Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy

Area of Science:

• Computer Science
• Distributed Systems
• Cloud Computing

Background:

• Fog computing environments face challenges with low data similarity and poor data tolerance.
• Inefficient job scheduling can lead to resource fragmentation and job starvation.

Purpose of the Study:

• To propose a novel hierarchical data job scheduling strategy for intelligent sensor-cloud in fog computing.
• To enhance resource utilization and minimize execution and response times in fog computer systems.

Main Methods:

• Developed a Hierarchical Data Job Scheduling (HDJS) strategy for Fog Computer (FC).
• HDJS dynamically adjusts job priorities to prevent starvation and maximize resource use.
• Utilizes key frames for resource information and intra-frame distribution for load balancing.

Main Results:

• Experimental results indicate the avoidance of job starvation and resource fragmentation.
• Demonstrated effective utilization of multi-core and multi-thread capabilities.
• Showed significant improvements in system resource utilization, execution time, and response time.

Conclusions:

• The proposed HDJS strategy effectively addresses data processing challenges in fog computing.
• HDJS optimizes resource allocation and performance in intelligent sensor-cloud environments.
• This approach offers a viable solution for enhancing the efficiency of fog computer systems.