scholarly journals Corrigendum to “A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing”

2021 ◽  
Vol 2021 ◽  
pp. 1-1
Author(s):  
Klervie Toczé ◽  
Simin Nadjm-Tehrani
Keyword(s):  
Edge Computing ◽  
Multiple Resources

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

2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
Klervie Toczé ◽  
Simin Nadjm-Tehrani
Keyword(s):  
Resource Management ◽  
Data Storage ◽  
Edge Computing ◽  
New Paradigm ◽  
Resource Location ◽  
Multiple Resources ◽  
Wide Range ◽  
Efficient Resource ◽  
Resource Type ◽  
The Impact

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.

Application of the multiple resources questionnaire (MRQ) to a complex gaming environment

2004 ◽  
Author(s):  
David B. Boles ◽  
Jeffrey B. Phillips ◽  
Jason R. Perdelwitz ◽  
Jonathan H. Bursk
Keyword(s):  
Multiple Resources ◽  
Gaming Environment

Smart Community Edge: Stream Processing Edge Computing Node for Smart Community Services

2020 ◽  
Vol 140 (9) ◽  
pp. 1030-1039
Author(s):  
W.A. Shanaka P. Abeysiriwardhana ◽  
Janaka L. Wijekoon ◽  
Hiroaki Nishi
Keyword(s):  
Stream Processing ◽  
Community Services ◽  
Edge Computing ◽  
Computing Node ◽  
Smart Community

Load Balancing for Energy-Harvesting Mobile Edge Computing

2021 ◽  
Vol E104.A (1) ◽  
pp. 336-342
Author(s):  
Ping ZHAO ◽  
Jiawei TAO ◽  
Abdul RAUF ◽  
Fengde JIA ◽  
Longting XU
Keyword(s):  
Energy Harvesting ◽  
Load Balancing ◽  
Edge Computing ◽  
Mobile Edge Computing

Dynamic Service Placement and Load Distribution in Edge Computing

2020 ◽  
Author(s):  
Adyson Magalhaes Maia ◽  
Yacine Ghamri-Doudane ◽  
Dario Vieira ◽  
Miguel Franklin de Castro
Keyword(s):  
Load Distribution ◽  
Edge Computing ◽  
Service Placement
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