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OLM: online LLC management for container-based cloud service

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Abstract

In contrast to the hypervisor-based virtualization method, the container-based scheme does not incur the overhead required by virtual machines since it requires neither a fully abstract hardware stack nor separate guest operating systems (OSes). In this virtualization method, the host OS controls the accesses of the containers to hardware resources. One container can thus be provided with resources such as CPU, memory and network, expectedly isolated from the others. However, due to the lack of architectural support, the last-level cache (LLC) is not utilized in an isolated manner, and thus, it is shared by all containers in the same cloud infrastructure. If a workload of a container leads to cache pollution, it negatively affects the performance of other workloads. To address this problem, we propose an efficient LLC management scheme. By monitoring the memory access pattern, the indirect LLC usage pattern of a container can be figured out. Then, our proposed scheme makes two groups at runtime without using any offline profiling data on containers. The first group is made up of cache-thrashing containers, which fill up the LLC without any temporal locality of data, and the second one consists of normal ones. For isolation, the two separate groups use different partitions of the LLC by the OS-based page coloring method. Our experimental study suggests that the performance of a normal container can be improved by up to 40% in the case of using our proposed scheme.

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Acknowledgements

This research was supported by (1) the DMC center at Samsung Electronics and by the National Research Foundation (NRF) Grant NRF-2013R1A1A2064629. It was also partly supported by (2) Institute for Information and Communications Technology Promotion (IITP) Grant funded by the Korea government (MSIP) (R0190-16-2012, High-Performance Big Data Analytics Platform Performance Acceleration Technologies Development) and partly supported by (3) the National Research Foundation (NRF) Grant (NRF-2016M3C4A7952587, PF Class Heterogeneous High-Performance Computer Development). In addition, this work was partly supported by (4) BK21 Plus for Pioneers in Innovative Computing (Dept. of Computer Science and Engineering, SNU) funded by National Research Foundation of Korea (NRF) (21A20151113068), and this research was supported by (5) the Korea government (MSIP) (NRF-2017R1A2B4004513, Optimizing GPGPU virtualization in multi-GPGPU environments through kernels concurrent execution-aware scheduling (National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP)). At last, this work was supported by (6) Institute for Information and Communications Technology promotion (IITP) Grant funded by the Korea government (MSIP) (2017-0-01733, General Purpose Secure Database Platform Using a Private Blockchain).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Republic of Korea

    Hanul Sung, Jeesoo Min & Hyeonsang Eom

  2. Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea

    Myungsun Kim

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  1. Hanul Sung
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Correspondence to Hyeonsang Eom.

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Sung, H., Kim, M., Min, J. et al. OLM: online LLC management for container-based cloud service. J Supercomput 74, 637–664 (2018). https://doi.org/10.1007/s11227-017-2181-9

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