Speculative region-based memory management for big data systems

Most real-world Big Data systems are written in managed languages. These systems suffer from severe memory problems due to the massive volumes of objects created to process input data. Allocating and deallocating a sea of objects puts a severe strain on the garbage collector, leading to excessive GC efforts and/or out-of-memory crashes. Region-based memory management has been recently shown to be effective to reduce GC costs for Big Data systems. However, all existing region-based techniques require significant user annotations, resulting in limited usefulness and practicality. This paper reports an ongoing project, aiming to design and implement a novel speculative region-based technique that requires only minimum user involvement. In our system, objects are allocated speculatively into their respective regions and promoted into the heap if needed. We develop an object promotion algorithm that scans regions for only a small number of times, which will hopefully lead to significantly improved memory management efficiency. We also present an OpenJDK-based implementation plan and an evaluation plan.