Name
Abstract | ||
|---|---|---|
In recent years, the resource demands in cloud environment have been increased incrementally. In order to effectively allocate the resources, the workload prediction of virtual machines (VMs) is a vital issue that makes the VM allocation more instantaneous and reduces the power consumption. In this paper, we propose a workload prediction method using Grey Forecasting model to allocate VMs, which is the first string in the research field. Firstly, we utilize the time-dependent of workload at the same period in every day, and forecast the VM workload tendency towards increasing or decreasing. Next, we compare the predicted value with previous time period on workload usage, then determine to migrate which VM wherein the physical machine (PM) for the balanced workload and lower power consumption. The simulation results show that our proposed method not only uses the fewer data to predict the workload accurately but also allocates the resource of VMs with power saving. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICOIN.2014.6799662 | ICOIN |
Keywords | Field | DocType |
grey interval forecasting,vm allocation,power aware computing,workload prediction,power consumption,vm migration,computer centres,power saving,vm workload tendency,virtual machines,cloud data center,telecommunication power management,resource allocation,pm,physical machine,vm workload prediction,grey forecasting model,cloud computing,power consumption reduction | Power saving,Workload prediction,Virtual machine,Computer science,Workload,Cloud data center,Real-time computing,Resource allocation,Cloud computing,Power consumption,Distributed computing | Conference |
Citations | PageRank | References |
8 | 0.48 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jhu-Jyun Jheng | 1 | 8 | 0.48 |
| Fan-Hsun Tseng | 2 | 186 | 17.21 |
| Han-Chieh Chao | 3 | 2502 | 214.00 |
| Li-Der Chou | 4 | 310 | 38.42 |