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An energy efficient RL based workflow scheduling in cloud computing
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AI
DOI：10.1016/j.eswa.2023.121038.png)
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Abstract
En 中文
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.
Keywords:
Task scheduling
Resource monitoring
Virtual machine (VM)
X-NOR Whirlpool Hashing Algorithm (X-NOR-WHA) and Jordan Normal Form-Deep Kronecker Neural Network (JNF-DKNN)
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W