139,203 research outputs found
Behavioural hybrid process calculus
Process algebra is a theoretical framework for the modelling and analysis of the behaviour of concurrent discrete event systems that has been developed within computer science in past quarter century. It has generated a deeper nderstanding of the nature of concepts such as observable behaviour in the presence of nondeterminism, system composition by interconnection of concurrent component systems, and notions of behavioural equivalence of such systems. It has contributed fundamental concepts such as bisimulation, and has been successfully used in a wide range of problems and practical applications in concurrent systems. We believe that the basic tenets of process algebra are highly compatible with the behavioural approach to dynamical systems. In our contribution we present an extension of classical process algebra that is suitable for the modelling and analysis of continuous and hybrid dynamical systems. It provides a natural framework for the concurrent composition of such systems, and can deal with nondeterministic behaviour that may arise from the occurrence of internal switching events. Standard process algebraic techniques lead to the characterisation of the observable behaviour of such systems as equivalence classes under some suitably adapted notion of bisimulation
Performance models of concurrency control protocols for transaction processing systems
Transaction processing plays a key role in a lot of IT infrastructures. It is widely used in a variety of contexts, spanning from database management systems to concurrent programming tools. Transaction processing systems leverage on concurrency control protocols, which allow them to concurrently process transactions preserving essential properties, as isolation and atomicity. Performance is a critical aspect of transaction processing systems, and it is unavoidably affected by the concurrency control. For this reason, methods and techniques to assess and predict the performance of concurrency control protocols are of interest for many IT players, including application designers, developers and system administrators. The analysis and the proper understanding of the impact on the system performance of these protocols require quantitative approaches. Analytical modeling is a practical approach for building cost-effective computer system performance models, enabling us to quantitatively describe the complex dynamics characterizing these systems. In this dissertation we present analytical performance models of concurrency control protocols. We deal with both traditional transaction processing systems, such as database management systems, and emerging ones, as transactional memories. The analysis focuses on widely used protocols, providing detailed performance models and validation studies. In addition, we propose new modeling approaches, which also broaden the scope of our study towards a more realistic, application-oriented, performance analysis
Towards Practical Graph-Based Verification for an Object-Oriented Concurrency Model
To harness the power of multi-core and distributed platforms, and to make the
development of concurrent software more accessible to software engineers,
different object-oriented concurrency models such as SCOOP have been proposed.
Despite the practical importance of analysing SCOOP programs, there are
currently no general verification approaches that operate directly on program
code without additional annotations. One reason for this is the multitude of
partially conflicting semantic formalisations for SCOOP (either in theory or
by-implementation). Here, we propose a simple graph transformation system (GTS)
based run-time semantics for SCOOP that grasps the most common features of all
known semantics of the language. This run-time model is implemented in the
state-of-the-art GTS tool GROOVE, which allows us to simulate, analyse, and
verify a subset of SCOOP programs with respect to deadlocks and other
behavioural properties. Besides proposing the first approach to verify SCOOP
programs by automatic translation to GTS, we also highlight our experiences of
applying GTS (and especially GROOVE) for specifying semantics in the form of a
run-time model, which should be transferable to GTS models for other concurrent
languages and libraries.Comment: In Proceedings GaM 2015, arXiv:1504.0244
TRACTABLE DATA-FLOW ANALYSIS FOR DISTRIBUTED SYSTEMS
Automated behavior analysis is a valuable technique in the development and maintainence of distributed systems. In this paper, we present a tractable dataflow analysis technique for the detection of unreachable states and actions in distributed systems. The technique follows an approximate approach described by Reif and Smolka, but delivers a more accurate result in assessing unreachable states and actions. The higher accuracy is achieved by the use of two concepts: action dependency and history sets. Although the technique, does not exhaustively detect all possible errors, it detects nontrivial errors with a worst-case complexity quadratic to the system size. It can be automated and applied to systems with arbitrary loops and nondeterministic structures. The technique thus provides practical and tractable behavior analysis for preliminary designs of distributed systems. This makes it an ideal candidate for an interactive checker in software development tools. The technique is illustrated with case studies of a pump control system and an erroneous distributed program. Results from a prototype implementation are presented
Towards a Practical Pedestrian Distraction Detection Framework using Wearables
Pedestrian safety continues to be a significant concern in urban communities
and pedestrian distraction is emerging as one of the main causes of grave and
fatal accidents involving pedestrians. The advent of sophisticated mobile and
wearable devices, equipped with high-precision on-board sensors capable of
measuring fine-grained user movements and context, provides a tremendous
opportunity for designing effective pedestrian safety systems and applications.
Accurate and efficient recognition of pedestrian distractions in real-time
given the memory, computation and communication limitations of these devices,
however, remains the key technical challenge in the design of such systems.
Earlier research efforts in pedestrian distraction detection using data
available from mobile and wearable devices have primarily focused only on
achieving high detection accuracy, resulting in designs that are either
resource intensive and unsuitable for implementation on mainstream mobile
devices, or computationally slow and not useful for real-time pedestrian safety
applications, or require specialized hardware and less likely to be adopted by
most users. In the quest for a pedestrian safety system that achieves a
favorable balance between computational efficiency, detection accuracy, and
energy consumption, this paper makes the following main contributions: (i)
design of a novel complex activity recognition framework which employs motion
data available from users' mobile and wearable devices and a lightweight
frequency matching approach to accurately and efficiently recognize complex
distraction related activities, and (ii) a comprehensive comparative evaluation
of the proposed framework with well-known complex activity recognition
techniques in the literature with the help of data collected from human subject
pedestrians and prototype implementations on commercially-available mobile and
wearable devices
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