies, and cache strategies according to the actual network environment and business needs, and further adjust various parameters to achieve the best optimization effect. Compression algorithms can reduce the amount of data, thereby reducing bandwidth requirements; acceleration technologies can increase data transmission speed and reduce latency; cache strategies can increase data reuse and reduce the amount of data transmitted repeatedly. The combined application of these technologies can significantly improve the efficiency and reliability of data transmission.

[0079] In addition, in the optimization process, network fluctuations and load changes in different scenarios also need to be considered. By dynamically adjusting the optimization strategy, it can better adapt to the changing network environment and ensure that efficient data transmission and synchronization performance can be maintained under different load conditions. This dynamic optimization capability makes WAN optimization technology more practical and flexible in actual applications.

[0080] In some implementations, in order to further improve the stability and security of the system, a monitoring and fault handling mechanism can also be introduced to promptly discover and resolve potential problems. By real-time monitoring of network performance and data transmission status, network failures can be identified and handled in a timely manner to avoid interruptions and delays in data transmission; through security monitoring and event response mechanisms, various security threats can be effectively responded to and the security of data transmission can be ensured. The implementation of these measures can provide a more solid guarantee for data transmission and synchronization.

[0081] This application can greatly improve the efficiency, reliability and security of cloud platforms during data synchronization by comprehensively using optimization technologies such as compression, acceleration and caching, and combining system stability and security considerations. This optimization process can not only reduce data transmission time and delay, improve bandwidth utilization, but also enhance system stability and security, and provide users with a better service experience. This comprehensive optimization method has broad application prospects and important practical significance, and is an important direction for the development of cloud platform data synchronization technology.

[0082] To implement WAN optimization technology, this problem can be abstracted into a cloud platform in terms of data transmission time, bandwidth utilization and latency, including:

[0083] Bandwidth utilization (B):

[0084] B=(CR) / C

[0085] B represents bandwidth utilization, ranging from 0 to 1; C represents the total bandwidth required for original data transmission (unit: bit / second); R represents the bandwidth requirement reduced by algorithms such as compression, acceleration and caching (unit: bit / second).

[0086] Data transmission time (T):

[0087] T=D / (B*W)

[0088] T represents the data transmission time (unit: seconds); D represents the amount of data to be transmitted (unit: bits); and W represents the available bandwidth (unit: bits / second).

[0089] Data synchronization delay (L):

[0090] L=T*(1-B)

[0091] L represents the delay of data synchronization (unit: seconds).

[0092] Data synchronization reliability (R):

[0093] R=1-(F / N)

[0094] R represents the reliability of data synchronization, ranging from 0 to 1; F represents the number of failures that occur during the data synchronization process; and N represents the total number of data synchronizations.

[0095] Data synchronization real-time (P):

[0096] P=1 / (1+L)

[0097] P indicates the real-time nature of data synchronization, ranging from 0 to 1.

[0098] Based on these models and variables, we can measure the effectiveness of a cloud platform in data synchronization. These indicators can help us understand how the optimization algorithm can reduce data transmission time, improve bandwidth utilization, reduce latency, and improve the reliability and real-time performance of data synchronization.

[0099] By integrating the above models, we can get the mathematical derivation process based on WAN optimization technology. In order to form a rigorous mathematical derivation, these models can be combined into an overall optimization goal. Assuming that the goal is to minimize the data transmission time (T) and data synchronization delay (L), while maximizing the data synchronization reliability (R) and real-time performance (P), we can define an overall optimization objective function as follows:

[0100] Objective function:

[0101] F(T,L,R,P)=α*T+β*L-γ*R-δ*P

[0102] Among them, α, β, γ and δ are weight coefficients, which can be adjusted according to the needs of the actual application scenario. The value of the weight coefficient can be between 0 and 1, and satisfies α+β+γ+δ=1.

[0103] In order to optimize the objective function, it is necessary to consider how to adjust algorithms such as compression, acceleration, and caching to reduce data transmission time and data synchronization delay, while improving data synchronization reliability and real-time performance. The following are the steps for mathematically deriving the above model:

[0104] Calculate bandwidth utilization (B):

[0105] B=(CR) / C

[0106] Substitute the bandwidth utilization (B) into the data transmission time model (T):

[0107] T=D / (B*W)

[0108] Substitute the data transmission time (T) into the data synchronization delay model (L):

[0109] L=T*(1-B)

[0110] The model for data synchronization reliability (R) and real-time performance (P) remains unchanged:

[0111] R=1-(F / N)

[0112] P=1 / (1+L)

[0113] Substitute into the objective function:

[0114] F(T,L,R,P)=α*(D / (B*W))+β*(T*(1-B))-γ*(1-(F / N))-δ*(1 / (1+L))

[0115] Find the partial derivative of the objective function and set it equal to zero to find the optimal solution. This usually requires numerical calculation based on the actual algorithm and data.

