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Register a new userOn Distributed Runtime Verification by Aggregate Computing
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Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Runtime verification is a computing analysis paradigm based on observing a system at runtime (to check its expected behaviour) by means of monitors generated from formal specifications. Distributed runtime verification is runtime verification in connection with distributed systems: it comprises both monitoring of distributed systems and using distributed systems for monitoring. Aggregate computing is a programming paradigm based on a reference computing machine that is the aggregate collection of devices that cooperatively carry out a computational process: the details of behaviour, position and number of devices are largely abstracted away, to be replaced with a space-filling computational environment. In this position paper we argue, by means of simple examples, that aggregate computing is particularly well suited for implementing distributed monitors. Our aim is to foster further research on how to generate aggregate computing monitors from suitable formal specifications.
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Since distributed software systems are ubiquitous, their correct functioning is crucially important. Static verification is possible in principle, but requires high expertise and effort which is not feasible in many eco-systems. Runtime verification can serve as a lean alternative, where monitoring mechanisms are automatically generated from property specifications, to check compliance at runtime. This paper contributes a practical solution for powerful and flexible runtime verification of distributed, object-oriented applications, via a combination of the runtime verification tool Larva and the active object framework ProActive. Even if Larva supports in itself only the generation of local, sequential monitors, we empower Larva for distributed monitoring by connecting monitors with active objects, turning them into active, communicating monitors. We discuss how this allows for a variety of monitoring architectures. Further, we show how property specifications, and thereby the generated monitors, provide a model that splits the blame between the local object and its environment. While Larva itself focuses on monitoring of control-oriented properties, we use the Larva front-end StaRVOOrS to also capture data-oriented (pre/post) properties in the distributed monitoring. We demonstrate this approach to distributed runtime verification with a case study, a distributed key/value store.
With numerous specialised technologies available to industry, it has become increasingly frequent for computer systems to be composed of heterogeneous components built over, and using, different technologies and languages. While this enables developers to use the appropriate technologies for specific contexts, it becomes more challenging to ensure the correctness of the overall system. In this paper we propose a framework to enable extensible technology agnostic runtime verification and we present an extension of polyLarva, a runtime-verification tool able to handle the monitoring of heterogeneous-component systems. The approach is then applied to a case study of a component-based artefact using different technologies, namely C and Java.
Stream Runtime Verification is a formal dynamic analysis technique that generalizes runtime verification algorithms from temporal logics like LTL to stream monitoring, allowing to compute richer verdicts than Booleans (including quantitative and arbitrary data). In this paper we study the problem of implementing an SRV engine that is truly extensible to arbitrary data theories, and we propose a solution as a Haskell embedded domain specific language. In spite of the theoretical clean separation in SRV between temporal dependencies and data computations, previous engines include ad-hoc implementations of a few data types, requiring complex changes to incorporate new data theories. We propose here an SRV language called hLola that borrows general Haskell types and embeds them transparently into an eDSL. This novel technique, which we call lift deep embedding, allows for example, the use of higher-order functions for static stream parameterization. We describe the Haskell implementation of hLola and illustrate simple extensions implemented using libraries, which require long and error-prone additions in other ad-hoc SRV formalisms.
Runtime verification is checking whether a system execution satisfies or violates a given correctness property. A procedure that automatically, and typically on the fly, verifies conformance of the systems behavior to the specified property is called a monitor. Nowadays, a variety of formalisms are used to express properties on observed behavior of computer systems, and a lot of methods have been proposed to construct monitors. However, it is a frequent situation when advanced formalisms and methods are not needed, because an executable model of the system is available. The original purpose and structure of the model are out of importance; rather what is required is that the system and its model have similar sets of interfaces. In this case, monitoring is carried out as follows. Two black boxes, the system and its reference model, are executed in parallel and stimulated with the same input sequences; the monitor dynamically captures their output traces and tries to match them. The main problem is that a model is usually more abstract than the real system, both in terms of functionality and timing. Therefore, trace-to-trace matching is not straightforward and allows the system to produce events in different order or even miss some of them. The paper studies on-the-fly conformance relations for timed systems (i.e., systems whose inputs and outputs are distributed along the time axis). It also suggests a practice-oriented methodology for creating and configuring monitors for timed systems based on executable models. The methodology has been successfully applied to a number of industrial projects of simulation-based hardware verification.
We study a stochastic game framework with dynamic set of players, for modeling and analyzing their computational investment strategies in distributed computing. Players obtain a certain reward for solving the problem or for providing their computational resources, while incur a certain cost based on the invested time and computational power. We first study a scenario where the reward is offered for solving the problem, such as in blockchain mining. We show that, in Markov perfect equilibrium, players with cost parameters exceeding a certain threshold, do not invest; while those with cost parameters less than this threshold, invest maximal power. Here, players need not know the system state. We then consider a scenario where the reward is offered for contributing to the computational power of a common central entity, such as in volunteer computing. Here, in Markov perfect equilibrium, only players with cost parameters in a relatively low range in a given state, invest. For the case where players are homogeneous, they invest proportionally to the reward to cost ratio. For both the scenarios, we study the effects of players arrival and departure rates on their utilities using simulations and provide additional insights.
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