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Task Allocation Framework For Software-Defined Fog v-RAN
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DOI：10.1109/JIOT.2021.3068878.png)
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Abstract
En 中文
The fifth-generation wireless technology (5G) has been developed with an aim to provide ubiquitous and scalable connectivity for Internet-of-Things (IoT) nodes. Likewise, the cloud radio access network (C-RAN) architecture can be exploited to enable efficient network access to IoT nodes. Nevertheless, the 5G C-RAN architecture is based on large data centers geographically located far apart, which introduces an inevitable overhead. Therefore, to supply real-time data services near by the data terminals, fog computing emerges as a promising solution. However, constrained physical fog resources and delay-sensitive services hinder the application of new virtualization technologies in the baseband unit (BBU) task allocation management of the fog network. To tackle these challenges, a task allocation framework for hierarchical software-defined fog virtual radio access networks (v-RANs) is proposed in this article. Precisely, we apply an enhanced ant colony optimization (ACO) in combination with a max-min algorithm to efficiently determine the optimal path for BBU task allocation management, while minimizing the transmission time for parallel task execution scheduling. Experimental results demonstrate that the queue delay in our approach is 98.38% and 98.82% lower than the round-robin (RR) algorithm and least connection technique (LCT), respectively.
Keywords:
Task analysis
Resource management
Cloud computing
Processor scheduling
Computer architecture
5G mobile communication
Internet of Things
5G
ant colony optimization (ACO)
fog computing
Internet of Things (IoT)
max-min
software-defined network (SDN)
task allocation
task scheduling
virtual radio access network (v-RAN)
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