DB-ACO: A Deadline-Budget Constrained Ant Colony Optimization for Workflow Scheduling in Clouds

Tao, Siyuan; Xia, Yuanqing; Ye, Lingjuan; Yan, Ce; Gao, Runze

doi:10.1109/tase.2023.3247973

Cited by 14 publications

(5 citation statements)

References 44 publications

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“…They have used a heuristic approach for optimizing the issue of energy consumption. In [22], they have presented a model called as DB-ACO which addresses the cost, budget and deadline during the execution of the workload. They have used Ant colony optimization method to optimize all these challenges.…”

Section: Literature Surveymentioning

confidence: 99%

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Quality of Service Aware Task Replanning Algorithm for Web-Service Provisioning in Cloud Environment

2024

IJIES

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In web service composition, the combination of multiple individual services to create complex workflows introduces additional challenges, and task scheduling is a crucial aspect that significantly impacts the overall system performance. The main challenge in web service composition task scheduling arises from the varying nature of services, which may have diverse processing requirements, data dependencies, and execution times. Hence, to address these challenges, the existing work have proposed many methods. But, as cloud environments often host these web services, the dynamic nature of cloud resources and the unpredictability of workloads add another layer of complexity to the task scheduling process. Due to this the existing works have attempted very less work on providing an efficient replanning task based on real-time changes in resource availability. Hence, this paper proposes a novel approach to cloud workload task scheduling in web service composition that incorporates efficient replanning and time complexity analysis called as Web service composition-efficient re-planning (WSC-ERP). The proposed approach takes into account various factors, such as task dependencies, and resource availability, to generate an optimized schedule that minimizes execution time and maximizes resource utilization. The WSC-ERP has been evaluated using the Montage scientific workload. The results show that the WSC-ERP provides better performance in terms of executional time, power sum, power average, energy consumption and reliability when compared with the Energy-Minimized Scheduling (EMS), and Evolutionary Computing based Web Service Composition (EC-WSC). The results show that the WSC-ERP has showed an improvement of 80.27% and 78.45% for average execution time, 89.98% and 87.49% for average power sum, 63.44% and 41.93% for average power average, 92.58% and 87.87% for average energy consumption, 4.97% and 4.07% for average reliability when compared with the existing EMS and EC-WSC models respectively.

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Section: Literature Surveymentioning

confidence: 99%

“…They have used Ant colony optimization method to optimize all these challenges. In summary, [18], [21], and [22] have used heterogenous computing environment, whereas all the existing work mainly focussed on the cloud environment. Also, all the research work focussed to analyse their models using complex workload types.…”

Section: Literature Surveymentioning

confidence: 99%

Quality of Service Aware Task Replanning Algorithm for Web-Service Provisioning in Cloud Environment

2024

IJIES

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“…Although the disadvantages of the cat swarm algorithm like the requirement of more iterations to achieve effective optimization still exist, these studies also guide future research. In addition, particle swarm algorithm [8][9][10], seagull optimization algorithm [11], and other emerging intelligent algorithms [12][13][14][15], have been proposed to optimize the cloud-computing resource scheduling. Among these algorithms, PSO may be more mature and have more application, while it is easy to fall into local optimum.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, by modifying the varied and crossover operators of the backtracking search algorithm (BSA) via neighborhood, the PSO-BSA algorithm has been proposed to accelerate the convergence speed [8]. Such as the ant colony optimization was carried out to improve the convergence accuracy and avoid local optima [12][13][14][15]. This algorithm has strong parallelism, and it is suitable for large-scale problems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Zhao,

Yan,

Lin

et al. 2024

CMC

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Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment. Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios. In this work, the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm (HPSO-EABC) has been proposed, which hybrids our presented Evolutionary Artificial Bee Colony (EABC), and Hierarchical Particle Swarm Optimization (HPSO) algorithm. The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm. Comprehensive testing including evaluations of algorithm convergence speed, resource execution time, load balancing, and operational costs has been done. The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm. Compared with the Particle Swarm Optimization algorithm, the HPSO algorithm not only improves the global search capability but also effectively mitigates getting stuck in local optima. As a result, the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed. Moreover, it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments, effectively reducing execution time and cost, which also is verified by the ablation experimental.

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“…Cloud computing remains a prominent framework for managing extensive distributed workflow module applications due to its adaptability, consistent performance, and robust oversight [1][2][3]. Through virtualization techniques, users can conduct multiple application tasks globally and concurrently.…”

Section: Introductionmentioning

confidence: 99%

Workflow Scheduling Scheme for Optimized Reliability and End-to-End Delay Control in Cloud Computing Using AI-Based Modeling

Khaleel,

Safran,

Alfarhood

et al. 2023

Mathematics

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In the context of cloud systems, the effectiveness of placing modules for optimal reliability and end-to-end delay (EED) is directly linked to the success of scheduling distributed scientific workflows. However, the measures used to evaluate these aspects (reliability and EED) are in conflict with each other, making it impossible to optimize both simultaneously. Thus, we introduce a scheduling algorithm for distributed scientific workflows that focuses on enhancing reliability while maintaining specific EED limits. This is particularly important given the inevitable failures of processing servers and communication links. To achieve our objective, we first develop an artificial intelligence-based model that merges an improved version of the wild horse optimization technique with a levy flight approach. This hybrid approach enhances the ability to explore new possibilities effectively. Additionally, we establish a viable strategy for sharing mapping decisions and stored information among processing servers, promoting scalability and robustness—essential qualities for large-scale distributed systems. This strategy not only boosts local search capabilities but also prevents premature convergence of the algorithm. The primary goal of this study is to pinpoint resource placements that strike a balance between global exploration and local exploitation. This entails effectively harnessing the search space and minimizing the inclination toward resources with a high likelihood of failures. Through experimentation in various system configurations, our proposed method consistently outperformed competing workflow scheduling algorithms. It achieved notably higher levels of reliability while adhering to the same EED constraints.

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DB-ACO: A Deadline-Budget Constrained Ant Colony Optimization for Workflow Scheduling in Clouds

Cited by 14 publications

References 44 publications

Quality of Service Aware Task Replanning Algorithm for Web-Service Provisioning in Cloud Environment

Quality of Service Aware Task Replanning Algorithm for Web-Service Provisioning in Cloud Environment

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Workflow Scheduling Scheme for Optimized Reliability and End-to-End Delay Control in Cloud Computing Using AI-Based Modeling

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