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Related Experiment Video

Updated: Mar 1, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

IntelliScheduler: an edge-cloud computing environment hybrid deep learning framework for task scheduling based on

L Raghavendar Raju¹, M Venkata Krishna Reddy², Sridhar Reddy Surukanti³

¹Dept. of Computer Science and Engineering, Matrusri Engineering College, Hyderabad, India. lraghavendarraju@matrusri.edu.in.

Scientific Reports

|February 27, 2026

Summary

IntelliScheduler uses deep reinforcement learning for adaptive task scheduling in edge-cloud systems. This approach significantly reduces task execution delays and improves Quality of Experience (QoE) in Internet of Things (IoT) applications.

Keywords:

Cloud computing Deep learning Edge computing Internet of things Reinforcement learning Task scheduling

Related Experiment Videos

Last Updated: Mar 1, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

Area of Science:

Computer Science
Artificial Intelligence
Distributed Systems

Background:

Edge-cloud computing is crucial for Internet of Things (IoT) applications requiring low latency.
Heterogeneous deadlines and dynamic workloads challenge Service Level Agreement (SLA) compliance and Quality of Service (QoS).
Existing cloud-centric and heuristic scheduling methods lack adaptability to changing conditions, leading to delays.

Purpose of the Study:

To develop an adaptive task scheduling framework for edge-cloud systems.
To minimize total task execution delay and improve resource utilization.
To enhance Service Level Agreement (SLA) compliance and Quality of Experience (QoE).

Main Methods:

Introduced IntelliScheduler, a hybrid actor-critic deep reinforcement learning framework.
Developed a runtime-aware state representation and a learning-based decision mechanism.
Implemented a learning-based optimal task scheduling (LbOTS) algorithm with latency-aware reward modeling.

Main Results:

Achieved up to 13% higher normalized reward and 15-75% better QoE compared to baseline methods.
Reported 67% lower training loss, 52-66% lower operational cost, and 80-90% lower rejection rates.
Demonstrated significant improvements in adaptive task scheduling efficiency.

Conclusions:

The proposed IntelliScheduler framework effectively addresses adaptive task scheduling challenges in edge-cloud environments.
The LbOTS algorithm optimizes task deployment for reduced latency and improved performance.
The adaptive learning formulation shows high relevance for dynamic edge-cloud scheduling scenarios.