TY - GEN
T1 - Service Placement and Request Scheduling for Data-intensive Applications in Edge Clouds
AU - Farhadi, Vajiheh
AU - Mehmeti, Fidan
AU - He, Ting
AU - Porta, Tom La
AU - Khamfroush, Hana
AU - Wang, Shiqiang
AU - Chan, Kevin S.
N1 - Funding Information:
This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
PY - 2019/4
Y1 - 2019/4
N2 - Mobile edge computing allows wireless users to exploit the power of cloud computing without the large communication delay. To serve data-intensive applications (e.g., augmented reality, video analytics) from the edge, we need, in addition to CPU cycles and memory for computation, storage resource for storing server data and network bandwidth for receiving user-provided data. Moreover, the data placement needs to be adapted over time to serve time-varying demands, while considering system stability and operation cost. We address this problem by proposing a two-time-scale framework that jointly optimizes service (data code) placement and request scheduling, under storage, communication, computation, and budget constraints. We fully characterize the complexity of our problem by analyzing the hardness of various cases. By casting our problem as a set function optimization, we develop a polynomial-time algorithm that achieves a constant-factor approximation under certain conditions. Extensive synthetic and trace-driven simulations show that the proposed algorithm achieves 90% of the optimal performance.
AB - Mobile edge computing allows wireless users to exploit the power of cloud computing without the large communication delay. To serve data-intensive applications (e.g., augmented reality, video analytics) from the edge, we need, in addition to CPU cycles and memory for computation, storage resource for storing server data and network bandwidth for receiving user-provided data. Moreover, the data placement needs to be adapted over time to serve time-varying demands, while considering system stability and operation cost. We address this problem by proposing a two-time-scale framework that jointly optimizes service (data code) placement and request scheduling, under storage, communication, computation, and budget constraints. We fully characterize the complexity of our problem by analyzing the hardness of various cases. By casting our problem as a set function optimization, we develop a polynomial-time algorithm that achieves a constant-factor approximation under certain conditions. Extensive synthetic and trace-driven simulations show that the proposed algorithm achieves 90% of the optimal performance.
UR - http://www.scopus.com/inward/record.url?scp=85068214325&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068214325&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM.2019.8737368
DO - 10.1109/INFOCOM.2019.8737368
M3 - Conference contribution
AN - SCOPUS:85068214325
T3 - Proceedings - IEEE INFOCOM
SP - 1279
EP - 1287
BT - INFOCOM 2019 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Conference on Computer Communications, INFOCOM 2019
Y2 - 29 April 2019 through 2 May 2019
ER -