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Chip multicore processors (CMPs) constitute the cherry-picked architecture for high-performance servers employed in supercomputers and cloud datacenters. In the last few years, Non-Uniform Memory Access (NUMA) multicore systems have become the dominant choice in these domains. Regardless of the technology advances enabling to pack an increasing number of cores and bigger caches on the same chip, contention for shared resources still represents an important challenge for the system software. Cores in CMPs typically share multiple resources, such as the last-level cache (LLC) or a DRAM controller. The competition for the usage of these resources leads to uneven performance degradation across co-running applications. Previous research has demonstrated that contention effects on CMPs can be mitigated via smart partitioning of the LLC or by distributing threads across groups of cores so as to even out the degree of competition on multiple LLCs or memory nodes. However, most existing resource-management strategies fail to effectively combine both contention-mitigating techniques, thus providing suboptimal results on NUMA multicores. In this paper, we analyze how to best combine these techniques to improve system-wide fairness, and, based on the conclusions of our analysis, propose a fair OS-level NUMA-aware resource manager that leverages dynamic contention-aware thread-to-socket mappings and cache-partitioning. We implemented our resource manager in the Linux kernel and assessed its effectiveness on a real dual-socket system featuring Intel Skylake processors. Our results show that it reduces unfairness by more than 17&#x0025; on average compared to Linux and a state-of-the-art NUMA-aware resource manager.

Divide&content: A Fair OS-Level Resource Manager for Contention Balancing on NUMA Multicores