Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As more public cloud computing platforms are emerging in the market, a great challenge for these Infrastructure as a Server (IaaS) providers is how to measure the cost and charge the Software as a Service (SaaS) clients for the cloud computing services. This problem is compounded as virtualization technology is deployed in many cloud platforms to consolidate servers and improve their utilization.  studies three different but related models for apportioning costs in a private or public cloud environment supported by virtualized data centers. With given workload placement scenarios and randomly selected workloads, these models estimate the cost for each workload. Through simulations and thorough comparisons of the results,  finally champion the RO-BURST model tailored for the service providers' need, that is characterized by robustness and burstiness. What is more,  import Cost Volatility Factors to ensure that our model is able to adjust itself to the market and multiform demands in por and hardware components, such as disks and CPU, showing its compatibility and extensibility.  also come up with a pricing strategy with respect to servers the workload employs, which generates an applicable and less placement-sensitive fee for the clients.
['Jianzong Wang', 'Rui Hua', 'Yifeng Zhu', 'Jiguang Wan', 'Changsheng Xie', 'Yanjun Chen']
I am writing a research paper on a idea RO-BURST: A Robust Virtualization Cost Model for Workload Consolidation over Clouds
[{"from": "human", "value": "I am writing a research paper on a idea RO-BURST: A Robust Virtualization Cost Model for Workload Consolidation over Clouds"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As more public cloud computing platforms are emerging in the market, a great challenge for these Infrastructure as a Server (IaaS) providers is how to measure the cost and charge the Software as a Service (SaaS) clients for the cloud computing services. This problem is compounded as virtualization technology is deployed in many cloud platforms to consolidate servers and improve their utilization.  studies three different but related models for apportioning costs in a private or public cloud environment supported by virtualized data centers. With given workload placement scenarios and randomly selected workloads, these models estimate the cost for each workload. Through simulations and thorough comparisons of the results,  finally champion the RO-BURST model tailored for the service providers' need, that is characterized by robustness and burstiness. What is more,  import Cost Volatility Factors to ensure that our model is able to adjust itself to the market and multiform demands in por and hardware components, such as disks and CPU, showing its compatibility and extensibility.  also come up with a pricing strategy with respect to servers the workload employs, which generates an applicable and less placement-sensitive fee for the clients."}]