Deep Reinforcement Learning for Energy-Efficient Task Scheduling in SDN-based IoT Network
Résumé
The growing demand and the diverse traffic patterns coming from various heterogeneous Internet of Things (IoT) systems place an increasing strain on the IoT infrastructure at edge network. Different edge resources (e.g. servers, routers, controllers, gateways) may illustrate different execution times and energy consumption for the same task. They should be capable of achieving high levels of performance to cope with the variability of tasks handling. However, edge nodes are often faced with issues to perform optimal resource distribution and energy-awareness policies in a way that makes effective runtime trade-offs to balance response time constraints, model fidelity, inference accuracy and task schedulability. To address these challenging issues, in this paper we present a dynamic task scheduling and resource management deep reinforcement learning approach for IoT traffic scheduling in SDN-based edge networks. First, we introduce the architectural design of our solution, with the specific objective of achieving high network performance. We formulate a task assignment and scheduling problem that strives to minimize the network latency, while ensuring energy efficiency. The evaluation of our approach offers better results compared against both deterministic and random task scheduling approaches, and show significant performances in terms of latency and energy consumption.
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