Zaixing Sun

Zaixing Sun

博士研究生

哈尔滨工业大学(深圳)

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.

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(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].

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(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.

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

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(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.

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(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].

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

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(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.

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(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.

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(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.

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(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.

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