Building on virtualization and programmability, 5G networks aim for concurrent support of application domains with different functional and QoS requirements, both across and within vertical domains. Towards these requirements, network slicing mechanisms allow the management and orchestration of the underlying pool of resources, typically within a single administrative domain. However, in several occasions, verticals are expected to have a large geographical footprint, often crossing the administrative borders of multiple network domains, placing a subsequent functional requirement for cross-domain orchestration. In this paper we describe our approach on cross-domain slicing operations for the case of Industrial Applications with strict and flexible QoS requirements, with a particular focus on Wind Power plant networks. We describe the design of our SDN-based orchestration TRL-7 prototype and further provide a detailed look on the testbed prepared for measurements in an operational Wind Power plant in Brande, Denmark.
The complexity of Business Intelligence activities has driven the proposal of several approaches for the effective modeling of Extract-Transform-Load (ETL) processes, based on the conceptual abstraction of their operations. Apart from fostering automation and maintainability, such modeling also provides the building blocks to identify and represent frequently recurring patterns. Despite some existing work on classifying ETL components and functionality archetypes, the issue of systematically mining such patterns and their connection to quality attributes such as performance has not yet been addressed. In this work, we propose a methodology for the identification of ETL structural patterns. We logically model the ETL workflows using labeled graphs and employ graph algorithms to identify candidate patterns and to recognize them on different workflows. We showcase our approach through a use case that is applied on implemented ETL processes from the TPC-DI specification and we present mined ETL patterns. Decomposing ETL processes to identified patterns, our approach provides a stepping stone for the automatic translation of ETL logical models to their conceptual representation and to generate fine-grained cost models at the granularity level of patterns.Peer ReviewedPostprint (author's final draft
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