The file caching problem is defined as follows. Given a cache of size k (a positive integer), the goal is to minimize the total retrieval cost for the given sequence of requests to files. A file f has size size(f ) (a positive integer) and retrieval cost cost(f ) (a non-negative number) for bringing the file into the cache. A miss or fault occurs when the requested file is not in the cache and the file has to be retrieved into the cache by paying the retrieval cost, and some other file may have to be removed (evicted ) from the cache so that the total size of the files in the cache does not exceed k.We study the following variants of the online file caching problem. Caching with Rental Cost (or Rental Caching ): There is a rental cost λ (a positive number) for each file in the cache at each time unit. The goal is to minimize the sum of the retrieval costs and the rental costs. Caching with Zapping : A file can be zapped by paying a zapping cost N ≥ 1. Once a file is zapped, all future requests of the file don't incur any cost. The goal is to minimize the sum of the retrieval costs and the zapping costs.We study these two variants and also the variant which combines these two (rental caching with zapping). We present deterministic lower and upper bounds in the competitive-analysis framework. We study and extend the online covering algorithm from [19] to give deterministic online algorithms. We also present randomized lower and upper bounds for some of these problems.
Can one choose a good Huffman code on the fly, without knowing the underlying distribution? Online Slot Allocation (OSA) models this and similar problems: There are n slots, each with a known cost. There are n items. Requests for items are drawn i.i.d. from a fixed but hidden probability distribution p. After each request, if the item, i, was not previously requested, then the algorithm (knowing c and the requests so far, but not p) must place the item in some vacant slot j i , at cost p i c(j i ). The goal is to minimize the total costThe optimal offline algorithm is trivial: put the most probable item in the cheapest slot, the second most probable item in the second cheapest slot, etc. The optimal online algorithm is First Come First Served (fcfs): put the first requested item in the cheapest slot, the second (distinct) requested item in the second cheapest slot, etc. The optimal competitive ratios for any online algorithm are 1 + H n−1 ∼ ln n for general costs and 2 for concave costs. For logarithmic costs, the ratio is, asymptotically, 1: fcfs gives cost opt + O(log opt).For Huffman coding, fcfs yields an online algorithm (one that allocates codewords on demand, without knowing the underlying probability distribution) that guarantees asymptotically optimal cost: at most opt + 2 log 2 (1 + opt) + 2.
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