245,699 research outputs found
SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems
The recent drive towards achieving greater autonomy and intelligence in
robotics has led to high levels of complexity. Autonomous robots increasingly
depend on third party off-the-shelf components and complex machine-learning
techniques. This trend makes it challenging to provide strong design-time
certification of correct operation.
To address these challenges, we present SOTER, a robotics programming
framework with two key components: (1) a programming language for implementing
and testing high-level reactive robotics software and (2) an integrated runtime
assurance (RTA) system that helps enable the use of uncertified components,
while still providing safety guarantees. SOTER provides language primitives to
declaratively construct a RTA module consisting of an advanced,
high-performance controller (uncertified), a safe, lower-performance controller
(certified), and the desired safety specification. The framework provides a
formal guarantee that a well-formed RTA module always satisfies the safety
specification, without completely sacrificing performance by using higher
performance uncertified components whenever safe. SOTER allows the complex
robotics software stack to be constructed as a composition of RTA modules,
where each uncertified component is protected using a RTA module.
To demonstrate the efficacy of our framework, we consider a real-world
case-study of building a safe drone surveillance system. Our experiments both
in simulation and on actual drones show that the SOTER-enabled RTA ensures the
safety of the system, including when untrusted third-party components have bugs
or deviate from the desired behavior
On Modeling and Analyzing Cost Factors in Information Systems Engineering
Introducing enterprise information systems (EIS) is usually associated with high costs. It is therefore crucial to understand those factors that determine or influence these costs. Though software cost estimation has received considerable attention during the last decades, it is difficult to apply existing approaches to EIS. This difficulty particularly stems from the inability of these methods to deal with the dynamic interactions of the many technological, organizational and projectdriven cost factors which specifically arise in the context of EIS. Picking up this problem, we introduce the EcoPOST framework to investigate the complex cost structures of EIS engineering projects through qualitative cost evaluation models. This paper extends previously described concepts and introduces design rules and guidelines for cost evaluation models in order to enhance the development of meaningful and useful EcoPOST cost evaluation models. A case study illustrates the benefits of our approach. Most important, our EcoPOST framework is an important tool supporting EIS engineers in gaining a better understanding of the critical factors determining the costs of EIS engineering projects
Designing a commutative replicated data type
Commuting operations greatly simplify consistency in distributed systems.
This paper focuses on designing for commutativity, a topic neglected
previously. We show that the replicas of \emph{any} data type for which
concurrent operations commute converges to a correct value, under some simple
and standard assumptions. We also show that such a data type supports
transactions with very low cost. We identify a number of approaches and
techniques to ensure commutativity. We re-use some existing ideas
(non-destructive updates coupled with invariant identification), but propose a
much more efficient implementation. Furthermore, we propose a new technique,
background consensus. We illustrate these ideas with a shared edit buffer data
type
GraphLab: A New Framework for Parallel Machine Learning
Designing and implementing efficient, provably correct parallel machine
learning (ML) algorithms is challenging. Existing high-level parallel
abstractions like MapReduce are insufficiently expressive while low-level tools
like MPI and Pthreads leave ML experts repeatedly solving the same design
challenges. By targeting common patterns in ML, we developed GraphLab, which
improves upon abstractions like MapReduce by compactly expressing asynchronous
iterative algorithms with sparse computational dependencies while ensuring data
consistency and achieving a high degree of parallel performance. We demonstrate
the expressiveness of the GraphLab framework by designing and implementing
parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and
Compressed Sensing. We show that using GraphLab we can achieve excellent
parallel performance on large scale real-world problems
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