Why (and How) Networks Should Run Themselves

The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols

A Model Driven Approach to Model Transformations

The OMG's Model Driven Architecture (MDA) initiative has been the focus of much attention in both academia and industry, due to its promise of more rapid and consistent software development through the increased use of models. In order for MDA to reach its full potential, the ability to manipulate and transform models { most obviously from the Platform Independent Model (PIM) to the Platform Specific Models (PSM) { is vital. Recognizing this need, the OMG issued a Request For Proposals (RFP) largely concerned with finding a suitable mechanism for trans- forming models. This paper outlines the relevant background material, summarizes the approach taken by the QVT-Partners (to whom the authors belong), presents a non-trivial example using the QVT-Partners approach, and finally sketches out what the future holds for model transformations

A systematic comparison of roundtrip software engineering approaches

Model-based software engineering contemplates several software development approaches in which models play an important role. One such approach is round-trip engineering. Very briefly, round-trip engineering is code generation from models, and models are updated whenever a code change occurs. The objective of this dissertation is to benchmark the comparative analysis of the round-trip engineering capability of the UML, Papyrus, Modelio and Visual Paradigm modeling tools. In more detailed terms, the work will focus on evaluating tools to automatically or semi-automatically support round-trip engineering processes for each selected diagram. Collaterally, this dissertation will allow us to gain insight into the current round-trip engineering landscape, establishing the state-of-the-art UML modeling tool support for this approach. Qualitative and quantitative analysis of the round-trip engineering capabilities of the tools show that the Papyrus, Modeling and Visual Paradigm tools yielded satisfactory results by applying the Reverse and Forward Engineering scenarios without changing the models and codes but applying the Round-trip engineering scenario with changes in model and code presented results with some gaps in model and code coherence. It was concluded that they arose because the semantic definition of the models was done informally. The conclusions drawn throughout the dissertation will answer the questions: How effective are current code generation tools for documenting application evolution? Where will it support the decision made? objectives and will support the recommendations of the best tools that address the round-trip engineering method.A engenharia de software baseada em modelo contempla várias abordagens de desenvolvimento de software nas quais os modelos desempenham um papel importante. Uma dessas abordagens é a Round-trip engineering. Muito brevemente, a Round-trip engineering é a geração de código a partir de modelos, e os modelos são atualizado sempre que ocorre uma alteração no código. O objetivo desta dissertação é a realização de um benchmarking da análise comparativa da capacidade de Round-trip engineering das ferramentas de modelação UML, Papyrus, Modelio e Visual Paradigm. Em termos mais detalhados, o trabalho se concentrará na avaliação de ferramentas para dar suporte automático ou semiautomático a processos de Round-trip engineering (engenharia direta e engenharia reversa) para cada diagrama selecionado. Colateralmente, esta dissertação permitirá alcançar uma visão do panorama atual da Round-trip engineering, estabelecendo o estado da arte do suporte de ferramentas de modelação em UML à dita abordagem. A analise qualitativa e quantitativamente da capacidade de Round-trip engineering das ferramentas mostro que, as ferramentas Papiro, Modelagem e Paradigma Visual apresentaram resultados satisfatórios aplicando os cenários de Reverse e Forward Engineering sem alterar os modelos e códigos e com alterações, mas aplicando o cenário Round-trip engineering com alterações nos modelo e código apresentaram resultados com algumas lacunas nomeadamente na coerência dos modelos e código. Concluiu-se que as mesmas surgiram por causa da definição semântica dos modelos ser feita de forma informal. As conclusões tiradas ao longo do trabalho respondera as perguntas: Qual a eficácia das ferramentas atuais de geração de código para documentar a evolução dos aplicativos? Onde apoiará a decisão tomada? que foram definidas nos objetivos e apoiarão as recomendações das melhores ferramentas que aborda o método Round-trip engineering

Enabling stream processing for people-centric IoT based on the fog computing paradigm

The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture

Evolving models in Model-Driven Engineering : State-of-the-art and future challenges

The artefacts used in Model-Driven Engineering (MDE) evolve as a matter of course: models are modified and updated as part of the engineering process; metamodels change as a result of domain analysis and standardisation efforts; and the operations applied to models change as engineering requirements change. MDE artefacts are inter-related, and simultaneously constrain each other, making evolution a challenge to manage. We discuss some of the key problems of evolution in MDE, summarise the key state-of-the-art, and look forward to new challenges in research in this area

From software architecture to analysis models and back: Model-driven refactoring aimed at availability improvement

Abstract Context With the ever-increasing evolution of software systems, their architecture is subject to frequent changes due to multiple reasons, such as new requirements. Appropriate architectural changes driven by non-functional requirements are particularly challenging to identify because they concern quantitative analyses that are usually carried out with specific languages and tools. A considerable number of approaches have been proposed in the last decades to derive non-functional analysis models from architectural ones. However, there is an evident lack of automation in the backward path that brings the analysis results back to the software architecture. Objective In this paper, we propose a model-driven approach to support designers in improving the availability of their software systems through refactoring actions. Method The proposed framework makes use of bidirectional model transformations to map UML models onto Generalized Stochastic Petri Nets (GSPN) analysis models and vice versa. In particular, after availability analysis, our approach enables the application of model refactoring, possibly based on well-known fault tolerance patterns, aimed at improving the availability of the architectural model. Results We validated the effectiveness of our approach on an Environmental Control System. Our results show that the approach can generate: (i) an analyzable availability model from a software architecture description, and (ii) valid software architecture models back from availability models. Finally, our results highlight that the application of fault tolerance patterns significantly improves the availability in each considered scenario. Conclusion The approach integrates bidirectional model transformation and fault tolerance techniques to support the availability-driven refactoring of architectural models. The results of our experiment showed the effectiveness of the approach in improving the software availability of the system

Fog-supported delay-constrained energy-saving live migration of VMs over multiPath TCP/IP 5G connections

The incoming era of the fifth-generation fog computing-supported radio access networks (shortly, 5G FOGRANs) aims at exploiting computing/networking resource virtualization, in order to augment the limited resources of wireless devices through the seamless live migration of virtual machines (VMs) toward nearby fog data centers. For this purpose, the bandwidths of the multiple wireless network interface cards of the wireless devices may be aggregated under the control of the emerging MultiPathTCP (MPTCP) protocol. However, due to the fading and mobility-induced phenomena, the energy consumptions of the current state-of-the-art VM migration techniques may still offset their expected benefits. Motivated by these considerations, in this paper, we analytically characterize and implement in software and numerically test the optimal minimum-energy settable-complexity bandwidth manager (SCBM) for the live migration of VMs over 5G FOGRAN MPTCP connections. The key features of the proposed SCBM are that: 1) its implementation complexity is settable on-line on the basis of the target energy consumption versus implementation complexity tradeoff; 2) it minimizes the network energy consumed by the wireless device for sustaining the migration process under hard constraints on the tolerated migration times and downtimes; and 3) by leveraging a suitably designed adaptive mechanism, it is capable to quickly react to (possibly, unpredicted) fading and/or mobility-induced abrupt changes of the wireless environment without requiring forecasting. The actual effectiveness of the proposed SCBM is supported by extensive energy versus delay performance comparisons that cover: 1) a number of heterogeneous 3G/4G/WiFi FOGRAN scenarios; 2) synthetic and real-world workloads; and, 3) MPTCP and wireless connections