Hu Rongdong, Jiang Jingfei, Liu Guangming, Wang Lixin
School of Computer, National University of Defense Technology, Changsha 410073, China.
School of Computer, National University of Defense Technology, Changsha 410073, China ; National Supercomputer Center, Tianjin 300457, China.
ScientificWorldJournal. 2014 Feb 20;2014:321231. doi: 10.1155/2014/321231. eCollection 2014.
Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements.
云提供商应在最大化资源利用率的同时确保服务质量。一种优化策略是根据应用程序的实际资源需求以细粒度模式及时分配资源。该策略的必要前提是提前获取未来负载信息。我们基于统计学习理论提出了一种适用于云计算环境复杂动态特性的多步超前负载预测方法KSwSVR。它集成了改进的支持向量回归算法和卡尔曼平滑器。使用从多种类型资源获取的公共跟踪数据来验证其预测准确性、稳定性和适应性，并与AR、BPNN和标准SVR进行比较。随后，基于预测结果，提出了一种简单高效的资源供应策略。CPU分配实验表明，它可以在满足服务水平协议要求的同时有效降低资源消耗。
