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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 to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis are used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource and less extensive towards the estimation, discovery, and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of nonfunctional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

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This paper presents ORWAR, a resource-efficient protocol for opportunistic routing in delay-tolerant networks. Our approach exploits the context of mobile nodes (speed, direction of movement and radio range) to estimate the size of a contact window. This knowledge is exploited to make better forwarding decisions and to minimize the probability of partially transmitted messages. As well as optimizing the use of bandwidth during overloads it helps to reduce energy consumption since partially transmitted messages are useless and waste transmission power. Another feature of the algorithm is the use of a differentiation mechanism based on message utility. This allows allocating more resources for high utility messages. More precisely, messages are replicated in the order of highest utility first, and removed from the buffers in the reverse order. To illustrate the benefit of such a scheme the global accumulated utility is used as a system-wide performance metric. Simulations illustrate the benefit of our model and show that ORWAR provides lower overhead and higher delivery rate, as well as higher accumulated utility compared to a number of well-known algorithms (including Maxprop and SprayAndWait).