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title={{Privacy mediators: helping IoT cross the chasm}},
Davies, Nigel Andrew Justin and Taft, Nina and
Satyanarayanan, Mahadev and Clinch, Sarah and
Amos, Brandon
Unease over data privacy will retard consumer acceptance of IoT
deployments. The primary source of discomfort is a lack of user
control over raw data that is streamed directly from sensors to the
cloud. This is a direct consequence of the over-centralization of
today’s cloud-based IoT hub designs. We propose a solution that
interposes a locally-controlled software component called a privacy
mediator on every raw sensor stream. Each mediator is in the same
administrative domain as the sensors whose data is being collected,
and dynamically enforces the current privacy policies of the owners
of the sensors or mobile users within the domain. This solution necessitates
a logical point of presence for mediators within the administrative
boundaries of each organization. Such points of presence
are provided by cloudlets, which are small locally-administered data
centers at the edge of the Internet that can support code mobility.
The use of cloudlet-based mediators aligns well with natural personal
and organizational boundaries of trust and responsibility.
title={{{Early Implementation Experience with Wearable Cognitive Assistance Applications}}},
author={Chen, Zhuo and Jiang, Lu and Hu, Wenlu and Ha, Kiryong and Amos, Brandon and Pillai, Padmanabhan and Hauptmann, Alex and Satyanarayanan, Mahadev},
A cognitive assistance application combines a wearable device such
as Google Glass with cloudlet processing to provide step-by-step
guidance on a complex task. In this paper, we focus on user assistance
for narrow and well-defined tasks that require specialized
knowledge and/or skills. We describe proof-of-concept implementations
for four different tasks: assembling 2D Lego models, freehand
sketching, playing ping-pong, and recommending context-relevant
YouTube tutorials. We then reflect on the difficulties we faced in
building these applications, and suggest future research that could
simplify the creation of similar applications.
title={{{The Case for Offload Shaping}}},
Wenlu Hu and Brandon Amos and Zhuo Chen and Kiryong Ha and
Wolfgang Richter and Padmanabhan Pillai and Benjamin Gilbert and
Jan Harkes and Mahadev Satyanarayanan
When offloading computation from a mobile device, we show
that it can pay to perform additional on-device work in order
to reduce the offloading workload. We call this offload shaping,
and demonstrate its application at many different levels
of abstraction using a variety of techniques. We show that
offload shaping can produce significant reduction in resource
demand, with little loss of application-level fidelity
title={{{Performance study of Spindle, a web analytics query engine
implemented in Spark}}},
author={Brandon Amos and David Tompkins},
booktitle={IEEE CloudCom},
This paper shares our experiences building and benchmarking Spindle as an open
source Spark-based web analytics platform. Spindle's design has been
motivated by real-world queries and data requiring concurrent, low latency
query execution. We identify a search space of Spark tuning options and study
their impact on Spark's performance. Results from a self-hosted six node
cluster with one week of analytics data (13.1GB) indicate tuning options such
as proper partitioning can cause a 5x performance improvement.
title={{{Global Parameter Estimation for a Eukaryotic Cell Cycle Model
in Systems Biology}}},
author={Tricity Andrew and Brandon Amos and David Easterling and Cihan Oguz and
William Baumann and John Tyson and Layne Watson},
booktitle={Summer Simulation Multiconference,
Society for Modeling and Simulation International},
The complicated process by which a yeast cell divides, known as the cell
cycle, has been modeled by a system of 26 nonlinear ordinary differential
equations (ODEs) with 149 parameters. This model captures the chemical
kinetics of the regulatory networks controlling the cell division process
in budding yeast cells. Empirical data is discrete and matched against
discrete inferences (e.g., whether a particular mutant cell lives or dies)
computed from the ODE solution trajectories. The problem of
estimating the ODE parameters to best fit the model to the data is a
149-dimensional global optimization problem attacked by the deterministic
algorithm VTDIRECT95 and by the nondeterministic algorithms differential
evolution, QNSTOP, and simulated annealing, whose performances are
title={{{Fortran 95 implementation of QNSTOP for global and
stochastic optimization}}},
author={Brandon Amos and David Easterling and Layne Watson and
Brent Castle and Michael Trosset and William Thacker},
booktitle={Spring Simulation Multiconference,
High Performance Computer Symposium,
Society for Modeling and Simulation International},
_venue={SpringSim (HPC)},
A serial Fortran 95 implementation of the QNSTOP algorithm is presented.
QNSTOP is a class of quasi-Newton methods for stochastic optimization with
variations for deterministic global optimization. This discussion provides
results from testing on various deterministic and stochastic optimization