Cloud and IoT applications have inquiring effects that can strongly influence today’s ever-growing internet life along with necessity to resolve numerous challenges for each application such as scalability, security, privacy, and reliability. During the deployment of IoT-based Cloud applications, the demand for Cloud tenants is dynamic that makes challenging to maintain scalability of the system. Developing an effective scaling technique is not merely a big concern, but how to achieve autonomic scaling results using future load prediction and migration policies is also a crucial phase. Also, to evaluate such auto-scaling strategy, certain Quality of Service (QoS) metrics must be recognized, explored and leveraged to enhance the performance of the system. Therefore, in this paper, a survey of existing auto-scaling, load prediction and VM migration techniques for IoT-based Cloud applications has been carried out along with the evaluation of various QoS parameters. Further, the future trends have also been discussed for performing auto-scaling in a Cloud environment.

云和物联网应用程序的查询效果会极大地影响当今不断增长的互联网生活，并有必要解决每个应用程序的众多挑战，例如可伸缩性，安全性，隐私性和可靠性。在部署基于IoT的云应用程序期间，对云租户的需求是动态的，这给维持系统的可扩展性带来了挑战。开发有效的扩展技术不仅是一个大问题，而且如何使用未来的负载预测和迁移策略获得自主扩展结果也是一个关键阶段。同样，为了评估这种自动扩展策略，必须识别，探索和利用某些服务质量（QoS）指标来增强系统性能。因此，在本文中，我们对现有的自动缩放功能进行了调查，已针对基于IoT的云应用程序执行了负载预测和VM迁移技术，并对各种QoS参数进行了评估。此外，还讨论了在云环境中执行自动扩展的未来趋势。