TY - GEN
T1 - Joint optimization of computing resources and data allocation for mobile edge computing (MEC)
T2 - 28th International Conference on Computer Communications and Networks, ICCCN 2019
AU - Shao, Xun
AU - Hasegawa, Go
AU - Kamiyama, Noriaki
AU - Liu, Zhi
AU - Masui, Hiroshi
AU - Ji, Yusheng
N1 - Funding Information:
The research was supported by ROIS NII Open Collaborative Research 2019-19FA03 andthe open collaborative research at National Institute of Informatics (NII) Japan (FY2018).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In recent years, the rapid development of cloud computing, networking, and mobile computing have substantially promoted mobile edge computing (MEC). Currently, most of the MEC services can be roughly divided into two categories: computation offloading to accelerate computation and save the energy of mobile devices and data services to shorten the latency between the content providers and the mobile users. Although emerging services such as user-specified transcoding and AR/VR systems require joint optimization of computing resource allocation and data placement, there is little research on it. In this work, we carry out an in-depth study on the interaction of computing resource allocation and data placement in mobile edge computing environments. Based on the analysis of the temporal and spatial characteristics of the two tasks, we propose a joint optimization framework that works with online manner. The proposed method employs hybrid timescales: a coarse-grained timescale to update the data placement and a fine-grained timescale to decide computing resource allocation. The proposed method achieves provable near-optimal performance without buffering users' requirements and does not assume that future trends in user requirements are predictable.
AB - In recent years, the rapid development of cloud computing, networking, and mobile computing have substantially promoted mobile edge computing (MEC). Currently, most of the MEC services can be roughly divided into two categories: computation offloading to accelerate computation and save the energy of mobile devices and data services to shorten the latency between the content providers and the mobile users. Although emerging services such as user-specified transcoding and AR/VR systems require joint optimization of computing resource allocation and data placement, there is little research on it. In this work, we carry out an in-depth study on the interaction of computing resource allocation and data placement in mobile edge computing environments. Based on the analysis of the temporal and spatial characteristics of the two tasks, we propose a joint optimization framework that works with online manner. The proposed method employs hybrid timescales: a coarse-grained timescale to update the data placement and a fine-grained timescale to decide computing resource allocation. The proposed method achieves provable near-optimal performance without buffering users' requirements and does not assume that future trends in user requirements are predictable.
KW - Data Placement
KW - Drift-Plus-Penalty Algorithm
KW - Mobile Edge Computing (MEC)
KW - Online Optimization
KW - Primal-Duality Algorithm
KW - Resource Allocation
UR - http://www.scopus.com/inward/record.url?scp=85073153353&partnerID=8YFLogxK
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U2 - 10.1109/ICCCN.2019.8847046
DO - 10.1109/ICCCN.2019.8847046
M3 - Conference contribution
AN - SCOPUS:85073153353
T3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
BT - ICCCN 2019 - 28th International Conference on Computer Communications and Networks
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 July 2019 through 1 August 2019
ER -