Shared heap management for memory-limited java virtual machines

Abstract

One scarce resource in embedded systems is memory. Multitasking makes the lack of memory problem even worse. Most current embedded systems, which do not provide virtual memory, simply divide physical memory and evenly assign contiguous memory chunks to multiple applications. Such simple memory management can frequently cause the lack of available memory for some applications, while others are not using the full amount of assigned memory. To overcome inefficiency in current memory management, we present an efficient heap management scheme that allows multiple applications to share heap space. To reduce overall heap memory usage, applications adaptively acquire subheaps out of shared pool of memory and release surplus subheaps to shared pool. As a result, applications see noncontiguous multiple subheaps as a heap in their address space. We target Java applications to implement our heap-sharing scheme in the KVM from Sun Microsystems. To protect fragmented heap space with a limited number of regions in memory protection unit (MPU), we maintain only a limited number of subheaps. We experimentally evaluate our heap management scheme with J2ME MIDP applications. Our static and dynamic schemes reduce heap memory usage, on average, by 30 and 27%, respectively. For both schemes, overheads are kept low. The execution times in our schemes are increased only by 0.01% for static scheme and 0.35% for dynamic scheme, on average.

JDiet: Footprint Reduction for Memory-constrained Systems

“…Utilizing well-known memory management techniques, the goal of JDiet is to allow a memory-constrained system to increase the size of its working set by consuming less physical memory than is normally required. As the amount of memory consumed by heap data far outweighs the code, stack, and other runtime data areas [Choi and Han, 2008], a generic memory management solution must address heap data usage. In much the same way as virtual memory, JDiet provides the illusion of a much largel heap 1 .…”

“…Kunkle and Cooperman [2008] [Bacon et al, 2002;Bonny and Henkel, 2007;Chen et al, 2005Chen et al, , 2003Clausen et al, 2000;Lekatsas et al, 2000;Lekatsas and Wolf, 1999;Rizzo, 1997;Shaham et al, 2001;Venstermans et al, 2007], heap sharing [Choi and Han, 2008], bytecode optimization [Vallée-Rai et al, 1999, prolific types [Shuf et al, 2002], colocation [Yu et al, 2008] and many other optimization/reduction techniques [Ananian and Rinard, 2003;Guo et al, 2006;McDowell et al, 1998;Tip et al, 1999].…”

“…Ekman and Stenstrom [2004] forecast that costs, physical space, and energy consumption of main memory will be a limiting factor in the near future. Multitasking systems exacerbate this problem [Choi and Han, 2008]. In multitasking computing systems a single process is unlikely to be granted all available memory, as it must be shared by multiple concurrently executing software processes.…”