[0116] Through the above mathematical derivation, the best compression, acceleration and caching strategies can be found to optimize data synchronization performance.

[0117] The effect of this application through improved WAN optimization technology is to increase its stability and security. In order to consider system stability and security in the cloud platform scenario, two new indicators can be introduced based on the original mathematical derivation: system stability (S) and security (Q). The following model will be used to measure these two indicators:

[0118] System stability(S):

[0119] S=1-(U / M)

[0120] S represents system stability, ranging from 0 to 1; U represents the number of system failures; and M represents the total operating time of the system.

[0121] Safety (Q):

[0122] Q=1-(V / N)

[0123] Q represents security, ranging from 0 to 1; V represents the number of security events; and N represents the total number of data synchronizations.

[0124] In order to optimize system stability and security, the original objective function needs to be modified. The new objective function is as follows:

[0125] Objective function:

[0126] F(T,L,R,P,S,Q)=α*T+β*L-γ*R-δ*P-ε*S-ζ*Q

[0127] Among them, α, β, γ, δ, ε and ζ are weight coefficients, which can be adjusted according to the requirements of actual application scenarios. The value of the weight coefficient can be between 0 and 1, and satisfies α+β+γ+δ+ε+ζ=1.

[0128] Now, system stability and security have been incorporated into the objective function. The following is the specific derivation process:

[0129] Calculate bandwidth utilization (B):

[0130] B=(CR) / C

[0131] Substitute the bandwidth utilization (B) into the data transmission time model (T):

[0132] T=D / (B*W)

[0133] Substitute the data transmission time (T) into the data synchronization delay model (L):

[0134] L=T*(1-B)

[0135] The models for data synchronization reliability (R), real-time performance (P), system stability (S), and security (Q) remain unchanged:

[0136] R=1-(F / N)

[0137] P=1 / (1+L)

[0138] S=1-(U / M)

[0139] Q=1-(V / N)

[0140] Substitute into the objective function:

[0141] F(T,L,R,P,S,Q)=α*(D / (B*W))+β*(T*(1-B))-γ*(1-(F / N))-δ*(1 / (1+L))-

[0142] ε*(1-(U / M))-ζ*(1-(V / N))

[0143] Find the partial derivative of the objective function and set it equal to zero to find the optimal solution. This usually requires numerical calculation based on the actual algorithm and data.

[0144] Through the above mathematical derivation, we can find the best compression, acceleration and caching strategies to optimize the cloud platform's performance, system stability and security during data synchronization.

[0145] The above content mainly discusses how to reduce data transmission time and latency and improve the reliability, real-time performance, system stability and security of data synchronization in the cloud platform scenario by optimizing algorithms such as compression, acceleration and caching. On this basis, several key indicators are introduced and mathematical models are designed to find the best strategy. The following is a detailed description of these technologies and a brief explanation of their significance.

[0146] How to calculate bandwidth utilization (B); this is a key metric because it reflects the bandwidth saved through algorithms such as compression, acceleration, and caching; the higher the bandwidth utilization, the more effective the optimization algorithm; to calculate bandwidth utilization, you need to know the total bandwidth required for the original data transmission (C) and the bandwidth required after optimization (R).

[0147] Calculation of data transmission time (T); This is another important indicator for measuring the effectiveness of data synchronization; It is necessary to know the amount of data to be transmitted (D) and the available bandwidth (W); By combining the bandwidth utilization, the time required for data transmission can be calculated.

[0148] Data synchronization latency (L); Latency is a key issue in the data synchronization process of cloud platforms as it may affect user experience and system performance; Data synchronization latency is calculated by combining data transmission time (T) and bandwidth utilization (B).

[0149] Indicators such as data synchronization reliability (R), real-time performance (P), system stability (S) and security (Q); these indicators respectively reflect the failure rate, response time, number of system failures and number of security incidents during the data synchronization process; by optimizing these indicators, it can be ensured that the cloud platform is more reliable, real-time, stable and secure when performing data synchronization.

