A capacity assessment method and storage medium for an IPTV service platform system

Abstract

This application provides a capacity assessment method and storage medium for an IPTV service platform system. It analyzes historical capacity usage data for each service area to derive a reference coefficient Z for that service area. i Number of requests per user access R i Average daily visits per user (P) i Based on the set number of users S and the reference coefficient Z for each service area i Number of requests per user access R i Average daily visits per user (P) i By determining the estimated access capacity requirement T for the target area and then combining this with the capacity component capacity TPSn, the number of capacity components required for the IPTV service platform system to provide IPTV services in the target area can be determined. This allows for analysis of data from the IPTV service platform system in existing service areas, providing a reference for the IPTV service platform system in other areas, enabling more accurate and reliable capacity assessments. This reduces the problem of high capacity resource costs or failure to meet actual system needs due to large discrepancies in capacity assessments.

Description

Technical Field

[0001] This application relates to the field of IPTV technology, and more specifically, to a capacity assessment method and storage medium for an IPTV service platform system. Background Technology

[0002] Capacity assessment refers to the process of planning, allocating, and adjusting computing resources (such as computing power, storage capacity, and network bandwidth) based on system requirements, resource availability, and performance goals. In the IPTV industry, each new service system requires capacity assessment before it is officially deployed. Due to the unique characteristic of IPTV being a province-specific network, there is currently no precise basis for determining the amount of capacity resources needed before a service system goes live. Therefore, the industry typically relies on empirical rules, statistical models, or manual adjustments for capacity planning and resource allocation. However, these simplified methods often fail to accurately predict and meet the actual needs of the system.

[0003] The industry uses simple models and rules, such as the Pareto Principle (80 / 20 rule), to describe system capacity requirements. The Pareto Principle focuses on the critical 20%, ignoring the other 80%. This simplification may overlook important details and factors, affecting the accuracy of the assessment. The Pareto Principle is often based on experience and observation, lacking specific data support. This means that in unconventional situations, its applicability is limited, requiring more detailed and accurate data for capacity requirement assessment. Furthermore, the Pareto Principle assumes an uneven distribution of resources and benefits, an assumption that is not always met; in some fields or projects, resource and benefit distribution may be more even. The Pareto Principle is based on a specific observation period and context, failing to consider change and dynamism. Capacity requirements may change over time, due to market fluctuations, or other factors, so relying solely on the Pareto Principle may not be sufficient to address such dynamic changes.

[0004] Therefore, existing technologies cannot fully account for the complexity and variability of systems, which may lead to inadequate capacity planning or wasted resources. Furthermore, these traditional methods typically rely on subjective capacity forecasting based on potential future user volume data, which may fail to accurately predict future demand, especially in the face of system changes, seasonal fluctuations, or unforeseen events, where the accuracy of these traditional methods will obviously decrease. Additionally, these traditional methods usually require significant human intervention and manual adjustments, including making capacity planning and resource allocation decisions based on experience. This is not only time-consuming but also prone to human error, reducing the efficiency and accuracy of capacity management.

[0005] In summary, the lack of suitable capacity assessment solutions within the IPTV industry makes it easy for developers to misjudge actual capacity needs, directly impacting the cost of capacity resources or causing performance issues that significantly affect the end-user experience due to insufficient capacity investment. Therefore, how to conduct accurate and reliable capacity assessments is a problem that needs to be solved in this field. Summary of the Invention

[0006] The purpose of this application is to provide a capacity assessment method and storage medium for an IPTV service platform system, so as to conduct reliable capacity assessment and reduce the problem of high capacity resource costs or failure to meet the actual needs of the system due to large differences in capacity assessment.

[0007] To achieve the above objectives, the embodiments of this application are implemented in the following manner:

[0008] In a first aspect, embodiments of this application provide a capacity assessment method for an IPTV service platform system, comprising: obtaining a predetermined number of users for the IPTV service platform system in a target area, wherein the predetermined number of users represents the number of users S that the IPTV service platform system is expected to serve when providing IPTV services in the target area; obtaining historical capacity usage data of the IPTV service platform system in each service area; and determining a reference coefficient Z for each service area based on the historical capacity usage data of each service area. i Number of requests per user access R i Average daily visits per user (P) i Based on the set number of users S and the reference coefficient Z for each service region. i Number of requests per user access R i Average daily visits per user (P) i The estimated access capacity requirement T for the target area is determined; the capacity component capability TPSn is obtained; and based on the estimated access capacity requirement T and the capacity component capability TPSn, the number of capacity components required for the IPTV service platform system to provide IPTV services in the target area is determined.

