我正在哈工大(深圳)攻读博士学位。我目前的研究方向是云计算环境中的资源与任务调度研究。我正在学习动态工作流调度、超启发式遗传编程和云-雾-边缘协同计算。

研究技术或观点

在优化调度问题的算法设计中,挖掘问题的结构和性质并探索确定性条件来避免无效搜索,可以达到三个目的:

  • 确定性地优化已知调度解;
  • 引导算法搜索高质量解区域;
  • 提高算法的搜索效率。
兴趣爱好
  • 云计算
  • 智能优化调度
  • 进化计算,特别是遗传编程
  • 云工作流调度/作业车间调度
  • 启发式/元启发式/超启发式 学习和优化
教育经历

论文

(2024). Evolving Scheduling Heuristics for Energy-Efficient Dynamic Workflow Scheduling in Cloud via Genetic Programming Hyper-heuristics. in Proceeding of International Conference on Intelligent Computing, 169–182.

PDF 引用 DOI

(2024). Multi-Tree Genetic Programming Hyper-Heuristic for Dynamic Flexible Workflow Scheduling in Multi-Clouds. IEEE Transactions on Services Computing, 17(5), 2687–2703. [2024年5月被选为热门论文].

PDF 引用 DOI

(2024). Virtual Machine Placement for Minimizing Image Retrieval Cost and Communication Cost in Cloud Data Center. IEEE Transactions on Network and Service Management, 21(2), 1998-2011.

PDF 引用 DOI

(2023). Efficient, economical and energy-saving multi-workflow scheduling in hybrid cloud. Expert Systems with Applications, 228, 120401.

PDF 引用 代码 DOI

(2023). An Energy-Efficient Scheduling Method for Real-Time Multi-workflow in Container Cloud. in Proceeding of 16th Annual International Conference on Combinatorial Optimization and Applications, 168–181.

PDF 引用 DOI

(2023). ET2FA: A Hybrid Heuristic Algorithm for Deadline-Constrained Workflow Scheduling in Cloud. IEEE Transactions on Services Computing, 16(3), 1807–1821. [2023年10月被选为热门论文].

PDF 引用 DOI

(2022). EHEFT-R: multi-objective task scheduling scheme in cloud computing. Complex & Intelligent Systems, 8(6), 4475–4482.

PDF 引用 DOI

(2021). T2FA: A Heuristic Algorithm for Deadline-Constrained Workflow Scheduling in Cloud with Multicore Resource. in Proceeding of IEEE 14th International Conference on Cloud Computing, 345–354.

PDF 引用 DOI

(2019). 基于块结构性质的花粉算法求解可重入作业车间调度问题. 机械工程学报, 55(16), 220–232.

PDF 引用 DOI

(2018). Salp swarm algorithm based on blocks on critical path for reentrant job shop scheduling problems. in Proceeding of International Conference on Intelligent Computing, 10954 LNCS, 638–648.

PDF 引用 DOI

(2018). Single-machine green scheduling to minimize total flow time and carbon emission. in Proceeding of International Conference on Intelligent Computing, 10954 LNCS, 670–678.

PDF 引用 DOI

联系方式