[0150] In order to optimize these indicators, an objective function is designed to combine these indicators and assign corresponding weight coefficients. The goal is to minimize data transmission time and latency while maximizing reliability, real-time, stability and security. By solving this objective function, the best compression, acceleration and caching strategies can be found.

[0151] WAN optimization technology can significantly reduce the time and delay of cross-regional disaster recovery data synchronization through compression, acceleration and caching algorithms, improve bandwidth utilization, and thus enhance the reliability and real-time performance of data synchronization. This helps reduce the time for disaster recovery, shorten business interruption time, improve the availability and stability of the overall system, and provide enterprises with more efficient disaster recovery protection.

[0152] (2) Multiple live-action guarantees are available in different locations

[0153] By adopting the multi-active technology in different locations, the main data center and the backup data center provide services to the outside world at the same time, ensuring high availability and disaster recovery capabilities of the business.

[0154] Remote multi-active technology is a solution that provides high availability and disaster recovery capabilities by running the primary data center and the backup data center at the same time. In the PaaS cloud platform in the regional-level remote disaster recovery scenario, the implementation of this technology involves several key aspects.

[0155] It is necessary to establish a primary and backup data center architecture across geographical locations. The primary data center is responsible for processing business requests and data processing, while the backup data center acts as a cold backup and continuously synchronizes data from the primary data center to achieve data redundancy and disaster recovery capabilities. This requires ensuring that the network connection between the primary and backup data centers is stable and reliable, and adopting appropriate data synchronization mechanisms.

[0156] Data synchronization and replication are the key to achieving multi-site active-active. Common data synchronization methods include synchronous replication and asynchronous replication. Synchronous replication requires that after writing data in the primary data center, the backup data center must wait for confirmation of successful writing before returning a response, ensuring data consistency, but may increase latency. Asynchronous replication allows the primary data center to return a response immediately after writing data, but the data in the backup data center may be delayed to a certain extent. Choose the appropriate data synchronization method based on business needs and data importance.

[0157] Load balancing and failover mechanisms also need to be implemented. Load balancing can distribute requests to appropriate nodes in the primary and backup data centers to achieve balanced resource utilization. The failover mechanism can detect failures in the primary data center and automatically switch to the backup data center to provide services, achieving high availability and disaster recovery capabilities. Common failover technologies include heartbeat-based fault detection and automatic switching, as well as DNS resolution to achieve traffic switching.

[0158] Security is also an important factor that needs to be considered in the PaaS cloud platform's multi-site active-active technology. Data encryption, identity authentication, access control and other security mechanisms need to be coordinated and implemented between the primary and backup data centers to ensure data security and privacy protection.

[0159] Remote multi-active technology is of great significance in PaaS cloud platforms. First, by running the primary and backup data centers at the same time, high availability of services can be achieved, and continuous services can be provided even when the primary data center fails or is unavailable. This reduces the risk of business interruption and improves user satisfaction and experience.

[0160] Remote multi-active technology also enhances the system's disaster recovery capabilities. When a catastrophic failure occurs in the primary data center, the backup data center can quickly take over services to ensure business continuity. This disaster recovery capability is crucial for the stable operation of key businesses, reducing the risk of data loss and service interruption, and improving system stability and reliability.

[0161] In summary, the multi-site active-active technology provides a high availability and disaster recovery solution in the PaaS cloud platform through the application of key technologies such as simultaneous operation of primary and backup data centers, data synchronization, load balancing and fault switching. The detailed solution of this technology needs to comprehensively consider multiple aspects such as network connection, data synchronization, load balancing, fault switching and security to achieve reliable business continuity and system stability.

[0162] (3) Load balancing failure

[0163] By introducing technologies such as load balancing and DNS resolution, traffic can be automatically switched to the backup data center when the primary data center fails, achieving rapid failover.

[0164] In the PaaS cloud platform in the regional-level off-site disaster recovery scenario, the introduction of technologies such as load balancing and DNS resolution can automatically switch traffic to the backup data center when the primary data center fails, thereby achieving rapid failover. The following is a detailed description of these technologies and a description of the solution.

[0165] Load balancing is a technology that distributes traffic to multiple servers or data centers to achieve balanced resource utilization and improve system availability. In the Paas cloud platform, by introducing a load balancer, traffic can be distributed between the primary data center and the backup data center. When the primary data center fails, the load balancer can automatically switch traffic to the backup data center to ensure the continuous availability of the business.

