Sunil Soman

AppScale: Scalable and Open AppEngine Application Development and Deployment

We present the design and implementation of AppScale, an open source extension to the Google AppEngine (GAE) Platform-as- a-Service (PaaS) cloud technology. Our extensions build upon the GAE SDK to facilitate distributed execution of GAE applications over virtualized cluster resources, including Infrastructure-as-a-Service (IaaS) cloud systems such as Amazon’s AWS/EC2 and Epucalyptus. AppScale provides a framework with which researchers can investigate the interaction between PaaS and IaaS systems as well as the inner workings of, and new technologies for, PaaS cloud technologies using real GAE applications.

Dynamic selection of application-specific garbage collectors

Much prior work has shown that the performance enabled by garbage collection (GC) systems is highly dependent upon the behavior of the application as well as on the available resources. That is, no single GC enables the best performance for all programs and all heap sizes. To address this limitation, we present the design, implementation, and empirical evaluation of a novel Java Virtual Machine (JVM) extension that facilitates dynamic switching between a number of very different and popular garbage collectors. We also show how to exploit this functionality using annotation-guided GC selection and evaluate the system using a large number of benchmarks. In addition, we implement and evaluate a simple heuristic to investigate the efficacy of switching automatically. Our results show that, on average, our annotation-guided system introduces less than 4% overhead and improves performance by 24% over the worst-performing GC (across heap sizes) and by 7% over always using the popular Generational/Mark-Sweep hybrid.

Isla Vista Heap Sizing: Using Feedback to Avoid Paging

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 applications 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

Application-specific garbage collection

Prior work, including our own, shows that application performance in garbage collected languages is highly dependent upon the application behavior and on underlying resource availability. We show that given a wide range of diverse garbage collection (GC) algorithms, no single system performs best across programs and heap sizes. We present a Java Virtual Machine extension for dynamic and automatic switching between diverse, widely used GCs for application-specific garbage collection selection. We describe annotation-guided, and automatic GC switching. We also describe a novel extension to extant on-stack replacement (OSR) mechanisms for aggressive GC specialization that is readily amenable to compiler optimization.

Task-aware garbage collection in a multi-tasking virtual machine

A multi-tasking virtual machine (MVM) executes multiple programs in isolation, within a single operating system process. The goal of a MVM is to improve startup time, overall system throughput, and performance, by effective reuse and sharing of system resources across programs (tasks). However, multitasking also mandates a memory management system capable of offering a guarantee of isolation with respect to garbage collection costs, accounting of memory usage, and timely reclamation of heap resources upon task termination.To this end, we investigate and evaluate, novel task-aware extensions to a state-of-the-art MVM garbage collector (GC). Our task-aware GC exploits the generational garbage collection hypothesis, in the context of multiple tasks, to provide performance isolation by maintaining task-private young generations. Task aware GC facilitates concurrent per-task allocation and promotion, and minimizes synchronization and scanning overhead. In addition, we efficiently track per-task heap usage to enable GC-free reclamation upon task termination. Moreover, we couple these techniques with a light-weight synchronization mechanism that enables per-task minor collection, concurrently with allocation by other tasks.We empirically evaluate the efficiency, scalability, and through-put that our task-aware GC system enables.

MTM2: Scalable Memory Management for Multi-tasking Managed Runtime Environments

Multi-tasking, managed runtime environments (MREs) for modern type-safe, object-oriented programming languages enable isolated, concurrent execution of multiple applications within a single operating system process. Multi-tasking MREs can potentially extract high-performance on desktop and hand-held systems through aggressive sharing of classes and compiled code, and by exploiting high-level dynamic program information. We investigate the performance of a state-of-the-art multi-taking MRE for concurrent program execution. We find that due to limited support for multi-tasking and performance isolation in the memory management subsystem, multi-tasking performs poorly compared to a production-quality, single-tasking MRE. We present MTM2: a comprehensive memory management system for concurrent multi-tasking. MTM2facilitates performance isolation and efficient heap space usage through on-demand allocation of application-private regions. MTM2mitigates fragmentation using a novel hybrid garbage collector that combines mark-sweep with opportunistic copying. Our evaluation shows that MTM2improves overall performance, scalability, and footprint for concurrent workloads over state-of-the-art, multi- and single-tasking MREs.