SOA pattern effect mitigation by neural network based pre-equalizer for 50G PON

Semiconductor optical amplifier (SOA) is widely used for power amplification in O-band, particularly for passive optical networks (PONs) which can greatly benefit its advantages of simple structure, low power consumption and integrability with photonics circuits. However, the annoying nonlinear pattern effect degrades system performance when the SOA is needed as a pre-amplifier in PONs. Conventional solutions for pattern effect mitigation are either based on optical filtering or gain clamping. They are not simple or sufficiently flexible for practical deployment. Neural network (NN) has been demonstrated for impairment compensation in optical communications thanks to its powerful nonlinear fitting ability. In this paper, for the first time, NN-based equalizer is proposed to mitigate the SOA pattern effect for 50G PON with intensity modulation and direct detection. The experimental results confirm that the NN-based equalizer can effectively mitigate the SOA nonlinear pattern effect and significantly improve the dynamic range of receiver, achieving 29-dB power budget with the FEC limit at 1e-2. Moreover, the well-trained NN model in the receiver side can be directly placed at the transmitter in the optical line terminal to pre-equalize the signal for transmission so as to simplify digital signal processing in the optical network unit

Semantic model-driven development of service-centric software architectures

Service-oriented architecture (SOA) is a recent architectural paradigm that has received much attention. The prevalent focus on platforms such as Web services, however, needs to be complemented by appropriate software engineering methods. We propose the model-driven development of service-centric software systems. We present in particular an investigation into the role of enriched semantic modelling for a modeldriven development framework for service-centric software systems. Ontologies as the foundations of semantic modelling and its enhancement through architectural pattern modelling are at the core of the proposed approach. We introduce foundations and discuss the benefits and also the challenges in this context

Quality-aware model-driven service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box character of services

Two ways to Grid: the contribution of Open Grid Services Architecture (OGSA) mechanisms to service-centric and resource-centric lifecycles

Service Oriented Architectures (SOAs) support service lifecycle tasks, including Development, Deployment, Discovery and Use. We observe that there are two disparate ways to use Grid SOAs such as the Open Grid Services Architecture (OGSA) as exemplified in the Globus Toolkit (GT3/4). One is a traditional enterprise SOA use where end-user services are developed, deployed and resourced behind firewalls, for use by external consumers: a service-centric (or ‘first-order’) approach. The other supports end-user development, deployment, and resourcing of applications across organizations via the use of execution and resource management services: A Resource-centric (or ‘second-order’) approach. We analyze and compare the two approaches using a combination of empirical experiments and an architectural evaluation methodology (scenario, mechanism, and quality attributes) to reveal common and distinct strengths and weaknesses. The impact of potential improvements (which are likely to be manifested by GT4) is estimated, and opportunities for alternative architectures and technologies explored. We conclude by investigating if the two approaches can be converged or combined, and if they are compatible on shared resources

Taming the cloud: Safety, certification and compliance for software services - Keynote at the Workshop on Engineering Service-Oriented Applications (WESOA) 2011

The maturity of IT processes, such as software development, can be and is often certified. Current trends in the IT industry suggest that software systems in the future will be very different from their counterparts today, with an increasing adoption of the Service-Oriented Architecture (SOA) design pattern and the deployment of Software-as-a-Service (SaaS) on Cloud infrastructures. In this talk we discuss some issues surrounding engineering Software Services for Cloud infrastructures and highlight the need for enhanced control, service-level agreement and compliance mechanisms for Software Services. Cloud Infrastructures and Service Mash-ups

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study