[0166] DNS resolution is the process of resolving a domain name to a corresponding IP address through the Domain Name System (DNS). In the Paas cloud platform, by configuring DNS records, the domain name can be pointed to the IP address of the primary data center. When the primary data center fails, the domain name can be pointed to the IP address of the backup data center by modifying the DNS record. In this way, when users access services through domain names, DNS resolution will forward the request to the backup data center to achieve rapid switching of traffic.

[0167] By comprehensively considering load balancing and DNS resolution technologies, a solution can be implemented to automatically switch traffic to the backup data center when the primary data center fails. The following is a detailed implementation plan:

[0168] Configure the load balancer: In the Paas cloud platform, deploy the load balancer and add the servers of the primary data center and the backup data center to the backend pool of the load balancer. The load balancer can distribute the traffic to different data centers according to certain rules based on the preset load balancing algorithm.

[0169] Set up health checks: To detect faults, the load balancer needs to perform health checks on the primary data center regularly. It detects the availability of the primary data center by sending heartbeats or timed requests. If the primary data center fails to respond or returns an error status code, the load balancer will identify it as a fault and automatically trigger a failover.

[0170] Configure DNS resolution: On the DNS server, configure the domain name resolution of the primary data center to point to the IP address of the primary data center. At the same time, configure the domain name resolution of the backup data center to point to the IP address of the backup data center. In this way, when the primary data center fails, the domain name can be modified to point to the IP address of the backup data center to achieve traffic switching.

[0171] Monitoring and automatic switching: The status of the primary data center is monitored in real time through the monitoring system. Once a failure of the primary data center is detected, the switching operation is automatically triggered. The switching process can include updating the configuration of the load balancer to distribute traffic only to the backup data center and updating the domain name resolution to point to the IP address of the backup data center. In this way, user requests will be automatically forwarded to the backup data center, achieving fast failover.

[0172] The value of this technology lies in the fast failover that automatically switches traffic to the backup data center when the primary data center fails. Through the combined use of load balancing and DNS resolution, the system traffic can be dynamically managed and adjusted to improve the system's availability and disaster recovery capabilities. This solution enables the Paas cloud platform to better cope with the failure of the primary data center and ensure business continuity and stability. At the same time, it also reduces reliance on manual intervention, realizes automated failover, and improves system efficiency and reliability.

[0173] (4) Data synchronization to ensure consistency

[0174] Combining remote multi-active technology and WAN optimization technology, fast data synchronization and consistency assurance can be achieved.

[0175] In the PaaS cloud platform in the regional-level off-site disaster recovery scenario, the combination of off-site multi-active technology and WAN optimization technology can achieve fast data synchronization and consistency assurance. The following is a detailed description of these technologies and a description of the solution.

[0176] Remote multi-active technology is a solution that provides high availability and disaster recovery capabilities by running the primary data center and the backup data center at the same time. In the Paas cloud platform, the primary data center is responsible for processing business requests and data processing, while the backup data center acts as a cold backup and continuously synchronizes the data of the primary data center to achieve data redundancy and disaster recovery capabilities. This requires ensuring that the network connection between the primary and backup data centers is stable and reliable, and adopting appropriate data synchronization mechanisms.

[0177] WAN optimization technology is a solution that reduces the time and delay of data synchronization and improves the reliability and real-time performance of data synchronization through algorithms such as compression, acceleration, and caching. In the Paas cloud platform, a variety of algorithms and methods can be used to optimize the efficiency and reliability of data synchronization across geographical locations. The use of data compression algorithms can reduce the size of data during transmission, thereby reducing the bandwidth and time required for transmission. Acceleration algorithms can reduce network latency by optimizing the path and method of data transmission. Cache can store copies of recently accessed data, quickly respond to data requests when needed, and reduce visits to remote data centers.

[0178] Combining the remote multi-active technology with WAN optimization technology can achieve the following solutions:

[0179] Data synchronization and consistency guarantee: Through remote multi-active technology, the main data center and the backup data center provide services to the outside world at the same time. When data is updated, the main data center synchronizes the data to the backup data center to ensure data consistency. At the same time, WAN optimization technology can reduce the time and delay of data synchronization, and improve the efficiency and real-time performance of data synchronization.

[0180] Data transmission based on WAN optimization: Optimize the efficiency and reliability of data transmission through compression, acceleration and caching algorithms. Use data compression algorithms to reduce data transmission volume and bandwidth consumption. Use acceleration algorithms to optimize data transmission paths and reduce network latency. Use caching technology to reduce access to remote data centers and improve the real-time and reliability of data synchronization.

