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Semantic-based QoS management in cloud systems: Current status and future challenges
Affiliation:1. Institute of Information Systems (IWI), University of Hamburg, Germany;2. Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Chile;1. School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, China;2. School of Computer Science and Engineering, Sun Yat-Sen University, China;3. School of Information and Communication Technology, Griffith University, Australia
Abstract:Cloud Computing and Service Oriented Architectures have seen a dramatic increase of the amount of applications, services, management platforms, data, etc. gaining momentum for the necessity of new complex methods and techniques to deal with the vast heterogeneity of data sources or services. In this sense Quality of Service (QoS) seeks for providing an intelligent environment of self-management components based on domain knowledge in which cloud components can be optimized easing the transition to an advanced governance environment. On the other hand, semantics and ontologies have emerged to afford a common and standard data model that eases the interoperability, integration and monitoring of knowledge-based systems. Taking into account the necessity of an interoperable and intelligent system to manage QoS in cloud-based systems and the emerging application of semantics in different domains, this paper reviews the main approaches for semantic-based QoS management as well as the principal methods, techniques and standards for processing and exploiting diverse data providing advanced real-time monitoring services. A semantic-based framework for QoS management is also outlined taking advantage of semantic technologies and distributed datastream processing techniques. Finally a discussion of existing efforts and challenges is also provided to suggest future directions.
Keywords:Cloud systems  Quality of service  Service oriented architectures  Semantics  Ontologies  Linked data  Sensor data  Big data
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