Studies on the resource workload demand in cloud computing environment aim at reducing resource wastage by optimizing the resource utilization in a cloud data center. Based on this goal, most of the existing approaches rely on resource management mechanisms such as resource allocation and Virtual Machine (VM) consolidation to reach an ideal solution for reducing resource wastage. Because of instability and high variability of the cloud resource usage and workloads, there is a demand for cloud providers to apply the prediction methods for forecasting the future cloud resource utilization. This paper employs a supervised statistical learning method, i.e., Support Vector Regression Technique (SVRT), to forecast the future usage of multi-attribute host resource. The method is particularly suitable to handle a non-linear cloud resource workload. To improve the prediction accuracy of SVRT, we decide Radial Basis Function as the kernel function of SVRT and apply Sequential Minimal Optimization Algorithm (SMOA) for the training and regression estimation of the prediction method. Besides, compared with the existing work, we consider the multi-attribute cloud resources other than the single resource. The method is employed under eight sets of real-world workloads, which are collected from BitBrain (BB), PlanetLab (PL) and Google Cluster Workload Traces (GCWT). Series of experiments conducted on the workload dataset show the effectiveness of our approach. Based on evaluation metrics, the final results show that the accuracy was enhanced by approximately 4%-16% and the error percentage was reduced by approximately 8%-60% compared with the state-of-the-art methods.

We all know the quick development in the wireless network area during the last decade and the importance of using sensors technique in our daily life. Specially, after the IoT revolution, but we all know some of the troubles that sensor faces, such as the littleness of energy resulted from its small size, which determines a small proportion of battery owned by each node of the sensor. In this paper we try to mitigate the burden that sensor nodes hold, whether a conventional sensor, cluster node or even Sink node as well as we try to improve the Sensor Networks data forwarding to the central BS and processing center by utilizing new protocol for connecting the sensor nodes directly to Wi-Fi Access Points connecting through ZigBee (SZW). This act as a gateway through bridging over the data from TCP/IP compact ZigBee compact and applies a different way. We called this process (Sensor ZigBee Wi-Fi connecting) SZW technique that will extend the life time of the network improving its operation and performance. Our results and simulation presented the effectiveness of the SZW protocol compared to previous techniques.