Effective memory management for mobile environments

Smartphones, tablets, and other mobile devices exhibit vastly different constraints compared to regular or classic computing environments like desktops, laptops, or servers. Mobile devices run dozens of so-called “apps” hosted by independent virtual machines (VM). All these VMs run concurrently and each VM deploys purely local heuristics to organize resources like memory, performance, and power. Such a design causes conflicts across all layers of the software stack, calling for the evaluation of VMs and the optimization techniques specific for mobile frameworks. In this dissertation, we study the design of managed runtime systems for mobile platforms. More specifically, we deepen the understanding of interactions between garbage collection (GC) and system layers. We develop tools to monitor the memory behavior of Android-based apps and to characterize GC performance, leading to the development of new techniques for memory management that address energy constraints, time performance, and responsiveness. We implement a GC-aware frequency scaling governor for Android devices. We also explore the tradeoffs of power and performance in vivo for a range of realistic GC variants, with established benchmarks and real applications running on Android virtual machines. We control for variation due to dynamic voltage and frequency scaling (DVFS), Just-in-time (JIT) compilation, and across established dimensions of heap memory size and concurrency. Finally, we provision GC as a global service that collects statistics from all running VMs and then makes an informed decision that optimizes across all them (and not just locally), and across all layers of the stack. Our evaluation illustrates the power of such a central coordination service and garbage collection mechanism in improving memory utilization, throughput, and adaptability to user activities. In fact, our techniques aim at a sweet spot, where total on-chip energy is reduced (20–30%) with minimal impact on throughput and responsiveness (5–10%). The simplicity and efficacy of our approach reaches well beyond the usual optimization techniques

Efficient Region-Based Memory Management for Resource-limited Real-Time Embedded Systems.

This paper presents a simple and efficient static analysis algorithm, combined with a region allocation policy for real-time embedded Java applications. The goal of this work is to provide a static analysis mechanism efficient enough to be integrated in an assisted-development environment, and to implement region-based memory management primitives suited for resource-limited platforms such as smart cards

Endicia Webtools Optimization and Migration

This project worked with Endicia to bring an external United States Postal Service system into Endicia’s environment. The system is used to calculate data needed to print shipping labels for packages. We built a full-stack server-to- database application that interfaces with Endicia’s services. We received requirements to prioritize performance and to handle hundreds of requests per second while never incurring downtime. Through this project, we made this service’s response times 30x faster. The program was then handed off for deployment

Memory Power Optimization of Java-Based Embedded Systems Exploiting Garbage Collection Information

Nowadays, Java is used in all types of embedded devices. For these memory-constrained systems, the automatic dynamicmemorymanager (Garbage Collector or GC) has been always a key factor in terms of the Java Virtual Machine (JVM) performance. Moreover, in current embedded platforms, power consumption is becoming as important as performance. Thus, in this paper we present an exploration, from an energy viewpoint, of the different possibilities of memory hierarchies for high-performance embedded systems when used by state-of-the-art GCs. This is a starting point for a better understanding of the interactions between the Java applications, the memory hierarchy and the GC. Hence, we subsequently present two techniques to reduce energy consumption on Java-based embedded systems, based on exploiting GC information. The first technique uses GC execution behavior to reduce leakage energy consumption taking advantage of the low-power mode of actual multi-banked SDRAM memories and it is intended for generational collectors. This technique can achieve a reduction up to 50% of SDRAM memory leakage. The second technique involves the inclusion of a software-controlled (scratchpad) memory that stores GC instructions under the JVM control to reduce the active energy consumption and also improve the performance of the target embedded system and it is aimed at all kind of garbage collectors. For this last technique we have experimented with two different approaches for selecting the GC code to be stored in the scratchpad memory: one static and one dynamic. Our experimental results show that the proposed dynamic scratchpad management approach for GCs enables up to 63% energy consumption reduction and 25% performance improvement during the collector phase, which means, in terms of JVM execution, a global reduction of 29% and 17% for energy and cycles respectively. Overall, this work outlines that the key for an efficient low-power implementation of Java Virtual Machines for high-performance embedded systems is the synergy between the GC choice, the memory architecture tuning, and the inclusion of power management schemes controlled by the JVM, exploiting knowledge of the GC behavior

The economics of garbage collection

This paper argues that economic theory can improve our understanding of memory management. We introduce the allocation curve, as an analogue of the demand curve from microeconomics. An allocation curve for a program characterises how the amount of garbage collection activity required during its execution varies in relation to the heap size associated with that program. The standard treatment of microeconomic demand curves (shifts and elasticity) can be applied directly and intuitively to our new allocation curves. As an application of this new theory, we show how allocation elasticity can be used to control the heap growth rate for variable sized heaps in Jikes RVM

A model-based approach for automatic recovery from memory leaks in enterprise applications

Large-scale distributed computing systems such as data centers are hosted on heterogeneous and networked servers that execute in a dynamic and uncertain operating environment, caused by factors such as time-varying user workload and various failures. Therefore, achieving stringent quality-of-service goals is a challenging task, requiring a comprehensive approach to performance control, fault diagnosis, and failure recovery. This work presents a model-based approach for fault management, which integrates limited lookahead control (LLC), diagnosis, and fault-tolerance concepts that: (1) enables systems to adapt to environment variations, (2) maintains the availability and reliability of the system, (3) facilitates system recovery from failures. We focused on memory leak errors in this thesis. A characterization function is designed to detect memory leaks. Then, a LLC is applied to enable the computing system to adapt efficiently to variations in the workload, and to enable the system recover from memory leaks and maintain functionality