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@article{171153,
        author = {Manjunatha K S and Nithin B M and Pramod J and Rehan Khan and Sathyanarayana S},
        title = {Dynamic Task Offloading and Resource Allocation in  Cloud-Edge Computing Using Swarm Intelligence and Deep Reinforcement Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {2532-2537},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171153},
        abstract = {The growing demand for real-time data processing in applications like image recognition and autonomous systems has made efficient task offloading and resource allocation critical in cloud-edge computing environments. This paper proposes an optimized hybrid framework that uses Deep Q Networks (DQN) for intelligent task offloading decisions and Particle Swarm Optimization with Minimum Completion Time (PSOMCT) for effective resource allocation. By dynamically deciding whether tasks should be processed on edge devices or offloaded to the cloud, the system minimizes latency and maximizes computational efficiency. gRPC ensures low-latency communication between system components, while MongoDB provides scalable and flexible data storage. The framework leverages YOLOv8 for real-time image processing, demonstrating significant improvements in performance, latency, and resource utilization. The results highlight the potential of the proposed system in optimizing task offloading and resource management in cloud-edge computing environments.},
        keywords = {Cloud-Edge Computing, Task offloading, Resource allocation, Deep Reinforcement Learning.},
        month = {December},
        }