Abstract-The performance of demand-driven caching depends on the locality of reference exhibited by the stream of requests made to the cache. In particular, it is expected that the stronger the locality of reference, the smaller the miss rate of the cache. For the Independent Reference Model, this amounts to a smaller miss rate when the popularity distribution of requested objects in the stream is more skewed. In this paper, we formalize this "folk theorem" through the companion concepts of majorization and Schur-concavity. This folk theorem is established for caches operating under a Random On-demand Replacement Algorithm (RORA). However, the result fails to hold in general under the (popular) LRU and CLIMB policies, but can be established when the input has a Zipf-like popularity pmf with large skewness parameter. In addition, we explore how the majorization of popularity distributions translates into comparisons of three well-known locality of reference metrics, namely the inter-reference time, the working set size and the stack distance.
Research has shown that TCP Vegas performs better than TCP Reno with respect to overall network utilization, stability, fairness, throughput, packet loss, and burstiness. In this paper, we analyze and improve the transient behavior of TCP Vegas, an important issue in today's large "bandwidth-delay product" networks. To quantify of our analysis, we introduce a new metric that captures the transient performance of TCP, namely, the (normailized) convergence time. We then consider the slow-start mechanism in TCP Vegas and show that with a properly configured parameter, the transient behavior of TCP Vegas improves with respect to convergence time.
We consider a cache operating under a demand-driven replacement policy when document requests are modeled according to the Independent Reference Model (IRM). We characterize the popularity pmf of the stream of misses from the cache, the so-called output of the cache, for a large class of demand-driven cache replacement policies. We measure strength of locality of reference in a stream of requests through the skewness of its popularity distribution. Using the notion of majorization to capture this degree of skewness, we show that for the policy A0 and the random policy, the output always has less locality of reference than the input. However, we show by counterexamples that this is not always the case under the LRU and CLIMB policies when the input is selected according to a Zipf-like pmf. In that case, conjectures are offered (and supported by simulations) as to when LRU or CLIMB caching indeed reduces locality of reference.
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