Abstract

Memory management is a critical issue for correctness and performance in real-time embedded systems. Recent work on real-time garbage collectors has shown that it is possible to provide guarantees on worst-case pause times and minimum mutator utilization time. This paper presents a new hierarchical real-time garbage collection algorithm for mixed-priority and mixed-criticality environments. With hierarchical garbage collection, real-time programmers can partition the heap into a number of heaplets and for each partition choose to run a separate collector with a schedule that matches the allocation behavior and footprint of the real-time task using it. This approach lowers worst-case response times of realtime applications by 26%, while almost doubling mutator utilization – all with only minimal changes to the application code. Categories and Subject Descriptors D.3.4 [Programming Languages]: Processors—interpreters, run-time environments; D.4.

Real time java on resource-constrained platforms with fiji vm

Real-time Java is quickly emerging as a platform for building safety-critical embedded systems. The real-time variants of Java, including [8, 15], are attractive alternatives to Ada and C since they provide a cleaner, simpler, and safer programming model. Unfortunately, current real-time Java implementations have trouble scaling down to very hard real-time embedded settings, where memory is scarce and processing power is limited. In this paper, we describe the architecture of the Fiji VM, which enables vanilla Java applications to run in very hard environments, including booting on bare hardware with only very rudimentary operating system support. We also show that our minimalistic approach delivers comparable performance to that of server-class production Java Virtual Machine implementations. 1

A Study of Concurrent Real-Time Garbage Collectors

Concurrent garbage collection is highly attractive for real-time systems, because offloading the collection effort from the executing threads allows faster response, allowing for extremely short deadlines at the microseconds level. Concurrent collectors also offer much better scalability over incremental collectors. The main problem with concurrent real-time collectors is their complexity. The first concurrent real-time garbage collector that can support fine synchronization, STOPLESS, has recently been presented by Pizlo et al. In this paper, we propose two additional (and different) algorithms for concurrent real-time garbage collection: CLOVER and CHICKEN. Both collectors obtain reduced complexity over the first collector STOPLESS, but need to trade a benefit for it. We study the algorithmic strengths and weaknesses of CLOVER and CHICKEN and compare them to STOPLESS. Finally, we have implemented all three collectors on the Bartok compiler and runtime for C # and we present measurements to compare their efficiency and responsiveness

Fine-grained Adaptive Biased Locking

Mutual-exclusion locking is the prevailing technique for protecting shared resources in concurrent programs. Fine-grained locking maximizes the opportunities for concurrent execution while preserving correctness, but increases both the number of locks and the frequency of lock operations. Adding to the frequency of these operations is the practice of using locks defensively - such as in library code designed for use in both concurrent and single-threaded scenarios. If the library does not protect itself with locks, an engineering burden is placed on the library's users; if the library does use locks, it punishes those who use it only from a single thread. Biased locking is a dynamic protocol for eliminating this trade-off, in which the underlying run-time system optimizes lock operations by biasing a lock to a specific thread when the lock is dynamically found to be thread-local. Biased locking protocols are distinguished by how many opportunities for optimization are found, and what performance trade-offs for non-local locks are experienced. Of particular concern is the relatively high cost involved in revoking the bias of a lock, which makes existing biased locking protocols susceptible to performance pathologies for programs with specific patterns of contention. This work presents the biased locking protocol used in Jikes RVM, a high-throughput Java virtual machine. The protocol, dubbed FABLE, builds on prior work by adding per-object-instance dynamic adaptation and inexpensive bias revocation. We describe the protocol, detail how it was implemented, and use it in offering the most thorough evaluation of Java locking protocols to date. FABLE is shown to provide speed-ups over traditional Java locking across a broad spectrum of benchmarks while being robust to cases previous protocols handled poorly

Reflexes: Abstractions for Highly Responsive Systems

Commercial Java virtual machines are designed to maximize the performance of applications at the expense of predictability. High throughput garbage collection algorithms, for example, can introduce pauses of 100 milliseconds or more. We are interested in supporting applications with response times in the tens of microseconds and their integration with larger timing-oblivious applications in the same Java virtual machine. We propose Reflexes, a new abstraction for writing highly responsive systems in Java and investigate the virtual machine support needed to add Reflexes to a Java environment. Our implementation of Reflexes was evaluated on several programs including an audio-processing application. We were able to run a Reflex at 22.05KHz with less than 0.2 % missed deadlines over 10 million observations, a result that compares favorably to an implementation written in C

Scheduling real-time garbage collection on uniprocessors

Managed languages such as Java and C# are increasingly being considered for hard real-time applications because of their productivity and software engineering advantages. Automatic memory management, or garbage collection, is a key enabler for robust, reusable libraries, yet remains a challenge for analysis and implementation of real-time execution environments. This article comprehensively compares leading approaches to hard real-time garbage collection. There are many design decisions involved in selecting a real-time garbage collection algorithm. For time-based garbage collectors on uniprocessors one must choose whether to use periodic, slack-based or hybrid scheduling. A significant impediment to valid experimental comparison of such choices is that commercial implementations use completely different proprietary infrastructures. We present Minuteman, a framework for experimenting with real-time collection algorithms in the context of a high-performance execution environment for real-time Java. We provide the first comparison of the approaches, both experimentally using realistic workloads, and analytically in terms of schedulability