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Transformer-Enhanced DQN Approach for Energy and Cost-Efficient Large-Scale dynamic workflow scheduling in Heterogeneous Environment

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IEEE INTERNET OF THINGS JOURNAL 2024年第22期11卷 37351-37367页

作者： Ding, Fan Yuan, Yaqian Lv, Lizhi Zhang, Rui Zhou, Wenbo Lanzhou Univ Technol Coll Comp & Commun Engn Lanzhou 50070 Peoples R China

In a heterogeneous workflow environment, the uncertainty of task execution times, dynamic resource changes, and task dependencies' evolution pose significant scheduling challenges. This article investigates how to make intelligent and adaptive scheduling decisions in a constantly changing heterogeneous cloud environment. We propose a novel scheduling approach, transformer-enhanced DQN (T-DQN) that combines the strengths of reinforcement learning (RL) and the Transformer model into a hybrid strategy. This method leverages the ability of RL to handle uncertainty and dynamics in the decision-making process while integrating the advantages of the Transformer model in dealing with long sequences and complex relationships. Our experimental evaluation shows that the T-DQN algorithm outperforms existing algorithms consistently in real-world workflow, dynamic scenarios, and high-load environments. T-DQN reduces makespan by up to 13.66%, improves energy by about 16.65%, and improves cost by 44.72% compared to the six other approaches. This performance is particularly significant in high-load environments, where T-DQN's adaptability and scalability minimize failure rates and optimize resource management, affirming its suitability as a robust solution to complex cloud computing challenges.

关键词： dynamic scheduling Task analysis Processor scheduling scheduling Heuristic algorithms Transformers Costs Cost deep reinforcement learning (DRL) dynamic workflow scheduling energy transformer

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Transfer Learning Based Multi-Objective Evolutionary Algorithm for dynamic workflow scheduling in the Cloud

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2024年第4期12卷 1200-1217页

作者： Xie, Huamao Ding, Ding Zhao, Lihong Kang, Kaixuan Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China Minist Educ Key Lab Big Data & Artificial Intelligence Transpo Beijing 100044 Peoples R China

Managing scientific applications in the Cloud poses many challenges in terms of workflow scheduling, especially in handling multi-objective workflow scheduling under quality of service (QoS) constraints. However, most studies address the workflow scheduling problem on the premise of the unchanged environment, without considering the high dynamics of the Cloud. In this paper, we model the constrained workflow scheduling in a dynamic Cloud environment as a dynamic multi-objective optimization problem with preferences, and propose a transfer learning based multi-objective evolutionary algorithm (TL-MOEA) to tackle the workflow scheduling problem of dynamic nature. Specifically, an elite-led transfer learning strategy is proposed to explore effective parameter adaptation for the MOEA by transferring helpful knowledge from elite solutions in the past environment to accelerate the optimization process. In addition, a multi-space diversity learning strategy is developed to maintain the diversity of the population. To satisfy various QoS constraints of workflow scheduling, a preference-based selection strategy is further designed to enable promising solutions for each iteration. Extensive experiments on five well-known scientific workflows demonstrate that TL-MOEA can achieve highly competitive performance compared to several state-of-art algorithms, and can obtain triple win solutions with optimization objectives of minimizing makespan, cost and energy consumption for dynamic workflow scheduling with user-defined constraints.

关键词： dynamic scheduling Cloud computing Processor scheduling Heuristic algorithms Quality of service Costs Optimization dynamic workflow scheduling multi-objective evolutionary algorithm transfer learning quality of service constraints preference optimization

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Genetic Programming for dynamic workflow scheduling in Fog Computing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第4期16卷 2657-2671页

