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Machine-learning-based cache partition method in cloud environment
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DOI：10.1007/s12083-021-01235-x.png)
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In the modern cloud environment, considering the cost of hardware and software resources, applications are often co-located on a platform and share such resources. However, co-located execution and resource sharing bring memory access conflict, especially in the Last Level Cache (LLC). In this paper, a lightweight method is proposed for partition LLC named by Classification-and-Allocation (C&A). Specifically, Support Vector Machine (SVM) is used in the proposed method to classify applications into the triple classes based on the performance change characteristic (PCC), and the Bayesian Optimizer (BO) is leveraged to schedule LLC to guarantee applications with the same PCC sharing the same part of LLC. Since the near-optimal partition can be found efficiently by leveraging BO-based scheduling with a few sampling steps, C&A can handle unseen and versatile workloads with low overhead. We evaluate the proposed method in several workloads. Experimental results show that C&A can outperform the state-of-art method KPart (El-Sayed et al in Proceedings of 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA) 104-117, 2018) by 7.45% and 22.50% respectively in overall system throughput and fairness, and reduces 20.60% allocation overhead.
Keyword:
Cloud
Cache Partition
Last Level Cache
Machine Learning
期刊
IF:
2.6
论文数:
2.2K
被引数:
2.9K
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PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
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