Garbage collection (GC) pauses are a notorious issue threatening the latency of applications. To mitigate this problem, state-of-the-art concurrent copying collectors allow GC threads to run simultaneously with application threads (mutators) in nearly all GC phases. However, the design of concurrent copying collectors does not always lead to low application latency. To this end, this work studies the behaviors of mainstream concurrent copying collectors in OpenJDK and mainly focuses on long application pauses under heavy workloads. By analyzing the design of those collectors, this work uncovers that lengthy pre-reclamation cycles (including GC phases before actual memory release), high GC frequency, and large metadata maintenance overhead are major factors for long pauses. Therefore, this work proposes Jade, a concurrent copying collector aiming to achieve both short pauses and high GC efficiency. Compared with existing collectors, Jade provides a group-wise collection mechanism to shorten pre-reclamation cycles while controlling GC frequency. It also embraces a generational heap layout and a single-phase algorithm to maximize young GC's throughput. The evaluation results on representative latency-critical applications show that Jade can reach sub-millisecond-level pauses even under heavy workloads and significantly improve applications' peak throughput compared with state-of-the-art concurrent collectors.

Hardware enclaves assist in constructing a trusted execution environment (TEE) to store private code and data and thus become an appealing solution to enhance applications’ security. Nevertheless, state-of-the-art enclave implementations like Intel Software Guard Extensions (SGX) have severe performance issues and hinder the deployment of more complicated applications, especially those written in high-level languages like Java. To reduce the performance overhead, prior work has partitioned applications or rebuilt lightweight language runtimes, but they either require manual labor from developers or fail to provide full-fledged support for existing applications. This work instead providesSAJ, a runtime built upon a full-fledged Java virtual machine (JVM) and thus requires no modifications to applications.SAJfirst analyzes the performance of vanilla JVMs running in enclaves and finds that the memory management overhead and boot phase are culprits for performance slowdown. For memory management,SAJintroduces SGX-aware heap layout and garbage collector, which reduces both GC and application execution time. As for the boot phase,SAJintroduces an address-conscious launching mechanism to improve the boot performance. The evaluation under representative Java applications shows thatSAJcan reduce the overall GC pause time, application time, and boot time by 2.93$\boldsymbol{\times}$, 2.58$\boldsymbol{\times}$, and 2.73$\boldsymbol{\times}$on average, respectively.

Managed workloads show strong demand for large memory capacity, which can be satisfied by a hybrid memory sub-system composed of traditional DRAM and the emerging non-volatile memory (NVM) technology. Nevertheless, NVM devices are limited by deficiencies like write endurance and asymmetric bandwidth, which threatens managed applications’ performance and reliability. Prior work has proposed different object placement mechanisms to mitigate problems introduced by NVM, but they require domain-specific knowledge on applications or significant change on managed runtime. By analyzing the performance of representative data-intensive workloads atop NVM, this paper finds that reducing write operations is key for performance and wear-leveling. To this end, this paper proposes GCMove, a transparent and efficient object placement mechanism for hybrid memories. GCMove embraces a lightweight write barrier for write detection and relies on garbage collections (GC) to copy objects into different devices according to their write-related behaviors. Compared with prior work, GCMove does not require significant changes in heap layout and thus can be easily integrated with mainstream copy-based garbage collection. The evaluation on various managed workloads shows that GCMove can eliminate 99.8% of NVM write operations on average and improve the performance by up to 19.81× compared with the NVM-only version.