Efficient and Dynamic Resource Provisioning Strategy for Data Processing Using Cloud Computing

Gadhavi, Lata; Bhavsar, Madhuri

doi:10.15866/irecos.v11i8.9681

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Whereas the first and second moving average model allocates the same weight to each element of the moving average database, the weighted moving average model allows any weight to be placed on each element, and the sum of all weights equals 1 [11]. Here, the active user requests and the utilization of resources for period F t are described in (12):…”

Section: Weighted Moving Average Methods (Wma)mentioning

confidence: 99%

“…The prediction is done by applying various moving average methods. In particular, the moving average methods FSMA and WMA are recommended to consider non-fluctuations in the short-term demand, as shown in our previous work [12]. An ontology-based dynamic resource provisioning for public cloud was implemented in the previous work [13].…”

Section: Algorithm 2: Workload Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction Based Efficient Resource Provisioning and Its Impact on QoS Parameters in the Cloud Environment

Gadhavi

Bhavsar

2018

IJECE

View full text Add to dashboard Cite

<p>The purpose of this paper is to provision the on demand resources to the end users as per their need using prediction method in cloud computing environment. The provisioning of virtualized resources to cloud consumers according to their need is a crucial step in the deployment of applications on the cloud. However, the dynamical management of resources for variable workloads remains a challenging problem for cloud providers. This problem can be solved by using a prediction based adaptive resource provisioning mechanism, which can estimate the upcoming resource demands of applications. The present research introduces a prediction based resource provisioning model for the allocation of resources in advance. The proposed approach facilitates the release of unused resources in the pool with quality of service (QoS), which is defined based on prediction model to perform the allocation of resources in advance. In this work, the model is used to determine the future workload prediction for user requests on web servers, and its impact toward achieving efficient resource provisioning in terms of resource exploitation and QoS. The main contribution of this paper is to develop the prediction model for efficient and dynamic resource provisioning to meet the requirements of end users.</p>

show abstract

Section: Weighted Moving Average Methods (Wma)mentioning

confidence: 99%

Section: Algorithm 2: Workload Predictionmentioning

confidence: 99%