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Throughput Maximization for Multiedge Multiuser Edge Computing Systems
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DOI：10.1109/JIOT.2021.3084509.png)
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Abstract
En 中文
The multiaccess edge computing/mobile-edge computing (MEC) is becoming a key technology toward full 5G. However, as it gets widely used, a fundamental problem is how to support as many service requests as possible under stringent Quality-of-Service (QoS) requirements and limited communications and computing resources. In this article, we study the long-term throughput maximization problem for multicell multiuser MEC systems. Different from most of the existing works that focus on energy or latency minimization problem for a single-edge system, a novel design is proposed from the service provider's perspective to maximize the system-wide throughput under latency bounds by jointly taking user association and resource allocation for both communications and computing into account. To capture the stochastic nature of MEC environments, a Markov decision process (MDP) is employed to model the queuing states for both mobile devices and MEC servers. By combining MDP and matching theory, a joint user association and resource allocation algorithm is given, where the resource allocation policy under given user-server association is solved. Extensive numerical results demonstrate the superiority of the proposed scheme in comparison with several existing approaches.
Keywords:
Task analysis
Servers
Resource management
Computational modeling
Cloud computing
Mobile handsets
5G mobile communication
Computation offloading
Markov decision process (MDP)
matching theory
multiaccess edge computing (MEC)
resource allocation
user association
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