In order to get programmatically, android.os.Process.myPid() function may be used.Īlternatively, it is possible to use /proc/self/stat file path which serves the same purpose. This file is located at /proc/ /stat, where is the ID of an application process. There is one Linux system file present on Android that is especially useful for identifying CPU times for individual apps. The goal is to find the sum of all these segments.
The graph below shows that CPU time can be represented as many individual time segments on a timeline. Usually, the CPU is not doing the work for the application during 100% of its lifetime. CPU timeĬPU time ( cpuTimeSec) is the time CPU spent doing work for a given application process, and is measured in seconds. The rest of this blog post will focus on collecting time-based metrics. Thus, it would significantly reduce the amount of data we need to collect. Since we’re aggregating analytics over time, time-based metrics would be a better fit as they allow for computing average percentages over any time interval during an app session with only a couple of data points. They are helpful when calculating the average CPU usage percentage over time.įrequency-based metrics tend to have wide ranges of values over short periods of time and are thus better suited for real-time monitoring. Time-based metrics: Based on time values provided by the system.Frequency-based metrics: Real-time data based on CPU frequency values.It is possible to divide all CPU metrics into two categories: Therefore, not all CPU monitoring approaches that are available on Linux work on Android. Note: The latest Android versions introduced permission limitations for accessing some Linux system files because of security reasons. This can be called either directly with the C language or using JVM wrappers provided by the Android SDK. Since Android is based on the Linux kernel, it keeps the system directory structure from the kernel. There are 2 primary sources for collecting CPU metrics on Android: To compute the above value, we must gather many intermediary metrics. This value shows a percentage of how much load the application has put on the CPU between 2 points in time. The primary metric that we are targeting is the relative average CPU usage. Based on this data, it is possible to obtain average values across product releases, compare them and act accordingly. The main idea of the CPU analysis is to track how much load the app puts on the device’s CPU while it’s running. Thus, it is important to understand how the application consumes CPU resources and to see the dynamics of such metrics across product releases. Features are constantly being added or improved, and all these modifications can potentially impact the performance of the application.
This means that the codebase grows and changes very quickly. Android applications such as Lyft’s apps are developed by a large number of contributors.
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