The Locator ID Separation Protocol (LISP) limits the growth of the Default-Free Zone routing tables by creating a highly aggregatable and quasi-static Internet core. However, LISP pushes the forwarding state to edge routers whose timely operation relies on caching of location to identity bindings. In this paper we develop an analytical model to study the asymptotic scalability of the LISP cache. Under the assumptions that (i) long-term popularity can be modeled as a constant Generalized Zipf distribution and (ii) temporal locality is predominantly determined by long-term popularity, we find that the scalability of the LISP cache is O(1) with respect to the amount of prefixes (Internet growth) and users (growth of the LISP site). We validate the model and discuss the accuracy of our assumptions using several one-day-long packet traces.
Denning was first to recognize the phenomenon of temporal locality in his definition of the working-set @cite_6 and together with Schwartz established the fundamental properties that characterize it @cite_20 . Although initially designed for the analysis of page caching in operating systems, the ideas were later reused in other fields including Web page and route caching.
@cite_20 @cite_6
2136719353 2144684817 2155239436 2096230186
Temporal locality of reference in Web request streams emerges from two distinct phenomena: the long-term popularity of Web documents and the short-term temporal correlations of references. We show that the commonly-used distribution of inter-request times is predominantly determined by the power law governing the long-term popularity of documents. This inherent relationship tends to disguise the existence of short-term temporal correlations. We propose a new and robust metric that enables accurate characterization of that aspect of temporal locality. Using this metric, we characterize the locality of reference in a number of representative proxy cache traces. Our findings show that there are measurable differences between the degrees (and sources) of temporal locality across these traces. The authors propose models for both temporal and spatial locality of reference in streams of requests arriving at Web servers. They show that simple models based on document popularity alone are insufficient for capturing either temporal or spatial locality. Instead, they rely on an equivalent, but numerical, representation of a reference stream: a stack distance trace. They show that temporal locality can be characterized by the marginal distribution of the stack distance trace, and propose models for typical distributions and compare their cache performance to the traces. They also show that spatial locality in a reference stream can be characterized using the notion of self-similarity. Self-similarity describes long-range correlations in the data set, which is a property that previous researchers have found hard to incorporate into synthetic reference strings. They show that stack distance strings appear to be strongly self-similar, and provide measurements of the degree of self-similarity in the traces. Finally, they discuss methods for generating synthetic Web traces that exhibit the properties of temporal and spatial locality measured in the data. There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged. In this paper we take a first step in this direction. We propose a framework for describing how reference streams are transformed as they pass through the Internet, based on three operations: aggregation, disaggregation, and filtering. We also propose metrics to capture the temporal locality of reference streams in this framework. We argue that these metrics (marginal entropy and interreference coefficient of variation) are more natural and more useful than previously proposed metrics for temporal locality; and we show that these metrics provide insight into the nature of reference stream transformations in the Web. Modern computer systems, as well as the Internet, use caching to maximize their efficiency. Nowadays, caching occurs in many different system layers. Analysis of these layers will lead to a deeper understanding of cache performance. ... comes from the uniprocessor environment. Spatial locality implies that the next data item in the address space is most likely to be used next, while temporal locality implies that the last data item used is most likely to be used next. Implementation is typically based on a fast but expensive memory (the price is affordable because, by definition, cache memory is small). Even if we use the same technology for the main memory and cache memory, the cache memory will be faster because smaller memories have a shorter access time. Recent research tries to split the CPU cache into two subcaches: one for spatial locality and one for temporal locality. # SMP On the SMP level, spatial and temporal