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 way to increase gateway throughput is to reduce the routing-table lookup time per packet. A routing-table cache can be used to reduce the average lookup time per packet and the purpose of this study is to determine the best management policies for this cache as well as its measured performance. The performance results of simulated caches for a gateway at MIT are presented. These results include the probability of reference versus previous access time, cache hit ratios, and the number of packets between cache misses. A simple, conservative analysis using the presented measurements shows that current gateway routing-table lookup time could be reduced by up to 65 . > Abstract The size of computer networks, along with their bandwidths, is growing exponentially. To support these large, high-speed networks, it is necessary to be able to forward packets in a few microseconds. One part of the forwarding operation consists of searching through a large address database. This problem is encountered in the design of adapters, bridges, routers, gateways, and name servers. Caching can reduce the lookup time if there is a locality in the address reference pattern. Using a destination reference trace measured on an extended local area network, we attempt to see if the destination references do have a significant locality. We compared the performance of MIN, LRU, FIFO, and random cache replacement algorithms. We found that the interactive (terminal) traffic in our sample had a quite different locality behavior than that of the noninteractive traffic. The interactive traffic did not follow the LRU stack model while the noninteractive traffic did. Examples are shown of the environments in which caching can help as well as those in which caching can hurt, unless the cache size is large.