AME-WPC: Advanced model for efficient workload prediction in the cloud

Cetinski, Katja; Jurič, Matjaž B.

doi:10.1016/j.jnca.2015.06.001

Cited by 62 publications

(26 citation statements)

References 43 publications

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“…If the average workload model is used, then the system will face the shortage of resources leading to performance degradation [15]. Machine learning methods which use historical data are commonly used for more accurate forecasting [16]. For example, Huang et al [17] use Recurrent neural network (RNN) with long short-term memory (LSTM) units to forecast server performance and load.…”

Section: Introductionmentioning

confidence: 99%

Short time prediction of cloud server round-trip time using a hybrid neuro-fuzzy network

Damaševičius¹,

Sidekerskienė²

2020

AIS

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The paper presents a cloud server roundtrip time prediction approach for cloud datacenters using neuro-fuzzy network with eight probability distribution functions (Normal, Rayleigh, Weibull, Gamma, Birnbaum-Saunders, Extreme Value, and Generalized Pareto) used for fuzzification and defuzzification. We predict the Round-Trip Time (RTT), i.e., the time for a network packet to travel from a client to a server and back. The proposed approach can achieve significant reduction in the short-time RTT prediction error, achieving an accuracy of 79.36%. The approach could be useful for increasing the efficiency of client-cloud systems, for example, when taking effective decisions for computational offloading, and contribute to the development of smart cloud computing.

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Section: Introductionmentioning

confidence: 99%

Short time prediction of cloud server round-trip time using a hybrid neuro-fuzzy network

Damaševičius¹,

Sidekerskienė²

2020

AIS

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“…Resource management is an important issue in the IaaS cloud service models that affects the efficiency of the cloud computing [2]. Workload prediction techniques are required to manage the cloud resources and increase the performance of the cloud computing [3]. According to the prediction of the workloads, resources are scaled up or down automatically to balance the workload among the computing resources.…”

Section: Introductionmentioning

confidence: 99%

“…The resource scaling efficiency depends on the utilized workload prediction method. The future workload prediction methods are classified to statistical methods [4] and machine-learning methods [3]. In the statistical methods, the prediction is done by matching the current workload history with the similar workload in the past.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, these methods are suitable for short-term workload prediction. They have a poor performance for long-term workload predictions [3]. In the literature, there are several statistical methods that have been applied to the workload prediction in the cloud such as: moving average [5], linear regression [6], Monte Carlo [7], and quadratic regression [6].…”

Section: Introductionmentioning

confidence: 99%

“…In the machinelearning methods, historical information about the previous workload is used to design a predicted model for the future workload. They overcame the problem of dealing with the long-term workload prediction that is suffered by the statistical methods [3]. There are several machine-learning methods that have been applied to the workload prediction in the literature such as: support vector machine (SVM) [8], regression tree [9], and artificial neural network (ANN) [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of an Enhanced Self-adapting Differential Evolution Algorithm to Workload Prediction in Cloud Computing

Attia¹,

Arafa²,

Sallam³

et al. 2019

IJITCS

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The demand for workload prediction approaches has recently increased to manage the cloud resources, improve the performance of the cloud services and reduce the power consumption. The prediction accuracy of these approaches affects the cloud performance. In this application paper, we apply an enhanced variant of the differential evolution (DE) algorithm named MSaDE as a learning algorithm to the artificial neural network (ANN) model of the cloud workload prediction. The ANN prediction model based on MSaDE algorithm is evaluated over two benchmark datasets for the workload traces of NASA server and Saskatchewan server at different look-ahead times. To show the improvement in accuracy of training the ANN prediction model using MSaDE algorithm, training is performed with other two algorithms: the back propagation (BP) algorithm and the self-adaptive differential evolution (SaDE) algorithm. Comparisons are made in terms of the root mean squared error (RMSE) and the average root mean squared error (ARMSE) through all prediction intervals. The results show that the ANN prediction model based on the MSaDE algorithm predicts the cloud workloads with higher prediction accuracy than the other algorithms compared with.

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PSO‐Based Hybrid Weighted k‐Nearest Neighbor Algorithm for Workload Prediction in Cloud Infrastructures

Yamuna

Vijay

Gomathi

2022

The New Advanced Society

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AME-WPC: Advanced model for efficient workload prediction in the cloud

Cited by 62 publications

References 43 publications

Short time prediction of cloud server round-trip time using a hybrid neuro-fuzzy network

Short time prediction of cloud server round-trip time using a hybrid neuro-fuzzy network

Application of an Enhanced Self-adapting Differential Evolution Algorithm to Workload Prediction in Cloud Computing

PSO‐Based Hybrid Weighted k‐Nearest Neighbor Algorithm for Workload Prediction in Cloud Infrastructures

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