Abstract
Recently, an increasing number of mobile users are eagerly using the cellular network in data applications. In particular, multimedia downloads generated by Internet-capable smart phones and other portable devices (such as tablets) has been widely recognized as the major source for strains in cellular networks, to a degree where service quality for all users is significantly impacted. Lately, patterns in both the content consumption as well as the Wi-Fi access by the users were alleged to be available. In this paper we introduce a technique to schedule the content for prefetching based on mobile usage patterns. This technique utilizes both a content profile as well as a bandwidth profile to schedule content for prefetching. Users can then use the cached version of the content in order to achieve a better user experience and reduce the peak-to-average ratio in mobile networks, especially during peak hours of the day. An experiment using real users traces was conducted and the results after applying the proposed evolutionary scheduling algorithm show that up to 70% of the user content requests can be fulfilled i.e. the content was successfully cached before request.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Shepard, C., Rahmati, A., Tossell, C., Kortum, P.: Livelab: measuring wireless networks and smartphone users in the field. ACM SIGMETRICS Perform (2010)
Song, C., et al.: Limits of predictability in human mobility. Science 327, 1018 (2010)
Larrabeiti, D., Romeral, R., Azcorra, A., Serrano, P.: Charging for web content pre-fetching in 3g networks. In: 4th International Workshop on Internet Charging and QoS Technology (2004)
El Gamal, H., Tadrous, J., Eryilmaz, A.: Proactive resource allocation: Turning predictable behavior into spectral gain. In: 8th Annual Allerton Conference on Communication, Control, and Computing (2010)
Handa, A.: Mobile data offload for 3g networks. Intellinet-Technology, Whitepaper (2009)
Johnston, M.: An evolutionary algorithm approach to multi-objective scheduling of space network communications. Intelligent Automation and Soft Computing (2008)
Kumar, A.: Multi-objective flow shop scheduling using metaheuristics. PHD thesis in Mechanical Engineering (2011)
Lee, K., Rhee, I.: Mobile data offloading: How much can wifi deliver. In: 10th Annual SIGCOMM Conference on Computer and Data Communication Networks (2010)
Pinedo, M.: Scheduling: Theory, algorithms and systems. Springer, New York (2012)
Cisco Technical Report: Report, Cisco Visual Networking Index (February 2012), http://www.cisco.com
Research, J.: Relief ahead for mobile data networks as 63% of traffic to move onto fixed networks via wifi and femtocells by 2015 (April 2011), http://juniperresearch.com/viewpressrelease.php?pr=240
Salomon, R.: Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. a survey of some theoretical and practical aspects of genetic algorithms. Biosystems (1996)
Suman, B., Kumar, P.: A survey of simulated annealing as a tool for single and multiobjective optimization. Journal of the Operational Research Society (2006)
Wang, Z., Xiaozhu Lin, F., Zhong, L., Chishtie, M.: How far can client-only solutions go for mobile browser speed? In: Int. World Wide Web Conf. (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shoukry, O.K., Fayek, M.B. (2013). Evolutionary Scheduling for Mobile Content Pre-fetching. In: Dediu, AH., MartÃn-Vide, C., Truthe, B., Vega-RodrÃguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2013. Lecture Notes in Computer Science, vol 8273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45008-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-45008-2_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45007-5
Online ISBN: 978-3-642-45008-2
eBook Packages: Computer ScienceComputer Science (R0)