MARVEL: Enabling Mobile Augmented Reality with Low Energy and Low Latency

Hyung-Sin Kim Intelligent, Real-Time, Systems

This paper presents MARVEL, a mobile augmented reality (MAR) system which provides a notation display service with imperceptible latency (<100 ms) and low energy consumption on regular mobile devices. In contrast to conventional MAR systems, which recognize objects using image-based computations performed in the cloud, MARVEL mainly utilizes a mobile device’s local inertial sensors for recognizing and tracking multiple objects, while computing local optical flow and offloading images only when necessary. We propose a system architecture which uses local inertial tracking, local optical flow, and visual tracking in the cloud synergistically. On top of that, we investigate how to minimize the overhead for image computation and offloading. We have implemented and deployed a holistic prototype system in a commercial building …

Authors: Kaifei Chen, Tong Li, Hyung-Sin Kim, David Culler, Randy Katz

Anna: A Crazy Fast, Super-Scalable, Flexibly Consistent KVS 🗺

Joe Hellerstein blog, Database Systems, Distributed Systems, Real-Time, Systems, Uncategorized 0 Comments

This article cross-posted from the DataBeta blog. There’s fast and there’s fast. This post is about Anna, a key/value database design from our team at Berkeley that’s got phenomenal speed and buttery smooth scaling, with an unprecedented range of consistency guarantees. Details are in our upcoming ICDE18 paper on Anna. Conventional wisdom (or at least Jeff Dean wisdom) says that you have to redesign your system every time you scale by 10x. As researchers, we asked the counter-cultural question: what would it take to build a key-value store that would excel across many orders of magnitude of scale, from a single multicore box to the global cloud? Turns out this kind of curiosity can lead to a system with pretty interesting practical …

Clipper: A Low-Latency Online Prediction Serving System

Daniel Crankshaw Intelligent, Real-Time, Systems

Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployment. In this paper, we introduce Clipper, a general-purpose low-latency prediction serving system. Interposing between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks and applications. Furthermore, by introducing caching, batching, and adaptive model selection techniques, Clipper reduces prediction latency and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. We evaluate Clipper on four common machine learning benchmark datasets and demonstrate its ability to meet the latency, accuracy, …

Authors: Dan Crankshaw, Xin Wang, Giulio Zhou, Michael J. Franklin, Joseph Gonzalez, Ion Stoica