基于用户行为分析的智能终端应用管理优化

随着智能终端的普及，涌现了多种多样的应用程序以满足用户需求.现有智能终端系统普遍使用基于LRU算法的Task killing机制管理后台应用程序，LRU算法只考虑了应用最近的使用情况，没有考虑用户使用习惯，可能导致后台应用程序被错误地终止，当用户切换回该应用程序时，会带来应用启动延迟增加、能耗增加、状态丢失等问题.本文设计并实现了一种基于贝叶斯网络的应用管理方法BNLP，并在Android移动终端上验证.该方法通过分析用户使用行为，预测后台应用程序即将被启动的概率，并据此进行应用管理.在LiveLab数据集上的实验表明，本文提出的BNLP模型相比于LRU算法应用程序重启率降低了17.2%，从而降低了延迟和能耗、提升了用户体验.

Application Management Optimization for Smart Terminals Based on User Behavior Analysis

With the widespread of smart terminals, there appear a lot of applications to satisfy all kinds of needs in users'' daily lives. Existing systems of smart terminals manage the background applications through an LRU-based task killing policy. LRU algorithm only considers applications'' recent using logs instead of users'' application using habits, it''ll increase the application restart ratio and also lead to problems like increasing application launch delay, increasing energy consumption and losing state. In this paper, it designs and implements a Bayesian network-based application management policy named BNLP, which analyzs users'' application using behavior to predict the probability of launching each application in the near future. Then BNLP will manage the background applications according to these probabilities. Experiment results show that our BNLP model can decrease the restart ratio of LRU by 17.2%, which greatly reduces launch delay, energy consumption and improves user experience.