Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks (2009.02186v1)

Abstract: The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources. Efficient scheduling of application tasks in such environments is challenging due to constrained resource capabilities, mobility factors in IoT, resource heterogeneity, network hierarchy, and stochastic behaviors. xisting heuristics and Reinforcement Learning based approaches lack generalizability and quick adaptability, thus failing to tackle this problem optimally. They are also unable to utilize the temporal workload patterns and are suitable only for centralized setups. However, Asynchronous-Advantage-Actor-Critic (A3C) learning is known to quickly adapt to dynamic scenarios with less data and Residual Recurrent Neural Network (R2N2) to quickly update model parameters. Thus, we propose an A3C based real-time scheduler for stochastic Edge-Cloud environments allowing decentralized learning, concurrently across multiple agents. We use the R2N2 architecture to capture a large number of host and task parameters together with temporal patterns to provide efficient scheduling decisions. The proposed model is adaptive and able to tune different hyper-parameters based on the application requirements. We explicate our choice of hyper-parameters through sensitivity analysis. The experiments conducted on real-world data set show a significant improvement in terms of energy consumption, response time, Service-Level-Agreement and running cost by 14.4%, 7.74%, 31.9%, and 4.64%, respectively when compared to the state-of-the-art algorithms.

• The paper introduces an Asynchronous Advantage Actor-Critic (A3C) based real-time scheduler employing Residual Recurrent Neural Networks (R2N2) for dynamic task scheduling in stochastic edge-cloud environments.
• It utilizes a decentralized multi-agent model with A3C and an R2N2 architecture to capture temporal relations in workloads and adapt to dynamic system conditions.
• Extensive simulations show the proposed A3C-R2N2 approach significantly outperforms state-of-the-art methods across metrics like energy usage, response time, SLA violations, and cost.

Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks

The research paper "Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks" presents a novel approach to task scheduling in fog computing environments that integrate mobile-edge and cloud resources. As the Internet of Things (IoT) expands, fog computing emerges as a crucial paradigm to handle the substantial data generated by IoT devices.

Overview

The paper introduces an Asynchronous Advantage Actor-Critic (A3C) based real-time scheduler employing Residual Recurrent Neural Networks (R2N2) within stochastic edge-cloud environments. Edge-cloud environments offer low-latency responses but pose challenges in terms of constrained resources, heterogeneity, and dynamic network conditions. The authors argue that conventional RL approaches lack adaptability and fail to leverage temporal workload patterns effectively.

Key Contributions

• System Model: The authors conceptualize a decentralized scheduling model using multiple agents, each with its own A3C architecture, enabling concurrent learning across distributed nodes.
• Recurrent Neural Network Architecture: The R2N2 model captures complex temporal relations between host and task parameters. It efficiently updates model parameters through skip connections in recurrent layers, ensuring rapid learning and adaptability.
• Optimization Methodology: The paper designs a policy gradient-based neural network to approximate the optimal mapping from current system state to actions (task allocations and migrations). The reward structure includes penalties for constraint violations and focuses on multiple metrics: energy consumption, response time, SLA violations, and operational costs.
• Experimental Validation: Extensive simulations using real-world datasets demonstrate significant efficiency improvements over state-of-the-art algorithms—energy usage by 14.4%, response time by 7.74%, SLA violations by 31.9%, and cost by 4.64%.

Implications and Future Directions

The deployment of the A3C-R2N2 model addresses key performance bottlenecks in edge-cloud computing environments. By accommodating temporal patterns and the stochastic nature of IoT workloads, this approach facilitates efficient task scheduling, critical for applications requiring immediate computational responses such as healthcare monitoring and traffic management.

Practically, this real-time scheduling framework has the potential to enhance operational efficiencies in smart cities and IoT-driven ecosystems. Theoretically, the model advances understanding in asynchronous learning methodologies, indicating promising avenues for integrating recurrent networks with reinforcement learning for complex task scheduling scenarios.

Moving forward, real-world implementations could include profiling techniques for continuous workload monitoring and dynamic parameter adjustment, ensuring the adaptability of scheduling decisions. Furthermore, privacy and security remain pertinent challenges that can be addressed through secure data mechanisms within decentralized environments.

By optimizing critical operational parameters, the research creates a comprehensive foundation for efficient fog computing deployments and heralds future innovations in AI-driven resource management systems.