Ramanathan, SaravananShivaraman, NitinSuryasekaran, SeimaEaswaran, ArvindBorde, EtienneSteinhorst, Sebastian2021-06-212021-06-212020https://dl.gi.de/handle/20.500.12116/36579Artificial Intelligence (AI) and Internet of Things (IoT) applications are rapidly growing in today’s world where they are continuously connected to the internet and process, store and exchange information among the devices and the environment. The cloud and edge platform is very crucial to these applications due to their inherent compute-intensive and resource-constrained nature. One of the foremost challenges in cloud and edge resource allocation is the efficient management of computation and communication resources to meet the performance and latency guarantees of the applications. Numerous research studies have been carried out to address this intricate problem. In this paper, the current state-of-the-art resource allocation techniques for the cloud continuum, in particular those that consider time-sensitive applications, are reviewed. Furthermore, we present the key challenges in the resource allocation problem for the cloud continuum, a taxonomy to classify the existing literature and the potential research gaps.enCloud computingEdge computingResource allocation and schedulingInternet of ThingsText/Journal Article10.1515/itit-2020-00132196-7032