Title :
A Bandwidth-Conscious Caching Scheme for Mobile Devices
Author :
Thyamagondlu, Badari N. ; Chu, Victor W. ; Wong, Raymond K.
Author_Institution :
Sch. of Comput. Sci. & Eng., Univ. of New South Wales, Sydney, NSW, Australia
fDate :
June 27 2013-July 2 2013
Abstract :
While a substantial amount of big data are consumed via mobile devices, accessing content via wireless data connections on mobile devices has its own set of challenges. Among these challenges, speed of data transfer is usually our first priority. Although there are many fast data connections available for Web surfing (3G, LTE etc.), the actual connection speed could vary significantly among different regions where fast connections may not be always available. As a result, the user experience of viewing information varies with different type of data connections in different locations. This paper proposes utilising the Type Of Data Connection (Bandwidth) to determine whether a dataset needs to be cached or pre-fetched to reduce the response time and thereby providing a better user experience. The role of the mobile device owner will form the basis of dataset construction criteria by using the technique of role mining. As the mobile devices are not confined to a particular space, an effort to trace the owner´s movement determines where the owner with the device is heading towards. This helps to identify the different connection speed patterns along the owner´s path, so that different caching or pre-fetching strategy can be deployed beforehand to aim for consistent quality of services.
Keywords :
Internet; bandwidth allocation; cache storage; data mining; mobile computing; Web surfing; bandwidth-conscious caching scheme; big data; connection speed patterns; data connection type; data transfer speed; dataset construction criteria; mobile devices; prefetching strategy; role mining technique; Context; Data mining; Educational institutions; Mobile communication; Mobile handsets; Servers; Trajectory;
Conference_Titel :
Big Data (BigData Congress), 2013 IEEE International Congress on
Conference_Location :
Santa Clara, CA
Print_ISBN :
978-0-7695-5006-0
DOI :
10.1109/BigData.Congress.2013.20