By C.K. Wong

ISBN-10: 3642693520

ISBN-13: 9783642693526

ISBN-10: 3642693547

ISBN-13: 9783642693540

A significant technological pattern for big database structures has been the advent of ever-larger mass garage structures. this permits computing facilities and company information processing installations to take care of on-line their application libraries, much less often used information records, transaction logs and backup copies less than unified approach regulate. Tapes, disks and drums are classical examples of mass garage media. The more moderen IBM 3851 Mass garage Facility, a part of the IBM 3850 Mass garage approach, represents a brand new course in mass garage improvement, specifically, it truly is two-dimensional. With the adulthood of magnetic bubble know-how, extra refined, great, multi-trillion-bit garage platforms aren't a ways sooner or later. whereas huge in potential, mass garage structures have often quite lengthy entry occasions. due to the fact that list entry possibilities aren't uniform, quite a few algorithms were devised to put the documents to diminish the common entry time. the 1st chapters of this ebook are dedicated regularly to such algorithmic stories in linear and two-dimensional mass garage structures. within the 3rd bankruptcy, we view the bubble reminiscence as greater than a garage medium. in truth, we talk about assorted constructions the place regimen operations, akin to information rearrangement, sorting, looking, etc., will be performed within the reminiscence itself, liberating the CPU for extra advanced initiatives. the issues mentioned during this booklet are combinatorial in nature.

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**Extra resources for Algorithmic Studies in Mass Storage Systems**

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J i j> 1 12(ij + i)d(i,j) . 23). Expansion in a Laurent series gives the final statement of the theorem. 6. Heuristic placement For heuristic placement, the expected frequency for the user placed in location i depends on the arrival time and on the rank of location i relative to the remaining vacant locations at the time when location i is filled. As a consequence, the analysis becomes much more tedious. 3 Let Dh denote the average distance after placement by our heuristic. -H~2»(23n2 +20n+33) 20n(3n + 1)(n - 2)(n - 3) .

1 Let Dr denote the average distance under random placement; then 2 E(Dr):::::(n - 1)/(3n + 1). 22) as required. 5. Optimal static placement For a given sequence {f t }, optimal static placement requires positioning the i-th smallest of {f t } in location i. Thus an implementation of this strategy would require waiting for all n arrivals before any allocation is made. 2 Let Do denote the average distance under optimal static placement. 23) ,; 7n/30 + 2... 25) 1). j=l j=l We shall compute E(fjfj) directly.

Let q be the number of those placed in locations 1 through j-1. ( j - 2 - (m + p + q) 2 n-s+l q ») s-l-r-q ( n-r-l) s-r-l + j - 1- (m + p + q) ). 36), we have after further manipulation E(ff i . I r,s,r~~s) = ------=----2(n - s + l)(n - r + 1) 1] . [ (i-1)(n-r) ++ - - - 1- - - (n-2) [ 2 (n-s+l)(n-r+1) (i-1)(j-l)(n-r)(n-3)-(i-l)(j-3)(n-r)(s-l) (n-2)(n-3) + (j-l)(n-2)-(j-2)(s-l) ] 2(n - 2) . ~~

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