Precise garbage collection for C

Rafkind, Jon; Wick, Adam; Regehr, John; Flatt, Matthew

doi:10.1145/1542431.1542438

Cited by 36 publications

(46 citation statements)

References 34 publications

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“…These examples include the checking of bounds information for validating array references [2,3], the con rmation of type information to validate cast operations [24], and the collection of object pointer information to perform garbage collection [33].…”

Section: Defending One Speci C Type Of Errorsmentioning

confidence: 99%

FreeGuard

Silvestro

Liu

Crosser

et al. 2017

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

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In spite of years of improvements to software security, heap-related attacks still remain a severe threat. One reason is that many existing memory allocators fall short in a variety of aspects. For instance, performance-oriented allocators are designed with very limited countermeasures against attacks, but secure allocators generally su er from signi cant performance overhead, e.g., running up to 10× slower. This paper, therefore, introduces FreeGuard, a secure memory allocator that prevents or reduces a wide range of heaprelated attacks, such as heap over ows, heap over-reads, use-afterfrees, as well as double and invalid frees. FreeGuard has similar performance to the default Linux allocator, with less than 2% overhead on average, but provides signi cant improvement to security guarantees. FreeGuard also addresses multiple implementation issues of existing secure allocators, such as the issue of scalability. Experimental results demonstrate that FreeGuard is very e ective in defending against a variety of heap-related attacks.

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Section: Defending One Speci C Type Of Errorsmentioning

confidence: 99%

FreeGuard

Silvestro

Liu

Crosser

et al. 2017

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

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“…Racket's precise single-threaded generational collector [11,5]. The main challenge in this implementation is Racket's page-granularity write barriers.…”

Section: This Work Presents An Implementation Of Clustered Collectionmentioning

confidence: 99%

“…We implemented Clustered Collection as a modification to the precise collector in Racket [11,5] version v5.90.0.9. The Racket collector is a single-threaded generational copying collector.…”

Section: Chapter 4 Implementationmentioning

confidence: 99%

Reducing pause times with clustered collection

Cutler

Morris

2015

SIGPLAN Not.

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Clustered Collection reduces garbage collection pauses in programs with large amounts of live data. A full collection of millions of live objects can pause the program for multiple seconds. Much of this work, however, is repeated from one collection to the next, particularly for programs that modify only a small fraction of their object graphs between collections.Clustered Collection reduces redundant work by identifying regions of the object graph which, once traced, need not be traced by subsequent collections. Each of these regions, or "clusters," consists of objects reachable from a single head object. If the collector can reach a cluster's head object, it skips over the cluster, and resumes tracing at the pointers that leave the cluster. If a cluster's head object is not reachable, or an object within a cluster has been written, the cluster collector may have to trace within the cluster. Clustered Collection is complete despite not tracing within clusters: it frees all unreachable objects.Clustered Collection is implemented as modifications to the Racket collector. Measurements of the code and data from the Hacker News web site show that Clustered Collection decreases full collection pause times by a factor of three. Hacker News works well with Clustered Collection because it keeps gigabytes of data in memory but modifies only a small fraction of that data. Other experiments demonstrate the ability of Clustered Collection to tolerate certain kinds of writes, and quantify the cost of finding clusters.

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“…Second, when transferring the data structures that are found to have been modified after the old version has completed startup operations-and thus cannot be automatically restored by mutable reinitialization-precise knowledge on data types in memory-which generally imposes a nontrivial annotation effort at full coverage [23,30]is only necessary for updated data structures that need to be type-transformed between versions. Building on this observation, MCR relies on a combination of precise [44] and conservative [16,17] garbage collection (GC) techniques to trace data structures and transfer them between versions even with partial type information. This mutable tracing strategy can drastically reduce the number of user-maintained annotations, only required when data structures with ambiguous type information-and thus normally traced conservatively-are changed by the update-and thus require precise tracing to unambiguously apply type transformations.…”

Section: Introductionmentioning

confidence: 99%

Mutable checkpoint-restart

Giuffrida

Iorgulescu

Tanenbaum

2014

Proceedings of the 15th International Middleware Conference on - Middleware '14

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The pressing demand to deploy software updates without stopping running programs has fostered much research on live update systems in the past decades. Prior solutions, however, either make strong assumptions on the nature of the update or require extensive and error-prone manual effort, factors which discourage live update adoption.This paper presents Mutable Checkpoint-Restart (MCR), a new live update solution for generic (multiprocess and multithreaded) server programs written in C. Compared to prior solutions, MCR can support arbitrary software updates and automate most of the common live update operations. The key idea is to allow the new version to restart as similarly to a fresh program initialization as possible, relying on existing code paths to automatically restore the old program threads and reinitialize a relevant portion of the program data structures. To transfer the remaining data structures, MCR relies on a combination of precise and conservative garbage collection techniques to trace all the global pointers and apply the required state transformations on the fly. Experimental results on popular server programs (Apache httpd, nginx, OpenSSH and vsftpd ) confirm that our techniques can effectively automate problems previously deemed difficult at the cost of negligible run-time performance overhead (2% on average) and moderate memory overhead (3.9x on average).

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Precise garbage collection for C

Cited by 36 publications

References 34 publications

FreeGuard

FreeGuard

Reducing pause times with clustered collection

Mutable checkpoint-restart

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