Project:

Workflow Schedule in cloud computing aims to schedule workflow tasks to cloud resources to optimize the cost of using cloud resources and performance of workflows.

Publications

Y. Yang, A. Chen. H. Ma, M. Zhang: Dual-tree Genetic Programming for Deadline-Constrained Dynamic Workflow Scheduling in Cloud, 20th edition of the International Conference on Service Oriented Computing (ICSOC) 2022 (to appear) (CORE Tier A) [pdf][code]

Y. Yang, G. Chen, H. Ma, M. Zhang, V Huang: Budget and SLA Aware Dynamic Workflow Scheduling in Cloud Computing with Heterogeneous Resources2021 IEEE Congress on Evolutionary Computation (CEC), pp. 2141-2148, 2021 [pdf][code]

Shen, Y., Chen, G., Ma, H., Zhang, M. (2025). Cost-Aware Dynamic Cloud Workflow Scheduling Using Self-attention and Evolutionary Reinforcement Learning. In: Service-Oriented Computing (ICSOC). Lecture Notes in Computer Science, vol 15405. Springer, 2024 (CORE Tier A)(Best Paper Award) paper.pdf (our code and data are publicly available at GitHub:YaShen998/GATES )

Datasets and simulators

Simulator Version 1: Dynamic Multi-Worflow Simulator developed by Victoria and Yifan (updated at 24/April/2024)

Code: ICSOC_code.zip

Notes:
  • My WorkflowScheduling-v3 environment has been added to the "env" folder, and parameter settings can be modified through workflow_scheduling_es_openai.yaml.
  • At the same time, other common files are fine-tuned by Yifan, which will not affect the running results of Albar's WorkflowScheduling-v2 environment at all.

The resources used for workflow scheduling will be put here
Simulator Version 2: for RL methods

Extended Simulator to mult-cloud workflow scheduling:

cllaw/AIML589-RL-DDMWS: Reinforcement learning algorithm for Dynamic Workflow Scheduling

Other workflow datasets:
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