[0009] In conjunction with the first aspect, in the first possible implementation of the first aspect, a reference coefficient Z for each service area is determined based on historical capacity usage data for each service area. i Number of requests per user access R i Average daily visits per user (P) iThis includes: For each service region: Based on historical capacity usage data for the current service region, determining the daily number of users accessing the service, the daily number of accesses, the daily number of access requests, the daily peak number of requests, and the daily average number of requests. The daily number of users accessing the service region reveals the number of users accessing the service region in a day; the daily number of accesses reveals the number of accesses generated in the current service region in a day; the daily number of access requests reveals the number of access requests generated in the current service region in a day; the daily peak number of requests reveals the maximum number of access requests at all times in the current service region during a day; and the daily average number of requests reveals the average number of access requests at all times in the current service region during a day. Based on the daily peak number of requests, the daily average number of requests, and the daily number of users accessing the service region, determining the reference coefficient Z for the current service region. i Based on the daily number of visits and the number of users visiting each day, determine the average daily number of visits P for users in the current service area. i Based on the daily number of visits and daily number of access requests, determine the number of single access requests R for users in the current service area. i .

[0010] In conjunction with the first possible implementation of the first aspect, in the second possible implementation of the first aspect, the reference coefficient Z for the current service area is determined based on the daily peak request count, daily average request count, and daily number of users accessing the service. i This includes: dividing the peak daily request count within the current service area by the average daily request count to calculate the daily request parameter value for that day; establishing a mapping relationship between the daily request parameter value and the daily number of users accessing the service area to form a parameter-user value pair for that day; and determining the reference coefficient Z for the current service area based on the daily parameter-user value pairs. i .

[0011] In conjunction with the first possible implementation of the first aspect, in the third possible implementation of the first aspect, the average daily number of visits P for users in the current service area is determined based on the daily number of visits and the number of users visiting within the day. i This includes: dividing the daily number of visits within the current service area by the daily number of users accessing the service area to obtain the average number of visits per user for that day; and calculating the average daily number of visits P for users within the current service area based on the average number of visits per user each day. i .

[0012] In conjunction with the first possible implementation of the first aspect, in the fourth possible implementation of the first aspect, the number of single access requests R per user in the current service area is determined based on the daily number of accesses and the number of access requests per day. iThis includes: dividing the total number of daily access requests within the current service area by the total number of daily accesses to obtain the number of single access requests for that day; and calculating the number of single access requests R per user in the current service area based on the number of single access requests per day. i .

[0013] In conjunction with the first possible implementation of the first aspect, in the fifth possible implementation of the first aspect, all times of day within the current service area are measured in seconds, based on a set user volume S and a reference coefficient Z for each service area. i Number of requests per user access R i Average daily visits per user (P) i Determine the estimated access capacity requirement T for the target area, including:

[0014] The estimated access capacity requirement T is calculated using the following formula:

[0015] F = R × P × S,

[0016]

[0017] Where F is the estimated total number of requests per day in the target region, R is the average number of single access requests per user across all service regions, P is the average number of daily accesses per user across all service regions, S is the number of users in the target region, Z is the average reference coefficient across all service regions, n is the total number of service regions, and R i P i Z i These represent the number of single access requests per user, the average number of accesses per user per day, and the reference coefficient α for the i-th service region. i Let be the weight corresponding to the i-th service area.

[0018] In conjunction with the fifth possible implementation of the first aspect, in the sixth possible implementation of the first aspect, based on the estimated access capacity requirement T and the capacity component capacity TPSn, the number of capacity components required for the IPTV service platform system to provide IPTV services in the target area is determined, including:

[0019] The number of capacity components is calculated using the following formula:

[0020]

[0021] Where x represents the number of capacity components required to provide IPTV service to a set number S of users in the target area, and TPSn is the capacity component capacity, revealing the number of access requests that the capacity component can handle per second. Indicates to Rounding up, Δx represents the number of redundant capacity components.

