To store, analyse and process the large volume of data generated by IoT traditional cloud computing, is used everywhere. However, the traditional cloud data centres have their limitations to handle high latency issues in time-critical applications of IoT and cloud. Their applications are computer gaming, e-healthcare, telemedicine and robot surgery. The high latency in IoTs and cloud includes high computational, communication latency (service) and network latencies. The vital requirement of IoT is to have minimum network, service and computation latencies for real-time applications. Network latency causes a delay in transmitting a message or communication from one location to another. Services that require data in real-time are almost impossible to access the data via the cloud. Traditional cloud computing approaches are unable to fulfil the quality-of-service (QoS) requirements in IoT devices. Researches related to latency reduction techniques are still in infancy. Some new approaches to minimize the latency for transmitting time-sensitive data in real-time are discussed in this paper for cloud and IoT devices. This research will help the researchers and industries to identify the techniques and technologies to minimize the latencies in IoT and cloud. The paper also discusses the research trends and the technical differences between the various technologies and techniques. With the increasing interest in the literature on latency minimization and its requirements for time-sensitive applications; it is important to systematically review and synthesize the approaches, tools, challenges and techniques to minimize latencies in IoT and cloud. This paper aims at systematically reviewing the state of the art of latency minimization to classify approaches, and techniques. The paper uses a PRISMA technique for a systematic review. The paper further identifies challenges and gaps in this regard for future research. We have identified 23 approaches and 32 technologies associated with latencies in the cloud and IoT. A total of 112 papers on latency reduction have been examined under this study. The existing research gaps and works for latency reduction in IoTs are discussed in detail. There are several challenges and gaps, which requires future research work for improving the latency minimization techniques and technologies. Finally, we present some open issues which will determine the future research direction.

存储，分析和处理由物联网传统云计算生成的大量数据的方法无处不在。但是，传统的云数据中心在处理IoT和云等时间紧迫的应用程序中的高延迟问题方面存在局限性。它们的应用是计算机游戏，电子医疗，远程医疗和机器人手术。物联网和云中的高延迟包括高计算，通信延迟（服务）和网络延迟。物联网的关键要求是为实时应用提供最小的网络，服务和计算延迟。网络等待时间导致从一个位置到另一位置传输消息或通信的延迟。需要实时数据的服务几乎不可能通过云访问数据。传统的云计算方法无法满足IoT设备中的服务质量（QoS）要求。与延迟减少技术有关的研究仍处于起步阶段。本文针对云和物联网设备讨论了一些最小化实时传输对时间敏感的数据的延迟的新方法。这项研究将帮助研究人员和行业识别技术和技术，以最大程度地减少物联网和云的延迟。本文还讨论了研究趋势以及各种技术之间的技术差异。随着对最小化等待时间及其对时间敏感应用的要求的文献越来越感兴趣；系统地审查和综合方法，工具，挑战和技术，以最大程度地减少物联网和云的延迟。本文旨在系统地回顾延迟最小化的技术现状，以对方法和技术进行分类。本文使用PRISMA技术进行系统评价。本文进一步指出了在这方面的挑战和差距，以供将来研究之用。我们已经确定了与云和物联网中的延迟相关的23种方法和32种技术。这项研究共审查了112篇有关减少等待时间的论文。详细讨论了物联网中现有的研究差距和减少延迟的工作。存在若干挑战和差距，这需要未来的研究工作来改进延迟最小化技术。最后，我们提出一些尚待解决的问题，这些问题将决定未来的研究方向。