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

Undermining User Privacy on Mobile Devices Using AI

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users

Android on x86

Computer scienc

Android on x86: An Introduction to Optimizing for Intel® Architecture

Android on x86: an Introduction to Optimizing for Intel® Architecture serves two main purposes. First, it makes the case for adapting your applications onto Intel’s x86 architecture, including discussions of the business potential, the changing landscape of the Android marketplace, and the unique challenges and opportunities that arise from x86 devices. The fundamental idea is that extending your applications to support x86 or creating new ones is not difficult, but it is imperative to know all of the technicalities. This book is dedicated to providing you with an awareness of these nuances and an understanding of how to tackle them. Second, and most importantly, this book provides a one-stop detailed resource for best practices and procedures associated with the installation issues, hardware optimization issues, software requirements, programming tasks, and performance optimizations that emerge when developers consider the x86 Android devices. Optimization discussions dive into native code, hardware acceleration, and advanced profiling of multimedia applications. The authors have collected this information so that you can use the book as a guide for the specific requirements of each application project. This book is not dedicated solely to code; instead it is filled with the information you need in order to take advantage of x86 architecture. It will guide you through installing the Android SDK for Intel Architecture, help you understand the differences and similarities between processor architectures available in Android devices, teach you to create and port applications, debug existing x86 applications, offer solutions for NDK and C++ optimizations, and introduce the Intel Hardware Accelerated Execution Manager. This book provides the most useful information to help you get the job done quickly while utilizing best practices

An Empirical Study on the Impact of Deep Parameters on Mobile App Energy Usage

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these deep parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. In this paper we empirically investigate this topic, combining a developer survey with systematic energy measurements. Our motivational survey of 25 Android developers suggests that developers do not understand, and largely ignore, the energy impact of deep parameters. To assess the potential implications of this practice, we propose a deep parameter energy profiling framework that can analyze the energy impact of deep parameters in an app. Our framework identifies deep parameters, mutates them based on our parameter value selection scheme, and performs reliable energy impact analysis. Applying the framework to 16 popular Android apps, we discovered that deep parameter-induced energy inefficiency is rare. We found only 2 out of 1644 deep parameters for which a different value would significantly improve its app\u27s energy efficiency. A detailed analysis found that most deep parameters have either no energy impact, limited energy impact, or an energy impact only under extreme values. Our study suggests that it is generally safe for developers to ignore the energy impact when choosing deep parameter values in mobile apps