Genetic Programming for Dynamic Workflow Scheduling in Fog Computing

Xu, Meng; Mei, Yi; Zhu, Shiqiang; Zhang, Beibei; Xiang, Tian; Zhang, Fangfang; Zhang, Mengjie

doi:10.1109/tsc.2023.3249160

Cited by 20 publications

(5 citation statements)

References 59 publications

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“…A new problem model and simulator are presented that consider all three types of devices as a single system for task execution. A novel Multi-Tree Genetic Programming (MTGP) method is proposed by Xu et al ( 2023 ) to automatically evolve scheduling heuristics for real-time decision-making at routing and sequencing points. Experiments show that MTGP significantly outperforms existing methods, achieving up to 50% reduction in makespan across different scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Saad,

Enam,

Qureshi

2024

Front. Big Data

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As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization. This paper proposes a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm to optimize multi-objective task scheduling in fog computing environments. The hybrid approach combines the strengths of GA and PSO, achieving effective exploration and exploitation of the search space, leading to improved performance compared to traditional single-algorithm approaches. The proposed hybrid algorithm results improved the execution time by 85.68% when compared with GA algorithm, by 84% when compared with Hybrid PWOA and by 51.03% when compared with PSO algorithm as well as it improved the response time by 67.28% when compared with GA algorithm, by 54.24% when compared with Hybrid PWOA and by 75.40% when compared with PSO algorithm as well as it improved the completion time by 68.69% when compared with GA algorithm, by 98.91% when compared with Hybrid PWOA and by 75.90% when compared with PSO algorithm when various tasks inputs are given. The proposed hybrid algorithm results also improved the execution time by 84.87% when compared with GA algorithm, by 88.64% when compared with Hybrid PWOA and by 85.07% when compared with PSO algorithm it improved the response time by 65.92% when compared with GA algorithm, by 80.51% when compared with Hybrid PWOA and by 85.26% when compared with PSO algorithm as well as it improved the completion time by 67.60% when compared with GA algorithm, by 81.34% when compared with Hybrid PWOA and by 85.23% when compared with PSO algorithm when various fog nodes are given.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Saad,

Enam,

Qureshi

2024

Front. Big Data

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“…Xu et al 17 introduced a fresh problem model for DWSFC (Dynamic Workflow Scheduling in Fog Computing) and unveiled a new simulator tailored precisely for this model. The simulator considered mobile devices, edge, and cloud servers as an integrated system capable of task execution.…”

Section: Related Work Fig:streammentioning

confidence: 99%

“…The incorporation of genetic operators aims to augment exploration during the initial iterations of the algorithm, especially when the search process has not yet converged to the vicinity of the optimal solution 17 . This adjustment has a direct impact on the convergence behavior, ensuring a more robust exploration of the solution space.…”

Section: Modified Firefly Algorithm For Hybrid Cloud-edge Schedulingmentioning

confidence: 99%

“…By harnessing the complementary strengths of both paradigms, researchers can unlock new frontiers in computational research, accelerating scientific discovery and innovation 15,16 . The emergence of hybrid cloud-edge environments brings forth promising opportunities for scientific workflows, yet efficient scheduling algorithms are paramount to fully harnessing their potential 17,18 . These environments amalgamate cloud and edge resources, each characterized by unique attributes.…”

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confidence: 99%

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Enhancing Workflow Efficiency: A Modified Firefly Algorithm for Hybrid Cloud-Edge Environments

Alsadie,

Alsulami

2024

Preprint

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Efficient scheduling of scientific workflows in cloud computing environments is essential for optimizing resource utilization and minimizing completion time. In this study, we comprehensively evaluate different scheduling algorithms, focusing on the Modified Firefly Optimization Algorithm (ModFOA) in comparison with existing methods like Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Our investigation considers key performance metrics such as makespan, resource utilization, and energy consumption across diverse configurations and scenarios. Scientific workflows often involve intricate tasks with dependencies, posing challenges for efficient scheduling. While existing algorithms have shown promise, they may not fully address the unique requirements of cloud environments, leading to suboptimal outcomes. Therefore, we propose evaluating ModFOA’s effectiveness in scheduling scientific workflows in the cloud. Through comparative analysis, ModFOA demonstrates superior performance in terms of makespan, achieving lower completion times across various configurations. Additionally, ModFOA exhibits competitive resource utilization and moderate energy consumption, positioning it as a promising solution for optimizing workflow scheduling in cloud environments. This study underscores the significance of selecting efficient scheduling algorithms and highlights ModFOA’s potential in improving workflow scheduling and resource management in cloud environments. Further research could focus on refining ModFOA parameters and validating its practicality in real-world cloud computing scenarios.

