TY - GEN
T1 - A study on flat and hierarchical system deployment for edge computing
AU - Li, Dawei
AU - Dong, Boxiang
AU - Wang, En
AU - Zhu, Michelle
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/12
Y1 - 2019/3/12
N2 - In this paper, we consider the server allocation problem for edge computing system deployment where each edge cloud is modeled as an M/M/c queue. Our goal is to minimize the overall average system response time of application requests generated by all mobile devices/users. We consider two approaches for edge cloud deployment: The flat deployment, where all edge clouds are co-located with the base stations, and the hierarchical deployment, where edge clouds can be co-located with other system components besides the base stations. In flat deployment, we demonstrate that the allocation of edge cloud servers should be balanced across all the base stations, if the application request arrival rates at the base stations are equal to each other; if the application request arrival rates are not the same, we propose a Largest Weighted Reduction Time First (LWRTF) algorithm to assign servers to edge clouds. Numerical comparisons of the proposed algorithm against several other reasonably designed heuristics verify that algorithm LWRTF has very good performances in terms of minimizing the average system response time. We also conduct preliminary study on hierarchical deployment for edge computing and show that the hierarchical deployment approach has great potentials in minimizing the overall average system response time.
AB - In this paper, we consider the server allocation problem for edge computing system deployment where each edge cloud is modeled as an M/M/c queue. Our goal is to minimize the overall average system response time of application requests generated by all mobile devices/users. We consider two approaches for edge cloud deployment: The flat deployment, where all edge clouds are co-located with the base stations, and the hierarchical deployment, where edge clouds can be co-located with other system components besides the base stations. In flat deployment, we demonstrate that the allocation of edge cloud servers should be balanced across all the base stations, if the application request arrival rates at the base stations are equal to each other; if the application request arrival rates are not the same, we propose a Largest Weighted Reduction Time First (LWRTF) algorithm to assign servers to edge clouds. Numerical comparisons of the proposed algorithm against several other reasonably designed heuristics verify that algorithm LWRTF has very good performances in terms of minimizing the average system response time. We also conduct preliminary study on hierarchical deployment for edge computing and show that the hierarchical deployment approach has great potentials in minimizing the overall average system response time.
KW - Edge computing
KW - average system response time
KW - edge cloud
KW - flat deployment
KW - hierarchical deployment
UR - http://www.scopus.com/inward/record.url?scp=85063911404&partnerID=8YFLogxK
U2 - 10.1109/CCWC.2019.8666572
DO - 10.1109/CCWC.2019.8666572
M3 - Conference contribution
AN - SCOPUS:85063911404
T3 - 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019
SP - 163
EP - 169
BT - 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019
A2 - Chakrabarti, Satyajit
A2 - Saha, Himadri Nath
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2019
Y2 - 7 January 2019 through 9 January 2019
ER -