[0022] In conjunction with the fifth possible implementation of the first aspect, in the seventh possible implementation of the first aspect, after determining the number of capacity components required for the IPTV service platform system to provide IPTV services in the target area, the method further includes: the IPTV service platform system adopting a capacity component dynamic adjustment strategy and a load balancing strategy to provide IPTV services in the target area.

[0023] Combining the seventh possible implementation of the first aspect, in the eighth possible implementation of the first aspect, the dynamic adjustment strategy of the capacity component is as follows: if the current number of online users in the target area... Not exceeding the trigger threshold The IPTV service platform system uses a capacity component of a first quantity A1 to process user requests, where the first quantity A1 and the trigger threshold are... They respectively satisfy:

[0024]

[0025]

[0026] Where F is the estimated total number of requests per day in the target region, and TPSn is the capacity of the capacity component; if the current number of online users in the target region... Exceeding the trigger threshold And the current number of online users The number of online users exceeding the number at the previous moment is used to determine the number of capacity components (A2) for handling user requests, based on the following formula:

[0027]

[0028] Where R is the average number of single access requests per user across all service regions, and P is the average number of daily accesses per user across all service regions; if the current number of online users in the target region... Exceeding the trigger threshold And the number of online users when the number of capacity components was last adjusted minus the current number of online users. If the difference is greater than TPSn, decrease the number of capacity components that handle user requests by one; otherwise, keep the number of capacity components that handle user requests unchanged.

[0029] Secondly, embodiments of this application provide a storage medium disposed within a device, comprising a stored program, wherein, when the program is executed, it controls the device containing the storage medium to execute the capacity assessment method of the IPTV service platform system as described in the first aspect or any possible implementation thereof.

[0030] Beneficial effects:

[0031] 1. This solution analyzes historical capacity usage data for each service area using the IPTV service platform system to determine the reference coefficient Z for each service area. i Number of requests per user access R i Average daily visits per user (P) i Based on the set number of users S and the reference coefficient Z for each service area i Number of requests per user access R i Average daily visits per user (P) i The process involves determining the estimated access capacity requirement T for the target area, and then, based on the estimated access capacity requirement T and the capacity component capacity TPSn, determining the number of capacity components required for the IPTV service platform system to provide IPTV services in the target area. This allows for analysis of data from the IPTV service platform system in existing service areas, providing a reference for the IPTV service platform system in other areas (or other projects) to conduct more accurate and reliable capacity assessments. This reduces the problem of high capacity resource costs or failure to meet actual system needs due to large discrepancies in capacity assessments.

[0032] 2. Calculate the reference coefficient Z for each service area. i Number of requests per user access R i Average daily visits per user (P) i When estimating parameters such as access capacity demand T, a relevant indicator calculation scheme was designed to make the calculated indicators and data more realistic and to provide a more accurate estimate of the access capacity demand T. This guides the IPTV service platform system in estimating the number of capacity components required to provide IPTV services in the target area, ensuring the accuracy and reliability of the estimate. Furthermore, given the rich and diverse data and services in the service area, using data from these service areas as support effectively overcomes the limited applicability of traditional methods (such as the Pareto principle).

[0033] 3. This solution further outlines the response strategy for the IPTV service platform system when providing IPTV services in the target area. It designs a dynamic adjustment strategy for capacity components, combined with mature load balancing strategies. This allows for dynamic adjustment of the number of capacity components based on the number of online users, enabling more precise matching of actual needs and avoiding excessive or insufficient resource allocation, thereby improving resource utilization efficiency. When the number of online users exceeds a trigger threshold, dynamic adjustments are made based on user access frequency and request frequency, providing sufficient processing capacity during peak periods to ensure a good user experience. The dynamic adjustment strategy can provide sufficient capacity components according to actual needs, while appropriately reducing resources during off-peak periods, thereby reducing unnecessary costs. This strategy can make timely adjustments based on actual conditions, adapting to changes in user volume and improving the robustness and stability of the IPTV service platform system. Furthermore, the dynamic adjustment strategy better achieves load balancing, preventing performance bottlenecks or failures caused by excessive load on certain nodes. In summary, this dynamic adjustment strategy helps the IPTV service platform system allocate resources more efficiently and flexibly, improving system stability and user experience, while also reducing costs to a certain extent and better meeting user needs.

