Residual Recurrent Neural Network (R2N2) and Intelligent Resource Optimization based Dynamic Scheduling for Edge-Cloud Computing Environments
Keywords:
Asynchronous-Advantage-Actor-Critic (A3C), Cloud Technology, Chaotic Crossover Tuna Swarm Optimizer (CCTSO), Deep Reinforcement Learning, Edge Computing, Residual Recurrent Neural Network (R2N2), Task SchedulingAbstract
Internet-of-Things (IoT) based applications has resulted use of Fog computing paradigm, which permit effortlessly exploiting both mobile-edge and cloud resources. Applications are hard to schedule due to restricted resource capabilities, IoT mobility factors, heterogeneity of resources, networking hierarchy, stochastic behaviors. Task arrival rates, task durations, resource needs are unpredictable in the edge-cloud ecosystem, making scheduling and resource monitoring problematic. In order to reduce parameters like Average Energy Consumption (AEC), Average Response Time (ART), Average Migration Time (AMT), Service Level Agreement Violations (SLAV), Chaotic Crossover Tuna Swarm Optimizer (CCTSO) presented in this work. CCTSO algorithm has also optimized application settings of hyper-parameters based on various user requirements. These requirements, together with information from the Resource Monitoring Service about the computer characteristics, are used by Residual Recurrent Neural Network (R2N2) model to predict the next scheduling options. R2N2 is known to update model parameters fast, whereas Asynchronous-Advantage-Actor-Critic (A3C) adaptation is recognized to dynamic conditions swiftly with less input. For stochastic Edge-Cloud contexts, A3C learning-based real-time scheduler that enables concurrent decentralized learning among many agents. When compared to other existing approaches, trials done on real-world data sets indicate substantial gains in terms of energy usage, reaction time, SLA, operation cost.
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