Elephant flows (elephants) refer to the sequences of packets that contribute only 10% of the total volume but consume over 90% of the network bandwidth. They often cause network congestion and should be efficiently managed. Present cloud data centers often involve host- and switch-based approaches to detect and schedule elephants, but suffer (1) each host and switch in the network needs to be customized, and (2) dynamic models and advanced policies are difficult to be applied. Software Defined Cloud (SDC) addresses these issues by enabling controller-based approaches. With the aid of Machine Learning (ML) technologies, SDC can achieve learning-based models, flexible deployment, and early detection and schedule of elephants for the optimization of network performance and energy usage in a dynamic and intelligent manner. On this purpose, this article emphases the significance of models describing elephants, surveys the mechanisms that may apply to model, detect, and schedule elephants for SDC to optimize the network performance and energy usage. To the best of our knowledge, this work is the first effort that reviews the techniques in all these related subtopics simultaneously in the context of energy cloud.

大象流（大象）是指仅占总流量10％但占用网络带宽90％以上的数据包序列。它们通常会导致网络拥塞，应进行有效管理。当前的云数据中心通常涉及基于主机和交换机的方法来检测和调度大象，但遭受（1）网络中的每个主机和交换机都需要自定义，并且（2）动态模型和高级策略难以应用。软件定义云（SDC）通过启用基于控制器的方法来解决这些问题。借助机器学习（ML）技术，SDC可以实现基于学习的模型，灵活的部署以及对大象的早期检测和调度，从而以动态，智能的方式优化网络性能和能耗。为此，本文强调了描述大象的模型的重要性，调查了可能适用于为SDC建模，检测和调度大象以优化网络性能和能源使用的机制。据我们所知，这项工作是在能源云的背景下同时审查所有这些相关子主题中的技术的第一项努力。