[0034] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A flowchart illustrating a capacity assessment method for an IPTV service platform system provided in this application embodiment.

[0037] Figure 2 This is historical capacity usage data for all services in a province in North China within a certain time period.

[0038] Figure 3 This is historical capacity usage data for a specific day within a certain time period for all services in a province in North China.

[0039] Figure 4 This is historical capacity usage data for all services in a southwestern province within a certain time period.

[0040] Figure 5 This is historical capacity usage data for a specific day within a certain time period for all services in a southwestern province.

[0041] Figure 6 This is historical capacity usage data for all services in a northwestern province within a certain time period.

[0042] Figure 7 This is historical capacity usage data for a specific day within a certain time period for all services in a northwestern province.

[0043] Figure 8 A diagram illustrating supporting documentation for the successful implementation of the project. Detailed Implementation

[0044] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0045] Please see Figure 1 , Figure 1 This is a flowchart illustrating a capacity assessment method for an IPTV service platform system provided in an embodiment of this application. The capacity assessment method for the IPTV service platform system may include steps S10, S20, S30, S40, and S50.

[0046] When an IPTV service platform system needs to provide IPTV services in a new area (referred to as the "target area" in this embodiment), capacity assessment is inevitably required to control investment costs, meet actual needs (it needs to be able to respond to user requests in a timely manner; if the capacity components are insufficient, the user experience will be greatly reduced), and ensure service quality. The capacity assessment method of the IPTV service platform system provided in this embodiment can be used to perform capacity assessment and determine the appropriate number of capacity components. The accuracy of capacity assessment is the key to balancing investment costs and user experience.

[0047] Therefore, step S10 can be run.

[0048] Step S10: Obtain the set number of users corresponding to the IPTV service platform system in the target area, where the set number of users represents the number of users S that the IPTV service platform system expects to serve when providing IPTV services in the target area.

[0049] In this embodiment, the set number of users corresponding to the IPTV service platform system in the target area can be obtained. The set number of users here represents the number of users S that the IPTV service platform system expects to serve when providing IPTV services in the target area, or the maximum number of users that need to make simultaneous requests.

[0050] After determining the number of users S, step S20 can be executed.

[0051] Step S20: Obtain historical capacity usage data of the IPTV service platform system in each service area.

[0052] In this embodiment, since the IPTV service platform system provides IPTV services in some service areas, the data generated by the IPTV service platform system during the process of providing IPTV services to these service areas can be used as guidance. It should be noted that the IPTV service provided to the service areas in this embodiment is the full range of services, not just a single service. This ensures the richness and diversity of services and avoids the problem of large prediction errors caused by service type deviations. The obtained data is the historical capacity usage data for each service area, including data at each moment (in seconds) within a period of time (e.g., 6 months, 1 year, 3 years), such as the number of users accessing the service, the number of access requests, and the capacity usage (in Mbps) at each moment. Figure 2-7 As shown.

[0053] After obtaining the historical capacity usage data for each service area, step S30 can be executed.

[0054] Step S30: Based on the historical capacity usage data of each service area, determine the reference coefficient Z for each service area. i Number of requests per user access R i Average daily visits per user (P) i .

[0055] In this embodiment, for each service area: based on the historical capacity usage data of the current service area, the daily number of users accessing the service area, the daily number of accesses, the daily number of access requests, the daily peak number of requests, and the daily average number of requests can be determined. The daily number of users accessing the service area reveals the number of users accessing the service area in a day, the daily number of accesses reveals the number of accesses generated in the current service area in a day, the daily number of access requests reveals the number of access requests generated in the current service area in a day, the daily peak number of requests reveals the maximum number of access requests at all times in the current service area in a day, and the daily average number of requests reveals the average number of access requests at all times in the current service area in a day.

[0056] Then, the reference coefficient Z for the current service region can be determined based on the daily peak request count, daily average request count, and daily number of users accessing the service. i .

[0057] For example, the peak daily request count for the current service area can be divided by the average daily request count for that day to calculate the daily request parameter value. Then, a mapping relationship is established between the daily request parameter value and the daily number of users accessing the service area, forming a parameter-user value pair for that day. Based on these daily parameter-user value pairs, the reference coefficient Z for the current service area is determined. iHere, the reference coefficient Z for the current service area can be calculated using a weighted average method. i .

