TY - GEN
T1 - Location Matters
T2 - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014
AU - Vaezpour, Seyed Yahya
AU - Wu, Kui
AU - Shoja, Gholamali C.
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2014
Y1 - 2014
N2 - Mobile Telecom Cloud (MTC) refers to cloud services provided by mobile telecommunication companies. Since mobile network operators support the last-mile Internet access to users, they have advantages over other cloud providers by providing users with better mobile connectivity and required QoS. As users continue to require higher bandwidth and lower delay, mobile companies can exploit their unique role in the Internet access to meet the increasing demands, which are hard to guarantee by other pure computing companies. In addition, to save cost in cloud services, mobile network operators are in a stronger position to utilize their existing infrastructure, which are geographically distributed in nature. The dilemma in meeting higher QoS demands while saving cost poses a big challenge to MTC providers. We tackle this challenge by strategically placing users' data in distributed switching centres to minimize the total system cost and maximize users' satisfaction. We formulate and solve the optimization problems using linear programming (LP)based branch-and-bound and LP with rounding. For scalability, we propose a similarity-based clustering method to group users into classes. Simulation results show that with the help of our optimization algorithms, we can effectively reduce the system cost and enhance users' QoS. © 2014 IEEE.
AB - Mobile Telecom Cloud (MTC) refers to cloud services provided by mobile telecommunication companies. Since mobile network operators support the last-mile Internet access to users, they have advantages over other cloud providers by providing users with better mobile connectivity and required QoS. As users continue to require higher bandwidth and lower delay, mobile companies can exploit their unique role in the Internet access to meet the increasing demands, which are hard to guarantee by other pure computing companies. In addition, to save cost in cloud services, mobile network operators are in a stronger position to utilize their existing infrastructure, which are geographically distributed in nature. The dilemma in meeting higher QoS demands while saving cost poses a big challenge to MTC providers. We tackle this challenge by strategically placing users' data in distributed switching centres to minimize the total system cost and maximize users' satisfaction. We formulate and solve the optimization problems using linear programming (LP)based branch-and-bound and LP with rounding. For scalability, we propose a similarity-based clustering method to group users into classes. Simulation results show that with the help of our optimization algorithms, we can effectively reduce the system cost and enhance users' QoS. © 2014 IEEE.
KW - Data Placement
KW - Mobile Telecom Cloud
KW - Quality of Service
UR - http://www.scopus.com/inward/record.url?scp=84903844141&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84903844141&origin=recordpage
U2 - 10.1109/MobileCloud.2014.34
DO - 10.1109/MobileCloud.2014.34
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781479925049
T3 - Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014
SP - 176
EP - 185
BT - Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014
PB - IEEE Computer Society
Y2 - 7 April 2014 through 10 April 2014
ER -
