Typed Architectures: architectural support for lightweight scripting

Typed Architectures: architectural support for lightweight scripting Kim et al., ASPLOS'17 JavaScript, Python, Ruby, Lua, and related dynamically typed scripting languages are increasingly popular for developing IoT applications. For example, the Raspberry Pi is associated with Python; Arduino and Intel's Galileo and Edison are associated with JavaScript. In these constrained hardware environments though, using JITs … Continue reading Typed Architectures: architectural support for lightweight scripting

Who controls the Internet? Analyzing global threats using property traversal graphs

Who controls the Internet? Analyzing global threats using property traversal graphs Simeonovski et al., WWW'17 Who controls the Internet? How much influence do they have? And what would happen if one of those parties launched an attack or was compromised and used to launch an attack? Previous works have looked at the individual core services, … Continue reading Who controls the Internet? Analyzing global threats using property traversal graphs

BOAT: Building auto-tuners with structured Bayesian optimization

BOAT: Building auto-tuners with structured Bayesian optimization Dalibard et al., WWW'17 Due to their complexity, modern systems expose many configuration parameters which users must tune to maximize performance... From the number of machines used in a distributed application, to low-level parameters such as compiler flags, managing configurations has become one of the main challenges faced … Continue reading BOAT: Building auto-tuners with structured Bayesian optimization

DeepSense: a unified deep learning framework for time-series mobile sensing data processing

DeepSense: a unified deep learning framework for time-series mobile sensing data processing Yao et al., WWW'17 DeepSense is a deep learning framework that runs on mobile devices, and can be used for regression and classification tasks based on data coming from mobile sensors (e.g., motion sensors). An example of a classification task is heterogeneous human … Continue reading DeepSense: a unified deep learning framework for time-series mobile sensing data processing

Usage patterns and the economics of the public cloud

Usage patterns and the economics of the public cloud Kilcioglu et al., WWW'17 Illustrating the huge diversity of topics covered at WWW, following yesterday's look at recovering mobile user trajectories from aggregate data, today's choice studies usage variation and pricing models in the public cloud. The basis for the study is data from 'a major … Continue reading Usage patterns and the economics of the public cloud

Trajectory recovery from Ash: User privacy is NOT preserved in aggregated mobility data

Trajectory recovery from Ash: User privacy is NOT preserved in aggregated mobility data Xu et al., WWW'17 Borrowing a little from Simon Wardley's marvellous Enterprise IT Adoption Cycle, here's roughly how my understanding progressed as I read through this paper: Huh? What? How? Nooooo, Oh No, Oh s*@\#! Xu et al. show us that even … Continue reading Trajectory recovery from Ash: User privacy is NOT preserved in aggregated mobility data