[0058] Assuming the historical capacity usage data for the current service region is for the past year (e.g., from January 1, 2023 to December 31, 2023), then the calculated daily request parameter values ​​for the current service region are 365, denoted as Z. ij , represents the intraday request parameter value of the i-th service region on the j-th day, and the intraday access user count for each day is denoted as S. ij Let represent the number of daily visitors to the i-th service region on day j. Then, the reference coefficient Z for the current service region (i.e., the i-th service region) can be calculated using the following formula. i :

[0059]

[0060] Where M is the number of days of historical capacity usage data for the current service area (i.e., the i-th service area), which corresponds to the total number of request parameter values ​​within a day in this formula.

[0061] Then, based on the daily number of visits and the number of users visiting each day, the average daily number of visits (P) for the current service area can be determined. i .

[0062] For example, the daily access count within the current service area can be divided by the daily number of users accessing the service area to obtain the average number of user accesses for that day (i.e., the average number of accesses per user); then, based on the daily average number of user accesses, the daily average number of user accesses P within the current service area can be calculated. i Here, the average daily number of visits P by users in the current service area can also be calculated using a weighted average method. i .

[0063] For example, continuing with the above assumption, the average number of visits per user calculated for the current service area is also 365, denoted as P. ij Let represent the average number of user visits on day j in the i-th service region. Therefore, the average daily number of visits P in the current service region (i.e., the i-th service region) can be calculated using the following formula. i :

[0064]

[0065] Furthermore, the number of single access requests (R) per user in the current service area can be determined based on the daily number of accesses and daily access requests. i .

[0066] For example, the number of daily access requests within the current service area can be divided by the total number of daily accesses to obtain the number of single access requests for that day. Then, based on the number of single access requests per day, the number of single access requests R per user in the current service area can be calculated. i Here, the number of single access requests R per user in the current service area can be calculated by averaging. i .

[0067] For example, continuing with the above hypothetical scenario, the number of single access requests calculated for the current service area is also 365, denoted as R. ij Let R represent the number of single access requests for the i-th service region on day j. Therefore, the number of single access requests R for the current service region (i.e., the i-th service region) can be calculated using the following formula. i :

[0068]

[0069] Determine the reference coefficient Z for each service area. i Number of requests per user access R i Average daily visits per user (P) i Then, step S40 can be run.

[0070] Step S40: Based on the set number of users S and the reference coefficient Z for each service area i Number of requests per user access R i Average daily visits per user (P) i The estimated access capacity requirement T for the target area is determined.

[0071] In this embodiment, since all times of day within each service area are measured in seconds (i.e., the interval between any two adjacent times is 1 second), the estimated access capacity requirement T can be calculated using the following formula:

[0072] F = R × P × S, (4)

[0073]

[0074] Where F is the estimated total number of requests per day in the target region, R is the average number of single access requests per user across all service regions, P is the average number of daily accesses per user across all service regions, S is the number of users in the target region, Z is the average reference coefficient across all service regions, n is the total number of service regions, and R i P i Z i These represent the number of single access requests per user, the average number of accesses per user per day, and the reference coefficient α for the i-th service region. iLet be the weight corresponding to the i-th service area.

[0075] In this embodiment, α i satisfy:

[0076]

[0077] Where, α i S represents the weight corresponding to the i-th service region. ij M represents the number of users accessing the service area on day j, M represents the number of days of historical capacity usage data for the service area, and n represents the total number of service areas.

[0078] After calculating the estimated access capacity requirement T, step S50 can be executed.

[0079] Step S50: Obtain the capacity component capability TPSn, and based on the estimated access capacity requirement T and the capacity component capability TPSn, determine the number of capacity components required for the IPTV service platform system to provide IPTV services in the target area.

[0080] In this embodiment, the number of capacity components can be calculated using the following formula:

[0081]

[0082] Where x represents the number of capacity components required to provide IPTV service to a set number S of users in the target area, and TPSn is the capacity component capacity, revealing the number of access requests that the capacity component can handle per second, measured in QPS (Queries Per Second). This embodiment uses TPSn = 3000 as an example, but is not limited thereto. Indicates to Rounding up, Δx represents the number of redundant capacity components. In this embodiment, it is taken as 2 or 3, but there is no limitation.

[0083] To facilitate understanding of this solution, an example is provided here:

[0084] Assuming the target area has a user count S = 100,000, the average number of single user requests R = 30, the average number of daily user visits P = 300, and the average reference coefficient of all service areas Z = 3, the user count S, the average number of single user requests R, and the average number of daily user visits P are substituted into formula (4) to calculate the estimated total number of daily requests F = 900,000,000 in the target area. Then, the estimated total number of daily requests F in the target area and the average reference coefficient Z of all service areas are substituted into formula (7) to calculate the estimated access capacity requirement T = 31,250. Then, the estimated access capacity requirement T and the capacity component capacity TPSn are substituted into formula (10). Here, the number of redundant capacity components Δx = 2. Therefore, the number of capacity components x = 13 can be calculated.

[0085] Here is a data comparison of project implementation:

[0086] For a certain project (customer name omitted), based on a total mobile user base of 14 million and an estimated TPS of 30,000 to 40,000 on the existing network, with each static container's TPS estimated at 2,000, the peak concurrency calculation method for a single data center using the Pareto principle is shown in Table 1 below:

[0087] Table 1. Capacity Assessment Based on the Pareto Principle

[0088]

[0089]

[0090] The recommended server configuration is shown in Table 2 below:

[0091] Table 2. Server Configuration Recommendations

[0092]

[0093] As can be seen, the 80 / 20 rule was used to evaluate the capacity, and the number of capacity components determined was 15 (12 units + 3 redundant units), which far exceeded the number needed to meet the actual demand, resulting in a significant increase in costs.

[0094] The capacity assessment method for the IPTV service platform system provided in this application shows that only three servers are needed to meet normal user service requirements. To avoid server malfunctions, a configuration of 3+1=4 (or 3+2=5) servers can be used, thus meeting user demand while also providing redundancy. The project has been successfully implemented and meets usage requirements. Figure 8 A diagram illustrating supporting documentation for the successful implementation of the project.

[0095] After determining the number of capacity components required for the IPTV service platform system to provide IPTV services in the target area, a response strategy for the IPTV service platform system to provide IPTV services in the target area can also be set. For example, the IPTV service platform system can use a dynamic capacity component adjustment strategy and a load balancing strategy to provide IPTV services in the target area. Here, the dynamic capacity component adjustment strategy is designed in this application, while the load balancing strategy is an existing mature solution (e.g., using a load balancer to distribute user requests to multiple capacity components to ensure that the load of each capacity component is relatively balanced; different load balancing algorithms can be used, such as round-robin, least connections, IP hash, etc., without limitation), which will not be elaborated here.

[0096] In this embodiment, the dynamic adjustment strategy for the capacity component is designed as follows:

[0097] If the current number of online users in the target region Not exceeding the trigger threshold The IPTV service platform system uses a capacity component of a first quantity A1 to process user requests, where the first quantity A1 and the trigger threshold are... They respectively satisfy:

[0098]

[0099] Where F is the estimated total number of requests per day in the target region, and TPSn is the capacity of the capacity component.

[0100] If the current number of online users in the target region Exceeding the trigger threshold And the current number of online users The number of online users exceeding the number at the previous moment is used to determine the number of capacity components (A2) for handling user requests, based on the following formula:

[0101]

[0102] Where R is the average number of single access requests per user across all service regions, and P is the average number of daily accesses per user across all service regions.

[0103] If the current number of online users in the target region Exceeding the trigger threshold And the number of online users when the number of capacity components was last adjusted minus the current number of online users. If the difference is greater than TPSn, the number of capacity components processing user requests can be reduced by one; otherwise, the number of capacity components processing user requests remains unchanged.

[0104] This application embodiment also provides a storage medium, which is disposed within a device and includes a stored program. When the program is executed, it controls the device where the storage medium is located to execute the capacity assessment method of the IPTV service platform system of this embodiment.

