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摘要
En 中文
DRAM-based cache is a practical approach to enhancing the performance of large-capacity SSDs. Due to DRAM’s limited capacity, cache sizes are significantly smaller than the data scale of workloads – and cache replacement schemes determine the cache hit ratio and SSD performance when a cache reaches full capacity. Prior work embarked on leveraging machine learning models (ML) to predict future access patterns in workloads, aiding cache replacement decisions for intelligent cache schemes inside SSDs. Unfortunately, existing ML-based cache schemes often overlook the impacts of data granularity – including page, request, and coarse granularities – of training datasets, model inference time overhead, and computational overhead on SSD performance. To address these challenges, we are motivated to propose an sample-inference-based intelligent cache scheme – eCache. eCache accurately predicts the future reuse distance of each sampled requested pages, which can enhance the accuracy of decision-making for the cache replacement. In particular, we design a parallel framework to curb the time overhead caused by model inference. We advocate for a random-group sampling inference method that utilizes the most accurate model while reducing computational overhead. Moreover, we implement eCache on the state-of-the-art SSD simulator, MQSim, and compare it against alternative cache schemes (i.e., CCache, NCache, LAC, and VS-batch). The experimental results unveil that compared with the other cache schemes, eCache significantly reduces the average response time by up to 79.68% with an average reduction of 44.08%. When compared with the page-granularity-ML-empowered cache schemes, eCache greatly curtails computational overhead by up to 85.23% with an average reduction of 66.00%.
Keyword:
Cache management schemes
machine learning
extreme gradient boosting
sample-inference
NAND flash memory
solid state disks
期刊
IF:
3.8
论文数:
5.4K
被引数:
9.8K

