期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 68, 期 9, 页码 8408-8420出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2019.2930601
关键词
Smart cars; caching; multi-access edge computing; 5G network; vehicular networks
资金
- Institute of Iunformation and communications Technology Planning and Evaluation (IITP) - Korea Government (MSIT) [2019-0-01287]
- MSIT, Korea, under the Grand Information Technology Research Center [IITP-2018-2015-0-00742]
- Evolvable Deep Learning Model Generation Platform for Edge Computing
- National Research Foundation of Korea [21A20131612192] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Remarkable prevalence of cloud computing has enabled smart cars to provide infotainment services. However, retrieving infotainment contents from long-distance data centers poses a significant delay, thus hindering to offer stringent latencyaware infotainment services. Multi-access edge computing is a promising option to meet strict latency requirements. However, it imposes severe resource constraints with respect to caching, and computation. Similarly, communication resources utilized to fetch the infotainment contents are scarce. In this paper, we jointly consider communication, caching, and computation (3C) to reduce infotainment content retrieval delay for smart cars. We formulate the problem as a mix-integer, nonlinear, and nonconvex optimization to minimize the latency. Furthermore, we relax the formulated problem from NP-hard to linear programming. Then, we propose a joint solution (3C) based on the alternative direction method of multipliers technique, which operates in a distributed manner. We compare the proposed 3C solution with various approaches, namely, greedy, random, and centralized. Simulation results reveal that the proposed solution reduces delay up to 9% and 28% compared to the greedy and random approaches, respectively.
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