Event Knowledge-based Prediction for Dynamic Resource Reallocation in Cloud Application Performance Management

• Main Authors: Sheng-Jhe Lan, 籃聖喆
• Other Authors: 孫雅麗
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/02945014781121375894

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 100 === In Application Performance Management (APM), the most common problem encountered by the administrators of many network service providers is how to sustain performance targets for the applications. Cloud computing offers the possibility to provide the resources on demand as Utility Computing. The internet services with highly changing demand can benefit from the implementation of cloud services rapid elasticity technology as live sport game broadcasting or online ticket booking system. We expect that specific events will cause significant increase the demand of applications. Cloud computing is elasticity and burstability, so it can help the internet services acquire necessary resources under the heavy variation of demand. However, system reallocation will take a resizing time and bring some cost and risk. The external demand prediction is an important component in dynamic resource allocation to provide target performance guarantees in cloud. In this work, we propose a learning-based prediction model and the real-time algorithm to forecast the external demand for this class of cloud applications. We use the learning method to understand the event knowledge based on the behaviors of historical events and consider the online measurements to predict the trend of external demand in next control period. We also develop safety margin-based prediction schemes to avoid the under-estimation errors of prediction. The experimental results show that our prediction method has more accurate prediction results than the traditional simple linear prediction methods. The use of safety margin only incurs a very small probability of under-estimation.