ALFE: A replacement policy to cache elephant flows in the presence of mice flooding

Abstract

Flow-based packet processing exists widely in a variety of network applications, where a large sized flow table is built to keep the alive flow records. To accelerate the search speed of the flow table, numerous systems employ cache mechanism to track the most recently referenced flows. However, network traffic exhibits some different characteristics from the workload of the general computational tasks, and classic replacement policies like LRU, Random, fail to perform well in the network scenarios. To develop a network-oriented flow cache replacement policy, we propose ALFE (Adaptive Least Frequently Evicted) based on the observations of traffic's heavy tailed feature and the statistically positive correlation between the flow size and the flow cache evict times. Specifically, the correlation helps us identify elephant flows at a tiny extra cost of a few more bits allocated to each flow entry. For those who are identified as possible elephant flows, ALFE favors their priorities in the cache, thus preventing them from being flooded by the massive mice flows. A prototype system employing ALFE policy is elaborately designed and implemented besides extensive simulations. Experimental results indicate that with 1K cache entries, ALFE can achieve up to 15% higher cache hit rate than LRU on real traces.