BUFFALO: Bloom Filter Forwarding Architecture for Large Organizations
In enterprise and data center networks, the scalability of the data plane becomes increasingly challenging as forwarding tables and link speeds grow. Simply building switches with larger amounts of faster memory is not appealing, since high-speed memory is both expensive and power hungry. Implementing hash tables in SRAM is not appealing either be- cause it requires significant overprovisioning to ensure that all forwarding table entries fit. Instead, we propose the BUFFALO architecture, which uses a small SRAM to store one Bloom filter of the addresses associated with each outgoing link. We provide a practical switch design leveraging flat addresses and shortest-path routing. BUFFALO gracefully handles false positives without reducing the packet-forwarding rate, while guaranteeing that packets reach their destinations with bounded stretch with high probability. We tune the sizes of Bloom filters to minimize false positives for a given memory size. We also handle routing changes and dynamically adjust Bloom filter sizes using counting Bloom filters in slow memory. Our extensive analysis, simulation, and prototype implementation in kernel-level Click show that BUFFALO significantly reduces memory cost, increases the scalability of the data plane, and improves packet-forwarding performance.