With the rapid development of information technology, edge computing has grown rapidly by pushing large amounts of computing to the edge of the network. However, due to the rapid growth of edge access devices and limited edge storage space, the edge cloud faces many challenges in addressing the workloads. In this paper, a cost-optimized resource scaling strategy is proposed based on load fluctuation. Firstly, the load prediction model is built based on DBN with supervised learning to predict the workloads of edge cloud. Then, a cost-optimized resource scaling strategy is presented, which comprehensively considers reservation planning and on-demand planning. In the reservation phase, the long-term resource reservation problem is planned as a two-stage stochastic programming problem, which is transformed into a deterministic integer programming problem. In the on-demand phase, the on-demand resource scaling problem planning is solved as an integer programming problem. Finally, extensive experiments are conducted to evaluate the performance of the proposed cost-optimized resource scaling strategy based on load fluctuation.

中文翻译：

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边缘云计算环境下基于负载波动的高效资源伸缩

随着信息技术的飞速发展，边缘计算通过将大量计算推向网络边缘而迅速发展。然而，由于边缘访问设备的快速增长和有限的边缘存储空间，边缘云在解决工作负载方面面临许多挑战。本文提出了一种基于负载波动的成本优化的资源扩展策略。首先，基于 DBN 建立负载预测模型，通过监督学习来预测边缘云的工作负载。然后，提出了一种成本优化的资源扩展策略，它综合考虑了预留计划和按需计划。在预留阶段，将长期资源预留问题规划为两阶段随机规划问题，转化为确定性整数规划问题。在按需阶段，按需资源扩展问题规划作为整数规划问题解决。最后，进行了大量实验以评估所提出的基于负载波动的成本优化资源扩展策略的性能。