To do so, a prediction‐based and power‐aware virtual machine allocation algorithm is proposed. Also, we present a three‐tier framework for energy‐efficient resource management in cloud data centers. Experim
These analysis results are also applied to develop an energy-efficient multicore list scheduling algorithm. The proposed power prediction method relies on the proposed tools with a network model and table-based computation along with the repository which analyze the profiling data, and therefore ...
The prediction model based on the traditional algorithm is simpler, but the parameters of the prediction process are difficult to determine objectively, the prediction accuracy is greatly affected by the parameters, and the model results are not stable enough. Another method is to apply a deep ...
Separating the datasets into training and testing sets is an essential first step when developing a machine learning-based model. However, a model driven by machine learning is necessary to generate further forecasts against the newly acquired dataset. 5.3. Prediction algorithm Following the development...
As before we can now use a simple visualization based on a sliding window average of 100 points to “smooth” the data and show the results of the true values against the prediction. As can be seen the fit is pretty good. Of course, this is not a rigorous statistical analysis, but ...
4 Dynamic Cloud Resource Allocation Algorithm 本文采用的是两阶段算法,第一阶段,使用预付费的资源来满足最低QoS的需求。 第二阶段,将non-deterministic user demand建模成随机变量,来动态分配on-demand的资源。 4.1 DCRA Flowchart Overview 在reservation phase,算法会决定满足最细骄傲用户需求的资源,来作为分配预付费...
For forecasting a server workload pattern in a cloud-based storage center, a cloud load prediction based on a weighted fractal support vector machine algorithm is presented34. In this study, parametric optimization using a method called particle optimization technique was created. A different approach...
(three hidden layers), respectively. Finally, we add a dense layer to ensure that our algorithm produces only a single value for a prediction. The epoch is set to 100 to ensure we gain the best model possible. To reduce training times and prevent overfitting of the model, training is ...
Fig. 3. Cloud-based services. Similar to GEE, many geographic data can be obtained from Google Cloud. A user can apply their own algorithm without downloading any data. This will be more convenient for SDG monitoring. Buontempo et al., 2019 ...
Support several prediction service frameworks, including TensorRT, PaddlePaddle, Anakin (a prediction service framework deeply optimized based on the PaddlePaddle). Prediction model library Match the model data and model operation environment (Container Image), and manage (adds/deletes/modifies) deployable...