Dynamic storage allocation: A survey and critical review

Wilson, Paul R.; Johnstone, Mark S.; Neely, Michael J.; Boles, David B.

doi:10.1007/3-540-60368-9_19

Cited by 359 publications

(430 citation statements)

References 94 publications

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“…Such behavior has important implications for memory management. In particular, it makes sense to cache freed blocks in anticipation of additional requests for the same size [732].…”

Section: Examples and Visualizationmentioning

confidence: 99%

“…In terms of system behavior it is possible to envision schedulers that exploit the short-range regularity of localized workloads, and adapt their behavior to best suit the current workload [243,754,668,755,694]. This is similar to recent approaches used in memory allocators, which cache freed blocks in anticipation of future requests for the same block sizes [732].…”

Section: Importancementioning

confidence: 99%

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Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

191

131

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Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.

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“…Such behavior has important implications for memory management. In particular, it makes sense to cache freed blocks in anticipation of additional requests for the same size [732].…”

Section: Examples and Visualizationmentioning

confidence: 99%

Section: Importancementioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

191

131

View full text Add to dashboard Cite

show abstract

“…Successful examples include the Lea allocator in Linux based systems [6], the Buddy allocator for Unix based systems [6] and variations of the Kingsley allocator in Windows XP [13] and FreeBSD based systems. Their embedded OS counterparts include the DM allocators of Symbian [11], Enea OSE [10], uClinux [9] and Windows CE [12].…”

Section: Related Workmentioning

confidence: 99%

“…This space is not used to store the application's data and can not be used for a future memory request. This is called internal fragmentation, which is common in requests of small memory blocks [6]. It can be prevented or reduced with the use of freelists, the best fit policy and the splitting mechanism, which will be analyzed in detail in the next section.…”

Section: Memory Fragmentationmentioning

confidence: 99%

Reducing memory fragmentation in network applications with dynamic memory allocators optimized for performance

Mamagkakis

Baloukas

Atienza

et al. 2006

Computer Communications

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The needs for run-time data storage in modern wired and wireless network applications are increasing. Additionally, the nature of these applications is very dynamic, resulting in heavy reliance on dynamic memory allocation. The most significant problem in dynamic memory allocation is fragmentation, which can cause the system to run out of memory and crash, if it is left unchecked. The available dynamic memory allocation solutions are provided by the real-time Operating Systems used in embedded or general-purpose systems. These state-of-the-art dynamic memory allocators are designed to satisfy the run-time memory requests of a wide range of applications. Contrary to most applications, network applications need to allocate too many different memory sizes (e.g., hundreds different sizes for packets) and have an extremely dynamic allocation and de-allocation behavior (e.g., unpredictable web-browsing activity). Therefore, the performance and the de-fragmentation efficiency of these allocators is limited. In this paper, we analyze all the important issues of fragmentation and the ways to reduce it in network applications, while keeping the performance of the dynamic memory allocator unaffected or even improving it. We propose highly customized dynamic memory allocators, which can be configured for specific network needs. We assess the effectiveness of the proposed approach in three representative real-life case studies of wired and wireless network applications. Finally, we show very significant reduction in memory fragmentation and increase in performance compared to state-of-the-art dynamic memory allocators utilized by real-time Operating Systems.

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“…There is a large body of work concerning general garbage collection techniques [36,64] and escape analysis for improving stack allocation in garbage collected systems [9,26]. The additional complexity of region inference and the polymorphic multiplicity analysis implemented in the ML Kit [8] allow more objects to be stack allocated than does traditional escape analyses, which allows only local, non-escaping values to be stack allocated.…”

Section: Related Workmentioning

confidence: 99%