[0181] Disaster recovery capability of multi-site active-active: Multi-site active-active technology provides high availability and disaster recovery capability by running the primary data center and backup data center simultaneously. In the event of a disaster or failure of the primary data center, the backup data center can immediately take over the business to ensure business continuity and stability. WAN optimization technology can speed up data synchronization, reduce data synchronization delays, and further improve data synchronization efficiency and reliability during disaster recovery.

[0182] In the PaaS cloud platform, it is critical to combine remote multi-active technology and WAN optimization technology to optimize the efficiency, real-time and reliability of data synchronization. Specific optimization solutions include:

[0183] Data compression and decompression: Use compression algorithms to compress the data to be transmitted, reduce the amount of data transmission, and reduce bandwidth consumption; after the compressed data is received in the backup data center, use the corresponding decompression algorithm to decompress it and restore the original data.

[0184] Acceleration algorithm: Reduce network latency by optimizing data transmission paths and methods; use technologies such as parallel transmission, data sharding, and data pre-fetching to improve the concurrency and efficiency of data transmission; use multi-threading or asynchronous IO methods for parallel transmission and send multiple data blocks at the same time to speed up data transmission.

[0185] Data caching: Introduce a caching mechanism at the storage level of the primary data center and the backup data center. Use a memory cache or distributed cache system to store copies of recently accessed data. When a data request arrives, first check whether the required data exists in the cache. If so, return the data in the cache directly, reducing access to remote data centers.

[0186] Asynchronous data synchronization: During the data update process, the primary data center can first send the data asynchronously to the backup data center, and then immediately return a response to the client; combining remote multi-active technology and WAN optimization technology can achieve fast data synchronization and consistency assurance, and improve the availability, disaster recovery capabilities and data synchronization efficiency of the Paas cloud platform in regional-level remote disaster recovery scenarios; the innovation of this technology lies in the introduction of WAN optimization technology to optimize the time and delay in the data transmission process, improve the efficiency and real-time performance of data synchronization, and thus enhance the disaster recovery capabilities of remote multi-active technology and the reliability of data synchronization.

[0187] (5) Virtualization reduces costs

[0188] Region-level off-site disaster recovery technology based on the PaaS cloud platform can realize the sharing of virtualized resources, reduce enterprise investment costs, and improve the scalability and flexibility of the system.

[0189] Region-level off-site disaster recovery technology based on the Paas cloud platform is a solution that shares and protects virtualized resources in multiple geographical locations. In this technology, multiple data centers collaborate with each other to provide high availability and disaster recovery capabilities, reduce enterprise investment costs and improve system scalability and flexibility.

[0190] This technology realizes the creation and management of virtual machines through a virtualization platform; virtualization abstracts physical hardware resources into virtual resources, allowing different departments or businesses to share the same set of resources; this sharing makes resource utilization more efficient and reduces the cost of purchasing and maintaining hardware equipment for enterprises.

[0191] Region-level off-site disaster recovery technology provides disaster recovery protection; by establishing multiple data centers in different geographical locations, data and applications are replicated and synchronized to ensure that when the primary data center fails, the backup data center can take over the business and continue to provide services; this disaster recovery capability ensures business continuity and stability and reduces the risk of business interruption due to disasters or failures.

[0192] Region-level off-site disaster recovery technology also improves the scalability and flexibility of the system; due to the sharing of virtualized resources, the allocation and use of resources can be dynamically adjusted according to business needs; the system can automatically schedule and allocate resources according to load conditions, improving the scalability and elasticity of the system; at the same time, this technology also supports the dynamic migration of virtual machines and applications, and can be flexibly deployed and adjusted between different data centers, thereby improving the flexibility and manageability of the system.

[0193] The region-level off-site disaster recovery technology based on the PaaS cloud platform reduces enterprise investment costs, improves system scalability and enhances flexibility through the sharing and disaster recovery protection of virtualized resources. The application of this technology enables enterprises to better cope with failures and disasters, ensure business continuity and stability, and provide enterprises with a more efficient, reliable and flexible IT infrastructure.

