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A Deep RL Approach on Task Placement and Scaling of Edge Resources for Cellular Vehicle-to-Network Service Provisioning
By conducting a complexity analysis, we prove that DDPG-based solutions achieve runtimes in the range of sub-milliseconds, meeting the strict latency requirements of C-V2N services.
V2N Service Scaling with Deep Reinforcement Learning
To this end, we employ Deep Reinforcement Learning (DRL) for vertical scaling in Edge computing to support vehicular-to-network communications.
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