TY - GEN
T1 - Numerical and simulation verification for optimal server allocation in edge computing
AU - Li, Dawei
AU - Asikaburu, Chigozie
AU - Shang, Jiacheng
AU - Wang, Ning
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/21
Y1 - 2021/4/21
N2 - In this paper, we consider the server allocation problem in edge computing. We consider a system model where there are a number of areas or locations, each of which has an associated Base Station (BS), where we can deploy an edge cloud with multiple servers. Each edge cloud will process application requests received at the corresponding BS from users in the corresponding area. The system manager/operator has a budget to deploy a given number of servers to the BSs. Our goal is to come up with a server allocation plan, i.e., how many servers to deploy at each of the BSs, such that the overall average turnaround time of application requests generated by all the users is minimized. In order to achieve the optimal solution for the problem, we resort to queueing theory and model each edge cloud as an M/M/c queue. Analysis on the problem motivates a Largest Weighted Reduction Time First (LWRTF) algorithm to assign servers to edge clouds. Numerical comparisons among various algorithms verify that Algorithm LWRTF has near-optimal performances in terms of minimizing the average turnaround time. Simulation results using the CloudSim Plus simulation tool also verify that Algorithm LWRTF achieves better performances compared to other reasonably designed heuristic algorithms.
AB - In this paper, we consider the server allocation problem in edge computing. We consider a system model where there are a number of areas or locations, each of which has an associated Base Station (BS), where we can deploy an edge cloud with multiple servers. Each edge cloud will process application requests received at the corresponding BS from users in the corresponding area. The system manager/operator has a budget to deploy a given number of servers to the BSs. Our goal is to come up with a server allocation plan, i.e., how many servers to deploy at each of the BSs, such that the overall average turnaround time of application requests generated by all the users is minimized. In order to achieve the optimal solution for the problem, we resort to queueing theory and model each edge cloud as an M/M/c queue. Analysis on the problem motivates a Largest Weighted Reduction Time First (LWRTF) algorithm to assign servers to edge clouds. Numerical comparisons among various algorithms verify that Algorithm LWRTF has near-optimal performances in terms of minimizing the average turnaround time. Simulation results using the CloudSim Plus simulation tool also verify that Algorithm LWRTF achieves better performances compared to other reasonably designed heuristic algorithms.
UR - https://www.scopus.com/pages/publications/85106719520
UR - https://www.scopus.com/pages/publications/85106719520#tab=citedBy
U2 - 10.1109/IEMTRONICS52119.2021.9422538
DO - 10.1109/IEMTRONICS52119.2021.9422538
M3 - Conference contribution
AN - SCOPUS:85106719520
T3 - 2021 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2021 - Proceedings
BT - 2021 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2021 - Proceedings
A2 - Chakrabarti, Satyajit
A2 - Paul, Rajashree
A2 - Gill, Bob
A2 - Gangopadhyay, Malay
A2 - Poddar, Sanghamitra
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2021
Y2 - 21 April 2021 through 24 April 2021
ER -