A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
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Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures used within the field of edge computing. Then, we review recent literature, consisting of a wide range of papers and categorise them using 4 perspectives, namely resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the research currently conducted. While the combination of dynamic load changes and mobility would seem to require novel edge-based resource estimation and discovery methods, we found these areas less studied. As for resource types, we observe that energy and data as a resource are not as well-studied as computation and communication resources. Finally, we find that few works are dedicated to the study of the footprint of resource management techniques.
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