[0105] In summary, this application provides a capacity assessment method and storage medium for an IPTV service platform system. By analyzing historical capacity usage data of the IPTV service platform system in each service area, a reference coefficient Z for each service area is determined. i Number of requests per user access R i Average daily visits per user (P) i Based on the set number of users S and the reference coefficient Z for each service area i Number of requests per user access R i Average daily visits per user (P) i The process involves determining the estimated access capacity requirement T for the target area, and then, based on the estimated access capacity requirement T and the capacity component capacity TPSn, determining the number of capacity components required for the IPTV service platform system to provide IPTV services in the target area. This allows for analysis of data from the IPTV service platform system in existing service areas, providing a reference for the IPTV service platform system in other areas (or other projects) to conduct more accurate and reliable capacity assessments. This reduces the problem of high capacity resource costs or failure to meet actual system needs due to large discrepancies in capacity assessments.

[0106] In calculating the reference coefficient Z for each service area i Number of requests per user access R i Average daily visits per user (P) i When estimating parameters such as access capacity demand T, a relevant indicator calculation scheme was designed to make the calculated indicators and data more realistic and to provide a more accurate estimate of the access capacity demand T. This guides the IPTV service platform system in estimating the number of capacity components required to provide IPTV services in the target area, ensuring the accuracy and reliability of the estimate. Furthermore, given the rich and diverse data and services in the service area, using data from these service areas as support effectively overcomes the limited applicability of traditional methods (such as the Pareto principle).

[0107] This solution further outlines the response strategy for the IPTV service platform system when providing IPTV services in the target area. It designs a dynamic adjustment strategy for capacity components, combined with mature load balancing strategies. This allows for dynamic adjustment of the number of capacity components based on the number of online users, enabling more precise matching of actual needs and avoiding excessive or insufficient resource allocation, thereby improving resource utilization efficiency. When the number of online users exceeds a trigger threshold, dynamic adjustments are made based on user access frequency and request frequency, providing sufficient processing capacity during peak periods to ensure a good user experience. The dynamic adjustment strategy can provide sufficient capacity components according to actual needs, while appropriately reducing resources during off-peak periods, thereby reducing unnecessary cost overhead. This strategy can make timely adjustments based on actual conditions, adapting to changes in user volume and improving the robustness and stability of the IPTV service platform system. Furthermore, the dynamic adjustment strategy better achieves load balancing, preventing performance bottlenecks or failures caused by excessive load on certain nodes. In summary, this dynamic adjustment strategy helps the IPTV service platform system allocate resources more efficiently and flexibly, improving system stability and user experience, while also reducing costs to a certain extent and better meeting user needs.

[0108] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0109] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A capacity assessment method for an IPTV service platform system, characterized in that, include: Obtain the set number of users corresponding to the IPTV service platform system in the target area, where the set number of users represents the number of users S that the IPTV service platform system expects to serve when providing IPTV services in the target area; Obtain historical capacity usage data of the IPTV service platform system in each service area; Based on historical capacity usage data for each service area, a reference coefficient Z is determined for each service area. i Number of requests per user access R i Average daily visits per user (P) i ; Based on the set user volume S and the reference coefficient Z for each service region i Number of requests per user access R i Average daily visits per user (P) i Determine the estimated access capacity requirement T for the target area; Obtain the capacity component capability TPSn, and based on the estimated access capacity requirement T and the capacity component capability TPSn, determine the number of capacity components required for the IPTV service platform system to provide IPTV services in the target area. Based on historical capacity usage data for each service area, a reference coefficient Z is determined for each service area. i Number of requests per user access R i Average daily visits per user (P) i ,include: For each service area: Based on historical capacity usage data for the current service area, the following are determined for each day: number of users accessing the service area within a day, number of accesses within a day, number of access requests within a day, peak number of access requests within a day, and average number of access requests within a day. The number of users accessing the service area within a day reveals the number of users accessing the service area in a day; the number of accesses within a day reveals the number of accesses generated in the current service area in a day; the number of access requests within a day reveals the number of access requests generated in the current service area in a day; the peak number of access requests within a day reveals the maximum number of access requests at all times in the current service area during a day; and the average number of access requests within a day reveals the average number of access requests at all times in the current service area during a day. Based on the daily peak request count, daily average request count, and daily number of users accessing the service area, a reference coefficient Z is determined for the current service region. i ; Based on the daily number of visits and the number of users visiting each day, determine the average daily number of visits P for users in the current service area. i ; Based on the daily number of visits and daily number of access requests, determine the number of single access requests R for users in the current service area. i ; All times of day within the current service area are displayed in seconds, based on a set user volume S and a reference coefficient Z for each service area. i Number of requests per user access R i Average daily visits per user (P) i Determine the estimated access capacity requirement T for the target area, including: The estimated access capacity requirement T is calculated using the following formula: F = R × P × S, Where F is the estimated total number of requests per day in the target region, R is the average number of single access requests per user across all service regions, P is the average number of daily accesses per user across all service regions, S is the number of users in the target region, Z is the average reference coefficient across all service regions, n is the total number of service regions, and R i P i Z i These represent the number of single access requests per user, the average number of accesses per user per day, and the reference coefficient α for the i-th service region. i Let be the weight corresponding to the i-th service area.

