Beyond RSS: Towards Intelligent Dynamic Memory Management (Work in Progress)
Authors: 
Christos Panagiotis Lamprakos, Sotirios Xydis, Peter Kourzanov, Manu Perumkunnil, Francky Catthoor, Dimitrios SoudrisAuthors Info & Claims
Christos Panagiotis Lamprakos, Sotirios Xydis, Peter Kourzanov, Manu Perumkunnil, Francky Catthoor, Dimitrios SoudrisAuthors Info & ClaimsPages 158 - 164
Abstract
The main goal of dynamic memory allocators is to minimize memory fragmentation. Fragmentation stems from the interaction between workload behavior and allocator policy. There are, however, no works systematically capturing said interaction. We view this gap as responsible for the absence of a standardized, quantitative fragmentation metric, the lack of workload dynamic memory behavior characterization techniques, and the absence of a standardized benchmark suite targeting dynamic memory allocation. Such shortcomings are profoundly asymmetric to the operation’s ubiquity.
This paper presents a trace-based simulation methodology for constructing representations of workload-allocator interaction. We use two-dimensional rectangular bin packing (2DBP) as our foundation. 2DBP algorithms minimize their products’ makespan, but virtual memory systems employing demand paging deem such a criterion inappropriate. We see an allocator’s placement decisions as a solution to a 2DBP instance, optimizing some unknown criterion particular to that allocator’s policy. Our end product is a data structure by design concerned with events residing entirely in virtual memory; no information on memory accesses, indexing costs or any other factor is kept.
We bootstrap our contribution’s utility by exploring its relationship to maximum resident set size (RSS). Our baseline is the assumption that less fragmentation amounts to smaller peak RSS. We thus define a fragmentation metric in the 2DBP substrate and compute it for both single- and multi-threaded workloads linked to 7 modern allocators. We also measure peak RSS for the resulting pairs. Our metric exhibits a monotonic relationship with memory footprint 94% of the time, as inferred via two-tailed statistical hypothesis testing with at least 99% confidence.
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