作者： Xu, Meng Mei, Yi Zhu, Shiqiang Zhang, Beibei Xiang, Tian Zhang, Fangfang Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Evolutionary Computat Res Grp Wellington 6140 New Zealand Zhejiang Lab Intelligent Robot Res Ctr Hangzhou 311121 Peoples R China

dynamic workflow scheduling in Fog Computing (DWSFC) is an important optimisation problem with many real-world applications. The current workflow scheduling problems only consider cloud servers but ignore the roles of mobile devices and edge servers. Some applications need to consider the mobile devices, edge, and cloud servers simultaneously, making them work together to generate an effective schedule. In this article, a new problem model for DWSFC is considered and a new simulator is designed for the new DWSFC problem model. The designed simulator takes the mobile devices, edge, and cloud servers as a whole system, where they all can execute tasks. In the designed simulator, two kinds of decision points are considered, which are the routing decision points and the sequencing decision points. To solve this problem, a new Multi-Tree Genetic Programming (MTGP) method is developed to automatically evolve scheduling heuristics that can make effective real-time decisions on these decision points. The proposed MTGP method with a multi-tree representation can handle the routing decision points and sequencing decision points simultaneously. The experimental results show that the proposed MTGP can achieve significantly better test performance (reduce the makespan by up to 50%) on all the tested scenarios than existing state-of-the-art methods.

关键词： Processor scheduling Task analysis Job shop scheduling dynamic scheduling Mobile handsets Cloud computing Servers dynamic workflow scheduling genetic programming fog computing

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Evolving scheduling Heuristics for Energy-Efficient dynamic workflow scheduling in Cloud via Genetic Programming Hyper-Heuristics 20th

Evolving Scheduling Heuristics for Energy-Efficient Dynamic ...

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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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A Genetic Programming-Based Hyper-Heuristic Approach for Multi-Objective dynamic workflow scheduling in Cloud Environment

A Genetic Programming-Based Hyper-Heuristic Approach for Mul...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Yu, Yongbo Shi, Tao Ma, Hui Chen, Gang Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

With the popularity of cloud computing, many organizations process their workflow tasks in cloud resources based on the Pay-As-Per-Use model. dynamic workflow scheduling (DWS) aims to allocate dynamically arriving workflow tasks to cloud resources with optimal makespan, cost, load-balancing, etc. To timely allocate arriving tasks, heuristics have been used to solve the DWS problem in cloud environment. However, most of them are manually designed, considering a single objective, and use simple features to allocate resources to workflow tasks. In practice, multiple objectives should be considered to provide trade-off heuristics for users to choose from. In this paper, we propose a genetic programming hyper-heuristic (GPHH) approach to automatically generate multiple heuristics for multi-objective DWS. Our experimental evaluation using benchmark datasets demonstrates the effectiveness of our proposed GPHH approach.

关键词： Cloud computing dynamic workflow scheduling genetic programming multi-objective GPHH

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Dual-Tree Genetic Programming for Deadline-Constrained dynamic workflow scheduling in Cloud 20th

Dual-Tree Genetic Programming for Deadline-Constrained Dynam...

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20th International Conference on Service-Oriented Computing (ICSOC)

作者： Yang, Yifan Chen, Gang Ma, Hui Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

ISBN: (纸本)9783031209833;9783031209840

dynamic workflow scheduling (DWS) aims to allocate abundant cloud resources to process a large number of heterogeneous workflows in order to minimize total operation cost and the penalty for violating deadline constraints. Instead of using manually designed heuristics that cannot work effectively across different problem instances, we develop a new Genetic Programming Hyper-Heuristic (GPHH) algorithm to automatically design scheduling heuristics for a newly formulated deadline-constrained dynamic workflow scheduling in cloud (DCD-WSC) problem. Different from previous works, our GPHH algorithm can design a pair of rules for Virtual Machine selection and task selection. A new dual-tree representation is proposed to jointly evolve the rule pair, enabling the algorithm to effectively control the inter-dependencies of the two rules. Experimental results show that our new algorithm can significantly outperform three baseline algorithms on a wide range of testing scenarios.

关键词： dynamic workflow scheduling Genetic programming hyper-heuristic Cloud computing Deadline constraint

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Multi-Tree Genetic Programming Hyper-Heuristic for dynamic Flexible workflow scheduling in Multi-Clouds

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2687-2703页

作者： Sun, Zaixing Mei, Yi Zhang, Fangfang Huang, Hejiao Gu, Chonglin Zhang, Mengjie Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518000 Peoples R China Guangdong Prov Key Lab Novel Secur Intelligence Te Shenzhen 518000 Peoples R China Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Evolutionary Computat Res Grp Wellington 6140 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

