Network Agile Preference-Based Prefetching for Mobile Devices
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Network Agile Preference-Based Prefetching for Mobile Devices JunZe Han1, Xiang-Yang Li1;2, Taeho Jung1, Jumin Zhao3, Zenghua Zhao4 1Department of Computer Science, Illinois Institute of Technology, USA 2 School of Software and TNLIST, Tsinghua University 3College of Information Engineering, Taiyuan University of Technology, China 4Department of Computer Engineering, Tianjin University, China Email: [email protected], [email protected], [email protected], [email protected] experience, in this work we present a seamless transparent solution that automatically prefetches contents and switches Abstract—For mobile devices, communication via cellular networks consumes more energy than via WiFi networks, and the connection between cellular and WiFi networks. To make suffers an expensive limited data plan. On the other hand, as the content prefetching work for mobile devices, a number of the coverage and the density of WiFI networks are smaller than challenges must be addressed. In general, we need to know those of the cellular networks, users cannot purely rely on WiFi when to prefetch, and what contents to prefetch such that the to access the Internet. In this work we present a behavior-aware user’s experience is not deteriorated and the overall energy and preference-based approach to prefetch news webpages for cost and monetary cost is reduced. In this work we propose a the user to visit in the near future, by exploiting the WiFi prefetching approach that is able to predict what webpages the network connections to reduce the energy and monetary cost. user will visit more likely in the near future and prefetch them We first design an efficient preference learning algorithm to keep via WiFi network at an appropriate time, such that when the track of the user’s changing interests, and then by predicting user indeed wants to visit these webpages later on, the user the appearance and durations of the WiFi network connections, our prefetch approach optimizes when to prefetch to maximize can directly access them from the prefetched buffer instead of the user experience while lowing the prefetch cost. Our prefetch accessing them via cellular network. approach also exploits the idle period of WiFi connections to reduce the tail-energy consumption. We implement our approach A large number of previous works on web prefetching [6], in iPhone and our extensive evaluations show that our system [7], [8], [9] are based on the URLs of webpages visited in the achieves about 60% hit ratio, saves about 50% cellular data usage, past. However, for news prefetch, simple URL-based approach and reduces the energy cost by 7%. does not work because newly posted news is usually assigned a new unpredictable URL. Thus it is hard to predict what I. INTRODUCTION kind of news a user will be interested in purely based on the URLs of the news. In this work, we present a prefetching According to a recent study [1], mobile web browsing is approach that combines the keyword-based and URL-based growing significantly and is expected to surpass the desktop approaches. Based on the websites and sections visited in the web browsing by 2015. Web browsing and news reading past, e.g., cnn.com, cnn.com/World, and the keywords in the account for a large proportion of the time and the data used news, we design an efficient preference learning algorithm to by smartphones: smartphone users with a data plan spend on keep track of the user’s changing interests for news so that we average 300 minutes per month browsing the web, which is can prefetch appropriate contents using WiFi. comparable to mobile voice usage [2]. A global smartphone study [2] shows that web browser is the single most popular On the other hand, many web prefetch techniques focus data application which accounts for 54% of data application on short-term prefetching [7], [10], [11], which prefetches face time and 50% of data volume for smartphones; and the webpages to be visited within a few seconds to a few reading news makes up for 68% of the user’s web browsing minutes at most. However, in this paper we focus on the time, which is one of the most frequent activities [3]. long-term prefetching instead which prefetches the webpages much earlier, e.g., half an hour to several hours, before the Typically there are two networking access approaches: user visits the webpages. A motivating example for the long- through cellular network and through WiFi network. Although term prefetching is as follows. Assume that a user Bob goes the cellular network is almost ubiquitous and its coverage to work at 8:00 am in the morning by train and he is used seems not to be a problem, the limited data plan, higher price to read news on his way to the office. Assume there is no and more energy consumption [4], [5] make the smartphone WiFi connection on