Rodrigo Bruno
Paulo Ferreira
ABSTRACT
Big Data applications suffer from unpredictable and unacceptably high pause times due to bad memory management (Garbage Collection, GC) decisions. This is a problem for all applications but it is even more important for applications with low pause time requirements such as credit-card fraud detection or targeted website advertisement systems, which can easily fail to comply with Service Level Agreements due to long GC cycles (during which the application is stopped). This problem has been previously identified and is related to Big Data applications keeping in memory (for a long period of time, from the GC's perspective) massive amounts of data objects.
Memory management approaches have been proposed to reduce the GC pause time by allocating objects with similar lifetimes close to each other. However, they either do not provide a general solution for all types of Big Data applications (thus only solving the problem for a specific set of applications), and/or require programmer effort and knowledge to change/annotate the application code.
This paper proposes POLM2, a profiler that automatically: i) estimates application allocation profiles based on execution records, and ii) instruments application bytecode to help the GC taking advantage of the profiling information. Thus, no programmer effort is required to change the source code to allocate objects according to their lifetimes. POLM2 is implemented for the OpenJDK HotSpot Java Virtual Machine 8 and uses NG2C, a recently proposed GC which supports multi-generational pretenuring. Results show that POLM2 is able to: i) achieve pauses as low as NG2C (which requires manual source code modification), and ii) significantly reduce application pauses by up to 80% when compared to G1 (default collector in OpenJDK). POLM2 does not negatively impact neither application throughput nor memory utilization.
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Index Terms
- POLM2: automatic profiling for object lifetime-aware memory management for hotspot big data applications
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