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- W1567221478 abstract "With the emerging of cloud computing, more and more enterprise organizations begin to migrate their applications to IaaS, which is a more flexible and cheaper alternative to traditional infrastructures. IaaS providers usually offer customers with resources in the form of VM and charge them in a time-based billing model. Meanwhile customers are allowed to dynamically apply for VM resources. However, highly dynamic workload makes customers difficultly determine how much capacity to provision. Furthermore, it is also a great challenge for customers to determine how to choose a VM provisioning scheme to match workload at a low cost. In this paper, we propose a workload prediction-based multi-VM provisioning mechanism to overcome these challenges, which contains an ARIMA workload predictor with dynamic error compensation (ARIMA-DEC) and a time-based billing aware multi-VM provisioning algorithm (TBAMP). The experimental results show that ARIMA-DEC predictor can obviously reduce SLA default rate and TBAMP algorithm can effectively save rental cost comparing to the existing algorithms." @default.
- W1567221478 created "2016-06-24" @default.
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- W1567221478 date "2013-12-02" @default.
- W1567221478 modified "2023-09-27" @default.
- W1567221478 title "A workload prediction-based multi-VM provisioning mechanism in cloud computing" @default.
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