Name
Abstract | ||
|---|---|---|
The popularity of cloud service spurs the increasing demands of cloud resources to the cloud service providers. Along with the new business opportunities, the pay-as-you-go model drastically changes the usage pattern and brings technology challenges to effective capacity planning. In this paper, we propose a new method for cloud capacity planning with the goal of fully utilizing the physical resources, as we believe this is one of the emerging problems for cloud providers. To solve this problem, we present an integrated system with intelligent cloud capacity prediction. Considering the unique characteristics of the cloud service that virtual machines are provisioned and de-provisioned frequently to meet the business needs, we propose an asymmetric and heterogeneous measure for modeling the over-estimation, and under-estimation of the capacity. To accurately forecast the capacity, we first divide the change of cloud capacity demand into provisioning and de-provisioning components, and then estimate the individual components respectively. The future provisioning demand is predicted by an ensemble time-series prediction method, while the future de-provisioning is inferred based on the life span distribution and the number of active virtual machines. Our proposed solution is simple and computational efficient, which make it practical for development and deployment. Our solution also has the advantages for generating interpretable predictions. The experimental results on the IBM Smart Cloud Enterprise trace data demonstrate the effectiveness, accuracy and efficiency of our solution. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/SCC.2012.8 | IEEE SCC |
Keywords | Field | DocType |
individual component estimation,cloud service provider,deprovisioning components,cloud resource,self-adaptive cloud capacity planning,capacity management,active virtual machine,cloud service,capacity underestimation modelling,active virtual machines,service quality maintenance,virtual machines,pay-as-you-go model,resource allocation,cloud service providers,future provisioning demand,planning,cloud capacity demand,life span distribution,cloud provider,effective capacity planning,cloud capacity planning,physical resource utilization,cloud computing,ibm smart cloud enterprise trace data,cloud resources,ensemble time-series prediction method,proposed solution,intelligent cloud capacity prediction,capacity overestimation modelling,time series,cost function,hardware,estimation,accuracy,mathematical model,time series analysis | Virtual machine,Computer science,Capacity management,Real-time computing,Service provider,Capacity planning,Provisioning,Resource allocation,Cloud testing,Distributed computing,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-4673-3049-7 | 18 | 0.86 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yexi Jiang | 1 | 253 | 14.60 |
| Chang-Shing Perng | 2 | 478 | 35.92 |
| Tao Li | 3 | 7216 | 393.45 |
| Rong N. Chang | 4 | 346 | 29.75 |