Prediction methods for effective resource provisioning in cloud computing: A survey

Kumar, K. Dinesh; Umamaheswari, E.

doi:10.3233/mgs-180292

Cited by 29 publications

(6 citation statements)

References 92 publications

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“…In (1), Xmin and Xmax refer to the dataset's minimum value and maximum value. After the normalization process, normalized data are considered as input for the network as shown in (2). Further, the input data is divide into two data sets, such as training and testing datasets, Fig.…”

Section: Data Preprocessing Methodsmentioning

confidence: 99%

“…The main drawback of this process is that the auto-scaling process faces difficulties in performing the scaling operations in the situation of sudden fluctuations in workload. On another side, a proactive approach performs resource scaling operations in advance [2]. The cloud resource management forecasts each cloud service's future workload and allocates the resources to their cloud services according to the predicted value.…”

Section: Introductionmentioning

confidence: 99%

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HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

Kumar

Umamaheswari

2020

Cybernetics and Information Technologies

Self Cite

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For cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach’s effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.

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Section: Data Preprocessing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

Kumar

Umamaheswari

2020

Cybernetics and Information Technologies

Self Cite

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“…The proposed work helpful to meet the needs of resource pooling and allocation. K. Dinesh Kumar et al [18] proposed a resourceprovisioning model with cost saving strategies to avoid loss of resources. A perdition method presented to avoid over costing, energy lost in data center, and cloud based environment, the proposed method predicted the basic need and upcoming starvation of resources allocation and managed the load as per define perdition.…”

Section: Related Workmentioning

confidence: 99%

Hybrid SFLA-UBS Algorithm for Optimal Resource Provisioning with Cost Management in Multi-cloud Computing

Hussain¹,

He²,

Zhu³

et al. 2021

IJACSA

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Multi-cloud is a vendor-based heterogeneous cloud paradigm in recent era of computing with dynamic infrastructural deployment. Multi-cloud provides all the essential and on-demand requirements of a virtual environment from various domains under a single service level agreement (SLA). Consumers from multitier domains can access all the available resources placed in a shared pool on service provider's side, as per their requirement. The shared pool of resources creates complexity in assigning the best and suitable resource to a particular virtual instance under the same services provider end. The complexity of resources in terms of accessibility from the various domains, dynamic allocation, security, and quality of services (QoS) raises concerns in the multi-cloud infrastructure. This complexity raise concern relates to optimal provisioning and cost management. In the proposed work a hybrid technique with a shuffled leapfrog algorithm and ubiquitous binary search (SLFA-UBS) to resolve these issues with optimal provisioning, dynamic allocation and better resource selection. The proposed work will help to create a need-based and demand-based resource pool with the appropriate selection of each resource. The proposed model also supports resource optimization with dynamic provisioning, cost-effective solution to achieve QoS in multi-cloud deployment on service provider end.

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“…The main disadvantage of this process is that the automatic resizing process has difficulty performing the resizing operations in the event of sudden fluctuations in the workload. A proactive approach allows resizing operations to be carried out in advance [11]. Cloud resource management forecasts the future workload of each cloud service and allocates the resources to their cloud services based on the expected value.…”

Section: Introductionmentioning

confidence: 99%

Fractional Rider Deep Long Short Term Memory Network for Workload Prediction-Based Distributed Resource Allocation Using Spark in Cloud Gaming

Désiré¹,

Francis²,

Kouassi³

et al. 2021

ENG

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The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour. The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it streamed the scenes as video series to play through network. Therefore, cloud gaming is a capable approach, which quickly increases the cloud computing platform. Obtaining enhanced user experience in cloud gaming structure is not insignificant task because user anticipates less response delay and high quality videos. To achieve this, cloud providers need to be able to accurately predict irregular player workloads in order to schedule the necessary resources. In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming. The workload of each resource is computed based on developed Fractional Rider Deep LSTM network. Moreover, resource allocation is performed by fractional Rider-based Harmony Search Algorithm (Rider-based HSA). This Fractional Rider-based HSA is developed by combining Fractional calculus (FC), Rider optimization algorithm (ROA) and Harmony search algorithm (HSA). Moreover, the developed Fractional Rider Deep LSTM is developed by integrating FC and Rider Deep LSTM. In addition, the multi-objective parameters, namely gaming experience loss QE, Mean Opinion Score (MOS), Fairness, energy, network parameters, and predictive load are considered for efficient resource allocation and workload prediction. Additionally, the developed workload prediction model achieved better performance using various parameters, like fairness, MOS, QE, energy and delay.

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Prediction methods for effective resource provisioning in cloud computing: A survey

Cited by 29 publications

References 92 publications

HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

Hybrid SFLA-UBS Algorithm for Optimal Resource Provisioning with Cost Management in Multi-cloud Computing

Fractional Rider Deep Long Short Term Memory Network for Workload Prediction-Based Distributed Resource Allocation Using Spark in Cloud Gaming

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