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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.

With the rapid growth of cloud computing services, the high energy consumption of cloud data centers has become a critical concern of the cloud computing society. While virtual machine (VM) consolidation is often used to reduce energy consumption, excessive VM consolidation may lead to local hot spots and increase the risk of equipment failure. One possible solution to this problem is to utilize thermal-aware scheduling, but existing approaches have trouble realizing the balance between SLA and energy consumption. This paper proposes a novel method to manage cloud data center resources based on thermal management (TM-VMC), which optimizes total energy consumption and proactively prevents hot spots from a global perspective. Its VM consolidation process includes four phases where the VMs scheduler uses an improved ant colony algorithm (UACO) to find appropriate target hosts for VMs based on server temperature and utilization status obtained in real-time. Experimental results show that the TM-VMC approach can proactively avoid data center hot spots and significantly reduce energy consumption while maintaining low Service Level Agreement (SLA) violation rates compared to existing mainstream VM consolidation algorithms with workloads from real-world data centers.