This paper proposes using dynamic workers and an energy saving system to efficiently schedule tasks and reduce idle workers. It introduces using particle swarm optimization and bucket sorting algorithms to assign tasks and identify idle workers that can be closed to save energy. The goal is to balance workload and achieve better energy savings for server nodes by properly allocating computing power.

1. Dynamic Workers for Scheduling
Authors: 唐子晴、邱柏誌
Advisor: Chien-Wen Wu

2. Introduction
• Scheduling is essential for backend services
• Adoption of “Energy Saving System”
• The paper introduces the dynamic workers to
finish the queued tasks by closing the idle
workers depending on the assigned tasks.

3. Literatures
• Simple Linux Utility for Resource Management,
Power Saving Guide
▫ https://computing.llnl.gov/linux/slurm/power_s
ave.html
• Scheduling Algorithms by Professor Dr. Peter
Brucker
• A Particle Swarm Optimization (PSO)-based
Heuristic for Scheduling Workflow Applications
in Cloud Computing Environments

4. Models
• Combination of “Energy Saving System” and
tasks assignment algorithm to identify the
closing idle workers.
• Sorting algorithm for importance of jobs (and
each job complexity) for deciding down time of
the idle workers.

5. Algorithms
• Particle Swarm Optimization(PSO)
▫ Solving task assignment problem
• Bucket sorting

6. Conclusion
• We expect to achieve a better energy saving
solution for server nodes (workers)
• A properly assigned computing power for loadbalancing

7. Thanks for Listening!