Abstract
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. 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.
Funding source: National Research Foundation Singapore
Award Identifier / Grant number: CREATE
Funding statement: This work was financially supported in part by the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) programme.
About the authors
Dr. Saravanan Ramanathan received the Ph. D. degree in Computer Science and Engineering from Nanyang Technological University (NTU), Singapore, in 2019. Currently, he is a research fellow at TUMCREATE, Singapore. His research interests include real-time cyber-physical systems and energy-efficient internet of things.
Nitin Shivaraman is a research associate at TUMCREATE and is currently pursuing a Joint-Ph. D. degree at Technische Universität München (TUM), Germany and NTU. He received the M. Sc. degree in Embedded Systems from NTU in 2013. His research focuses on communication strategies for power-efficiency and fault-tolerance in IoT networks.
Seima Suriyasekaran is a project officer at TUMCREATE and is currently pursuing a Master’s degree in Computer Science and Engineering at NTU. She received her B. E. degree in Electronics and Communications Engineering from Anna University in 2011. Her research interests include application of deep learning and energy modeling for IoT devices.
Assoc. Prof. Dr. Arvind Easwaran is an Associate Professor in the School of Computer Science and Engineering at NTU. He received a PhD degree in Computer and Information Science from the University of Pennsylvania, USA, in 2008. In NTU, his research focuses on the design and analysis of real-time and cyber-physical systems, including their application in domains such as automotive, manufacturing and energy systems.
Assoc. Prof. Dr. Etienne Borde is an Associate Professor in the School of Computer Engineering at Telecom Paris (Paris, France) since 2011. He received his Ph. D. degree in Computer Science from the same university in 2009. His research interests are: component-based software engineering, architecture description languages, model transformation, code generation and formal verification.
Assoc. Prof. Dr. Sebastian Steinhorst is an Associate Professor in Electrical and Computer Engineering at TUM where he leads the Embedded Systems and Internet of Things group. His research centers around hardware-software architecture co-design of secure decentralized embedded and cyber-physical systems in the application areas of industrial IoT, automotive and smart energy. He is also a Co-Program PI at TUMCREATE.
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