During the performance test, 20 concurrency is required, and the number of concurrent applications i

1. Classic Formula 1:

In general, the following empirical formula is used to estimate the average number of concurrent users and peak data of the system.

1) The average number of concurrent users is c = nl t

2) The peak spine brightness of the number of concurrent users c' = c + 3 * root number c

c is the average number of concurrent users, n is the number of login sessions, l is the average length of the login session, and t is the length of time for the value investigation.

c' is the peak number of concurrent users.

Example 1, suppose system a, the system has 3000 users, and the average number of users per day is about 400 users to access the system (which can be obtained from the system logs), for a typical user, the average time from login to exit in a day is 4 hours, and in a day, the user will only use the system for 8 hours.

In this case, the average number of concurrent users is: c = 400 x 4 8 = 200

The peak number of concurrent users is c' = 200 + 3 * root number 200 = 243

Example 2, a company designed a payroll system for its 170,000 employees, employees can access the system to query their own salary information, but not everyone will use this system, assuming that only 50% of them will use the system regularly, and 70% of these people use the system once in the last week of each month, and the average time of using the system is 5 minutes.

The average number of concurrent users in the last week of the month is (9 to 5).

n = 170000* = 11900

c= 11900*5/60/8 = 124

2. General formula 2:

For most scenarios, we use (total number of users, statistical time) * impact factor (generally 3) to estimate the concurrency.

For example, taking the subway as an example, the number of passengers per day is 50,000 people, the morning peak is 7 to 9 o'clock, and the evening peak is 6 to 7 o'clock, according to the 8 2 principle, 80% of passengers will take the subway during the peak period, then the number of people who go to the subway ticket gate is 50000*80% (3*60*60)=, about 4 people s, taking into account factors such as security checks, entrance closures, etc., the actual number of people piled up at the ticket gate must be larger than this, assuming that everyone needs 3 seconds to enter the station, The actual concurrency should be 4 people s*3s=12, of course, the impact factor can be increased according to the actual situation!

3. According to the PV calculation formula:

For example, a **, the daily PV is about 1000W, according to the 2 8 principle, we can think that 80% of the 1000W PV is completed in 9 hours a day (human energy is limited), then TPS is:

1000w*80% (9*3600)=s, take the empirical factor 3, then the concurrency should be:

Fourth, according to TPS estimates:

The formula is c = think time + 1)*tps

5. Calculated according to the number of system users:

For example, the company's OA system or total users have 2000 people; Peak**500 people; But these 500 people are not a concept that exists as concurrent users. That is, it does not indicate the actual pressure on the server; It is possible that 40% of the attention is paid to the news bulletin board on the homepage and the like (note that watching the news at this stage cannot cause pressure on the server); 20% of users are querying information or operating**; 20% of users are in a daze; 20% jump between pages; In this case, only the real 20% of the users are having a substantial impact on the server.

We will treat this query and operation as a business scope; This part of the business concurrent users is directly referred to as the number of concurrent users

1.Calculate the average number of concurrent users: c=nl t

2.Peak number of concurrent users: c' c+3 root number c

In equation (1), c is the average number of concurrent users; n is the number of login sessions; l is the average length of the login session; t refers to the length of the time period examined.

Equation (2) gives the calculation method of the peak number of concurrent users, where c' is the peak number of concurrent users, and c is the average number of concurrent users obtained in equation (1). The formula is derived from the estimation of the buried answer of the user's login session that conforms to the Poisson distribution.

Suppose there is an OA system with 3000 users, and on average there are about 400 users per day to access the system, (log file view) for a typical user, the average time from login to logout of the system is 4 hours in a day, and the user only uses the system for 8 hours in a day.

According to Equation (1) and Equation (2), we get:

c = 400*4/8 = 200

c' 200 + 3 * root number 200 = 242

However, the general practice is to use 10% of the number of users accessing the system per day as the average number of concurrent users. The maximum number of concurrent users is multiplied by a value, 2 or 3

Suppose the user asks the system to process up to 100 login requests per second, 10 25 50 75 100 concurrent users to perform the login operation, and then observe the response time and transactions per second of the system under different loads. If the number of users is 100 and the response time is still allowed, the number of users should be increased, such as 120. Personally, I understand that this number of users is set by what we often call the equivalence class and boundary value method.