2. The capacity assessment method for an IPTV service platform system according to claim 1, characterized in that, Based on the daily peak request count, daily average request count, and daily number of users accessing the service area, a reference coefficient Z is determined for the current service region. i ,include: Divide the peak daily number of requests in the current service area by the average daily number of requests for that day to calculate the daily request parameter value for that day. Establish a mapping relationship between the daily request parameter values ​​and the daily number of users accessing the site, forming parameter-user value pairs for the day; Based on the daily parameter-user value pairs, the reference coefficient Z for the current service area is determined. i .

3. The capacity assessment method for an IPTV service platform system according to claim 1, characterized in that, Based on the daily number of visits and the number of users visiting each day, determine the average daily number of visits P for users in the current service area. i ,include: Divide the daily access count within the current service area by the daily access user count to obtain the average number of user accesses for that day. Calculate the average daily number of user visits P in the current service area based on the average number of user visits per day. i .

4. The capacity assessment method for an IPTV service platform system according to claim 1, characterized in that, Based on the daily number of visits and daily number of access requests, determine the number of single access requests R for users in the current service area. i ,include: Divide the number of daily access requests within the current service area by the total number of daily accesses for that day to obtain the number of single access requests for that day. Based on the number of single access requests per day, calculate the number of single access requests R for users in the current service area. i .

5. The capacity assessment method for an IPTV service platform system according to claim 1, characterized in that, Based on the estimated access capacity requirement T and the capacity component capacity TPSn, the number of capacity components required for the IPTV service platform system to provide IPTV services in the target area is determined, including: The number of capacity components is calculated using the following formula: Where x represents the number of capacity components required to provide IPTV service to a set number S of users in the target area, and TPSn is the capacity component capacity, revealing the number of access requests that the capacity component can handle per second. Indicates to Rounding up, Δx represents the number of redundant capacity components.

6. The capacity assessment method for an IPTV service platform system according to claim 1, characterized in that, After determining the number of capacity components required for the IPTV service platform system to provide IPTV services in the target area, the method further includes: The IPTV service platform system employs a dynamic capacity component adjustment strategy and a load balancing strategy to provide IPTV services in the target area.

7. The capacity assessment method for an IPTV service platform system according to claim 6, characterized in that, The dynamic adjustment strategy for capacity components is as follows: If the current number of online users in the target region Not exceeding the trigger threshold The IPTV service platform system uses a capacity component of a first quantity A1 to process user requests, wherein the first quantity A1 and the trigger threshold are... They respectively satisfy: Where F is the estimated total number of requests per day in the target area, and TPSn is the capacity of the capacity component; If the current number of online users in the target region Exceeding the trigger threshold And the current number of online users The number of online users exceeding the number at the previous moment is used to determine the number of capacity components (A2) for handling user requests, based on the following formula: Where R is the average number of single access requests per user across all service regions, and P is the average number of daily accesses per user across all service regions. If the current number of online users in the target region Exceeding the trigger threshold And the number of online users when the number of capacity components was last adjusted minus the current number of online users. If the difference is greater than TPSn, reduce the number of capacity components that handle user requests by one. Otherwise, keep the number of capacity components that handle user requests unchanged.

8. A storage medium, characterized in that, The storage medium is disposed within the device and includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to execute the capacity assessment method of the IPTV service platform system according to any one of claims 1 to 7.

Images