[0194] In the PaaS cloud platform, sharing virtualized resources through region-level off-site disaster recovery technology can reduce the investment cost of enterprises while improving the scalability and flexibility of the system. The following is a detailed description of this technology and a description of the solution:

[0195] Region-level off-site disaster recovery technology is a technology that implements virtualized resource sharing and disaster recovery protection in the PaaS cloud platform. It establishes multiple data centers in different geographical locations, each of which is equipped with a virtualization platform and related hardware facilities to achieve replication and synchronization of virtual machines and virtualized resources. In this way, even if a data center fails or suffers a disaster, other data centers can continue to provide services to ensure business continuity.

[0196] The following key technologies are involved in the regional disaster recovery technology:

[0197] Virtual machine replication and synchronization: Through virtual machine replication and synchronization technology, the virtual machine image of the primary data center is replicated to the backup data center. This can be achieved by exporting, transferring and importing virtual machine images. The replication and synchronization of virtual machines needs to ensure the consistency and integrity of data, and the stability and reliability of the network.

[0198] Data synchronization and replication: In addition to virtual machine replication, data in the virtualized environment also needs to be synchronized and replicated, including virtual disks, storage volumes, and databases. Data synchronization and replication require a reliable data synchronization mechanism to ensure data consistency between the primary data center and the backup data center. Common synchronization methods include synchronous replication and asynchronous replication, and you can choose an appropriate synchronization strategy based on business needs.

[0199] Load balancing and fault switching: In order to achieve sharing and disaster recovery of virtualized resources, load balancing and fault switching need to be implemented between different data centers. Load balancing can distribute requests to available nodes in each data center according to real-time load conditions to achieve balanced resource utilization. The fault switching mechanism can detect failures in the primary data center and automatically switch requests to the backup data center to achieve rapid fault recovery and business continuity.

[0200] Network connectivity and latency optimization: In region-level off-site disaster recovery technology, the network connectivity between the primary data center and the backup data center is very important. It is necessary to ensure that the network connection between data centers is stable and reliable, and use network optimization technology to reduce the latency of data synchronization and improve system performance and response speed. Commonly used network optimization technologies include compression, acceleration, and caching algorithms.

[0201] The significance of this technology is that it realizes the sharing and disaster recovery protection of virtualized resources, bringing multiple values: By sharing virtualized resources, enterprises can reduce the procurement and maintenance costs of hardware equipment. Different departments or businesses can share the same set of resources, improve resource utilization and avoid resource waste. Through region-level off-site disaster recovery technology, resource allocation and use can be dynamically adjusted according to business needs. The system can automatically adjust resource allocation according to load conditions to improve the scalability and elasticity of the system. The flexibility of the virtualization platform makes resource allocation and management more convenient. Through region-level off-site disaster recovery technology, virtual machines and applications can be flexibly migrated between different data centers to achieve dynamic resource scheduling and flexible business deployment.

[0202] The present application also provides a regional-level off-site disaster recovery system based on the Paas cloud platform. The system adopts off-site multi-active technology and WAN optimization technology to provide services simultaneously through off-site multi-active technology, and reduces the time and delay of cross-geographical data synchronization through WAN optimization technology, thereby improving the reliability and real-time performance of data synchronization.

[0203] The system uses WAN optimization technology to establish a cloud platform data synchronization measurement model, and improves overall efficiency through optimization algorithms. The cloud platform data synchronization measurement model includes:

[0204] A bandwidth calculation module, used to calculate bandwidth utilization to reflect the bandwidth demand reduced by data compression algorithms, acceleration algorithms and caching algorithms;

[0205] The data transmission time calculation module is used to substitute the calculated bandwidth utilization into the data transmission time model to obtain the time required for data transmission;

[0206] The delay calculation module is used to substitute the time required for data transmission into the data synchronization delay model to calculate the delay.

[0207] The present application also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned Region-level off-site disaster recovery method based on the Paas cloud platform is implemented.

[0208] The present application also provides a computer-readable storage medium storing a computer program, which enables a computer to execute the above-mentioned Region-level off-site disaster recovery method based on the Paas cloud platform.

[0209] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0210] In summary, the regional-level off-site disaster recovery technology based on the PaaS cloud platform reduces the investment cost of enterprises and improves the scalability and flexibility of the system through the sharing and disaster recovery protection of virtualized resources. The detailed solution of this technology includes the technical implementation of key aspects such as virtual machine replication and synchronization, data synchronization and replication, load balancing and fault switching, and network connectivity and latency optimization. It provides enterprises with reliable disaster recovery capabilities and ensures business continuity and stability.

