Abstract
With increasing market competition among commercial cloud computing infrastructures, major cloud service providers are building co-located data centers to deploy online services and offline jobs in the same cluster to improve resource utilization. However, at present, related researches on the characteristics of co-location are still immature. Therefore, stable solutions for resource collaborative scheduling are still essentially absent, which restricts the further optimization of co-location technology. In this paper, we present performance prediction-based co-located task scheduling (PPCTS) model to perform fine-grained resource allocation under Quality of Service (QoS) constraints. PPCTS improves the overall cluster CPU resource utilization to 56.211%, which is competitive to the current related works. Besides, this paper fully implements a co-located system prototype. Compared with the traditional simulator-based scheduling research work, the prototype proposed in this paper is easier to be migrated to the real production environment.
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References
Batista, G.E., Keogh, E.J., Tataw, O.M., De Souza, V.M.: CID: an efficient complexity-invariant distance for time series. Data Min. Knowl. Disc. 28(3), 634–669 (2014)
Chen, Q., Wang, Z., Leng, J., Li, C., Zheng, W., Guo, M.: Avalon: towards QoS awareness and improved utilization through multi-resource management in datacenters. In: Proceedings of the ACM International Conference on Supercomputing, pp. 272–283 (2019)
Chen, S., Delimitrou, C., Martínez, J.F.: Parties: QoS-aware resource partitioning for multiple interactive services. In: Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 107–120 (2019)
Jiang, C., Fan, T., Gao, H., Shi, W., Wan, J.: Energy aware edge computing: a survey. Comput. Commun. 151, 556–580 (2020)
Jiang, C., Han, G., Lin, J., Jia, G., Shi, W., Wan, J.: Characteristics of co-allocated online services and batch jobs in internet data centers: a case study from Alibaba cloud. IEEE Access 7, 22495–22508 (2019)
Jiang, C., et al.: Characterizing co-located workloads in Alibaba cloud datacenters. IEEE Trans. Cloud Comput. 1 (2020)
Liu, Q., Yu, Z.: The elasticity and plasticity in semi-containerized co-locating cloud workload: a view from Alibaba trace. In: Proceedings of the ACM Symposium on Cloud Computing, pp. 347–360 (2018)
Lo, D., Cheng, L., Govindaraju, R., Ranganathan, P., Kozyrakis, C.: Heracles: improving resource efficiency at scale. In: Proceedings of the 42nd Annual International Symposium on Computer Architecture, pp. 450–462 (2015)
Lu, C., Ye, K., Xu, G., Xu, C.Z., Bai, T.: Imbalance in the cloud: an analysis on Alibaba cluster trace. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 2884–2892. IEEE (2017)
Mars, J., Tang, L., Hundt, R., Skadron, K., Soffa, M.L.: Bubble-up: increasing utilization in modern warehouse scale computers via sensible co-locations. In: Proceedings of the 44th annual IEEE/ACM International Symposium on Microarchitecture, pp. 248–259 (2011)
Patel, T., Tiwari, D.: CLITE: efficient and QoS-aware co-location of multiple latency-critical jobs for warehouse scale computers. In: 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 193–206. IEEE (2020)
Tian, H., Zheng, Y., Wang, W.: Characterizing and synthesizing task dependencies of data-parallel jobs in Alibaba cloud. In: Proceedings of the ACM Symposium on Cloud Computing, pp. 139–151 (2019)
Xu, F., Wang, S., Yang, W.: Cloud resource scheduling algorithm based on game theory. Comput. Sci. 46(6A), 295–299 (2019)
Zhao, S., Xue, S., Chen, Q., Guo, M.: Characterizing and balancing the workloads of semi-containerized clouds. In: 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), pp. 145–148. IEEE Computer Society (2019)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61972118), the Science and Technology Project of State Grid Corporation of China (Research and Application on Multi-Datacenters Cooperation & Intelligent Operation and Maintenance, No. 5700-202018194A-0-0-00), and the Science and Technology Project of State Grid Corporation of China (No. SGSDXT00DKJS1900040).
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Yuan, T., Ou, D., Wang, J., Jiang, C., Cérin, C., Yan, L. (2022). PPCTS: Performance Prediction-Based Co-located Task Scheduling in Clouds. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_16
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DOI: https://doi.org/10.1007/978-3-030-95391-1_16
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