A cost-aware auto-scaling approach using the workload prediction in service clouds

2013 ◽  
Vol 16 (1) ◽  
pp. 7-18 ◽  
Author(s):  
Jingqi Yang ◽  
Chuanchang Liu ◽  
Yanlei Shang ◽  
Bo Cheng ◽  
Zexiang Mao ◽  
...  
2019 ◽  
Vol 8 (3) ◽  
pp. 8011-8014

Paper: Scientific and Web applications are major sources of Internet traffic that requires resources such as Memory ,CPU and Network are on demand. Cloud computing and virtualization are the boons for such resource demand applications from various users. Service models of cloud computing provide a platform for many applications to use resources as pay per use model. In Cloud, Auto-scaling with manage Service Level Agreement (SLA) of resources is one of the main challenges to meet the current demand for resources. To maintain the performance of the cloud, which provision resources based on a heuristic for workload prediction is prime importance. In this paper, we address auto-scaling as a problem to forecast near-future demand of resource using a KNN machine learning methods suggest the optimized model for the dynamic variation of CPU utilization


2017 ◽  
Vol 137 (3) ◽  
pp. 521-531
Author(s):  
Yoko Hirashima ◽  
Kenta Yamasaki ◽  
Tomohiro Morimura ◽  
Norihisa Komoda

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