实验室博士生李杰，邓玉辉老师等人联合撰写的论文《Energy-Aware Virtual Machine Scheduling with Hybrid Optimization for Cloud Data Centers》被《 IEEE Transactions on Sustainable Computing》录用。论文将于2025年正式发表。
Abstract—The reduction of energy consumption will be even more urgent in cloud data centers due to the explosive increase of application data. Virtual machine (VM) integration is a relatively standard technology currently applied for computing facilities of data centers. However, excessive VM consolidation can easily lead to local hot spots that lower the energy efficiency and reliability of data centers. In addition, on account of the impact of heat recirculation in data centers, the traditional VM scheduling strategy cannot comprehensively ponder optimizing the holistic data center energy, which encompasses both server energy and cooling energy. To handle these issues, we proposed EAVMS- an Energy-Aware VM Scheduling approach for minimizing the holistic energy consumption of data centers. EAVMS adopts a two-phase approach to gain energy efficiency while guaranteeing QoS. First, EAVMS leverages a Blended Genetic algorithm and Simulated Annealing algorithm (BGSA) to optimize the initial placement of VMs. Second, EAVMS utilizes a dynamic migration algorithm to achieve effective migration by setting a maximum server temperature threshold without violating the service level agreement (SLA) that cuts down energy consumption by moderating the hot spots of servers. We conducted extensive experiments using two real-world traces (i.e., PlanetLab and Google Cluster datasets) to evaluate the effectiveness of EAVMS. The experimental results unveil that our approach is capable of saving 3.23%-43.07% in the holistic energy consumption of cloud data centers with only a tiny service performance degradation compared to other state-of-the-art alternatives (e.g., MJPM, GRANITE, TAS, XINT-GA, and Random).