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- W3216509920 abstract "By interacting with the environment through trial and error, a deep reinforcement learning (DRL) -based controller is able to learn the patterns underlying a highly dynamic environment, paving the way for self-optimized decision making. Leveraging this capability, we propose a Deep Q-learning (DQL) -based algorithm to perform joint service function chain (SFC) integration and routing tasks for service requests, each with specific stringent end-to-end delay and bandwidth constraints in a cloud-based distributed Network Function Virtualization (NFV).Our design goal is to maximize the number of dynamically provisioned service requests in the network over a time horizon, thus indirectly contributing to maximizing network throughput.Numerical results show that the trained DQL-based algorithm achieves over 95% of the average request acceptance rate and over 96% of the offered load. This performance result is comparable to that of a Resource-Constrained Shortest Path algorithm while achieving a much shorter execution time, approximately 10 times faster." @default.
- W3216509920 created "2021-12-06" @default.
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- W3216509920 date "2021-10-01" @default.
- W3216509920 modified "2023-10-18" @default.
- W3216509920 title "Joint Service Function Chain Embedding and Routing in Cloud-based NFV: A Deep Q-Learning Based Approach" @default.
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- W3216509920 doi "https://doi.org/10.1109/5gwf52925.2021.00037" @default.
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