Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 8, Issue 11, Pages 9407-9421Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3058363
Keywords
Streaming media; Resource management; Servers; Quality of service; Delays; Computational modeling; Optimization; Mobile-edge computing (MEC); network stability; resource allocation; total cost saving
Categories
Funding
- National Key Research and Development Program of China [2020YFB1807500]
- National Natural Science Foundation of China [62001357]
- Guangdong Basic and Applied Basic Research Foundation [2020A1515110496, 2020A1515110079]
- Hunan Provincial Nature Science Foundation [2018JJ2535]
- Chile CONICYT FONDECYT [1181809]
- Chile CONICYT FONDEF [ID16I10466]
- Key Research and Development Programs of Shaanxi [2019ZDLGY13-07, 2019ZDLGY13-04]
- Ministry of Science and Technology of China
- Science and Technology Development Fund, Macau SAR [037/2017/AMJ]
- Guangdong-Hong Kong-Macau Joint Laboratory Program [2020B1212030009]
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This article focuses on the resource optimization problem in mobile edge computing systems and proposes a dynamic subchannel allocation and resource allocation algorithm (DSARA), demonstrating that the algorithm can achieve the minimum total cost value.
Mobile-edge computing (MEC) is a promising technology, which allows reducing latency and energy consumption, thereby making the user experience better. Although MEC can support various types of services, differentiated Quality-of-Service (QoS) requirements bring difficulties and challenges to the allocation of radio resources and computing resources of the MEC system. In this article, we jointly optimize subchannel allocation, as well as the local central processing unit (CPU) speed scaling, user association, subcarrier assignment, power allocation, and video quality decision for MEC systems to study the total cost saving problem. Considering the traffic variations, we develop an online algorithm by using the Lyapunov optimization technique to solve this problem, referred to as dynamic subchannel allocation and resource allocation (DSARA). Particularly, the proposed DSARA algorithm only needs to track the state of the current network without requiring any prior knowledge. Besides, we prove that our proposed algorithm can asymptotically achieve the minimum total cost value (such as minimizing the power consumption and maximizing quality satisfaction). Simulation results show that the DSARA can achieve a good tradeoff between the total cost and delay, and outperforms the existing schemes in terms of the total cost expenditure.
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