RT Journal Article
T1 Three-Tier Capacity and Traffic Allocation for Core, Edges, and Devices for Mobile Edge Computing
A1 Lin, Ying-Dar
A1 Lai, Yuan-Cheng
A1 Huang, Jian-Xun
A1 Chien, Hsu-Tung
AB In order to satisfy the 5G requirements of ultra-low latency, mobile edge computing (MEC)-based architecture, composed of three-tier nodes, core, edges, and devices, is proposed. In MEC-based architecture, previous studies focused on the controlplane issue, i.e., how to allocate traffic to be processed at different nodes to meet this ultra-low latency requirement. Also important is how to allocate the capacity to different nodes in the management plane so as to establish a minimal-capacity network. The objectives of this paper is to solve two problems: 1) to allocate the capacity of all nodes in MEC-based architecture so as to provide a minimal-capacity network and 2) to allocate the traffic to satisfy the latency percentage constraint, i.e., at least a percentage of traffic satisfying the latency constraint. In order to achieve these objectives, a two-phase iterative optimization (TPIO) method is proposed to try to optimize capacity and traffic allocation in MEC-based architecture. TPIO iteratively uses two phases to adjust capacity and traffic allocation respectively because they are tightly coupled. In the first phase, using queuing theory calculates the optimal traffic allocation under fixed allocated capacity, while in the second phase, allocated capacity is further reduced under fixed traffic allocation to satisfy the latency percentage constraint. Simulation results show that MEC-based architecture can save about 20.7% of capacity of two-tier architecture. Further, an extra 12.2% capacity must be forfeited when the percentage of satisfying latency is 90%, compared to 50%.
PB IEEE
SN 1932-4537
YR 2018
FD 2018-07-03
LK https://hdl.handle.net/10016/28282
UL https://hdl.handle.net/10016/28282
LA eng
NO This work was supported in part by H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant number 761586), andMinistry of Science and Technology, Taiwan for financially supporting thisresearch under Contract No. MOST 106-2218-E-009-018.
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RD 20 may. 2024