So it will essentially create 10,000 objects of type ‘Chunk’ and occupy a lot of memory.
In the second loop of the code snippet above, we are creating a new chunk object for each iteration of the loop. A large number of small allocations can also cause heap fragmentation. The more objects an application allocates, the more frequently garbage collector will be forced to run – which eats up resources needed to boost user experience and responsiveness. The basic rule of thumb here is that garbage collection is not free. So what can you do to keep your system from running out of memory? Read on for some general guidelines for improving the memory usage and overall performance of Android.
Memory Optimization, Best Practices For Android If an application reaches the maximum heap size and needs to allocate more memory, the system will throw an OutOfMemoryError. Android will always start an application process with an average heap size and will then grow it up to the maximum limit on that device for an app. For example, the size on a Galaxy S3 is 64MB, whereas on a Nexus5 device, it is 192MB. Most devices running Android 2.3 or later will return this size as 24MB or higher. You can check the getMemor圜lass() API of ActivityManager service, it will tell you the maximum heap size available for any application on a device. However there are some other options also, like killing whichever cached process will give the maximum memory gain for the system.Įvery application on Android has a maximum heap size limit (varies for each device). Cached processes can be killed in LRU (least recently used) order. Be aware that the system will kill one or more cached processes if it needs more memory for any running process. For example, if an application is launched and then the user presses the ‘Home’ button, then that application’s process will be added in the list of cached processes, that is if it does not have a running service. All other launched applications will go into the list of cached processes to allow for easier and faster switching between applications.
A running process is the foremost application running on the device or an application with a service running actively in the background. This is reflected by USS (unique set size) and PSS (proportional set size) in ‘meminfo.’Īnother important thing to keep in mind when investigating opportunities for memory optimization is that Android divides the application processes based on running vs cached processes. While investigating an application’s RAM usage, it is important to keep shared memory usage in mind since we should only be looking at the private dirty memory that is being used by our application. Every new application process is then forked from the zygote process so it is able to access all the shared RAM pages loaded by it. So whenever a device boots up, a process called zygote loads the common framework code. In order to optimize the memory usage, Android tries to share some framework resources or common classes in memory across processes. Most of the memory in a running application is dirty memory and this is the one you should watch out for. It can be expensive, especially when running in a background process.
Dirty RAMĭirty RAM is the memory that cannot be paged out.
Android knows these pages can be recovered from the disk, so they can be paged out if the system needs memory somewhere else. Any files or resources which are present on the disk, such as code, are kept in mmap’ed pages. Unlike PCs, Android does not offer swap space for memory, however it does use paging and memory-mapping. There are two kinds of memories when it comes to Android: Clean RAM and Dirty RAM. This article covers best practices for memory usage. While there are many mechanisms to reduce Android footprint and reduce memory overhead (such as headless Android mode, low memory Android configurations, etc.) ensuring that the application code also effectively uses available memory is important. While phones and tablets are getting very powerful (with quad core processors and 2+GB RAM having become the de facto standard) this is certainly not the case with many other IoT devices where due to cost margins, the need of the day is still lower powered CPUs and lesser RAM (as RAM is an expensive part of any device BOM). However, getting Android to actually work effectively on diverse platforms is quite challenging. Developing a new OEM product based on Android as an embedded OS makes a lot of sense compared to say, only using Linux as we have covered before. The OS is already making its way into a host of other smart devices, like Google Glass for example, in a movement toward what’s being called “ the internet of things” or IoT. For those tracking the evolution of Android, it is evident that the future of the Android based ecosystem goes far beyond just phones and tablets.