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“…Besides, Burke et al [29] also applied GP based hyper heuristic on two dimensional strip packing problems. Furthermore, GP based hyper heuristic approaches can also handle travelling salesman problems [99], satisfiability testing problems [66],web service allocation problems [179] and job shop problems [68,85,207,208,217].…”

Section: Genetic Programming Hyper Heuristicmentioning

confidence: 99%

Explainable Genetic Programming for Evolving Routing Policies of Uncertain Capacitated Arc Routing Problems

Wang

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The Uncertain Capacitated Arc Routing Problem (UCARP) is a well-known combinatorial optimization problem that has many real-world applications. Genetic programming has successfully evolved routing policies that can make real-time routing decisions for uncertain arc routing problems. Although the evolved routing policies are highly effective, they are typically very complex thus hard for real users to understand and trust. Therefore, it is necessary to improve the interpretability of GP-evolved routing policies.The overall goal of this thesis is to develop both intrinsic and post-hoc methods to improve the interpretability of GP-evolved routing policies for UCARP.First, this thesis proposes a novel intrinsic genetic programming approach that simplifies the routing policies during the evolutionary process using a niching technique. The simplified routing policies are stored in an external archive. This thesis also develops new elitism, parent selection, and breeding schemes for generating offspring from the original population and the archive. The experimental results show that the newly proposed approach can achieve significantly better test effectiveness than the current state-of-the-art genetic programming hyper heuristic methods for UCARP. The evolved routing policies are smaller, thus potentially more interpretable.Second, this thesis develops several multi-objective genetic programming algorithms to evolve a Pareto front of routing policies with different interpretability and effectiveness, so that the end users can choose based on their preferences. This thesis handles two main challenges, i.e., objective selection bias issue and stochastic fitness evaluation issue, of designing Multi-Objective GP (MOGP) for UCARP. To handle the objective selection bias issue, an $\alpha$ dominance strategy is proposed. To further improve the $\alpha$ dominance strategy, this thesis develops a new $\alpha$ value adjustment scheme. To handle the stochastic fitness evaluation issue, this thesis proposes an archive strategy. The new archive strategy uses an external archive to store the potentially effective individuals that could have been lost during the traditional GP process. The individuals in the archive contribute back to the population in the breeding process. Finally, this thesis combines all the above strategies to develop a new MOGP algorithm. The experimental results show that the new MOGP algorithm outperforms state-of-the-art algorithms for UCARP in terms of HV and IGD. The evolved routing policies are much smaller, thus potentially more interpretable.Lastly, this thesis proposes a new post-hoc explanation approach to explaining the effective but complex GP-evolved routing policies. This thesis proposes two metrics that can evaluate the quality of a local explanation for GP-evolved routing policies. Then, this thesis investigates the feasibility of using a linear model to construct a local explanation for a complex GP-evolved routing policy in a decision situation. After that, this thesis further improves the local explanation method using Particle Swarm Optimization (PSO) to learn an interpretable linear model that accurately explains the local behavior of the routing policy for each decision situation, and extends the local explanation method to a global explanation method. The experimental results and case studies show that the proposed method can obtain accurate and understandable explanations of GP-evolved routing policies for UCARP.From the intrinsic aspect, both GP program simplification method and multi-objective GP methods are proposed. The GP program simplification approach addresses the case where only a single interpretable routing policy is required, i.e. where the user's preferences are known before the routing policy is generated. The multi-target approach is aimed at situations where several different interpretable routing policies are needed, because sometimes we cannot know the user's preferences in advance, and the user can choose their own routing policy according to their preferences. From the post-hoc aspect, this thesis proposes local and global explanation methods to further improve the interpretability of existing complex GP-evolved routing policies. Furthermore, the local and global explanation methods not only produce text explanations but also visual explanations.

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Genetic Programming for Dynamic Workflow Scheduling in Fog Computing

Cited by 20 publications

References 59 publications

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Enhancing Workflow Efficiency: A Modified Firefly Algorithm for Hybrid Cloud-Edge Environments

Explainable Genetic Programming for Evolving Routing Policies of Uncertain Capacitated Arc Routing Problems

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