Performance evaluation of Fractal component based systems

International audienceComponent based system development is now a well accepted design approach in software engineering. Numerous component models have been proposed and for most of them, specific software tools allow building Component Based System (CBS). Although these tools perform several checks on the built system, few of them provide formal verification of behavioural properties nor performance evaluation of the resulting system. In this context, we have developed a general method associating to a CBS, a formal model, based on Stochastic Well formed Nets, a class of high level Petri Nets, allowing qualitative behavioural analysis together with performance evaluation of this CBS. The definition of the model heavily depends on the (run time) component model used to describe the CBS. In this paper, we instantiate our method to Fractal CBS and its reference Java implementation Julia. The method starts from the Fractal architectural description of a system, and defines rules to systematically generate elements models of the CBS and their interactions. We then apply a structured method both for qualitative and performance analysis taking into account the given implementation of the Fractal model. The main interest of our method is to take advantage of the compositional definition of such systems to carry out an efficient analysis. The paper concentrates on performance evaluation and presents our method step by step with an illustrative example

Doctor of Philosophy

dissertationA modern software system is a composition of parts that are themselves highly complex: operating systems, middleware, libraries, servers, and so on. In principle, compositionality of interfaces means that we can understand any given module independently of the internal workings of other parts. In practice, however, abstractions are leaky, and with every generation, modern software systems grow in complexity. Traditional ways of understanding failures, explaining anomalous executions, and analyzing performance are reaching their limits in the face of emergent behavior, unrepeatability, cross-component execution, software aging, and adversarial changes to the system at run time. Deterministic systems analysis has a potential to change the way we analyze and debug software systems. Recorded once, the execution of the system becomes an independent artifact, which can be analyzed offline. The availability of the complete system state, the guaranteed behavior of re-execution, and the absence of limitations on the run-time complexity of analysis collectively enable the deep, iterative, and automatic exploration of the dynamic properties of the system. This work creates a foundation for making deterministic replay a ubiquitous system analysis tool. It defines design and engineering principles for building fast and practical replay machines capable of capturing complete execution of the entire operating system with an overhead of several percents, on a realistic workload, and with minimal installation costs. To enable an intuitive interface of constructing replay analysis tools, this work implements a powerful virtual machine introspection layer that enables an analysis algorithm to be programmed against the state of the recorded system through familiar terms of source-level variable and type names. To support performance analysis, the replay engine provides a faithful performance model of the original execution during replay

Strategies for including cloud-computing into an engineering modeling workflow

With the advent of cloud computing, high-end computing, networking, and storage resources are available on-demand at a relatively low price point. Internet applications in the consumer and increasingly in the enterprise space are making use of these resources to upgrade existing applications and build new ones. This is made possible by building decentralized applications that can be integrated with one another through web-enabled application programming interfaces (APIs). However, in the fields of engineering and computational science, cloud computing resources have been utilized primarily to augment existing high-performance computing hardware, but engineering model integrations still occur by the use of software libraries. In this research, a novel approach is proposed where engineering models are constructed as independent services that publish web-enabled APIs. To enable this, the engineering models are built as stateless microservices that solve a single computational problem. Composite services are then built utilizing these independent component models, much like in the consumer application space. Interactions between component models is orchestrated by a federation management system. This proposed approach is then demonstrated by disaggregating an existing monolithic model for a cookstove into a set of component models. The component models are then reintegrated and compared with the original model for computational accuracy and run-time. Additionally, a novel engineering workflow is proposed that reuses computational data by constructing reduced-order models (ROMs). This framework is evaluated empirically for a number of producers and consumers of engineering models based on computation and data synchronization aspects. The framework is also evaluated by simulating an engineering design workflow with multiple producers and consumers at various stages during the design process. Finally, concepts from the federated system of models and ROMs are combined to propose the concept of a hybrid model (information artefact). The hybrid model is a web-enabled microservice that encapsulates information from multiple engineering models at varying fidelities, and responds to queries based on the best available information. Rules for the construction of hybrid models have been proposed and evaluated in the context of engineering workflows

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

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

Modular System for Shelves and Coasts (MOSSCO v1.0) - a flexible and multi-component framework for coupled coastal ocean ecosystem modelling

Shelf and coastal sea processes extend from the atmosphere through the water column and into the sea bed. These processes are driven by physical, chemical, and biological interactions at local scales, and they are influenced by transport and cross strong spatial gradients. The linkages between domains and many different processes are not adequately described in current model systems. Their limited integration level in part reflects lacking modularity and flexibility; this shortcoming hinders the exchange of data and model components and has historically imposed supremacy of specific physical driver models. We here present the Modular System for Shelves and Coasts (MOSSCO, http://www.mossco.de), a novel domain and process coupling system tailored---but not limited--- to the coupling challenges of and applications in the coastal ocean. MOSSCO builds on the existing coupling technology Earth System Modeling Framework and on the Framework for Aquatic Biogeochemical Models, thereby creating a unique level of modularity in both domain and process coupling; the new framework adds rich metadata, flexible scheduling, configurations that allow several tens of models to be coupled, and tested setups for coastal coupled applications. That way, MOSSCO addresses the technology needs of a growing marine coastal Earth System community that encompasses very different disciplines, numerical tools, and research questions.Comment: 30 pages, 6 figures, submitted to Geoscientific Model Development Discussion

A software tool for simulating practical chemistry

A software package has been written to allow a user to build and manipulate a simple chemistry experiment. Using a toolbox of equipment the apparatus can be interactively designed and the necessary chemicals added from a database. Selection of the appropriate physical and reaction conditions allows the experiment to be run both in real and virtual time, snapshots of the experiment being stored for subsequent modification and replay. The structure of the reaction data file allows any reaction to be designed with yields and both forward and backward reaction rates. Thus, the user has the opportunity to experiment with the best apparatus layout, reactant composition and physical conditions in order to achieve an optimal result. Some extensions of the current software are discussed