Amalgamation of Neural Network and Genetic Algorithm for Efficient Workload Prediction in Data Center

Pushpalatha, R; Ramesh, Bharathi

doi:10.1007/978-981-16-0443-0_6

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…This is associated to weight with bias parameter K r and K v . Fitness function is calculated in Equation (10).…”

Section: Optimize the Sapgan Parameters Using Gpcamentioning

confidence: 99%

“…To construct an exact workload prediction approach that handle extremely variable workloads, the workload pattern correlations must be successfully collected in response to highly variance of workload patterns 9 . The original workload data features are extracted to decrease the workload data dimensionality as well as prediction faults for precise workload prediction to overcome the high dimensionality difficulty 10 …”

Section: Introductionmentioning

confidence: 99%

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Workload prediction for enhancing power efficiency of cloud data centers using optimized self‐attention‐based progressive generative adversarial network

Saravanan,

Santhosh Babu

2023

Int J Communication

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SummaryNowadays, future workload prediction is an important requirement in cloud data centers to maintain flexibility and scalability of resources. However, due to unexpected peaks, drops in workload, noise, and redundancy in user requests, there is a considerable variance in resource demands, making it difficult to accurately predict workloads. Therefore, a self‐attention‐based progressive generative adversarial network (SAPGAN) optimized with Giza Pyramids Construction Algorithm (GPCA)‐based workload prediction is proposed for Sustainable Cloud Data Centers (CDC). At first, redundant data in historical data obtained through CDC are filtered utilizing Markov chain random field (MCRF) co‐simulation method. These pre‐processed historical data are supplied to the SAPGAN. The SAPGAN weight parameters are optimized by GPCA. The proposed method is analyzed using 2 benchmark datasets: HTTP traces from Saskatchewan and NASA. The simulation is implemented in JAVA. The performance metrics is examined to verify the efficacy of the proposed technique. The performance of the proposed approach provides 28.70%, 11.87%, and 14.79% higher accuracy; 30.15%, 11.72%, and 18.34% lesser energy consume for the dataset of NASA; and 5.32%, 2.45%, and 5.67% higher accuracy; 12.36%, 24.24%, and 34.16% lesser energy consume for the dataset of Saskatchewan HTTP traces compared with existing methods, such as auto‐adaptive learning in a dynamic cloud environment (AADEA‐WLP‐CDC), a neural network model depending on biphase adaptive learning for anticipating cloud data center workload (BALNN‐WLP‐CDC) and multiple scale ensemble of deep learning framework for multistep‐ahead cloud workload prediction (EMD‐LSTM‐GAN‐WLP‐CDC).

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“…This is associated to weight with bias parameter K r and K v . Fitness function is calculated in Equation (10).…”

Section: Optimize the Sapgan Parameters Using Gpcamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Workload prediction for enhancing power efficiency of cloud data centers using optimized self‐attention‐based progressive generative adversarial network

Saravanan,

Santhosh Babu

2023

Int J Communication

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“…This is computed as the efficiency of the proposed method to improve power consumption while predicting workloads that is calculated using Equation (10),…”

Section: Recallmentioning

confidence: 99%

“…The accurate workload prediction model can proficiently deal the resource management decisions 9 . Normally, the process of workload prediction focuses has a number of challenges, random variations at workload demand forms, which are directly proportional to the allocation as well as resources distribution 10‐15 . It can lead to underestimation of resources and overestimation 16 .…”

Section: Introductionmentioning

confidence: 99%

“…9 Normally, the process of workload prediction focuses has a number of challenges, random variations at workload demand forms, which are directly proportional to the allocation as well as resources distribution. [10][11][12][13][14][15] It can lead to underestimation of resources and overestimation. 16 Energy consumption is unacceptable in a dynamic cloud environment.…”

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confidence: 99%

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COSCO2: AI‐augmented evolutionary algorithm based workload prediction framework for sustainable cloud data centers

Karthikeyan¹,

Balamurugan²,

Cyriac

et al. 2022

Trans Emerging Tel Tech

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Workload prediction is the necessary factor in the cloud data center for maintaining the elasticity and scalability of resources. However, the accuracy of workload prediction is very low, because of redundancy, noise, and low accuracy for workload prediction in cloud data center. Therefore, in this article, a tree hierarchical deep convolutional neural network (T-CNN) optimized with sheep flock optimization algorithm based work load prediction is proposed for sustainable cloud data centers. Initially, the historical data from the cloud data center is preprocessed using kernel correlation method. The proposed T-CNN approach is used for workload prediction in dynamic cloud environment. The weight parameters of the T-CNN model are optimized by sheep flock optimization algorithm. The proposed COSCO2 method has accurately predicts the upcoming workload and reduces extravagant power consumption at cloud data centers. The proposed approach is evaluated utilizing two benchmark datasets:(i) NASA, (ii) Saskatchewan HTTP traces. The simulation of this model is implemented in java tool and the parameters are calculated. From the simulation, the proposed method attains 20.

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Multivariate time series ensemble model for load prediction on hosts using anomaly detection techniques

Bawa,

Rana,

Tekchandani

2024

Cluster Comput

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Amalgamation of Neural Network and Genetic Algorithm for Efficient Workload Prediction in Data Center

Cited by 9 publications

References 13 publications

Workload prediction for enhancing power efficiency of cloud data centers using optimized self‐attention‐based progressive generative adversarial network

Workload prediction for enhancing power efficiency of cloud data centers using optimized self‐attention‐based progressive generative adversarial network

COSCO2: AI‐augmented evolutionary algorithm based workload prediction framework for sustainable cloud data centers

Multivariate time series ensemble model for load prediction on hosts using anomaly detection techniques

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