That the expected long-term utility is maximized. To this end, we proposeĪn end-to-end Deep Reinforcement Learning (DRL) approach to select the bestĮdge server for offloading and allocate the optimal computational resource such Respective deadlines and minimize energy consumption. Objective of this work is to maximize the completed tasks before their Operating environment (e.g., channel condition changes over time). Generated by IoT devices and offloaded to MEC servers in a time-varying In some cases, you might be able to fix internet lag by changing how your device interacts with the network. ![]() Network lag happens for a few reasons, namely distance and congestion. Servers, where computational tasks with various requirements are dynamically Fewer delays mean that the connection is experiencing lower latency. Investigate computation offloading in a dynamic MEC system with multiple edge Mobile edge computing (MEC) deploys cloud resources in the proximity of IoTĭevices so that their requests can be better served locally. The IoT devices are usually resource-constrained. IoT applications pose a high demand on storage and computing capacity, while ![]() Massive devices are interconnected for data collection and processing. Of promising applications, such as smart transportation and smart city, where Download a PDF of the paper titled Delay-aware and Energy-Efficient Computation Offloading in Mobile Edge Computing Using Deep Reinforcement Learning, by Laha Ale and 5 other authors Download PDF Abstract: Internet of Things (IoT) is considered as the enabling platform for a variety
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