[0211] Compared with the existing technology, this application introduces WAN optimization technology and adopts compression, acceleration and caching algorithms, which can significantly reduce data transmission time and delay, improve bandwidth utilization, and enhance the reliability and real-time performance of data synchronization, thereby reducing off-site disaster recovery time, shortening business interruption time, improving system availability and stability, and providing enterprises with more efficient disaster recovery protection.

[0212] On this basis, by introducing key indicators such as bandwidth utilization, data transmission time, data synchronization delay, data synchronization reliability, real-time, system stability and security, and designing mathematical models and objective functions, we can find the best compression, acceleration and caching strategies to further optimize the data synchronization process.

[0213] Compared with the existing technology, this application combines the cloud platform scenario and uses WAN optimization technology to achieve fast, reliable, secure and real-time data synchronization. It also achieves comprehensive optimization of indicators through the design of mathematical models and objective functions, and realizes off-site disaster recovery protection more efficiently.

[0214] The application scenarios of the region-level off-site disaster recovery technology based on the Paas cloud platform of this application include fields such as finance, telecommunications, and medical care that require high reliability and disaster recovery capabilities. In addition, multi-location deployment at a long distance from the data center can also be applied to cross-regional and globalized enterprise business. This technology has significant commercial value. This technology can help enterprises reduce the losses caused by system failures and the time of business interruption, ensure the continuity and stability of the enterprise's business, and improve customer satisfaction and brand image. In addition, through the sharing and flexibility of virtualized resources, it can also reduce the investment costs of enterprises and improve overall operational efficiency.

[0215] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any technician familiar with the profession, without departing from the scope of the technical solution of the present invention, according to the technical essence of the present invention, any simple modification, equivalent replacement and improvement made to the above embodiment still falls within the protection scope of the technical solution of the present invention.

Claims

1. A region-level off-site disaster recovery method based on Paas cloud platform, characterized in that: In the Paas cloud platform, remote multi-active technology and WAN optimization technology are used to provide services simultaneously through remote multi-active technology, and WAN optimization technology is used to reduce the time and delay of cross-geographical data synchronization, thereby improving the reliability and real-time performance of data synchronization; the following steps are included: WAN optimization technology uses data compression algorithms, acceleration algorithms, and caching algorithms to reduce data transmission delays and network congestion, and improve data synchronization efficiency and speed; Introducing remote multi-active technology, so that the primary and backup data centers can provide services to the outside world at the same time, ensuring high service availability and disaster recovery capabilities; Use load balancing and fault switching mechanisms to automatically switch traffic to the backup data center when the primary data center fails; Among them, a cloud platform data synchronization measurement model is established using WAN optimization technology, and the overall efficiency is improved through optimization algorithms. The cloud platform data synchronization measurement model includes: calculating bandwidth utilization to reflect the bandwidth demand reduced by data compression algorithms, acceleration algorithms and caching algorithms; then substituting the calculated bandwidth utilization into the data transmission time model to obtain the time required for data transmission; substituting the time required for data transmission into the data synchronization delay model to calculate the delay.

2. The region-level off-site disaster recovery method based on Paas cloud platform according to claim 1 is characterized in that: The WAN optimization technology described above reduces the size of data during transmission and reduces the bandwidth and time required for transmission through a data compression algorithm, optimizes the path and method of data transmission through an acceleration algorithm to reduce network latency, and sets caches at the source and target ends of data transmission through a cache algorithm to reduce dependence on the network; the WAN optimization technology described above limits the amount of data transmitted and adjusts the priority of data transmission, and adjusts the priority of data transmission according to the importance and urgency of the data to optimize the efficiency and reliability of data transmission; the WAN optimization technology described above avoids excessive occupation of bandwidth resources by managing network bandwidth and controlling traffic.

3. The region-level off-site disaster recovery method based on Paas cloud platform according to claim 1 is characterized in that: In the cloud platform data synchronization measurement model, the reliability of data synchronization is measured by the ratio of the number of failures occurring during the synchronization process to the total number of failures. Real-time performance is the inverse of delay, reflecting the immediacy of data synchronization. In the cloud platform data synchronization measurement model, various indicators including data transmission time, data synchronization delay, synchronization reliability and synchronization real-time performance are comprehensively considered to form an objective function. The indicators in the objective function are optimized through WAN optimization technology. The weight coefficient in the objective function is adjusted according to the actual application scenario to ensure that the focus of optimization meets specific needs.

