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首页> 外文期刊>ACM Transactions on Embedded Computing Systems >Shared Heap Management for Memory-Limited Java Virtual Machines
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Shared Heap Management for Memory-Limited Java Virtual Machines

机译:内存受限的Java虚拟机的共享堆管理

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摘要

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.
机译:嵌入式系统中的一种稀缺资源是内存。多任务使缺乏内存的问题更加严重。当前的大多数不提供虚拟内存的嵌入式系统都只是简单地划分物理内存,然后将连续的内存块平均分配给多个应用程序。这种简单的内存管理经常会导致某些应用程序缺少可用内存,而其他应用程序则没有使用全部分配的内存。为了克服当前内存管理中的低效率,我们提出了一种有效的堆管理方案,该方案允许多个应用程序共享堆空间。为了减少总体堆内存使用,应用程序可以自适应地从共享内存池中获取子堆,并将多余的子堆释放到共享池中。结果,应用程序将不连续的多个子堆视为其地址空间中的堆。我们以Java应用程序为目标,以在Sun Microsystems的KVM中实现我们的堆共享方案。为了保护内存保护单元(MPU)中具有有限数量区域的碎片堆空间,我们仅维护有限数量的子堆。我们通过J2ME MIDP应用程序实验性地评估了堆管理方案。我们的静态和动态方案分别平均减少了30%和27%的堆内存使用量。对于这两种方案,开销都保持较低。我们的方案的执行时间平均仅对静态方案增加了0.01%,对于动态方案仅增加了0.35%。

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