Towards resource-efficient cloud systems: Avoiding over-provisioning in demand-prediction based resource provisioning

Chen, Liuhua; Shen, Haiying

doi:10.1109/bigdata.2016.7840604

Cited by 9 publications

(2 citation statements)

References 27 publications

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“…Ullrich et al [73], also presented a detailed survey of the challenges and approaches for resource demand prediction in cloud computing environment. A resource demand prediction algorithm was proposed in [74] to overcome resource over-provisioning. Using the Fast-Fourier Transform (FFT), burst and noise were eliminated to improve the accuracy of the prediction.…”

Section: ) Resource Demand Predictionmentioning

confidence: 99%

Vehicular Cloud Resource Management, Issues and Challenges: A Survey

Danquah

Altılar

2020

IEEE Access

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Recent advancements in the automotive industry have led to the design of smart vehicles with high capacity resources for communication, sensing, processing, and storage of data. In the near future, it is envisaged that these resources will be harnessed and utilized to provide cloud services such as storage as a service, computation as a service, and sensing as a service. This paradigm of computing termed vehicular cloud, presents a lot of opportunities for the deployment of delay-sensitive applications in vehicular environment. However, high mobility of vehicles and rapidly changing topology of vehicular networks introduce new challenges such as instability of resources which make the management of resources in vehicular cloud complicated. Therefore, vehicular cloud computing require robust and mobility-aware resource management solutions to be designed. In order to achieve this goal, researchers and engineers need to be abreast with the techniques of resource management in vehicular cloud. This paper presents a detailed survey of resource management tasks in vehicular clouds. A thorough introduction to vehicular cloud is presented initially. Then we identify and examine all the resource management tasks that are carried out in vehicular cloud by classifying them into three different phases: pre-resource assignment phase, resource-assignment phase, and post resource assignment phase. Proposed solutions to resource management challenges in literature at each phase are reviewed in detail. Unlike the existing surveys on vehicular clouds, this study covers all aspects of resource management such as resource brokering, resource demand prediction, resource allocation, and resource scheduling. Following the extensive survey, we also propose frameworks for resource monitoring and resource brokering for vehicular cloud. Finally, open issues and research directions for vehicular cloud resource management are presented.

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Section: ) Resource Demand Predictionmentioning

confidence: 99%

Vehicular Cloud Resource Management, Issues and Challenges: A Survey

Danquah

Altılar

2020

IEEE Access

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“…An overview of the challenges and approaches for forecasting usage of resources in cloud computing can be found in the study [1]. To prevent resource over-provisioning, Chen et al [2] developed a forecast method exclusively for burst workload. To eliminate bursts and sounds, this technique employs the Fast Fourier Transform (FFT) algorithm, which increases prediction accuracy.…”

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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A Resource Demand Prediction Method Based on EEMD in Cloud Computing

Chen

Wang

2018

Procedia Computer Science

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Towards resource-efficient cloud systems: Avoiding over-provisioning in demand-prediction based resource provisioning

Cited by 9 publications

References 27 publications

Vehicular Cloud Resource Management, Issues and Challenges: A Survey

Vehicular Cloud Resource Management, Issues and Challenges: A Survey

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

A Resource Demand Prediction Method Based on EEMD in Cloud Computing

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