Optimising makespan and energy consumption in task scheduling for parallel systems

Stewart, R.; Raith, Andrea; Sinnen, Oliver

doi:10.1016/j.cor.2023.106212

Cited by 12 publications

(3 citation statements)

References 49 publications

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“…Stewart et al [48] proposed a bi-objective integer linear programming method in the cloud in order to optimize energy consumption and makespan time.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

Liu¹,

Kosarirad

Meisami

et al. 2023

Processes

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Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks.

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“…Stewart et al [48] proposed a bi-objective integer linear programming method in the cloud in order to optimize energy consumption and makespan time.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

Liu¹,

Kosarirad

Meisami

et al. 2023

Processes

View full text Add to dashboard Cite

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“…Task scheduling for user applications in cloud computing has VOLUME 11, 2023 gained attention. Numerous heuristics have been proposed, but finding optimal scheduling remains difficult due to the NP-hard nature of the problem [19], [20], [21].…”

Section: Related Work a Cloud Computing Centers With Heterogeneous Co...mentioning

confidence: 99%

An Integrated Optimization-Based Algorithm for Energy Efficiency and Resource Allocation in Heterogeneous Cloud Computing Centers

2023

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At a significant moment in the rapid development of cloud technology, large-scale cloud computing centers have emerged. With the emergence of the internet and artificial intelligence, enormous computing resources are required to process data and train machine learning models. The architecture of cloud computing centers involves millions of computing resources, and improper management of these resources can increase operating costs and exert tremendous pressure on the environment. This study proposes an optimized computing resource and energy management algorithm for computing centers with heterogeneous computing resources from the perspective of Green IT. Specifically, this study models the energy consumption at each point in time and the relationship between tasks and also considers the calculation of data backup. This approach will be expanded to optimize decisions for all computing tasks in computing centers based on the sequence of tasks and energy consumption while considering heterogeneous computing resources, energy efficiency, task scheduling, and execution time. By modeling this issue as a highly nonlinear optimization problem and utilizing mathematical programming and Lagrangian relaxation, we propose an optimized energy management algorithm to effectively manage computing resources and create cloud computing centers with high performance and low energy consumption.

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“…However, the challenges of long research cycles and expensive investment costs present formidable obstacles that are not easily surmounted 2 . Several studies have demonstrated that energy consumption can be effectively reduced through the implementation of green scheduling, which involves the consideration of traditional scheduling objectives and environmental factors 3 , 4 . In sustainable manufacturing, the energy issue is taken into account at three levels: the product level, the machine level, and the production system level 5 .…”

Section: Introductionmentioning

confidence: 99%

Learning dispatching rules via novel genetic programming with feature selection in energy-aware dynamic job-shop scheduling

2023

Sci Rep

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The incorporation of energy conservation measures into production efficiency is widely recognized as a crucial aspect of contemporary industry. This study aims to develop interpretable and high-quality dispatching rules for energy-aware dynamic job shop scheduling (EDJSS). In comparison to the traditional modeling methods, this paper proposes a novel genetic programming with online feature selection mechanism to learn dispatching rules automatically. The idea of the novel GP method is to achieve a progressive transition from exploration to exploitation by relating the level of population diversity to the stopping criteria and elapsed duration. We hypothesize that diverse and promising individuals obtained from the novel GP method can guide the feature selection to design competitive rules. The proposed approach is compared with three GP-based algorithms and 20 benchmark rules in the different job shop conditions and scheduling objectives considered energy consumption. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules. Overall, the average improvement over the best-evolved rules by the other three GP-based algorithms is 12.67%, 15.38%, and 11.59% in the meakspan with energy consumption (EMS), mean weighted tardiness with energy consumption (EMWT), and mean flow time with energy consumption (EMFT) scenarios, respectively.

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Optimising makespan and energy consumption in task scheduling for parallel systems

Cited by 12 publications

References 49 publications

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

An Integrated Optimization-Based Algorithm for Energy Efficiency and Resource Allocation in Heterogeneous Cloud Computing Centers

Learning dispatching rules via novel genetic programming with feature selection in energy-aware dynamic job-shop scheduling

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