KSwSVR: A New Load Forecasting Method for Efficient Resources Provisioning in Cloud

Hu, Rongdong; Jiang, Jingfei; Liu, Guangming; Wang, Lixin

doi:10.1109/scc.2013.67

Cited by 20 publications

(4 citation statements)

References 23 publications

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“…The combination of the above-mentioned techniques are also used to predict the future workload. These include SVR and Kalman filter [124], ARIMA and RNN [125], ARIMA and wavelet decomposition [126] (Fig. 9).…”

Section: Workload Categorization and Predictionmentioning

confidence: 99%

Energy efficiency in cloud computing data centers: a survey on software technologies

2022

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Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage.

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Section: Workload Categorization and Predictionmentioning

confidence: 99%

Energy efficiency in cloud computing data centers: a survey on software technologies

2022

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“…The approach proposed by [7] combines a threshold-based method for communication reduction in collecting utilisation data in distributed systems and couples this with a k-means clustering where a forecasting model predicts the centroid values to infer resource utilisation values of individual machines. Alternative modelling approaches including support vector regression, Markov chain models and exponential smoothing based models are presented by [8], [9], [10] and [11]. Several previous papers use the same datasets we use to evaluate their proposed models, enabling comparison among the different approaches [7]- [10].…”

Section: Related Workmentioning

confidence: 99%

Fast-Fourier-Forecasting Resource Utilisation in Distributed Systems

Pritz¹,

Perez²,

Leung³

2020

Preprint

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Distributed computing systems often consist of hundreds of nodes (machines), executing tasks with different resource requirements. Efficient resource provisioning and task scheduling in such systems are non-trivial and require close monitoring and accurate forecasting of the state of the system, specifically resource utilisation at its constituent machines. Two challenges present themselves towards these objectives. First, collecting monitoring data entails substantial communication overhead. This overhead can be prohibitively high, especially in networks where bandwidth is limited. Second, forecasting models to predict resource utilisation should be accurate and also need to exhibit high inference speed. Mission critical scheduling and resource allocation algorithms use these predictions and rely on their immediate availability. To address the first challenge, we present a communicationefficient data collection mechanism. Resource utilisation data is collected at the individual machines in the system and transmitted to a central controller in batches. Each batch is processed by an adaptive data-reduction algorithm based on Fourier transforms and truncation in the frequency domain. We show that the proposed mechanism leads to a significant reduction in communication overhead while incurring only minimal error and adhering to accuracy guarantees. To address the second challenge, we propose a deep learning architecture using complex Gated Recurrent Units to forecast resource utilisation. This architecture is directly integrated with the above data collection mechanism to improve inference speed of the presented forecasting model. Using two real-world datasets, we demonstrate the effectiveness of our approach, both in terms of forecasting accuracy and inference speed. Our approach resolves several challenges encountered in resource provisioning frameworks and can also be generically applied to other forecasting problems.

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“…Proactive resource allocation [5], [8], [9] approaches apply VM resource allocation based on resource demand prediction. Gong et al [5] developed the PRedictive Elastic reSource Scaling (PRESS) system for dynamic fine-grained resource allocation to VMs in order to reduce resource costs and avoid application SLA performance violations.…”

Section: Related Workmentioning

confidence: 99%

“…Although the authors argue that their techniques can support VM allocation, they did not integrate them into any allocation method. Hu et al [8] present KSwSVR, a system for fine-grained VM resource allocation that makes prediction of the VM resource demand multiple steps ahead in the future based on the combination of an improved version of an SVM and a Kalman filter. The proposed proactive VM resource allocation solutions have the drawback that they predict the demand of only one resource or of multiple resources separately without taking into account cross-correlation between resources.…”

Section: Related Workmentioning

confidence: 99%

Cross-Correlation Prediction of Resource Demand for Virtual Machine Resource Allocation in Clouds

Minarolli

Freisleben

2014

2014 Sixth International Conference on Computational Intelligence, Communication Systems and Networks

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Cloud computing is aimed at offering elastic resource allocation on demand in a pay-as-you-go fashion to cloud consumers. To achieve this goal in automatic manner, a resource scaling mechanism is needed that maintains application performance according to Service Level Agreements (SLA) and reduces resource costs at the same time. In this paper, we present a cross-correlation prediction approach based on machine learning that predicts resource demands of multiple resources of virtual machines running in a cloud infrastructure. Based on these predictions, a proactive resource allocation scheme is applied that assigns only the required resources to virtual machines to keep their cost to a minimum. Experimental results with the web serving multi-tier application benchmark of CloudSuite show the effectiveness of our approach compared to a non-cross-correlation prediction technique in achieving better prediction accuracy and better application performance.

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KSwSVR: A New Load Forecasting Method for Efficient Resources Provisioning in Cloud

Cited by 20 publications

References 23 publications

Energy efficiency in cloud computing data centers: a survey on software technologies

Energy efficiency in cloud computing data centers: a survey on software technologies

Fast-Fourier-Forecasting Resource Utilisation in Distributed Systems

Cross-Correlation Prediction of Resource Demand for Virtual Machine Resource Allocation in Clouds

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