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Low latency services such as credit-card fraud detection and website targetedadvertisement rely on Big Data platforms (e.g., Lucene, Graphchi, Cassandra)which run on top of memory managed runtimes, such as the JVM. These platforms,however, suffer from unpredictable and unacceptably high pause times due toinadequate memory management decisions (e.g., allocating objects with verydifferent lifetimes next to each other, resulting in memory fragmentation).This leads to long and frequent application pause times, breaking Service LevelAgreements (SLAs). This problem has been previously identified and results showthat current memory management techniques are ill-suited for applications thathold in memory massive amounts of middle to long-lived objects (which is thecase for a wide spectrum of Big Data applications).  Previous works try to reduce such application pauses by allocating objectsoff-heap or in special allocation regions/generations, thus alleviating thepressure on memory management. However, all these solutions require acombination of programmer effort and knowledge, source code access, or off-lineprofiling, with clear negative impact on programmer productivity and/orapplication performance.  This paper presents ROLP, a runtime object lifetime profiling system. ROLPprofiles application code at runtime in order to identify which allocationcontexts create objects with middle to long lifetimes, given that such objectsneed to be handled differently (regarding short-lived ones). This profilinginformation greatly improves memory management decisions, leading to long taillatencies reduction of up to 51% for Lucene, 85% for GraphChi, and 60% forCassandra, with negligible throughput and memory overhead. ROLP is implementedfor the OpenJDK 8 HotSpot JVM and it does not require any programmer effort orsource code access.

ROLP: Runtime Object Lifetime Profiling for Big Data Memory Management