Adaptive and Concurrent Garbage Collection for Virtual Machines
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| Titel: | Adaptive and Concurrent Garbage Collection for Virtual Machines |
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| Autoren: | Haque, Md. Enamul, Zobaed, Sm, Hussain, Razin Farhan, Islam, Aminul |
| Publikationsjahr: | 2020 |
| Bestand: | ScholarSpace at University of Hawaii at Manoa |
| Schlagwörter: | Soft Computing: Theory Innovations and Problem Solving Benefits, adaptive garbage collection, machine learning, matrix factorization, memory management, virtual machines |
| Beschreibung: | An important issue for concurrent garbage collection in virtual machines (VM) is to identify which garbage collector (GC) to use during the collection process. For instance, Java program execution times differ greatly based on the employed GC. It has not been possible to identify the optimal GC algorithms for a specific program before exhaustively profiling the execution times for all available GC algorithms. In this paper, we present an adaptive and concurrent garbage collection (ACGC) technique that can predict the optimal GC algorithm for a program without going through all the GC algorithms. We implement this technique in the Java virtual machine and test it using standard benchmark suites. ACGC learns the algorithms’ usage pattern from different training program features and generates a model for future programs. Feature generation and selection are two important steps of our technique, which creates different attributes to use in the learning step. Our experimental evaluation shows improvement in selecting the best GC. Additionally, our approach is helpful in finding better heap size settings for improved program execution. |
| Publikationsart: | conference object |
| Dateibeschreibung: | 10 pages; application/pdf |
| Sprache: | English |
| Relation: | Proceedings of the 53rd Hawaii International Conference on System Sciences; https://hdl.handle.net/10125/63950; https://doi.org/10.24251/HICSS.2020.211 |
| DOI: | 10.24251/HICSS.2020.211 |
| Verfügbarkeit: | https://hdl.handle.net/10125/63950 https://doi.org/10.24251/HICSS.2020.211 |
| Rights: | Attribution-NonCommercial-NoDerivatives 4.0 International ; https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Dokumentencode: | edsbas.98B5CFDD |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | An important issue for concurrent garbage collection in virtual machines (VM) is to identify which garbage collector (GC) to use during the collection process. For instance, Java program execution times differ greatly based on the employed GC. It has not been possible to identify the optimal GC algorithms for a specific program before exhaustively profiling the execution times for all available GC algorithms. In this paper, we present an adaptive and concurrent garbage collection (ACGC) technique that can predict the optimal GC algorithm for a program without going through all the GC algorithms. We implement this technique in the Java virtual machine and test it using standard benchmark suites. ACGC learns the algorithms’ usage pattern from different training program features and generates a model for future programs. Feature generation and selection are two important steps of our technique, which creates different attributes to use in the learning step. Our experimental evaluation shows improvement in selecting the best GC. Additionally, our approach is helpful in finding better heap size settings for improved program execution. |
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| DOI: | 10.24251/HICSS.2020.211 |