3 research outputs found
Empirically Assessing Opportunities for Prefetching and Caching in Mobile Apps
Network latency in mobile software has a large impact on user experience,
with potentially severe economic consequences. Prefetching and caching have
been shown effective in reducing the latencies in browser-based systems.
However, those techniques cannot be directly applied to the emerging domain of
mobile apps because of the differences in network interactions. Moreover, there
is a lack of research on prefetching and caching techniques that may be
suitable for the mobile app domain, and it is not clear whether such techniques
can be effective or whether they are even feasible. This paper takes the first
step toward answering these questions by conducting a comprehensive study to
understand the characteristics of HTTP requests in over 1000 popular Android
apps. Our work focuses on the prefetchability of requests using static program
analysis techniques and cacheability of resulting responses. We find that there
is a substantial opportunity to leverage prefetching and caching in mobile
apps, but that suitable techniques must take into account the nature of apps'
network interactions and idiosyncrasies such as untrustworthy HTTP header
information. Our observations provide guidelines for developers to utilize
prefetching and caching schemes in app development, and motivate future
research in this area.Comment: ASE 201
Mobile-App Analysis and Instrumentation Techniques Reimagined with DECREE
A large number of mobile-app analysis and instrumentation techniques have
emerged in the past decade. However, those techniques' components are difficult
to extract and reuse outside their original tools, their evaluation results are
hard to reproduce, and the tools themselves are hard to compare. This paper
introduces DECREE, an infrastructure intended to guide such techniques to be
reproducible, practical, reusable, and easy to adopt in practice. DECREE allows
researchers and developers to easily discover existing solutions to their
needs, enables unbiased and reproducible evaluation, and supports easy
construction and execution of replication studies. The paper describes DECREE's
three modules and its potential to fundamentally alter how research is
conducted in this area
Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms
Prefetching web pages is a well-studied solution to reduce network latency by
predicting users' future actions based on their past behaviors. However, such
techniques are largely unexplored on mobile platforms. Today's privacy
regulations make it infeasible to explore prefetching with the usual strategy
of amassing large amounts of data over long periods and constructing
conventional, "large" prediction models. Our work is based on the observation
that this may not be necessary: Given previously reported mobile-device usage
trends (e.g., repetitive behaviors in brief bursts), we hypothesized that
prefetching should work effectively with "small" models trained on mobile-user
requests collected during much shorter time periods. To test this hypothesis,
we constructed a framework for automatically assessing prediction models, and
used it to conduct an extensive empirical study based on over 15 million HTTP
requests collected from nearly 11,500 mobile users during a 24-hour period,
resulting in over 7 million models. Our results demonstrate the feasibility of
prefetching with small models on mobile platforms, directly motivating future
work in this area. We further introduce several strategies for improving
prediction models while reducing the model size. Finally, our framework
provides the foundation for future explorations of effective prediction models
across a range of usage scenarios.Comment: MOBILESoft 202