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Exploring gate-limited analytical models for high-performance network storage servers
Authors:Glenford Mapp  Dhawal Thakker  Orhan Gemikonakli
Affiliation:School of Engineering and Information Sciences, Middlesex University, Hendon Campus, London, UK, NW4 4BT;Department of Computer Science Loughborough University Area FJ, Garendon Wing, Holywell Park Loughborough, Leicestershire LE11 3TU, UK Email: L.Guan@lboro.ac.uk Phone: +44 (0)1509 635661;Department of Computing & the Digital Environment Faculty of Engineering and Computing Coventry University Priory St, Coventry CV1 5FB, UK Email: Xingang.Wang@coventry.ac.uk Phone: +44 (0)24 7688 7688;Department of Computing School of Informatics University of Bradford Richmond Road, Bradford, West Yorkshire BD7 1DP, UK Email: I.U.Awan@Bradford.ac.uk Phone: +44(0)1274 233952;Chair of Computer Networks Faculty of Computer Science Technical University of Dresden D-01062 Dresden Email: waltenegus.dargie@tu-dresden.de Phone: +49 351 463 38352
Abstract:Gate-limited service is a type of service discipline found in queueing theory and can be used to describe a number of operational environments, for example, large transport systems such as buses, trains or taxis, etc. Recently, there has been the observation that such systems can also be used to describe interactive Internet Services which use a Client/Server interaction. In addition, new services of this genre are being developed for the local area. One such service is a Network Memory Server (NMS) being developed here at Middlesex University. Though there are several examples of real systems that can be modelled using gate-limited service, it is fair to say that the analytical models which have been developed for gate-limited systems have been difficult to use, requiring many iterations before practical results can be generated. In this paper, a detailed gate-limited bulk service queueing model based on Markov chains is explored and a numerical solution is demonstrated for simple scenarios. Quantitative results are presented and compared with a mathematical simulation. The analysis is used to develop an algorithm based on the concept of optimum operational points. The algorithm is then employed to build a high-performance server which is capable of balancing the need to prefetch for streaming applications while promptly satisfying demand misses. The algorithm is further tested using a systems simulation and then incorporated into an Experimental File System (EFS) which showed that the algorithm can be used in a real networking environment.
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