  Artificial 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. The heterogeneity of cloud
resources (processors, memory, storage, bandwidth), variable cost structure and
unpredictable workload patterns make the design of resource allocation
techniques complex. 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.