Multi-cloud is a promising paradigm due to its advantages such as avoiding vendor lock-in and optimising costs. This article focuses on dynamic flexible workflow scheduling with minimum total monetary cost in multi-clouds, considering multiple categories of services for each cloud with different configurations and billing methods. Existing studies generally ignore the characteristics and states of each individual cloud when making schedules, which may be ineffective regarding cost savings and quality of service. To address this issue, we propose to introduce a cloud selection decision on top of the existing task selection and resource selection decisions to help us select appropriate resource for task in an overall cost-effective cloud. To automatically learn the task, cloud and resource selection rules simultaneously, we propose a new genetic programming with multi-tree representation based on a customised discrete event-driven dynamic workflow scheduling simulator. Simulation results based on two real-world data traces show that the proposed algorithm performs significantly better than the state-of-the-art algorithms in terms of reducing the rental costs and deadline deviation, and improving the success rate. The results also show that the superiority of the proposed algorithm lies in the ability to select an appropriate cloud resource for a task.

关键词： dynamic scheduling Cloud computing Task analysis Costs Heuristic algorithms Processor scheduling Genetic programming Deadline constraint dynamic workflow scheduling genetic programming multi-clouds hyper-heuristic

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Budget and SLA Aware dynamic workflow scheduling in Cloud Computing with Heterogeneous Resources

Budget and SLA Aware Dynamic Workflow Scheduling in Cloud Co...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Yang, Yifan Chen, Gang Ma, Hui Zhang, Mengjie Huang, Victoria Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand Univ Waikato Sch Comp & Math Sci Hamilton 3240 New Zealand

ISBN: (纸本)9781728183923

workflow with different patterns and sizes arrive at a cloud data center dynamically to be processed at virtual machines in the data center, with the aim to minimize overall cost and makespan while satisfying Service Level Agreement (SLA) requirement. To efficiently schedule workflows, manually designed heuristics are proposed in the literature. However, it is time consuming to manually design heuristics. The designed heuristics may not work effectively for heterogeneous workflow since only simple problem related factors are considered in the heuristics. Further, most of the existing approaches ignore the deadline constraints set in SLAs. Genetic Programming Hyper Heuristic (GPHH) can be used to automatically design heuristics for scheduling problems. In this paper, we propose a GPHH approach to automatically generate heuristics for the dynamic workflow scheduling problem, with the goal of minimizing the VM rental fees and SLA penalties. Experiments have been conducted to evaluate the performance of the proposed approach. Compared with several existing heuristics and conventional Genetic Programming (GP) approaches, the proposed dynamic workflow scheduling Genetic Programming (DWSGP) has better performance and is highly adaptable to variations in cloud environment.

关键词： dynamic workflow scheduling cloud computing genetic programming

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Cooperative Coevolutionary Genetic Programming Hyper-Heuristic for Budget Constrained dynamic Multi-workflow scheduling in Cloud Computing 1

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23rd European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP) Held as Part of EvoStar Conference

作者： Escott, Kirita-Rose Ma, Hui Chen, Gang Victoria Univ Wellington Wellington 6012 New Zealand

ISBN: (数字)9783031300356

ISBN: (纸本)9783031300349;9783031300356

dynamic Multi-workflow scheduling (DMWS) in cloud computing is a well-known combinatorial optimisation problem. It is a great challenge to tackle this problem by scheduling multiple workflows submitted at different times and meet user-defined quality of service objectives. scheduling with user-defined budget constraints is becoming increasingly important due to cloud dynamics associated with on-demand provisioning, instance types, and pricing. To address the Budget-Constrained dynamic Multi-workflow scheduling (BC-DMWS) problem, a novel Cooperative Coevolution Genetic Programming (CCGP) approach is proposed. Two heuristic rules, namely VM Selection/Creation Rule (VMR) and Budget Alert Rule (BAR), are learned automatically by CCGP. VMR is used to allocate ready tasks to either existing or newly rented VM instances, while BAR makes decisions to downgrade VM instances so as to meet the budget constraint. Experiments show significant performance and success rate improvement compared to state-of-the-art algorithms.

关键词： Cloud computing dynamic workflow scheduling Genetic programming Hyper heuristic Cooperative coevolution Budget constraint

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