Cache What You Need to Cache

The SSD has been playing a significantly important role in caching systems due to its high performance-to-cost ratio. Since the cache space is typically much smaller than that of the backend storage by one order of magnitude or even more, write density (defined as writes per unit time and space) of the SSD cache is therefore much more intensive than that of HDD storage, which brings about tremendous challenges to the SSD’s lifetime. Meanwhile, under social network workloads, quite a lot writes to the SSD cache are unnecessary. For example, our study on Tencent’s photo caching shows that about 61% of total photos are accessed only once, whereas they are still swapped in and out of the cache. Therefore, if we can predict these kinds of photos proactively and prevent them from entering the cache, we can eliminate unnecessary SSD cache writes and improve cache space utilization. To cope with the challenge, we put forward a “one-time-access criteria” that is applied to the cache space and further propose a “one-time-access-exclusion” policy. Based on these two techniques, we design a prediction-based classifier to facilitate the policy. Unlike the state-of-the-art history-based predictions, our prediction is non-history oriented, which is challenging to achieve good prediction accuracy. To address this issue, we integrate a decision tree into the classifier, extract social-related information as classifying features, and apply cost-sensitive learning to improve classification precision. Due to these techniques, we attain a prediction accuracy greater than 80%. Experimental results show that the one-time-access-exclusion approach results in outstanding cache performance in most aspects. Take LRU, for instance: applying our approach improves the hit rate by 4.4%, decreases the cache writes by 56.8%, and cuts the average access latency by 5.5%.