Garbage collection: algorithms for automatic dynamic memory management
Garbage collection: algorithms for automatic dynamic memory management
On effectiveness of GC in Java
Proceedings of the 2nd international symposium on Memory management
Heap profiling for space-efficient Java
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Tuning garbage collection for reducing memory system energy in an embedded java environment
ACM Transactions on Embedded Computing Systems (TECS)
Dynamic Storage Allocation: A Survey and Critical Review
IWMM '95 Proceedings of the International Workshop on Memory Management
Automatic heap sizing: taking real memory into account
Proceedings of the 4th international symposium on Memory management
An energy efficient garbage collector for java embedded devices
LCTES '05 Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Shared heap management for memory-limited java virtual machines
ACM Transactions on Embedded Computing Systems (TECS)
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Multitasking is one of capabilities we often want to have in memory-constrained embedded systems. To support multiple address spaces within a small physical memory, a simple memory management frequently encounters the lack of available memory. Our paper presents an efficient heap memory management scheme that reduces memory footprints by adaptively sharing heaps among multiple tasks in JVM environments. We modified KVM from Sun Microsystems so that Java applications acquire or release heaps in a shared pool on an as-needed basis. To protect address spaces among tasks in the absence of virtual memory capabilities, we use memory protection units (MPUs) by incorporating them into our heap sharing scheme. Our experiments with J2ME MIDP applications show significant reductions by 33% on average, ranging from 6% to 50% in memory usage over the execution. The overheads of our scheme in garbage collection are kept low. The execution times in our scheme increase only by 0.2% on average.