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- W4385296385 abstract "In recent times, many modern businesses have been adopting cloud computing platforms to deploy their workflow applications. However, scheduling workflows and allocating resources is a difficult task due to factors such as task dependency, heterogeneity, and computational intensity. Resource allocation can pose significant challenges in terms of execution time and cost. As a result, there is a need to reduce energy consumption, makespan, and cost simultaneously, which is often framed as a multi-objective optimization problem. In this paper, we have proposed an energy efficient RL-based workflow scheduling framework for cloud computing. The framework integrates the X-NOR Whirlpool hashing algorithm to enhance security by allowing legitimate users into the cloud environment. The Linearly Weighted Moving Average - Sea Lion Optimization method is used to compute minimal parameters, the Jordan Normal Form - Deep Kronecker Neural Network is utilized for resource monitoring, and the Fuzzy Self-Defense algorithm is employed for efficient selection of virtual machines. We considered the Heterogeneous Computing Scheduling Problem (HCSP) and the Grid Workload Archive (GWA) - T-12 Bitbrains datasets to compare our proposed framework with existing works. Based on the result analysis, the proposed LJC Framework outperforms the QoS-HEFT, TCCS, and SHEFTEX algorithms, achieving better performance." @default.
- W4385296385 created "2023-07-28" @default.
- W4385296385 creator A5009473167 @default.
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- W4385296385 date "2023-12-01" @default.
- W4385296385 modified "2023-10-06" @default.
- W4385296385 title "An Energy Efficient RL Based Workflow Scheduling in Cloud Computing" @default.
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- W4385296385 doi "https://doi.org/10.1016/j.eswa.2023.121038" @default.
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