Deadline‐constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing

Liu, Li; Zhang, Miao; Buyya, Rajkumar; Fan, Qi

doi:10.1002/cpe.3942

Cited by 108 publications

(67 citation statements)

References 27 publications

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“…Next, the TDRS is imported into the established battery model. Finally, the usable battery capacity is incorporated into the battery model parameters, and is identified by the GA [29]. Furthermore, battery degradation not only features the capacity reduction, but also embodies the variation of the OCV-SOC relationship.…”

Section: The Capacity Estimation Algorithmmentioning

confidence: 99%

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Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

et al. 2020

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In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium-ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circuit voltage, the SOC and the available capacity in the scope of whole lifespan, is constructed to describe the capacity attenuation, and the battery available capacity is identified by a genetic algorithm (GA), together with the parameters related to SOC. The square root cubature Kalman filter (SRCKF) is employed to estimate the SOC with the consideration of capacity degradation. The experimental results demonstrate the effectiveness and feasibility of the co-estimation scheme.Energies 2020, 13, 1410 2 of 15 initial values, and the latter is not suitable for online estimation, as it usually costs long shelving time to acquire the OCV value. With the development of computation technologies and machine learning, a variety of artificial intelligence-based, data-driven methods, such as neural networks [6] and support vector machines [7], are proposed for SOC estimation by establishing black-box models. Data-driven methods feature a strong nonlinear mapping capability with high accuracy; however, these approaches show high complexity, and require a considerable amount of training data. Alternatively, model-based methods have been widely investigated and applied for SOC estimation, thanks to the capabilities of online application, high precision and the independence of initial values. Conventional modeling manners mainly include electrochemical models and the equivalent circuit models (ECMs). Compared with complicated electrochemical models, ECM is commonly used to describe the electrical behavior of batteries, and subsequently to estimate the SOC due to its simplification and preferable precision. Yanwen Li et al. proposed a multi-model probability fusion algorithm to describe the battery's electrical characteristics, and subsequently estimate the SOC [8]. In model-based methods, the combination of the battery model and the intelligent filtering algorithm is a hotspot in SOC estimation research. The frequently used filtering algorithms include Kalman filtering (KF) [9], the H-infinity filter (HIF) [10], particle filter (PF) [11], and their various extensions. In particular, the extended KF (EKF) is widely employed to execute SOC estimation using a first-order Taylor expansion on the basis of the battery's nonlinear model [12]. Nonetheless, the second and higher order expansion is usually neglected, thus leading to slow a convergence rate, and even divergence. The unscented KF (UKF) is exploited to estimate battery SOC, based on the recursive unscented transformation to approximate the nonlinear observation without Taylor polynomial expansions [13]. The UKF shows better estimation precision and robustness than the EKF in strong, nonlinear systems [14]. On the basis of the radial-spherical cubature criterion, the cubature Kalman filter (CKF) leverages a set of volume points to appro...

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Section: The Capacity Estimation Algorithmmentioning

confidence: 99%

“…Next, the TDRS is imported into the established battery model. Finally, the usable battery capacity is incorporated into the battery model parameters, and is identified by the GA [29].…”

Section: The Capacity Estimation Algorithmmentioning

confidence: 99%

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

et al. 2020

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“…Li Liu, Miao Zhang and et al presented a co evolutionary genetic algorithm [11] to scientific workflow scheduling in cloud. In this approach the crossover and mutation probability to speed up the convergence of the optimal solution.…”

Section: Related Workmentioning

confidence: 99%

Design of Energy Aware Scheduling Algorithm for Executing Scientific Workflows in Cloud

Balamurugan¹,

Saraswathi²

2019

IJEAT

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The usage of cloud computing and its resources for the execution of scientific workflow is a rapidly increasing demand. The Scientific applications are generally large in scale; even a single scientific workflow includes more number of complex tasks. Execution of these tasks can be made successful only by deploying it in the cloud virtual machines, because only cloud environment can only provide very large number of computing assets. In cloud, every processing resource is given as Virtual Machine. Any scientific workflow deployed in the cloud needs large number of virtual machines so; huge amount of computational energy is spent by the virtual machines to execute multifaceted scientific workflows. Hence there arises the need to utilize the cloud resources in an energy efficient way. Also, if the virtual machines are planned to schedule in an energy efficient manner there is an increase of makepsan of the workflow which is going to be an important parameter for completing the workflow within the deadline. So, the need for executing scientific workflows in energy efficient way with reduced makespan becomes a major issue among the researchers. It also becomes very challenging task to executing a scientific workflow in within the given deadline of a task in the given workflow. To address these issues, a new Energy Aware workflow scheduling algorithm is proposed and designed with improved makespan for the execution of different scientific applications in cloud environment.

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“…The biotic pollination needs a pollinator agent to complete the pollination process but abiotic pollination does not need an agent to complete the pollination process. In pollination, process insects go to different flowers and insects bypass some species of flower and this process is called as Flower consistency [12]. Flower pollination process is classified into two parts that are cross-pollination and Self-pollination.…”

Section: Proposed Approachmentioning

confidence: 99%

Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking

Khurana¹,

Singh²

2018

ICST Transactions on Scalable Information Systems

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Workflow scheduling is one of the most challenging tasks in cloud computing. It uses different workflows and quality of service requirements based on the deadline and cost of the tasks. The main goal of workflow scheduling algorithm is to optimize the time and cost by using virtual machine migration. This algorithm computes the subset problem and decision problem in NP time. It works on the decision-making process to reduce the time and cost of computation on the server side. This paper proposes hybrid optimization to optimize the virtual machine locally and globally. The PEFT algorithm is used for initialization and worked as a heuristic algorithm. This algorithm reduces the error of random initialization of optimization. The proposed algorithm based upon Flower pollination with Grey Wolf Optimization using hybrid approach shows significant end effective results than flower pollination with genetic algorithm. The proposed approach also considered the reliability parameter on different workflows.

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Deadline‐constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing

Cited by 108 publications

References 27 publications

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

Design of Energy Aware Scheduling Algorithm for Executing Scientific Workflows in Cloud

Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking

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