Name
Abstract | ||
|---|---|---|
We propose a new, principled approach to adaptive heap sizing based on control theory. We review current state-of-the-art heap sizing mechanisms, as deployed in Jikes RVM and HotSpot. We then formulate heap sizing as a control problem, apply and tune a standard controller algorithm, and evaluate its performance on a set of well-known benchmarks. We find our controller adapts the heap size more responsively than existing mechanisms. This responsiveness allows tighter virtual machine memory footprints while preserving target application throughput, which is ideal for both embedded and utility computing domains. In short, we argue that formal, systematic approaches to memory management should be replacing ad-hoc heuristics as the discipline matures. Control-theoretic heap sizing is one such systematic approach.
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Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2464157.2466481 | ISMM |
Keywords | Field | DocType |
systematic approach,principled heap sizing,control theory,standard controller algorithm,tighter virtual machine memory,memory management,control-theoretic heap sizing,principled approach,current state-of-the-art heap,control problem,heap size,hotspot,virtual machines,ergonomics | Control theory,Virtual machine,Control theory,Computer science,Parallel computing,Real-time computing,Heap (data structure),Heuristics,Memory management,Utility computing,Sizing,Computer programming | Conference |
ISBN | Citations | PageRank |
978-1-4503-2100-6 | 6 | 0.47 |
References | Authors | |
27 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David R. White | 1 | 631 | 41.35 |
| Jeremy Singer | 2 | 226 | 21.41 |
| Jonathan M. Aitken | 3 | 26 | 6.92 |
| Richard E. Jones | 4 | 187 | 13.99 |