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724
Resource Prediction-Based Edge Collaboration Scheme for Improving QoE
1Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea.
Sensors (Basel, Switzerland)
|December 28, 2021
Summary
This study introduces a resource prediction-based edge collaboration scheme to enhance the Quality of Experience (QoE) for Internet of Things (IoT) devices. The new method improves task processing efficiency and reduces completion times by intelligently predicting and utilizing edge computing resources.
Area of Science:
- Computer Science
- Networking
- Distributed Systems
Background:
- The proliferation of Internet of Things (IoT) devices outpaces their native computational capabilities.
- Edge servers offer enhanced computing resources but face efficiency limitations with large device numbers.
- Existing edge collaboration schemes neglect crucial factors like resource availability and communication latency, impacting Quality of Experience (QoE).
Purpose of the Study:
- To propose a novel resource prediction-based edge collaboration scheme to improve QoE for IoT applications.
- To address the limitations of existing schemes by incorporating resource prediction and inter-server communication considerations.
Main Methods:
- Developed a scheme that estimates computing resource needs based on incoming device tasks.
- Implemented probabilistic collaboration between edge servers guided by predicted resource availability.
- Utilized a delay model and a greedy algorithm for resource allocation, factoring in computation and buffering times.
Main Results:
- The proposed scheme demonstrates significantly improved QoE compared to existing methods.
- Achieved a higher success rate in task processing.
- Reduced task completion time.
Conclusions:
- Resource prediction-based edge collaboration is effective in enhancing IoT application performance.
- Considering predicted resources and communication delays is vital for optimizing QoE in edge computing.
- The proposed scheme offers a viable solution for efficient task processing in resource-constrained IoT environments.
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