design and performance analysis of a dram-based statistics counter array architecture
The problem of maintaining efficiently a large number (say millions) of statistics counters that need to be updated at very high speeds (e.g. 40 Gb/s) has received considerable research attention in recent years. This problem arises in a variety of router management and data streaming applications where large arrays of counters are used to track various network statistics and implement various counting sketches. It proves too costly to store such large counter arrays entirely in SRAM while DRAM is viewed as too slow for providing wirespeed updates at such high speeds. In this paper, we propose a DRAM-based counter architecture that can effectively maintain wirespeed updates to large counter arrays. The proposed approach is based on the observation that modern commodity DRAM architectures, driven by aggressive performance roadmaps for consumer applications (e.g. video games), have advanced architecture features that can be exploited to make a DRAM-based solution practical. In particular, we propose a randomized DRAM architecture that can harness the performance of modern commodity DRAM offerings by interleaving counter updates to multiple memory banks. The proposed architecture makes use of a simple randomization scheme, a small cache, and small request queues to statistically guarantee a near-perfect load-balancing of counter updates to the DRAM banks. The statistical guarantee of the proposed scheme is proven using a novel combination of convex ordering and large deviation theory. Our proposed counter scheme can support arbitrary increments and decrements at wirespeed, and it can support different number representations, including both integer and floating point number representations.
statistics counter convex ordering large deviation theory network measurement majorization