A Resource Scheduling Method for Cloud Data Centers Based on Thermal Management

Abstract

With the continuous growth of cloud computing services, the high energy consumption of cloud data centers has become an urgent problem to be solved. Virtual machine consolidation (VMC) is an important way to optimize energy consumption, however excessive consolidation may lead to local hotspots and increase the risk of equipment failure. Thermal-aware scheduling can solve this problem, but it is difficult to strike a balance between SLA and energy consumption. To solve the above problems, we propose a method for scheduling cloud data center resources based on thermal management (TM-VMC), which optimizes total energy consumption and proactively prevents hotspots from a global perspective. It includes four phases of the VM consolidation process, dynamically schedules VMs by detecting server temperature and utilization status in real time, and finds suitable target hosts based on an improved ant colony algorithm (UACO) for the VMs. We compare the TM-VMC approach with several existing mainstream VM consolidation algorithms under workloads from real-world data centers. Simulation experimental results show that the TM-VMC approach can proactively avoid data center hotspots and significantly reduce energy consumption while maintaining low SLA violation rates.

A Hybrid Optimization Algorithm for Efficient Virtual Machine Migration and Task Scheduling Using a Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique

“…This research paper's structure and organization have been thoughtfully chosen to provide a thorough and methodical explanation of the ICWRS algorithm and how it is used to optimize load balancing, job scheduling, and virtual machine migration [6]. The algorithm's theoretical underpinnings, experimental design, outcomes, comparative evaluations, and debates are covered in detail in the parts that follow.…”