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.
A program's working set W ( t, T ) at time t is the set of distinct pages among the T most recently referenced pages. Relations between the average working-set size, the missing-page rate, and the interreference-interval distribution may be derived both from time-average definitions and from ensemble-average (statistical) definitions. An efficient algorithm for estimating these quantities is given. The relation to LRU (lease recently used) paging is characterized. The independent-reference model, in which page references are statistically independent, is used to assess the effects of interpage dependencies on working-set size observations. Under general assumptions, working-set size is shown to be normally distributed. Probably the most basic reason behind the absence of a general treatment of resource allocation in modern computer systems is an adequate model for program behavior. In this paper a new model, the “working set model,” is developed. The working set of pages associated with a process, defined to be the collection of its most recently used pages, provides knowledge vital to the dynamic management of paged memories. “Process” and “working set” are shown to be manifestations of the same ongoing computational activity; then “processor demand” and “memory demand” are defined; and resource allocation is formulated as the problem of balancing demands against available equipment.