Computing in the RAIN: a reliable array of independent nodes

The RAIN project is a research collaboration between Caltech and NASA-JPL on distributed computing and data-storage systems for future spaceborne missions. The goal of the project is to identify and develop key building blocks for reliable distributed systems built with inexpensive off-the-shelf components. The RAIN platform consists of a heterogeneous cluster of computing and/or storage nodes connected via multiple interfaces to networks configured in fault-tolerant topologies. The RAIN software components run in conjunction with operating system services and standard network protocols. Through software-implemented fault tolerance, the system tolerates multiple node, link, and switch failures, with no single point of failure. The RAIN-technology has been transferred to Rainfinity, a start-up company focusing on creating clustered solutions for improving the performance and availability of Internet data centers. In this paper, we describe the following contributions: 1) fault-tolerant interconnect topologies and communication protocols providing consistent error reporting of link failures, 2) fault management techniques based on group membership, and 3) data storage schemes based on computationally efficient error-control codes. We present several proof-of-concept applications: a highly-available video server, a highly-available Web server, and a distributed checkpointing system. Also, we describe a commercial product, Rainwall, built with the RAIN technology

Quantum attacks on Bitcoin, and how to protect against them

The key cryptographic protocols used to secure the internet and financial transactions of today are all susceptible to attack by the development of a sufficiently large quantum computer. One particular area at risk are cryptocurrencies, a market currently worth over 150 billion USD. We investigate the risk of Bitcoin, and other cryptocurrencies, to attacks by quantum computers. We find that the proof-of-work used by Bitcoin is relatively resistant to substantial speedup by quantum computers in the next 10 years, mainly because specialized ASIC miners are extremely fast compared to the estimated clock speed of near-term quantum computers. On the other hand, the elliptic curve signature scheme used by Bitcoin is much more at risk, and could be completely broken by a quantum computer as early as 2027, by the most optimistic estimates. We analyze an alternative proof-of-work called Momentum, based on finding collisions in a hash function, that is even more resistant to speedup by a quantum computer. We also review the available post-quantum signature schemes to see which one would best meet the security and efficiency requirements of blockchain applications.Comment: 21 pages, 6 figures. For a rough update on the progress of Quantum devices and prognostications on time from now to break Digital signatures, see https://www.quantumcryptopocalypse.com/quantum-moores-law

Link-time smart card code hardening

This paper presents a feasibility study to protect smart card software against fault-injection attacks by means of link-time code rewriting. This approach avoids the drawbacks of source code hardening, avoids the need for manual assembly writing, and is applicable in conjunction with closed third-party compilers. We implemented a range of cookbook code hardening recipes in a prototype link-time rewriter and evaluate their coverage and associated overhead to conclude that this approach is promising. We demonstrate that the overhead of using an automated link-time approach is not significantly higher than what can be obtained with compile-time hardening or with manual hardening of compiler-generated assembly code

Study of fault-tolerant software technology

Presented is an overview of the current state of the art of fault-tolerant software and an analysis of quantitative techniques and models developed to assess its impact. It examines research efforts as well as experience gained from commercial application of these techniques. The paper also addresses the computer architecture and design implications on hardware, operating systems and programming languages (including Ada) of using fault-tolerant software in real-time aerospace applications. It concludes that fault-tolerant software has progressed beyond the pure research state. The paper also finds that, although not perfectly matched, newer architectural and language capabilities provide many of the notations and functions needed to effectively and efficiently implement software fault-tolerance

Real time stream processing for Internet of things and sensing environments

Includes bibliographical references.2015 Fall.Improvements in miniaturization and networking capabilities of sensors have contributed to the proliferation of Internet of Things (IoT) and continuous sensing environments. Data streams generated in such settings must keep pace with generation rates and be processed in real time. Challenges in accomplishing this include: high data arrival rates, buffer overflows, context-switches during processing, and object creation overheads. We propose a holistic framework that addresses the CPU, memory, network, and kernel issues involved in stream processing. Our prototype, Neptune, builds on the Granules cloud runtime and leverages its support for scheduling packets and communications based on publish/subscribe, peer to peer, and point-to-point. The framework maximizes bandwidth utilization in the presence of small messages via the use of buffering and dynamic compactions of packets based on their entropy. Our use of thread-pools and batched processing reduces context switches and improves effective CPU utilizations. The framework alleviates memory pressure that can lead to swapping, page faults, and thrashing through efficient reuse of objects. To cope with buffer overflows we rely on flow control and throttling the preceding stages of a processing pipeline. Our correctness criteria included deadlock/livelock avoidance, and ordered and exactly-once processing. Our benchmarks demonstrate the suitability of the Granules/Neptune combination and we contrast our performance with Apache Storm, the dominant stream-processing framework developed by Twitter. At a single node, we are able to achieve a processing rate of ~2 million stream packets per-second. In a distributed cluster setup, we are able to achieve a processing rate of ~100 million stream packets per-second with a near-optimal bandwidth utilization