Tối ưu hóa viễn thông và thích nghi Kỹ thuật Heuristic P14

Many of today’s data intensive applications have the common need to access exceedingly large databases in a shared fashion, simultaneously with many other copies of themselves or similar applications. Often these multiple instantiations of the client application are geographically distributed, and therefore access the database over wide area networks. As the size of these ‘industrial strength’ databases continue to rise, particularly in the arena of Internet, Intranet and Multimedia servers, performance problems due to poor scalabilty are commonplace. ...
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Tối ưu hóa viễn thông và thích nghi Kỹ thuật Heuristic P14 Telecommunications Optimization: Heuristic and Adaptive Techniques. Edited by David W. Corne, Martin J. Oates, George D. Smith Copyright © 2000 John Wiley & Sons Ltd ISBNs: 0-471-98855-3 (Hardback); 0-470-84163X (Electronic)14Exploring EvolutionaryApproaches to DistributedDatabase ManagementMartin J. Oates and David Corne14.1 IntroductionMany of today’s data intensive applications have the common need to access exceedinglylarge databases in a shared fashion, simultaneously with many other copies of themselves orsimilar applications. Often these multiple instantiations of the client application aregeographically distributed, and therefore access the database over wide area networks. Asthe size of these ‘industrial strength’ databases continue to rise, particularly in the arena ofInternet, Intranet and Multimedia servers, performance problems due to poor scalabilty arecommonplace. Further, there are availability and resilience risks associated with storing alldata in a single physical ‘data warehouse’, and many systems have emerged to help improvethis by distributing the data over a number of dispersed servers whilst still presenting theappearance of a single logical database The Internet is a large scale distributed file system, where vast amounts of highlyinterconnected data are distributed across many number of geographically dispersed nodes.It is interesting to note that even individual nodes are increasingly being implemented as acluster or ‘farm’ of servers. These ‘dispersed’ systems are a distinct improvement overmonolithic databases, but usually still rely on the notion of fixed master/slave relationships(mirrors) between copies of the data, at fixed locations with static access configurations. For‘fixed’ systems, initial file distribution design can still be complex and indeed evolutionaryTelecommunications Optimization: Heuristic and Adaptive Techniques, edited by D. Corne, M.J. Oates and G.D. Smith© 2000 John Wiley & Sons, Ltd236 Telecommunications Optimization: Heuristic and Adaptive Techniquesalgorithms have been suggested in the past for static file distribution by March and Rho(1994, 1995) and Cedano and Vemuri (1997), and for Video-on Demand like services byTanaka and Berlage (1996). However as usage patterns change, the efficiency of theoriginal distribution can rapidly deteriorate and the administration of such systems, beingmainly manual at present, can become labour intensive as an alternative solution, Bichevand Olafsson (1998) have suggested and explored a variety of automated evolutionarycaching techniques. However, unless such a dispersed database can dynamically adjustwhich copy of a piece of data is the ‘master’ copy, or indeed does away with the notion of a‘master copy’, then it is questionable whether it can truly be called a ‘distributed’ database. The general objective is to manage varying loads across a distributed database so as toreliably and consistently provide near optimal performance as perceived by clientapplications. Such a management system must ultimately be capable of operating over arange of time varying usage profiles and fault scenarios, incorporate considerations formultiple updates and maintenance operations, and be capable of being scaled in a practicalfashion to ever larger sized networks and databases. To be of general use, the system musttake into consideration the performance of both the back-end database servers, and thecommunications networks, which allow access to the servers from the client applications. Where a globally accessible service is provided by means of a number of distributed andreplicated servers, accessed over a communications network, the particular allocation ofspecific groups of users to these ‘back-end’ servers can greatly affect the user perceivedperformance of the service. Particularly in a global context, where user load variessignificantly over a 24 hour period, peak demand tends to ‘follow the sun’ from Europethrough the Americas and on to the Asia Pacific region. Periodic re-allocation of groups ofusers to different servers can help to balance load on both servers and communications linksto maintain an optimal user-perceived Quality of Service. Such re-configuration/re-allocation can also be usefully applied under server node or communications link failureconditions, or during scheduled maintenance. The management of this dynamic access configuration/load balancing in near real timecan rapidly become an exceedingly complex task, dependent on the number of nodes, levelof fragmentation of the database, topography of the networ ...