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			<titleStmt><title level='a'>Coeus: Clustering (A)like Patterns for Practical Machine Intelligent Hybrid Memory Management</title></titleStmt>
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				<publisher>IEEE</publisher>
				<date>05/01/2022</date>
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				<bibl> 
					<idno type="par_id">10469909</idno>
					<idno type="doi">10.1109/CCGrid54584.2022.00071</idno>
					
					<author>Thaleia Dimitra Doudali</author><author>Ada Gavrilovska</author>
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			<abstract><ab><![CDATA[Emerging workloads benefit from massive memory capacities provided by hybrid memory platforms. Recent system-level hybrid memory management solutions integrate machine learning methods to better predict complex data access behaviors. Given the substantial associated learning overheads, such solutions train parallel recurrent neural networks to learn the access patterns at the granularity of a page for a carefully selected page subset. Our observation reveals that the size of this subset varies immensely across workload classes, sizes and patterns. Increasing the granularity at the level of a page group will help reduce the aggregate learning overheads. Yet, unsupervised machine learning clustering methods are not practical to use in this context. Instead, this paper builds Coeus - a page grouping mechanism for machine learning-based hybrid memory management. Coeus is simple, robust and efficient. Coeus leverages data reuse insights to fine-tune the granularity at which patterns are interpreted by the system. As a result, Coeus creates large clusters of pages that share the same access behavior, in a practical way. Coeus reduces by almost 3x the associated learning overheads. In addition, Coeus achieves 3x higher application performance, by the combined effects of applying machine learning to more pages and by performing management operations at better granularity, compared to configurations of existing hybrid memory managers.]]></ab></abstract>
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