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
The emergence of low-power 32-bit Systems-on-Chip (SoCs), which integrate a 32-bit MCU, radio, and flash, presents an opportunity to re-examine design points and trade-offs at all levels of the system architecture of networked sensors. To this end, we develop a post-SoC/32-bit design point called Hamilton, showing that using integrated components enables a ∼$7 core and shifts hardware modularity to design time. We study the interaction between hardware and embedded OSes, identifying that (1) post-SoC motes provide lower idle current (5.9 µA) than traditional 16-bit motes, (2) 32-bit MCUs are a major energy consumer (e.g., tick increases idle current >50 times), comparable to radios, and (3) thread-based concurrency is viable, requiring only 8.3 µs of context switch time. We design a …
Authors: Hyung-Sin Kim, Michael Andersen, Kaifei Chen, Sam Kumar, William J. Zhao, Kevin Ma, David Culler
With the advent of network function virtualization (NFV), outsourcing network processing to the cloud is growing in popularity amongst enterprises and organizations. Such outsourcing, however, poses a threat to the security of the client’s traffic because the cloud is notoriously susceptible to attacks. We present SafeBricks, a system that shields generic network functions (NFs) from an untrusted cloud. SafeBricks ensures that only encrypted traffic is exposed to the cloud provider, and preserves the integrity of both traffic and the NFs. At the same time, it enables clients to reduce their trust in NF implementations by enforcing least privilege across NFs deployed in a chain. SafeBricks does not require changes to TLS, and safeguards the interests of NF vendors as well …
Authors: Rishabh Poddar, Chang Lan, Raluca Ada Popa, Sylvia Ratnasamy
Many big data systems are written in garbage-collected languages and GC has a substantial impact on throughput, responsiveness and predicability of these systems. However, despite decades of research, there is still no “Holy Grail” of GC: a collector with no measurable impact, even on real-time applications. Such a collector needs to achieve freedom from pauses, high GC throughput and good memory utilization, without slowing down application threads or using substantial amounts of compute resources. In this paper, we propose a step towards this elusive goal by reviving the old idea of moving GC into hardware. We discuss the trends that make it the perfect time to revisit this approach and present the design of a hardware-assisted GC that aims to …
Authors: Martin Maas, Krste Asanovic, John Kubiatowicz
R is a popular statistical programming language with a number of extensions that support data processing and machine learning tasks. However, interactive data analysis in R is usually limited as the R runtime is single threaded and can only process data sets that fit in a single machine’s memory. We present SparkR, an R package that provides a frontend to Apache Spark and uses Spark’s distributed computation engine to enable large scale data analysis from the R shell. We describe the main design goals of SparkR, discuss how the high-level DataFrame API enables scalable computation and present some of the key details of our implementation.
Authors: Shivaram Venkataraman, Ali Ghodsi, Ion Stoica
More and more applications and web services generate larger and larger amounts of confidential data, such as user and financial data. On one hand, these systems must use encryption to ensure confidentiality, while on the other hand, they want to use compression to reduce costs and increase performance. Unfortunately, encryption and compression are in tension, leading many existing systems to support one but not the other. We propose MiniCrypt, the first big data keyvalue store that reconciles encryption and compression, without compromising performance. At the core of MiniCrypt is an observation on data compressibility trends in key-value stores, which enables grouping key-value pairs in small key packs, together with a set of new distributed systems techniques for retrieving, updating, merging …
Authors: Wenting Zheng, Raluca Ada Popa, Ion Stoica, Rachit Agarwal, Frank Li
As enterprises move to cloud-based analytics, the risk of cloud security breaches poses a serious threat. Encrypting data at rest and in transit is a major first step. However, data must still be decrypted in memory for processing, exposing it to an attacker who has compromised the operating system or hypervisor. Trusted hardware such as Intel SGX has recently become available in latest-generation processors. Such hardware enables arbitrary computation on encrypted data while shielding it from a malicious OS or hypervisor. However, it still suffers from a significant side channel: access pattern leakage. We present Opaque, a package for Apache Spark SQL that enables very strong security for SQL queries: data encryption, computation verification, and access pattern leakage protection (a.k.a. …
Authors: Wenting Zheng, Raluca Ada Popa, Ion Stoica, Joseph Gonzalez, Ankur Dave, Jethro Beekman
Many shared computing clusters allow users to utilize excess idle resources at lower cost or priority, with the proviso that some or all may be taken away at any time. But, exploiting such dynamic resource availability and the often fluctuating markets for them requires agile elasticity and effective acquisition strategies. Proteus aggressively exploits such transient revocable resources to do machine learning (ML) cheaper and/or faster. Its parameter server framework, AgileML, efficiently adapts to bulk additions and revocations of transient machines, through a novel 3-stage active-backup approach, with minimal use of more costly non-transient resources. Its BidBrain component adaptively allocates resources from multiple EC2 spot markets to minimize average cost per work as transient resource availability and cost change over time. …
Authors: Aaron Harlap, Alexey Tumanov, Andrew Chung, Gregory R. Ganger, Phil Gibbons
Modern resource management frameworks for largescale analytics leave unresolved the problematic tension between high cluster utilization and job’s performance predictability—respectively coveted by operators and users. We address this in Morpheus, a new system that: 1) codifies implicit user expectations as explicit Service Level Objectives (SLOs), inferred from historical data, 2) enforces SLOs using novel scheduling techniques that isolate jobs from sharing-induced performance variability, and 3) mitigates inherent performance variance (e.g., due to failures) by means of dynamic reprovisioning of jobs. We validate these ideas against production traces from a 50k node cluster, and show that Morpheus can lower the number of deadline violations by 5x to 13x, while retaining cluster-utilization, and lowering cluster footprint by 14% to 28%. We demonstrate …
Authors: C. Curino, I. Menache, S. Narayanamurthy, Alexey Tumanov, J. Yaniv, R. Mavlyutov, I. Goiri, S. Krishnan, J. Kulkarni, S. Rao
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
