Dynamic Provisioning of System Topologies in the Cloud

Ritter, Thomas; Mitschang, Bernhard; Mega, Cataldo

doi:10.1007/978-1-4471-2819-9_34

Cited by 7 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The neural network is widely used as a prediction approach in different areas of service computing [10]. Specifically, to predict the workload, sliding window technique [16], exponential smoothing method [17], and auto regression model [18] are used as predicting methods. The fuzzy neural network is also used to obtain better results of prediction in the case of workload.…”

Section: Related Workmentioning

confidence: 99%

A Self-Adaptive Resource Provisioning Approach using Fuzzy Logic for Cloud-Based Applications

Mateen¹,

Nasrullah²,

Hayat³

et al. 2020

IJCDS

View full text Add to dashboard Cite

The resource adaptation on demand is an important factor in the field of cloud computing. During runtime, autonomic resource provisioning is not an easy task to choose the accurate amount of resources for service-based cloud applications. For this reason, it is required to guess the future demands for self-adaptive resources to deal with the irregular requests based on runtime workload changes of service-based cloud applications. In this paper, an efficient approach to increase the utilization of resources is proposed that is based on self-adaptive computing with fuzzy logic. Additionally, the proposed fuzzy logic approach enhanced the performance of planning phase for better decision making. Based on fuzzy logic, cloud applications having self-learning provisioning resources outperformed the hybrid resource provisioning approach. To calculate the quality of the proposed technique, real-world ClarkNet and NASA workload traces are used. The results of experiments show that the proposed technique has decreased the entire cost and has boosted the utilization of resources as compared to the other contemporary techniques.

show abstract

Section: Related Workmentioning

confidence: 99%