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Before deploying the Genesys Web Engagement (GWE) solution to your production site, you must estimate the size of solution that will be able to handle the expected user load. Genesys recommends that you download the GWE Sizing Calculator, an Excel workbook that you can use to help calculate the number of Web Engagement Server nodes required for your production deployment.
The process of estimation starts from input values, usually given in the terms of business operations; for example, daily visit rates, or page view rates. Using some math, and having in mind the workflow that is applied to the input traffic, you can then produce the expected load values in terms of requests per second. Applying these values to the experimentally produced measurements, you can estimate the size of the solution required to be deployed.
Input Data for Load Estimation
To estimate the load, you need to know the following data:
- Average visits rate and page views rate (per hour)
- Maximum visits rate and page views rate (per hour)
To estimate your disk space consumption, it is helpful to have the following information about the configuration of the solution:
- Number of business events per page or visit
- Portion of categorized events
You can use these numbers as inputs into the GWE Sizing Calculator.
Basic Load Estimation
Visits Rate Estimation
Having the maximum or average visits count and page view count per hour (or day), you can get visits rate and visit depth:
- Visits per second = Visits per hour / 3600 = Visits per Day / 24*3600
- Visit depth = Page views per hour / Visits per hour = Page views per day / Visits per day
Events Rate Estimation
Having the following input data about solution configuration:
- Average number of business events (timeout, search, and so on) per page – page custom events
- Average number of user events (SignIn/SignOut/UserInfo) per visit – custom visit events
- The fact that every visit produces one system event (visit started) and every page view produces two system events (PageEntered and PageExited)
The calculator can now estimate the average event rate produced by user visits:
- Events per second = Visits per second * (custom visit events + 1) + (Visits per Seconds * Visit Depth) * (custom page events + 2)
Requests Per Second
Assuming that each visit also contains two requests for loading client scripts and DSL, and every page view also contains one request for categorization info:
- Requests per second = Visits per second * 1 + (Visits per second * Visit depth) * 2 + Events per second
Peak Load Estimation
Having only average values for visit and page views, you can only predict some average load, and, consequently, only an average solution size that will be able to handle such load. However, the user load is not evenly distributed during the day, so you must estimate possible peak load during busy hours.
To cover that scenario, you can take your Visits as the Poisson distributed flow. To estimate the possible peak load, use the following calculation:
- Maximum Visits per Second = Average Visits per Second + 4 * SQRT (Average Visits per Second)
Now, having the Maximum Visits per Second value, it is possible to estimate the Maximum Events per Second and the Maximum Requests per Second, using the same approach as for Basic Load, but replacing average visits rate with maximum visits rate.
Load Estimation Example
Visits Rate Estimation
Having the maximum visits and page view rates per hour, you're calculating visits rate and visit’s depth. For example, having the following values:
- maximum number of visits is 3000 per hour
- maximum number of page views is 32000 per hour
The estimated visit depth is 32000/3000 = 10.7 pages.
- Average load: 3000 visits per hour = 0.83 visits per second
- Maximum load: 0.83 + 4*sqrt(0.83) = 4.5 visits per second
Events Rate Estimation
Given two business events per page, and about 10 percent of visits producing additional events:
- Average events per second = 0.83*(1 + 0.1) + (0.83*10.7)*(2 + 2) = 37 events per second
Requests per Second
Assuming that every visit generates one request for script and another for data, and every page view generates one request for categories:
- Average requests per second = 37 events + 0.83*2 + (0.83*10.7)*1 = 47 requests per second
Peak Load Estimation
Using Maximum Visits per second instead of Average Visits per Second, the following values can be calculated:
- Maximum events per second = 4.5*(1 + 0.1) + (4.5*10.7)*(2 + 2) = 197 events per second
- Maximum requests per second = 197 + 4.5*2 + (4.5*10.7)*1 = 254 requests per second
Minimum Solution Size
The solution deployed should handle all user input, and have some N+1 redundancy.
Recommended solution size for Web Engagement Server—three nodes (to support high availability and load balancing). To avoid "split brain" issues, keep an uneven number of nodes in the cluster. The cluster is considered as “ready-to-work” only after a majority of nodes have joined. A majority is N / 2 + 1, where N is the total number of nodes configured for the cluster.
The minimum solution size for Cassandra and disk space requirements are described in the sections below.
- Cassandra—three nodes (to provide data consistency and to allow a fault tolerance of one node). The consistency level must be LOCAL_QUORUM.
Disk Space Usage
￼Disk space usage directly depends on the following factors:
- The event rate in the solution
- The average count of the categories applied to each event
- The replication factor specified for Cassandra (and Elasticsearch)
Visit—series of page views from the same uniquely identified browser. Note that pages opened in different tabs or windows of the same browser will be treated as part of the same visit.
Average visits per second—service load in terms of customer site.
Request—a single request to the service. A single page view produces a series of requests to the Web Engagement Server:
- Loading of monitoring script
- Loading of categorization information
- Sending information about PageEntered, PageExited, and business events back to the Web Engagement Server
Requests per second (RPS) – actual load in terms of service performance.