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Job-Deadline-Guarantee-Based Joint Flow Scheduling and Routing Scheme in Data Center Networks.
Long Suo1, Han Ma2, Wanguo Jiao1
1College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|January 11, 2024
Summary
Meeting deadlines is critical for delay-stringent Internet of Things (IoT) applications. This study proposes a joint flow scheduling and routing (JFSR) scheme to maximize jobs meeting deadlines in data center networks (DCNs).
Area of Science:
- Computer Science
- Network Engineering
- Cloud Computing
Background:
- Internet of Things (IoT) applications on cloud platforms often have strict latency and deadline requirements.
- Meeting these deadlines is crucial for Quality of Service (QoS) and revenue in delay-stringent applications.
- Efficient flow scheduling and routing in data center networks (DCNs) are key to reducing job execution times.
Purpose of the Study:
- To address the challenge of guaranteeing deadlines for multi-stage jobs in DCNs.
- To propose an efficient heuristic scheme that jointly optimizes flow scheduling and routing.
- To maximize the number of jobs that successfully meet their deadlines.
Main Methods:
- Formulated the joint flow scheduling and routing optimization problem for multi-stage jobs.
- Decomposed the intractable problem into inter-coflow and intra-coflow scheduling sub-problems.
- Developed an iterative coflow scheduling and routing (ICSR) algorithm for optimizing path and bandwidth allocation.
Main Results:
- The proposed joint flow scheduling and routing (JFSR) scheme effectively addresses deadline constraints.
- Simulation results demonstrate a significant increase in the number of jobs meeting their deadlines.
- The scheme successfully balances job scheduling and routing for improved performance.
Conclusions:
- The JFSR scheme offers an efficient solution for deadline-aware job scheduling in DCNs.
- This approach is vital for enhancing the reliability of delay-stringent IoT applications.
- The proposed method provides a practical strategy for optimizing resource utilization and performance in cloud-based IoT systems.
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