A quality of service architecture
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HOTOS '99 Proceedings of the The Seventh Workshop on Hot Topics in Operating Systems
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ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
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Telecommunications companies currently present a large demand for flexible and efficient data management system architectures. Data Management Systems are providers of data access services, or simply data services, which perform functions that manage, manipulate or deliver data. In order for such systems to provide high quality data services that adequately meet the special requirements of telecommunications companies, they should comprise tile following dimensions: (i) the data service users (humans or client applications) should be granted sufficient control to select data services according to their specific Quulity of Service (QoS) requirements (ii) the provided data services must present explicit descriptions of their non-functional - quality - properties along with their functional characteristics, and (iii) dynamic mechanisms should be adopted for adaptive data service resource management in order to achieve tile best of performance under any circumstances. Recent developments in the areas of data management and database middleware fall short in proposing approaches for data service delivery that emphasise these dimensions and especially the QoS aspect of a data service. In this paper we present QuDAS, a QoS-based data access provisioning system that is strictly designed on the lines of tile above criteria. The main contribution of QuDAS is that data services are capable of publishing their functional and quality properties as a special set of meta-data, namely, interfaces, protocols and tariffs. Based on this meta-data users can select any set of data services that is tailored to their functional and QoS needs. Additionally, QuDAS achieves to dynamically manage service resource usage and automatically balance its workload by means of tariffs, which are continuously adjusted driven by tile valying user demand for data services.