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20th International Conference on Intelligent Computing (ICIC)

作者： Sun, Zaixing Zhang, Fangfang Mei, Yi Huang, Hejiao Gu, Chonglin Qian, Bin Zhang, Mengjie Harbin Inst Technol Shenzhen Shenzhen 518000 Peoples R China Victoria Univ Wellington Wellington 6140 New Zealand Kunming Univ Sci & Technol Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819755776;9789819755783

With the rapid development of cloud computing, the issue of how to reduce energy consumption has attracted a great deal of attention. Especially for dynamic workflow scheduling, dependency constraints between tasks and high quality of service requirements, such as real-time requirements and deadline constraints, make it very challenging. This paper focuses on the energy-efficient scheduling problem, which jointly considers the impact of finer-grained tasks with CPU and memory configurations on energy consumption. A dynamic workflow scheduling simulator is developed to mimic the scheduling process in real-world scenarios. Then, we propose a Cooperative Coevolution genetic programming to learn heuristics for both the task selection decision and the instance selection decision, using the simulator for heuristic evaluation. The scheduling heuristics obtained by Cooperative Coevolution genetic programming evolution can then be used to make real-time decisions in dynamic environments. The simulation results show that the proposed method has managed to obtain better scheduling heuristics than the baseline methods in terms of energy consumption and resource utilization.

关键词： Cloud Computing Dynamic Workflow Scheduling genetic programming hyper-heuristics

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Enhancing generalization in genetic programming hyper-heuristics through mini-batch sampling strategies for dynamic workflow scheduling

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INFORMATION SCIENCES 2024年 678卷

作者： Yang, Yifan Chen, Gang Ma, Hui Hartmann, Sven Zhang, Mengjie Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington 6012 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6012 New Zealand Tech Univ Clausthal Dept Informat D-38678 Clausthal Zellerfeld Germany

genetic programming hyper -heuristics (GPHH) have been successfully used to evolve scheduling rules for Dynamic Workflow Scheduling (DWS) as well as other challenging combinatorial optimization problems. The method of sampling training instances has a significant impact on the generalization ability of GPHH, yet they are rarely addressed in existing research. This article aims to fill this gap by proposing a GPHH algorithm with a sampling strategy to thoroughly investigate the impact of six instance sampling strategies on algorithmic generalization, including one rotation strategy, three mini -batch strategies, and two hybrid strategies. Experiments across four scenarios with varying settings reveal that: (1) mini -batch with random sampling can outperform rotation in generalizing to unseen workflow scheduling problems under the same computational cost;(2) employing a hybrid strategy that combines rotation and mini -batch further enhances the generalization ability of GPHH;and (3) mini -batch and hybrid strategies can effectively enable heuristics trained on small-scale training instances generalizing well to large-scale unseen ones. These findings highlight the potential of mini -batch strategies in GPHH, offering improved generalization performance while maintaining diversity and suggesting promising avenues for further exploration in GPHH domains.

关键词： Dynamic workflow scheduling genetic programming hyper-heuristics Generalization Mini-batch

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Multi-Objective Optimization of Application Deployment Strategies in Integrated Cloud-Fog Computing Environments 2

Multi-Objective Optimization of Application Deployment Strat...

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2nd IEEE International Conference on Software Services Engineering (SSE)

作者： Han, Zheng Wang, Chen Fang, Zhengxin Ma, Hui Chen, Gang Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington New Zealand Natl Inst Water & Atmospher Res Dept HPC & Data Sci Christchurch New Zealand

ISBN: (纸本)9798350368529;9798350368512

In the domain of Cloud computing, Fog computing is integrated with the Cloud to offer a balanced approach that combines Cloud's scalability with Fog's low latency, enabling efficient software application deployment. However, many current studies overlook the unpredictability of future user requests, such as assuming all requests are known beforehand. User requests often arrive dynamically and may have different quality of service (QoS) preferences. Therefore we need effective methods to handle dynamic application deployment with multiple objectives. This paper tackles this gap by modeling a multi-objective application deployment problem that considers dynamically arriving users' requests on application deployment in a Cloud-Fog environment. We further introduce a multi-objective genetic programming hyper-Heuristic based approach to automatically generate a set of deployment rules that can be chosen according to users' QoS preferences. These rules are generated with different trade-offs of two optimization objectives, i.e., minimizing cost and latency, which can be used for deploying applications dynamically. Our experimental evaluation using real-world data demonstrates that our GPHH approach can generate effective heuristics for deploying applications in an integrated Cloud-Fog environment.

关键词： Cloud Computing Fog Computing Multi-Objective Optimisation genetic programming hyper-heuristics

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A Two-stage genetic programming hyper-heuristic Approach with Feature Selection for Dynamic Flexible Job Shop Scheduling

A Two-stage Genetic Programming Hyper-heuristic Approach wit...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Zhang, Fangfang Mei, Yi Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

ISBN: (纸本)9781450361118

Dynamic flexible job shop scheduling (DFJSS) is an important and a challenging combinatorial optimisation problem. genetic programming hyper-heuristic (GPHH) has been widely used for automatically evolving the routing and sequencing rules for DFJSS. The terminal set is the key to the success of GPHH. There are a wide range of features in DFJSS that reflect different characteristics of the job shop state. However, the importance of a feature can vary from one scenario to another, and some features may be redundant or irrelevant under the considered scenario. Feature selection is a promising strategy to remove the unimportant features and reduce the search space of GPHH. However, no work has considered feature selection in GPHH for DFJSS so far. In addition, it is necessary to do feature selection for the two terminal sets simultaneously. In this paper, we propose a new two-stage GPHH approach with feature selection for evolving routing and sequencing rules for DFJSS. The experimental studies show that the best solutions achieved by the proposed approach are better than that of the baseline method in most scenarios. Furthermore, the rules evolved by the proposed approach involve a smaller number of unique features, which are easier to interpret.

关键词： Feature Selection Dynamic Flexible Job Shop Scheduling genetic programming hyper-heuristics

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