document: Scalable techniques for memory-efficient CDN simulations Since CDN simulations are known to be highly memory-intensive , in this paper , we argue the need for reducing the memory requirements of such simulations . We propose a novel memory-efficient data structure that stores cache state for a small subset of popular objects accurately and uses approximations for storing the state for the remaining objects . Since popular objects receive a large fraction of the requests while less frequently accessed objects consume much of the memory space , this approach yields large memory savings and reduces errors . We use bloom filters to store approximate state and show that careful choice of parameters can substantially reduce the probability of errors due to approximations . We implement our techniques into a user library for constructing proxy caches in CDN simulators . Our experimental results show up to an order of magnitude reduction in memory requirements of CDN simulations , while incurring a 5-10 % error .
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