TY - GEN
T1 - Adaptive delay-tolerant scheduling for efficient cellular and WiFi usage
AU - Yetim, Ozlem Bilgir
AU - Martonosi, Margaret Rose
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/8
Y1 - 2014/10/8
N2 - Today's mobile devices offer multiple network connectivity options with orders of magnitude differences in cost, power, speed and reliability. Given this high variability, dynamic optimization of radio connectivity choice is promising. To increase the flexibility and payoff of such optimizations, we recognize that many applications have significant delay tolerance, which we exploit to schedule data transmissions. This paper proposes and evaluates techniques for cost-optimizing connectivity choice based on application delay tolerance, as well as on predictions of upcoming data usage and connectivity availability. We explore optimal (MILP-based) and heuristic approaches for optimizing this choice while abiding by application performance requirements. Our work studies how errors in predicting data usage or network connectivity impact each approach's success at cost reduction. We evaluate the technique through both simulation and a prototype on an Android smartphone. Overall, our technique averages more than 2× reduction in cellular data usage, and for some scenarios, the reduction is as high as 5×. In addition, the Android prototype also demonstrates the importance of accounting for radio switching overhead and TCP flow migration time.
AB - Today's mobile devices offer multiple network connectivity options with orders of magnitude differences in cost, power, speed and reliability. Given this high variability, dynamic optimization of radio connectivity choice is promising. To increase the flexibility and payoff of such optimizations, we recognize that many applications have significant delay tolerance, which we exploit to schedule data transmissions. This paper proposes and evaluates techniques for cost-optimizing connectivity choice based on application delay tolerance, as well as on predictions of upcoming data usage and connectivity availability. We explore optimal (MILP-based) and heuristic approaches for optimizing this choice while abiding by application performance requirements. Our work studies how errors in predicting data usage or network connectivity impact each approach's success at cost reduction. We evaluate the technique through both simulation and a prototype on an Android smartphone. Overall, our technique averages more than 2× reduction in cellular data usage, and for some scenarios, the reduction is as high as 5×. In addition, the Android prototype also demonstrates the importance of accounting for radio switching overhead and TCP flow migration time.
UR - http://www.scopus.com/inward/record.url?scp=84908892394&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84908892394&partnerID=8YFLogxK
U2 - 10.1109/WoWMoM.2014.6918945
DO - 10.1109/WoWMoM.2014.6918945
M3 - Conference contribution
AN - SCOPUS:84908892394
T3 - Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014, WoWMoM 2014
BT - Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014, WoWMoM 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2014
Y2 - 19 June 2014
ER -