Service oriented networking

This paper introduces a new paradigm for service oriented networking being developed in the FUSION project(1). Despite recent proposals in the area of information centric networking, a similar treatment of services - where networked software functions, rather than content, are dynamically deployed, replicated and invoked - has received little attention by the network research community to date. Our approach provides the mechanisms required to deploy a replicated service instance in the network and to route client requests to the closest instance in an efficient manner. We address the main issues that such a paradigm raises including load balancing, resource registration, domain monitoring and inter-domain orchestration. We also present preliminary evaluation results of current work

Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

Modeling context-aware systems using TOSCA

Cloud Computing is widely accepted as a provider of virtual resources over the Internet, it is due to its economical and technical benefits, such as on-demand self-service, resource pooling and rapid elasticity capabilities. To exploit these properties reliably, it is needed to automate their internal processes for application provisioning, configuration and management. One of the standards that has been developed in the last years with this aim is the Topology and Orchestration Specification for Cloud Applications (TOSCA) by OASIS. TOSCA is a standard which enables application developers to describe applications and their management as models that incorporate components and their relations among each other. Due to the emerging of new fields such as the Internet of Things, Industry 4.0 and the upcoming Fog Computing paradigm, which depend more and more on dynamically changing situations and therefore reconfiguration of applications in such a scenario, a systematic modelling approach which handles situational dependencies directly in the models of these applications is needed. The goal of the thesis is to identify suitable modeling concepts from the field of context- aware systems and apply one of these on the TOSCA language to incorporate the inherent nature of change of future applications in the field of Cloud/Fog Computing, Internet of Things and Industry 4.0

A UML Profile for the Design, Quality Assessment and Deployment of Data-intensive Applications

Big Data or Data-Intensive applications (DIAs) seek to mine, manipulate, extract or otherwise exploit the potential intelligence hidden behind Big Data. However, several practitioner surveys remark that DIAs potential is still untapped because of very difficult and costly design, quality assessment and continuous refinement. To address the above shortcoming, we propose the use of a UML domain-specific modeling language or profile specifically tailored to support the design, assessment and continuous deployment of DIAs. This article illustrates our DIA-specific profile and outlines its usage in the context of DIA performance engineering and deployment. For DIA performance engineering, we rely on the Apache Hadoop technology, while for DIA deployment, we leverage the TOSCA language. We conclude that the proposed profile offers a powerful language for data-intensive software and systems modeling, quality evaluation and automated deployment of DIAs on private or public clouds

Gathering solutions and providing APIs for their orchestration to implement continuous software delivery

In traditional IT environments, it is common for software updates and new releases to take up to several weeks or even months to be eventually available to end users. Therefore, many IT vendors and providers of software products and services face the challenge of delivering updates considerably more frequently. This is because users, customers, and other stakeholders expect accelerated feedback loops and significantly faster responses to changing demands and issues that arise. Thus, taking this challenge seriously is of utmost economic importance for IT organizations if they wish to remain competitive. Continuous software delivery is an emerging paradigm adopted by an increasing number of organizations in order to address this challenge. It aims to drastically shorten release cycles while ensuring the delivery of high-quality software. Adopting continuous delivery essentially means to make it economical to constantly deliver changes in small batches. Infrequent high-risk releases with lots of accumulated changes are thereby replaced by a continuous stream of small and low-risk updates. To gain from the benefits of continuous delivery, a high degree of automation is required. This is technically achieved by implementing continuous delivery pipelines consisting of different application-specific stages (build, test, production, etc.) to automate most parts of the application delivery process. Each stage relies on a corresponding application environment such as a build environment or production environment. This work presents concepts and approaches to implement continuous delivery pipelines based on systematically gathered solutions to be used and orchestrated as building blocks of application environments. Initially, the presented Gather'n'Deliver method is centered around a shared knowledge base to provide the foundation for gathering, utilizing, and orchestrating diverse solutions such as deployment scripts, configuration definitions, and Cloud services. Several classification dimensions and taxonomies are discussed in order to facilitate a systematic categorization of solutions, in addition to expressing application environment requirements that are satisfied by those solutions. The presented GatherBase framework enables the collaborative and automated gathering of solutions through solution repositories. These repositories are the foundation for building diverse knowledge base variants that provide fine-grained query mechanisms to find and retrieve solutions, for example, to be used as building blocks of specific application environments. Combining and integrating diverse solutions at runtime is achieved by orchestrating their APIs. Since some solutions such as lower-level executable artifacts (deployment scripts, configuration definitions, etc.) do not immediately provide their functionality through APIs, additional APIs need to be supplied. This issue is addressed by different approaches, such as the presented Any2API framework that is intended to generate individual APIs for such artifacts. An integrated architecture in conjunction with corresponding prototype implementations aims to demonstrate the technical feasibility of the presented approaches. Finally, various validation scenarios evaluate the approaches within the scope of continuous delivery and application environments and even beyond

An extensible application topology definition and annotation framework

This thesis introduces a framework for decision support during the design of applications for the cloud, or migration of existing applications to a cloud environment. For this purpose, a GENeralized Topology Language (GENTL) is introduced and mappings from existing languages to GENTL are provided. An annotation scheme for GENTL, which can capture annotations to topologies and topology elements is designed and instantiations for different annotation types are given. A framework implementing import functionalities for the topology languages Blueprint and TOSCA is presented. The framework enables the annotation of topologies with documentation annotations, references to external resources and incorporates a series of annotations which can be used to retrieve cost calculations from the external decision support system Nefolog

A Cloud-Based Framework for Machine Learning Workloads and Applications

[EN] In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models.This work was supported by the project DEEP-Hybrid-DataCloud ``Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud'' that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant 777435Lopez Garcia, A.; Marco De Lucas, J.; Antonacci, M.; Zu Castell, W.; David, M.; Hardt, M.; Lloret Iglesias, L.... (2020). A Cloud-Based Framework for Machine Learning Workloads and Applications. IEEE Access. 8:18681-18692. https://doi.org/10.1109/ACCESS.2020.2964386S1868118692