Nara: Learning Network-Aware Resource Allocation Algorithms for Cloud Data Centres

• URL: http://arxiv.org/abs/2106.02412v1
• Date: Fri, 4 Jun 2021 10:56:49 GMT
• Title: Nara: Learning Network-Aware Resource Allocation Algorithms for Cloud Data Centres
• Authors: Zacharaya Shabka, Georgios Zervas
• Abstract summary: Nara is a framework based on reinforcement learning and graph neural networks (GNN) to learn network-aware allocation policies. It can accept up to 33% more requests than the best baseline when deployed on DCNs with up to the order of $10times more compute nodes than the DCN seen during training. It is able to maintain its policy's performance on DCNs with the order of $100times$ more servers than seen during training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data centres (DCs) underline many prominent future technological trends such as distributed training of large scale machine learning models and internet-of-things based platforms. DCs will soon account for over 3\% of global energy demand, so efficient use of DC resources is essential. Robust DC networks (DCNs) are essential to form the large scale systems needed to handle this demand, but can bottleneck how efficiently DC-server resources can be used when servers with insufficient connectivity between them cannot be jointly allocated to a job. However, allocating servers' resources whilst accounting for their inter-connectivity maps to an NP-hard combinatorial optimisation problem, and so is often ignored in DC resource management schemes. We present Nara, a framework based on reinforcement learning (RL) and graph neural networks (GNN) to learn network-aware allocation policies that increase the number of requests allocated over time compared to previous methods. Unique to our solution is the use of a GNN to generate representations of server-nodes in the DCN, which are then interpreted as actions by a RL policy-network which chooses from which servers resources will be allocated to incoming requests. Nara is agnostic to the topology size and shape and is trained end-to-end. The method can accept up to 33\% more requests than the best baseline when deployed on DCNs with up to the order of $10\times$ more compute nodes than the DCN seen during training and is able to maintain its policy's performance on DCNs with the order of $100\times$ more servers than seen during training. It also generalises to unseen DCN topologies with varied network structure and unseen request distributions without re-training.

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