Scalable Resource and Performance Management and Profiling for Cloud-enabled Applications

• Main Authors: Cheng-En Du, 杜承恩
• Other Authors: Yeali S. Sun
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/03167573049481337247

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 102 === Virtualization has become the norm in today’s business. As the development of cloud computing technology advances, increasing number of enterprises move their existing physical machine hosted applications to virtualized cloud environments. Since the control and coordination of the use of hardware such as I/O and memory access are quite different between a hypervisor and a native operating system, when measuring the resource usage of an application one must take into account those consumed by the virtualization system modules on behalf of the application. In other words, it is important to know what an application’s resource needs are prior to transitioning it to the virtual environment in the initial planning phase. Particularly to answer the question of how many resources must be provisioned to the application given the anticipated workload and the performance goal. In this work, we first study the execution flow in both environments and identify a set of multi-dimensional usage parameters including CPU, memory, disk I/O, and network I/O to capture all aspects of resource consumption of an application especially in a virtualized environment. A workload-performance-resource (WPR) model is proposed where resource usages of an application both in native and in virtual under different workloads and performance goal are collected and the Canonical Correlation Analysis (CCA) method is used to construct the many-to-many relationship between the native resource usage parameters and the virtual resource usage parameters. In the model, multiple relationship patterns are accounted to achieve good prediction accuracy. We note that the prediction errors were high when it moved across certain critical resource allocation boundaries and a single model cannot accurately characterize changes in application behavior. We then propose a continuous retraining scheme called a sliding window-based retraining model to revise the WPR model as the workload increases. The experimental results show that the CCA-based WPR model can accurately capture the complex multi-dimensional resource usage relationship between in native and in a virtualized environment. When the target workload is within the range in the training set, the prediction error rate is less than 5%. The sliding window-based retraining breaks and recaps to update the training set to reconstruct a new CCA-based WPR model. We can successfully control the prediction error rate under 10% as the complex behavior of resource usage of the application in its VM changes as the intensity of workload increases.