the train and Bob has a WiFi connection users prefer using the WiFi network. It is thus natural to switch at home. By profiling the news types and frequently visited to WiFi network connection whenever possible. In order to webpages by Bob on the train, we can prefetch these contents reduce energy and monetary cost while not sacrificing the user for Bob when he is about to leave home for work. Although The research of Li is partially supported by NSF CNS-1035894, NSF the contents prefetched might be obsolete or invalided in the ECCS-1247944, NSF ECCS-1343306, NSF CMMI 1436786, National Natural future, we assume that the user’s experience will not drop by Science Foundation of China under Grant No. 61170216, No. 61228202. reading the news prefetched about an hour ago. For the long-term mobile prefetching described above, clients’ access latency. Thus without global webpage access another difficulty for prefetching is to decide when to prefetch information, we cannot adapt the long-term prefetching into the contents. Several challenges make the decision of prefetch personal prefetching for our problem. timing difficult: (1) if a user has a WiFi network connection now, we need to predict when the user will move outside the B. Mobile Prefetch coverage region of the WiFi network; (2) even if we know when the WiFi network connection will be lost, we need to Compared to desktop prefetching techniques, the prefetch- estimate the time needed for prefetching the contents so that ing techniques for mobile device need to be energy and data the prefetched contents are mostly up-to-date when the user efficient. Several prefetching schemes [18], [19], [20] have reads it; (3) the prefetching activities should not interfere the been proposed which make prefetch decision by considering user’s regular network usage activities (otherwise, the user the power consumption, data access rate, data update rate, and experience will be deteriorated). data size. Specially, Higgins et al. [20] presented a cost-benefit analysis to decide when to prefetch based on the performance In summary, the main contributions of this work are as such as latency reduction, the cost of energy and monetary cost follows. We design an efficient keyword-based and URL-based or data usage. Some prefetching schemes [9], [21], [22] also preference learning algorithm to predict the news webpage that take advantage of the users’ spatiotemporal access patterns for the user will be interested in. We also present a network con- web contents. Lymberopoulos et al. [9] presented a prefetching nection prediction approach that can start contents prefetching scheme that predicts what webpages a user is likely to request automatically. In addition, we propose a prefetch scheduling as well as when these requests are likely to occur. Parate algorithm to exploit idle time of the connection to prefetch the et al. [21] proposed an approach to predict which app will webpages. We implemented the prototype system on iPhone be used next and when it will be used, and then prefetch and conducted extensive evaluations on the performance of application content to fast app launch. Kamaraju et al. [22] our system. The experimental results show that our system presented a context-aware delivery paradigm that prefetches achieves about 60% hit ratio, saves about 50% cellular data video contents by exploiting locations and times in which the usage, and reduces the energy cost by 7%. networks experience excess of resources. However none of the prior prefetching schemes are designed for news webpage II. RELATED WORK prefetching and make prefetch decisions based on the network A. Desktop Prefetch condition prediction. In particularly, as news webpages are frequently updated and require high freshness, it is desirable Web prefetching has been studied for a long time. The pri- that we can delay the prefetching as late as possible, which mary objective of prefetching technique in desktop computer requires that we can predict the future network condition. Thus is to reduce the latency of webpage loading and increase the priors prefetching cannot be directly adapted to news webpage hit ratio of the prefetched webpages. prefetching. 1) Short-Term Prefetching: Short-term prefetching tech- nique aims at predicting the webpages to be visited imme- III. PROBLEM FORMULATION AND CHALLENGES diately say the next one or two webpages. The Markov model A. Problem Formulation has been exploited in the short-term prefetching to predict next webpages to be visited by analyzing the past visit sequence [6], Given the user’s visited webpages and the past network [8], [12]. In HTML5 [13] and Firefox browser [11], a strategy conditions, we want to prefetch news webpages via WiFi “link prefetching” for website optimization is introduced: network that the user is likely to visit when the WiFi network the browser can prefetch specified webpages based on the is not available.