Abstract
The clouding server providers usually take workload consolidation to maximize server utilization. For eliminating performance interference due to the competition among multiple shared resources, resource partitioning becomes an important problem in daily commercial servers scenario. However, partitioning the critical multiple resources coordinately is particularly challenging due to the complex contention behaviors and the large search space to be explored for finding the optimal solution.
In this paper, we propose GCNPart, which focuses on allocating the optimal shared compete resource partition for colocated applications to optimize system performance. The existing resource partitioning frameworks lack analysis and good modeling of applications, resulting in inefficiencies or lack of generality. We formulate the resource partitioning problem as a sequential decision problem. GCNPart builds an accurate application performance model based on graph convolutional neural networks (GCN) to learn the mapping relationships from multiple resources to applications, and then constructs deep reinforcement learning (DRL) model to consider temporal information for real-time resource partitioning decisions. The extensive experiments evaluate that compared with the existing resource partitioning frameworks, GCNPart improves system throughput by 5.35% \(\sim \) 26.57%.
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Acknowledgment
This work is supported by Key-Area Research and Development Program of Guangdong Province 2021B0101310002; State Key Laboratory of Computer Architecture, ICT, CAS, under Grant No. CARCHB202013; National Science Foundation of China (62141412, 61872201); Science and Technology Development Plan of Tianjin (20JCZDJC00610); Fundamental Research Funds for the Central Universities.
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Chen, R., Shi, H., Wu, J., Li, Y., Liu, X., Wang, G. (2023). GCNPart: Interference-Aware Resource Partitioning Framework with Graph Convolutional Neural Networks and Deep Reinforcement Learning. In: Meng, W., Lu, R., Min, G., Vaidya, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2022. Lecture Notes in Computer Science, vol 13777. Springer, Cham. https://doi.org/10.1007/978-3-031-22677-9_30
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