How to Reduce Smartphone Traffic Volume by 30%?

Qian, Feng; Huang, Junxian; Erman, Jeffrey; Mao, Zeyu; Sen, Subhabrata; Spatscheck, Oliver

doi:10.1007/978-3-642-36516-4_5

Cited by 26 publications

(17 citation statements)

References 16 publications

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“…According to the results reported in [29], objects that have an image or video content-type and also most objects with binary data (e.g. app/octet-stream) already are in compressed form and there is very little room for additional saving.…”

Section: Selective Compressionmentioning

confidence: 90%

Network assisted latency reduction for mobile web browsing

Sehati

Ghaderi

2016

Computer Networks

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Section: Selective Compressionmentioning

confidence: 90%

Network assisted latency reduction for mobile web browsing

Sehati

Ghaderi

2016

Computer Networks

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“…Various works show the benefit of compression for transmitting less network data and thus reducing the energy cost [194,255,262]. We analysed the compression performance of the default compression strategy in Android 1 on the actual messages from the collected WhatsApp logs.…”

Section: Message Sizementioning

confidence: 99%

Energy Modelling and Fairness for Efficient Mobile Communication

Alonso¹,

Jon²

2016

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Energy consumption and its management have been clearly identified as a challenge in computing and communication system design, where energy economy is obviously of paramount importance for battery powered devices. This thesis addresses the energy efficiency of mobile communication at the user end in the context of cellular networks.We argue that energy efficiency starts by energy awareness and propose EnergyBox, a parametrised tool that enables accurate and repeatable energy quantification at the user end using real data traffic traces as input. EnergyBox offers an abstraction of the underlying states for operation of the wireless interfaces and allows to estimate the energy consumption for different operator settings and device characteristics. The tool is used throughout the thesis to quantify and reveal inefficient data communication patterns of widely used mobile applications.We consider two different perspectives in the search of energy-efficient solutions. From the application perspective, we show that systematically quantifying the energy consumption of design choices (e.g., communication patterns, protocols, and data formats) contributes to a significantly smaller energy footprint. From the system perspective, we devise a cross-layer solution that schedules packet transmissions based on the knowledge of the network parameters that impact the energy consumption of the handset. These attempts show that application level decisions require a better understanding of possible energy apportionment policies at system level.Finally, we study the generic problem of determining the contribution of an entity (e.g., application) to the total energy consumption of a given system (e.g., mobile device). We compare the state-of-the-art policies in terms of fairness leveraging cooperative game theory and analyse their required information and computational complexity. We show that providing incentives to reduce the total energy consumption of the system (as part of fairness) is tightly coupled to the policy selection. Our study provides guidelines to select an appropriate policy depending on the characteristics of the system. This work has been supported by the Swedish National Graduate School of Computer Science (CUGS), Sweden. v Populärvetenskaplig sammanfattningMobila tjänster som utnyttar kommunikationöver mobila nätverkär till stor nytta för mobiltelefonanvändare. Mobilanvändarna fokuserar på interaktionen med applikationerna och förväntar sig att kunna kommunicera när det behövs utan att tänka på batteriets livslängd. Dessvärre resulterar ineffektiv användning av trådlösa gränssnitt (t.ex. WiFi eller mobilnät) i snabb batteriurladdning. Energiförbrukning och energihanteringär en av de stora utmaningarna för informationsteknologin just nu. Den här avhandlingen behandlar energieffektiviteten i mobilkommunikation med fokus på de mobila enheterna och användarens upplevelse. Den energiförbrukning som orsakas av kommunikationär inte proportionell mot mängden data som skickas vilket beror på hur data skickasöver t...

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“…3) Selective Compression: According to the results reported in [20], objects that have an image or video content-type and also most objects with binary data (e.g. app/octet-stream) already are in compressed form and there is very little room for saving.…”

Section: ) Resource Profilermentioning

confidence: 99%

WebPro: A proxy-based approach for low latency web browsing on mobile devices

Sehati¹,

Ghaderi²

2015

2015 IEEE 23rd International Symposium on Quality of Service (IWQoS)

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Abstract-To load a webpage, a web browser first downloads the base HTML file of the page in order to discover the list of objects referenced in the page. This process takes roughly one round-trip time and constitutes a significant portion of the web browsing delay on mobile devices as wireless networks suffer from longer transmission and access delays compared to wired networks. In this work, we propose a solution for eliminating this initial delay, which is transparent to end systems, does not require modifying HTTP, and is well suited for web browsing on mobile devices. Our solution, called WebPro, relies on a network proxy that builds an up-to-date database of resource lists for the websites visited frequently by network users. The proxy resides in the wired part of the network, and hence can afford to pro-actively build and refresh the resource list database periodically. When a request for a webpage comes to the proxy, it simultaneously fetches the base HTML and all referenced objects required to render the webpage using the corresponding resource list stored in the local database. We have built a working prototype of WebPro and have conducted live experiments over WiFi and LTE networks. Our results show an average of 26% reduction in page load time for a mix of popular web sites chosen from categories such as news, sports and shopping. Moreover, in comparison to another best known proxy-based solution, WebPro provides delay reductions ranging from 5% to 51% for a variety of web sites.

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How to Reduce Smartphone Traffic Volume by 30%?

Cited by 26 publications

References 16 publications

Network assisted latency reduction for mobile web browsing

Network assisted latency reduction for mobile web browsing

Energy Modelling and Fairness for Efficient Mobile Communication

WebPro: A proxy-based approach for low latency web browsing on mobile devices

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