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Memory errors are a notorious source of security vulnerabilities that can lead to service interruptions, information leakage and unauthorized access. Because such errors are also difficult to debug, the absence of timely patches can leave users vulnerable to attack for long periods of time. A variety of approaches have been introduced to combat these errors, but these often incur large runtime overheads and generally abort on errors, threatening availability. This paper presents Archipelago, a runtime system that takes advantage of available address space to substantially reduce the likelihood that a memory error will affect program execution. Archipelago randomly allocates heap objects far apart in virtual address space, effectively isolating each object from buffer overflows. Archipelago also protects against dangling pointer errors by preserving the contents of freed objects after they are freed. Archipelago thus trades virtual address space---a plentiful resource on 64-bit systems---for significantly improved program reliability and security, while limiting physical memory consumption by tracking the working set of an application and compacting cold objects. We show that Archipelago allows applications to continue to run correctly in the face of thousands of memory errors. Across a suite of server applications, Archipelago's performance overhead is 6% on average (between -7% and 22%), making it especially suitable to protect servers that have known security vulnerabilities due to heap memory errors.

Dynamic storage allocation has become increasingly important in many applications, in part due to the use of the object-oriented paradigm. At the same time, processor speeds are increasing faster than memory speeds and programs are increasing in size faster than memories. In this paper, we investigate efforts to predict heap object reference and lifetime behavior at the time objects are allocated. Our approach uses profile-based optimization, and considers a variety of different information sources present at the time of object allocation to predict the object's reference frequency and lifetime. Our results, based on measurements of six allocation intensive programs, show that program references to heap objects are highly predictable and that our prediction methods can successfully predict the behavior of these heap objects. We show that our methods can decrease the page fault rate of the programs measured, sometimes dramatically, in cases where the physical memory available to the program is constrained.