Profit Maximization by Forming Federations of Geo-Distributed MEC Platforms

This paper has been presented at: Seventh International Workshop on Cloud Technologies and Energy Efficiency in Mobile Communication Networks (CLEEN 2019). How cloudy and green will mobile network and services be? 15 April 2019 - Marrakech, MoroccoIn press / En prensaMulti-access edge computing (MEC) as an emerging technology which provides cloud service in the edge of multi-radio access networks aims to reduce the service latency experienced by end devices. When individual MEC systems do not have adequate resource capacity to fulfill service requests, forming MEC federations for resource sharing could provide economic incentive to MEC operators. To this end, we need to maximize social welfare in each federation, which involves efficient federation structure generations, federation profit maximization by resource provisioning configuration, and fair profit distribution among participants. We model the problem as a coalition game with difference from prior work in the assumption of latency and locality constraints and also in the consideration of various service policies/demand preferences. Simulation results show that the proposed approach always increases profits. If local requests are served with local resource with priority, federation improves profits without sacrificing request acceptance rates.This work was partially supported by the Ministry of Science and Technology, Taiwan, under grant numbers 106-2221-E-009-004 and by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant number 761586)

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

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 is 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 non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa