Cloud resource demands, especially some unclear and emergent resource demands, are growing rapidly with the development of cloud computing, big data and artificial intelligence. The traditional cloud resource allocation methods do not support the emergent mode in guaranteeing the timeliness and optimization of resource allocation. This paper proposes a resource allocation algorithm for emergent demands in cloud computing. After building the priority of resource allocation and the matching distances of resource performance and resource proportion to respond to emergent resource demands, a multi-objective optimization model of cloud resource allocation is established based on the minimum number of the physical servers used and the minimum matching distances of resource performance and resource proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, is presented to solve the multi-objective optimization model, which not only improves the quality and distribution uniformity of the solution set but also accelerates the solving speed. The experimental results show that our algorithm can not only allocate resources quickly and optimally for emergent demands but also balance the utilization of all kinds of resources.

中文翻译：

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云计算中紧急需求资源分配的多目标优化

随着云计算，大数据和人工智能的发展，云资源需求（尤其是一些不清楚的和紧急的资源需求）正在迅速增长。传统的云资源分配方法在保证资源分配的及时性和优化性方面不支持紧急模式。针对云计算中的紧急需求，本文提出了一种资源分配算法。在建立资源分配的优先级以及资源性能和资源比例的匹配距离以响应紧急资源需求之后，基于所使用的物理服务器的最小数量和最小匹配，建立了云资源分配的多目标优化模型。资源性能和资源比例的距离。然后，一种改进的进化算法，提出了RAA-PI-NSGAII求解多目标优化模型，不仅提高了解集的质量和分布均匀性，而且加快了求解速度。实验结果表明，该算法不仅可以快速，优化地分配突发资源，而且可以平衡各种资源的利用。