To date, the most commonly used criterion for invoking garbage collection (GC) is based on heap usage; that is garbage collection is invoked when the heap or an area inside the heap is full. This approach can suffer from two performance shortcomings, untimely garbage collection invocations and large volumes of surviving objects. In this work, we explore a new GC triggering approach called MicroPhase that exploits two observations, (i) allocation requests occur in phases and (ii) the phase boundaries coincide with times when most objects also die, to proactively invoke garbage collection yielding high efficiency. We extended the HotSpot virtual machine from Sun Microsystems to support MicroPhase and conducted experiments using 20 benchmarks. The experimental results indicate that our technique can reduce the GC times in 19 applications. The differences in GC overhead range from an increase of 1 % to a decrease of 26 % when the heap is set to be twice the maximum live-size. As a result, MicroPhase can improve the the overall performance of 13 benchmarks. The performance differences range from a degradation of 2.5 % to an improvement of 14%.

Managed runtime environments (MREs) employ garbage collection (GC) for automatic memory management. However, GC induces pressure on the virtual memory (VM) manager, since it may touch pages that are not related to the working set of the application. Paging due to GC can significantly hurt performance, even when the application’s working set fits into physical memory. We present a feedback-directed heap resizing mechanism to avoid GC-induced paging, using information from the operating system (OS). We avoid costly GCs when there is physical memory available, and trade off GC for paging when memory is constrained Our mechanism is simple and uses allocation stall events during GC alone to trigger heap resizing, without user participation or OS kernel modification. Our system enables significant performance improvements when real memory is restricted and similar to, or better performance than, the current state-of-the-art MRE, when memory is unconstrained. 1.

Abstract. A generational collection strategy utilizing a single nursery cannot efficiently manage objects in application servers due to variance in their lifespans. In this paper, we introduce an optimization technique designed for application servers that exploits an observation that remotable objects are commonly used as gateways for client requests. Objects instantiated as part of these requests (remote objects) often live longer than objects not created to serve these remote requests (local objects). Thus, our scheme creates remote and local objects in two separate nurseries; each is properly sized to match the lifetime characteristic of the residing objects. We extended the generational collector in HotSpot to support the proposed optimization and found that given the same heap size, the proposed scheme can improve the maximum throughput of an application server by 14% over the default collector. It also allows the application server to handle 10% higher workload prior to memory exhaustion. 1

For garbage-collected applications, dynamically-allocated objects are contained in a heap. Programmer productivity improves significantly if there is a garbage collector to automatically de-allocate objects that are no longer needed by the applications. However, there is a run-time performance overhead in garbage collection, and this cost is sensitive to heap size H: a smaller H will trigger more collection, but a large H can cause page faults, as when H exceeds the size M of main memory allocated to the application. This paper presents a Heap Sizing Rule for how H should vary with M. The Rule can help an application trade less page faults for more garbage collection, thus reducing execution time. It is based on a heap-aware Page Fault Equation that models how the number of page faults depends on H and M. Experiments show that this rule outperforms the default policy used by JikesRVM’s heap size manager. Specifically, the number of faults and the execution time are reduced for both static and dynamically changing M. 1.