We propose and analyze a distributed cooperative
caching strategy based on the Evolutive Summary Counters
(ESC), a new data structure that stores an approximated record
of the data accesses in each computing node of a search engine.
The ESC capture the frequency of accesses to the elements
of a data collection, and the evolution of the access patterns
for each node in a network of computers. The ESC can be
efficiently summarized into what we call ESC-summaries to
obtain approximate statistics of the document entries accessed
by each computing node.
We use the ESC-summaries to introduce two algorithms that
manage our distributed caching strategy, one for the distribution
of the cache contents, ESC-placement, and another one for the
search of documents in the distributed cache, ESC-search. While
the former improves the hit rate of the system and keeps a large
ratio of data accesses local, the latter reduces the network traffic
by restricting the number of nodes queried to find a document.
We show that our cooperative caching approach outperforms
state of the art models in both hit rate, throughput, and location
recall for multiple scenarios, i.e., different query distributions
and systems with varying degrees of complexity.
