Dynamic Task Offloading and Resource Allocation for Mobile-Edge Computing in Dense Cloud RAN

With the unprecedented development of smart mobile devices (SMDs), e.g., Internet-of-Things devices and smartphones, various computation-intensive applications are explosively increasing in ultradense networks (UDNs). Mobile-edge computing (MEC) has emerged as a key technology to alleviate the compu...

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Vydáno v:IEEE internet of things journal Ročník 7; číslo 4; s. 3282 - 3299
Hlavní autoři: Zhang, Qi, Gui, Lin, Hou, Fen, Chen, Jiacheng, Zhu, Shichao, Tian, Feng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Cloud computing
Cloud radio access network (C-RAN)
Computation offloading
Computational modeling
Computer simulation
Decomposition
Delays
Edge computing
Electronic devices
Energy efficiency
Lyapunov optimization
Mobile computing
mobile-edge computing (MEC)
Network latency
Nonlinear programming
Optimization
Radio
Resource allocation
Resource management
Resource scheduling
Servers
Task analysis
task offloading
Task scheduling
ultradense network (UDN)
Wireless networks
ISSN:2327-4662, 2327-4662
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Shrnutí:With the unprecedented development of smart mobile devices (SMDs), e.g., Internet-of-Things devices and smartphones, various computation-intensive applications are explosively increasing in ultradense networks (UDNs). Mobile-edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of SMDs and decrease service latency for computation-intensive applications. With the benefits of network function virtualization, MEC can be integrated with the cloud radio access network (C-RAN) in UDNs for computation and communication cooperation. However, with stochastic computation task arrivals and time-varying channel states, it is challenging to offload computation tasks online with energy-efficient computation and radio resource management. In this article, we investigate the task offloading and resource allocation problem in MEC-enabled dense C-RAN, aiming at optimizing network energy efficiency. A stochastic mixed-integer nonlinear programming problem is formulated to jointly optimize the task offloading decision, elastic computation resource scheduling, and radio resource allocation. To tackle the problem, the Lyapunov optimization theory is introduced to decompose the original problem into four individual subproblems which are solved by convex decomposition methods and matching game. We theoretically analyze the tradeoff between energy efficiency and service delay. Extensive simulations evaluate the impacts of system parameters on both energy efficiency and service delay. The simulation results also validate the superiority of the proposed task offloading and resource allocation scheme in dense C-RAN.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2020.2967502