A calendar based Internet content pre-caching agent for small computing devices

Komninos, Andreas; Dunlop, Mark

doi:10.1007/s00779-007-0153-4

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…For mobile devices, prefetching has been used to support disconnected operation [18,40], or to reduce access latency and power consumption [4,7,40]. Predicting a user's web accesses typically relies on determining the probability of the user accessing web content based on previously accessed content, by extracting sequential and set patterns [14,26], as well as temporal features for higher prefetching accuracy [22].…”

Section: Related Workmentioning

confidence: 99%

Prefetching mobile ads

Mohan

Nath

Riva

2013

Proceedings of the 8th ACM European Conference on Computer Systems

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Mobile app marketplaces are dominated by free apps that rely on advertising for their revenue. These apps place increased demands on the already limited battery lifetime of modern phones. For example, in the top 15 free Windows Phone apps, we found in-app advertising contributes to 65% of the app's total communication energy (or 23% of the app's total energy). Despite their small size, downloading ads each time an app is started and at regular refresh intervals forces the network radio to be continuously woken up, thus leading to a high energy overhead, so-called 'tail energy' problem. A straightforward mechanism to lower this overhead is to prefetch ads in bulk and serve them locally. However, the prefetching of ads is at odds with the real-time nature of modern advertising systems wherein ads are sold through real-time auctions each time the client can display an ad. This paper addresses the challenge of supporting ad prefetching with minimal changes to the existing advertising architecture. We build client models predicting how many ad slots are likely to be available in the future. Based on this (unreliable) estimate, ad servers make client ad slots available in the ad exchange auctions even before they can be displayed. In order to display the ads within a short deadline, ads are probabilistically replicated across clients, using an overbooking model designed to ensure that ads are shown before their deadline expires (SLA violation rate) and are shown no more than required (revenue loss). With traces of over 1,700 iPhone and Windows Phone users, we show that our approach can reduce the ad energy overhead by over 50% with a negligible revenue loss and SLA violation rate.

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Section: Related Workmentioning

confidence: 99%

Prefetching mobile ads

Mohan

Nath

Riva

2013

Proceedings of the 8th ACM European Conference on Computer Systems

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“…However, these approaches are not directly applicable to the mobile world because they do not take into account the constraints of latency and energy consumption imposed by mobile devices. In the context of mobile devices, prefetching has been used to support disconnected operation [18,33], or to reduce access latency and power consumption [4,6,33]. Specifically, to minimize energy consumption when prefetching web content, the content is prefetched more aggressively when the available bandwidth changes to higher rates [6] or content with high access probability, low update rate, a small data size, and a high retrieval delay is fetched with higher priority [33].…”

Section: Related Workmentioning

confidence: 99%

PocketWeb

Lymberopoulos

Riva

Strauß

et al. 2012

Proceedings of the Seventeenth International Conference on Architectural Support for Programming Languages and Operating System

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The high network latencies and limited battery life of mobile phones can make mobile web browsing a frustrating experience. In prior work, we proposed trading memory capacity for lower web access latency and a more convenient data transfer schedule from an energy perspective by prefetching slowly-changing data (search queries and results) nightly, when the phone is charging. However, most web content is intrinsically much more dynamic and may be updated multiple times a day, thus eliminating the effectiveness of periodic updates.This paper addresses the challenge of prefetching dynamic web content in a timely fashion, giving the user an instant web browsing experience but without aggravating the battery lifetime issue. We start by analyzing the web access traces of 8,000 users, and observe that mobile web browsing exhibits a strong spatiotemporal signature, which is different for every user. We propose to use a machine learning approach based on stochastic gradient boosting techniques to efficiently model this signature on a per user basis. The machine learning model is capable of accurately predicting future web accesses and prefetching the content in a timely manner. Our experimental evaluation with 48,000 models trained on real user datasets shows that we can accurately prefetch 60% of the URLs for about 80-90% of the users within 2 minutes before the request. The system prototype we built not only provides more than 80% lower web access time for more than 80% of the users, but it also achieves the same or lower radio energy dissipation by more than 50% for the majority of mobile users.

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“…Perhaps the closest work to ours is Komninos and Dunlop's work on pre-caching web content for mobile devices, based on entries on one's calendar [7]. For example, if a person had the name of a place in her calendar, one that was atypical for that user, then the system would try to pre-cache related content for maps, hotels, and so on.…”

Section: Data Caching and Synchronizationmentioning

confidence: 99%

GurunGo

González

Hong

2010

Proceedings of the Eleventh Workshop on Mobile Computing Systems &Amp; Applications - HotMobile '10

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Networked devices like desktop computers and mobile phones make it possible for people to access any of the billions of web pages available on the Internet. However, mobile devices are fundamentally different from desktop PCs in terms of input speeds, screen size, and network speeds, making it harder in practice to find information when on the go. In this paper, we introduce GurunGo, a system that monitors a person's activities on their PC for mobile data types-kinds of data likely to be useful to a person when mobile-and then proactively copies these snippets of data onto his mobile device, thus making it easier to find that information when mobile. Our initial prototype finds and extracts mobile data types from web pages that are browsed on a desktop computer, annotates it with additional relevant information, and copies it to a mobile device in the background. We discuss the design and implementation of GurunGo, as well as some of the tradeoffs and design rationale.

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A calendar based Internet content pre-caching agent for small computing devices

Cited by 7 publications

References 20 publications

Prefetching mobile ads

Prefetching mobile ads

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