Memory management is the examination of an application’s memory to ensure that it is properly allocated in storage to optimize the application’s performance. Memory management involves organizing memory in storage, monitoring the capacity of the storage space, and performing memory disposal.
Memory management is necessary for optimizing the overall performance of applications and for rightsizing their infrastructure. When an application stores too much memory, additional infrastructure is used, unnecessarily increasing infrastructure costs. Also, an application’s productivity may be slowed when not enough memory storage is available. Memory can be used to create shortcuts and patterns to optimize an application’s performance when tasks are recognized. Infrastructure and performance are directly tied to costs and as many companies already face tight margins, memory management can help limit these burdens.
Memory management is an automated process that is executed by the Java Virtual Machine (JVM). One responsibility of the JVM is overseeing the heap, which is the space where all the objects created by a Java program are held. The heap is important because it stores information that the application can use to run more efficiently. Objects in the heap can be called on when relevant to provide shortcuts and locate patterns, using previously learned information.
The Just-In-time (JIT) Compiler, traditionally located in the Java Virtual Machine (JVM), is responsible for memory disposal. When memory is not relevant or useful to the program, the JIT is responsible for disposing of it. In the JIT, the garbage collector locates unused objects to clear memory space.
When the garbage collector categorizes objects, objects are placed at different locations in the heap. There are different storage locations in the heap, where objects are situated based on how long they’ve remained in the heap. Objects can be moved into the tenured location after surviving many processes and can eventually become permanent in the memory.
Memory management in other languages may not be an automated process. C and C++ memory management are examples of manual processes, which require programmers to deliberately perform these functions. The JIT compiler is the critical component that differentiates Java memory management from other programs.
Python memory management involves a private heap containing all Python objects and data structures, while Linux memory management is a complex system with many configurable settings.
Most JVM vendors still operate with a pre-cloud mindset. Most JVMs aren’t designed to maximize the benefits created by cloud technologies, such as the added opportunities for efficiency, scalability and cost optimization. Pre-cloud technology limits the growth of companies looking to expand into the cloud and ultimately hinders their performance capabilities. This is why many companies are failing to reach cloud cost optimization; when technology isn’t optimized for the cloud, companies will bear the cost.
When optimizing the performance of JVMs for the cloud, the differences between cloud native and traditional JVMs become apparent. Traditionally, JIT compilers are located in the JVM, so each JVM performs the compilation process separately. This model uses resources inefficiently, and infrastructure limitations can inhibit application performance. In cloud-native environments, the compiler moves from inside each JVM to a shared space in the cloud so one compiler performs compilation for all the shared resources.
Traditional Java applications cannot run at an acceptable speed during warm up while the application compiles components, which is a time-consuming performance delay. These applications cannot store information about their past executions. When an application is started, the JVM must perform the same routines every time, as they profile, analyze and compile the same data. Alternatively, cloud native JVMs can store profiles, allowing the JVM to run code during warm up, reducing the inefficiencies of the warm-up process.
Memory management is also slowed in traditional JVMs because the JITs must pause applications to perform garbage collection. These pauses hurt overall application performance because the application must pause its services and also go through the warm-up process again. Also, when the garbage collectors are run, their pauses cannot be predicted. When an application is unknowingly paused, it may be misidentified as an application error. These pauses may lead to false error reports, creating unnecessary performance issues when new instances are created as the response. A cloud native JVM eliminates this problem with the pauseless garbage collecting feature.
Azul Platform Prime is the world’s only cloud native JVM, taking JVMs out of a pre-cloud mindset and into the cloud world. Azul’s Intelligence Cloud also hosts a pauseless garbage collector, known as the Cloud Native Compiler. Azul Platform Prime is an easy, effective way to improve the performance of revenue-generating applications while reducing infrastructure costs by up to 50%. Unlike other vendors, Azul’s products are designed for the cloud. These powerful optimizations result in a faster and more efficient memory management process.
A truly superior Java platform that can cut your infrastructure costs in half.