4. The region-level off-site disaster recovery method based on Paas cloud platform according to claim 1 is characterized in that: In the cloud platform data synchronization measurement model, the system stability is measured by the ratio of the number of system failures to the total operating time, and the system security is represented by the ratio of the number of security events to the total number of data synchronizations; by introducing monitoring and fault handling mechanisms, potential problems can be discovered and resolved in a timely manner.

5. The region-level off-site disaster recovery method based on Paas cloud platform according to claim 1 is characterized in that: The cloud platform data synchronization measurement model is represented by a cloud platform that comprehensively measures the time required for data transmission, bandwidth utilization, and latency; wherein the overall optimization goal of the cloud platform is expressed as follows: Assuming that the overall optimization goal is to minimize the data transmission time T and data synchronization delay L, while maximizing the data synchronization reliability R and real-time performance P, the function of the overall optimization goal is: F(T,L,R,P)=α*T+β*L-γ*R-δ*P Among them, α, β, γ and δ are weight coefficients, which can be adjusted according to the needs of the actual application scenario. The value of each weight coefficient is between 0 and 1, and satisfies α+β+γ+δ=1.

6. The region-level off-site disaster recovery method based on Paas cloud platform according to claim 1 is characterized in that: The remote multi-active technology provides a high availability and disaster recovery solution by running the main data center and the backup data center at the same time; in the remote multi-active technology, data synchronization methods include synchronous replication and asynchronous replication, and the appropriate data synchronization method is selected according to business requirements and the importance of data; the remote multi-active technology includes a load balancing mechanism and a fault switching mechanism, the load balancing mechanism is used to distribute requests to appropriate nodes in the main and backup data centers to achieve balanced utilization of resources, and the fault switching mechanism is used to detect faults in the main data center and automatically switch to the backup data center to provide services; in the fault switching mechanism, fault detection and automatic switching based on heartbeat detection are adopted, and DNS resolution technology is used to achieve traffic switching.

7. The region-level off-site disaster recovery method based on Paas cloud platform according to claim 6 is characterized in that: The remote multi-active technology comprehensively considers the DNS resolution technology in the load balancing mechanism and the failover mechanism to automatically switch the traffic to the backup data center when the primary data center fails, specifically including: Configure the load balancer: In the Paas cloud platform, deploy the load balancer and add the servers of the primary data center and the backup data center to the backend pool of the load balancer. The load balancer distributes the traffic to different data centers according to certain rules based on the preset load balancing algorithm. Set up health checks: The load balancer regularly performs health checks on the primary data center to detect faults. The health check detects the availability of the primary data center by sending heartbeats or timed requests. If the primary data center fails to respond or returns an error status code, the load balancer will identify it as a fault and automatically trigger a failover. Configure DNS resolution: On the DNS server, configure the domain name resolution of the primary data center to point to the IP address of the primary data center. At the same time, configure the domain name resolution of the backup data center to point to the IP address of the backup data center. When the primary data center fails, modify the DNS record to point the domain name to the IP address of the backup data center to switch traffic. Monitoring and automatic switching: The status of the primary data center is monitored in real time through the monitoring system, and the switching operation is automatically triggered when a failure of the primary data center is detected; the switching process includes updating the configuration of the load balancer to distribute traffic only to the backup data center, and updating the domain name resolution to point to the IP address of the backup data center, so that user requests will be automatically forwarded to the backup data center, realizing fast fault switching.

8. A region-level off-site disaster recovery system based on Paas cloud platform, characterized by: The system adopts remote multi-active technology and WAN optimization technology to provide services simultaneously, and uses WAN optimization technology to reduce the time and delay of cross-geographical data synchronization, thereby improving the reliability and real-time performance of data synchronization; The system uses WAN optimization technology to establish a cloud platform data synchronization measurement model and improves overall efficiency through optimization algorithms. The cloud platform data synchronization measurement model includes: A bandwidth calculation module, used to calculate bandwidth utilization to reflect the bandwidth demand reduced by data compression algorithms, acceleration algorithms and caching algorithms; The data transmission time calculation module is used to substitute the calculated bandwidth utilization into the data transmission time model to obtain the time required for data transmission; The delay calculation module is used to substitute the time required for data transmission into the data synchronization delay model to calculate the delay.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the region-level off-site disaster recovery method based on the Paas cloud platform as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the Region-level off-site disaster recovery method based on the Paas cloud platform as described in any one of claims 1 to 7.

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