User-profile-driven collaborative bandwidth sharing on mobile phones

The advent of smart phones, along with the paradigm shift towards cloud-based services, presents new challenges to the cellular backbone infrastructure. Cisco predicts that mobile data traffic will double every year through 2014, with a CAGR of 108% from 2009 to 2014, reaching 3.6 exabytes per month. We propose to exploit the potential of smart phones in proximity cooperatively, using their resources to reduce the demand on the cellular infrastructure, through a decision framework called RACE (Resource Aware Collaborative Execution). RACE enables the use of other mobile devices in the promixity as mobile data relays. RACE is a Markov Decision Process (MDP) optimization framework that takes user profiles and user preferences to determine the degree of collaboration. Both centralized and decentralized policies are developed and validated through simulation using real mobile usage traces. We implemented a simple prototype on a network of HTC G1 phones running the Android 1.5 operating system to demonstrate the viability of the system.

[1]  Jiye Yu,et al.  iLink: search and routing in social networks , 2007, KDD '07.

[2]  Margaret Martonosi,et al.  LOCALE: Collaborative Localization Estimation for Sparse Mobile Sensor Networks , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[3]  Byung-Gon Chun,et al.  Augmented Smartphone Applications Through Clone Cloud Execution , 2009, HotOS.

[4]  Jane Yung-jen Hsu,et al.  Collaborative Localization: Enhancing WiFi-Based Position Estimation with Neighborhood Links in Clusters , 2006, Pervasive.

[5]  Haiyun Luo,et al.  PERM: A Collaborative System for Residential Internet Access , 2006 .

[6]  Eric Horvitz,et al.  SearchTogether: an interface for collaborative web search , 2007, UIST.

[7]  Liviu Iftode,et al.  Context-aware Battery Management for Mobile Phones , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[8]  Ahmad Rahmati,et al.  Context-for-wireless: context-sensitive energy-efficient wireless data transfer , 2007, MobiSys '07.

[9]  Lenin Ravindranath,et al.  COMBINE: leveraging the power of wireless peers through collaborative downloading , 2007, MobiSys '07.

[10]  Takahiro Hara,et al.  A collaborative Web browsing system for multiple mobile users , 2004, Fourth Annual IEEE International Conference on Pervasive Computing and Communications (PERCOM'06).

[11]  Romit Roy Choudhury,et al.  Micro-Blog: sharing and querying content through mobile phones and social participation , 2008, MobiSys '08.

[12]  Venkatesh Akella,et al.  Markov decision process (MDP) framework for optimizing software on mobile phones , 2009, EMSOFT '09.

[13]  Steven D. Gribble,et al.  Flashproxy: transparently enabling rich web content via remote execution , 2008, MobiSys '08.

[14]  Kang G. Shin,et al.  Improving TCP performance over wireless networks with collaborative multi-homed mobile hosts , 2005, MobiSys '05.

[15]  Geoffrey H. Kuenning,et al.  Saving portable computer battery power through remote process execution , 1998, MOCO.