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Edge computing is promoted to meet increasing performance needs ofdata-driven services using computational and storage resources close to the enddevices, at the edge of the current network. To achieve higher performance inthis new paradigm one has to consider how to combine the efficiency of resourceusage at all three layers of architecture: end devices, edge devices, and thecloud. While cloud capacity is elastically extendable, end devices and edgedevices are to various degrees resource-constrained. Hence, an efficientresource management is essential to make edge computing a reality. In thiswork, we first present terminology and architectures to characterize currentworks within the field of edge computing. Then, we review a wide range ofrecent articles and categorize relevant aspects in terms of 4 perspectives:resource type, resource management objective, resource location, and resourceuse. This taxonomy and the ensuing analysis is used to identify some gaps inthe existing research. Among several research gaps, we found that research isless prevalent on data, storage, and energy as a resource, and less extensivetowards the estimation, discovery and sharing objectives. As for resourcetypes, the most well-studied resources are computation and communicationresources. Our analysis shows that resource management at the edge requires adeeper understanding of how methods applied at different levels and gearedtowards different resource types interact. Specifically, the impact of mobilityand collaboration schemes requiring incentives are expected to be different inedge architectures compared to the classic cloud solutions. Finally, we findthat fewer works are dedicated to the study of non-functional properties or toquantifying the footprint of resource management techniques, includingedge-specific means of migrating data and services.

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing