Energy efficiency is a first-order concern for mobile devices. Although garbage collection is central to the Android platform, the energy costs of garbage collection are not well understood. This is likely due to major methodological hurdles: first, heterogeneous hardware and inherently multi-tenanted software; second, UI-induced waits confound classic performance analysis methodologies; and third, difficulty in measuring energy, which is often not supported by the device, and if it is, has low temporal resolution. These hurdles have clouded our understanding of the energy costs of GC, and may have led to missed opportunities for more energy efficient mobile software. The principal contributions of this paper are new methodologies and a study of the energy costs of garbage collection that uses them. We introduce: i) new insight into the deep problems that UI interactions bring to the performance analysis of Android applications; ii) methodologies that address the problem of cost attribution when the resolution of the measure is coarser than the events being studied; iii) methodologies that control for the hardware heterogeneity found on modern devices; iv) a methodology that emulates the multi-tenanted environment found on mobile devices; and v) an implementation that allows us to quantify the tradeoff due to parallelism in GC. We perform these experiments using a suite of real-world applications, a subset of the DaCapo benchmarks, and a microbenchmark. We show: i) that waits introduced by UI interactions make performance analysis of Android applications deeply challenging; ii) that single-threaded GC typically outperforms parallel GC on mobile devices; iii) that garbage collection has a significant energy overhead on Android applications and is sensitive to GC algorithm and configuration; iv) that different subsystems’ energy consumption is affected by GC choice and configuration. This work sheds new light on energy overheads on mobile devices and we hope may motivate new designs in this impactful domain.

The performance of mobile devices directly affects billions of people worldwide. Yet, despite memory management being key to their responsiveness, energy efficiency, and cost, mobile devices are understudied in the literature. A paucity of suitable methodologies and benchmarks is likely both a cause and a consequence of this gap. It also reflects the challenges of evaluating mobile devices due to: i) their inherently multi-tenanted nature, ii) the scarcity of widely-used open source workloads suitable as benchmarks, iii) the challenge of determinism and reproducibility given mobile devices' extensive use of GPS and network services, iv) the complexity of mobile performance criteria.

Automatic memory management relieves programmers of the burden of having to reason about object lifetimes in order to soundly reclaim allocated memory. However, this automation comes at a cost. The cost and benefits of garbage collection relative to manual memory management have been the subject of contention for a long time, and will likely remain so. However, until now, the question is surprisingly under-studied. We examine the costs and benefits of garbage collection through four studies, exploring: We conduct this study in a contemporary setting using recent CPU microarchitectures, and novel methodologies including a mark-sweep collector built upon off-the-shelf free-list allocators, allowing us to shed new light on garbage collection overheads in a modern context.

We introduce a novel type system for enforcing secure information flow in an imperative language. Our work is motivated by the problem of statically checking potential information leakage in Android applications. To this end, we design a lightweight type system featuring Android permission model, where the permissions are statically assigned to applications and are used to enforce access control in the applications. We take inspiration from a type system by Banerjee and Naumann to allow security types to be dependent on the permissions of the applications. A novel feature of our type system is a typing rule for conditional branching induced by permission testing, which introduces a merging operator on security types, allowing more precise security policies to be enforced. The soundness of our type system is proved with respect to non-interference. A type inference algorithm is also presented for the underlying security type system, by reducing the inference problem to a constraint solving problem in the lattice of security types. In addition, a new way to represent our security types as reduced ordered binary decision diagrams is proposed.