This chapter discusses the false rates of Bloom filters in a distributed environment. A Bloom filter (BF) is a space-efficient data structure to support probabilistic membership query. In distributed systems, a Bloom filter is often used to summarize local services or objects and this Bloom filter is replicated to remote hosts. This allows remote hosts to perform fast membership query without contacting the original host. However, when the services or objects are changed, the remote Bloom replica may become stale. This chapter analyzes the impact of staleness on the false positive and false negative for membership queries on a Bloom filter replica. An efficient update control mechanism is then proposed based on the analytical results to minimize the updating overhead. This chapter validates the analytical models and the update control mechanism through simulation experiments.
