Traffic Aware Based Communication Energy Optimization Of Terminal

With the rapid development of 3G/4G mobile network technology and worldwide popularity of intelligent devices,mobile Internet is growing at an unprecedented rate to subvert the people's lives.Significantly enhance of computing performance makes a wide variety of applications such as streaming media players,VOIP,video conference,etc.run in smart devices fluently,which greatly enriched and changed the way people live.They have become an indispensable part of life.At the same time,the development of battery technology did not break the bottleneck and it is difficult to maintain a large number of applications running for a period time.Taking into account the network communication energy consumption accounted for the major part of the mobile terminal,energy optimization of mobile Internet has become a hot area of research.The presence of the tail effect is the main reason of low terminal communication efficiency because after data transmission completion,interface does not immediately switch to IDLE state but to maintain the high power state for a period of time in order to quickly response the subsequent network transmission.This waiting time is the tail of energy during which there is no data transmission.Articles [2],[13] pointed out that this part of this energy accounted for nearly 60 % of the total energy consumption of the interface.Reducing tail effect is challenging problem.The tail mechanism is designed to achieve the quickly response to upcoming data transmission.For energy optimization,we need consider the tradeoff between energy saving and promotion overhead which needs several message exchanges between terminal and base station and consumes CPU computation resource,causes time delay.Considering the data request come from applications,it is better to study the traffic pattern of application and search the distribution in time so that interface can dynamically adjust RRC state according to current data transmission pattern.In this paper,we collected the traffic trace of 3G/4G from 15 users.According to analysis of trace from different applications such as web browser,new reading,streaming and so on,we discovered the period transfer and the impact on energy consumption and radio resource of terminal.As for this problem,we propose period detect algorithm to distinguish these transmission and reshape traffic for energy saving.Further,for traffic pattern learning,we build model for data streaming from applications and verified that the data transmission has strong temporal correlation.Based on observation,we design high-efficiency energy optimization scheme,called TATO.TATO is mainly consist of three parts.The core idea is to train SVM model to learn traffic model,based on which TATO predicts arrive time of following data transmission within predefined time.With the predict result,TATO adopts the fast dormancy mechanism to adjust RRC state to cut unnecessary tail time to achieve energy saving without much influence on user perspective experience.The advantage of TATO is obvious: it accomplish significant energy saving with little influence on application running;moreover,it is light-weight and runs in application level without requirement to system and other application.We implemented trace-driven simulation based on trace and result shows it is feasible and can saves 50%-60% energy on average across different applications.