Resource Demand Prediction in Multi-Tenant Service Clouds

Verma, Manish; Gangadharan, G. R.; Ravi, Vadlamani; Narendra, Nanjangud C.

doi:10.1109/ccem.2013.6684440

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…Many studies have been conducted on various predictions in cloud computing. From the perspective of research objectives, some researchers have studied server load prediction [ [6][7][8][9][10], VM load prediction [11,12], VM utilization prediction [13,14], host utilization prediction [15], web application workload prediction [16], cloud service workload prediction [17][18][19], workflow workload prediction [20], service quality prediction [21], and workload characterization [22][23][24]. Toumi et al [6] described a server load according to the submitted task types and the submission rate and applied a stream mining technique to predict server loads.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Jing

Wang

2019

Journal of Electrical and Computer Engineering

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Dynamic resource scheduling is a critical activity to guarantee quality of service (QoS) in cloud computing. One challenging problem is how to predict future host utilization in real time. By predicting future host utilization, a cloud data center can place virtual machines to suitable hosts or migrate virtual machines in advance from overloaded or underloaded hosts to guarantee QoS or save energy. However, it is very difficult to accurately predict host utilization in a timely manner because host utilization varies very quickly and exhibits strong instability with many bursts. Although machine learning methods can accurately predict host utilization, it usually takes too much time to ensure rapid resource allocation and scheduling. In this paper, we propose a hybrid method, EEMD-RT-ARIMA, for short-term host utilization prediction based on ensemble empirical mode decomposition (EEMD), runs test (RT), and autoregressive integrated moving average (ARIMA). First, the EEMD method is used to decompose the nonstationary host utilization sequence into relatively stable intrinsic mode function (IMF) components and a residual component to improve prediction accuracy. Then, efficient IMF components are selected and then reconstructed into three new components to reduce the prediction time and error accumulation due to too many IMF components. Finally, the overall prediction results are obtained by superposing the prediction results of three new components, each of which is predicted by the ARIMA method. An experiment is conducted on real host utilization traces from a cloud platform. We compare our method with the ARIMA model and the EEMD-ARIMA method in terms of error, effectiveness, and time-cost analysis. The results show that our method is a cost-effective method and is more suitable for short-term host utilization prediction in cloud computing.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Jing

Wang

2019

Journal of Electrical and Computer Engineering

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“…Researches on resource demand prediction are mainly focused on how to save energy [16], improve performance [7,24], increase profit [20,14,18] and so on. To optimize resource management and task scheduling, Fahimeh Ramezani et al [13] introduce a prediction method for predicting VM workload pattern and VM migration time using fuzzy expert system.…”

Section: Related Workmentioning

confidence: 99%

A High-Accuracy Self-Adaptive Resource Demands Predicting Method in Iaas Cloud Environment

Chen¹,

Zhu²,

Di³

et al. 2015

NNW

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In IaaS (Infrastructure as a Service) cloud environment, users are provisioned with virtual machines (VMs). However, the initialization and resource allocation of virtual machines are not instantaneous and usually minutes of time are needed. Therefore, to realize efficient resource provision, it is necessary to know the accurate amount of resources needed to be allocated in advance. For this purpose, this paper proposes a high-accuracy self-adaptive prediction method using optimized neural network. The characters of users' demands and preferences are analyzed firstly. To deal with the specific circumstances, a dynamic self-adaptive prediction model is adopted. Some basic predictors are adopted for resource requirements prediction of simple circumstances. BP neural network with self-adjusting learning rate and momentum is adopted to optimize the prediction results. High-accuracy self-adaptive prediction is realized by using the prediction results of basic predictors with different weights as training data besides the historical data. Feedback control is introduced to improve the whole operation performance. Statistic validation of the method is conducted adopting multiple evaluation criteria. The experiment results show that the method is promising for effectively predicting resource requirements in the cloud environment.

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“…In order to increase prediction accuracy, Xu et al [6] propose the GFSS-ANFIS/SARIMA prediction model, which integrates Seasonal Autoregressive Integrated Moving Average Model (ARIMA) and Generalized Fuzzy Soft Sets with Adaptive Neuro Fuzzy Inference System. Data mining and statistical techniques are used in Verma et al [7] resource prediction framework for multi-tenant service clouds to forecast resource demands in order to minimize resources and provisioning time. To obtain precise performance forecasts in hybrid clouds, Imai et al [8] suggest a model which has workload-tailored elastic compute unit (WECU) as a computing power unit.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Bio-Inspired Optimization-based Cloud Resource Demand Prediction using Improved Support Vector Machine

Sanjay,

Sreedharan

2024

IJACSA

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In order to furnish diverse resource requirements in cloud computing, numerous resources are integrated into a data centre. How to deliver resources in a timely and accurate manner to meet user expectations is a significant concern. However, the resource demands of users fluctuate greatly and frequently change regularly. It's possible that the resource provision won't happen on time. Furthermore, because some physical resources are shut down to save energy, there may occasionally not be enough of them to meet user requests. Therefore, it's critical to offer resource provision proactively to ensure positive user involvement using cloud computing. To enable resource provision in advance, it is essential to accurately estimate future resource demands. Using machine learning techniques, we offer a unique approach in this study that tries to identify key features, accelerating the forecast of cloud resource consumption. Finding the classification method with the greatest fit and maximum classification accuracy is crucial when predicting cloud resource consumption. The attribute selection method is used to decrease the dataset. The categorization process is then given the reduced data. The hybrid attribute selection method used in the investigation, which combines the bio-inspired algorithm genetic algorithm, the pulse-coupled neural network, and the particle swarm optimization algorithm, improves classification accuracy. The accuracy of prediction employing this technique is examined using a variety of performance criteria. When it comes to predicting the demand for cloud resources, the experimental results show that the suggested machine learning method performs more effectively than traditional machine learning models.

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Resource Demand Prediction in Multi-Tenant Service Clouds

Cited by 9 publications

References 23 publications

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

A High-Accuracy Self-Adaptive Resource Demands Predicting Method in Iaas Cloud Environment

Hybrid Bio-Inspired Optimization-based Cloud Resource Demand Prediction using Improved Support Vector Machine

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