Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions - Saeik, Firdose; Avgeris, Marios; Spatharakis, Dimitrios; Santi, Nina; Dechouniotis, Dimitrios; Violos, John; Leivadeas, Aris; Athanasopoulos, Nikolaos; Mitton, Nathalie; Papavassiliou, Symeon (Computer networks (Amsterdam, Netherlands : 1999), 2021)
Next generation communication networks are expected to accommodate a high number of new and resource-voracious applications that can be offered to a large range of end users. Even though end devices are becoming more powerful, the available local resources cannot cope with the requirements of these applications. This has created a new challenge called task offloading, where computation intensive tasks need to be offloaded to more resource powerful remote devices. Naturally, the Cloud Computing is a well-tested infrastructure that can facilitate the task offloading. However, Cloud Computing as a centralized and distant infrastructure creates significant communication delays that cannot satisfy the requirements of the emerging delay-sensitive applications. To this end, the concept of Edge Computing has been proposed, where the Cloud Computing capabilities are repositioned closer to the end devices at the edge of the network. This paper provides a detailed survey of how the Edge and/or Cloud can be combined together to facilitate the task offloading problem. Particular emphasis is given on the mathematical, artificial intelligence and control theory optimization approaches that can be used to satisfy the various objectives, constraints and dynamic conditions of this end-to-end application execution approach. The survey concludes with identifying open challenges and future directions of the problem at hand.