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Disaster-and-Evacuation-Aware Backup Datacenter Placement Based on Multi-Objective Optimization

delete2019-01-01
delete12
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OA
AI
X
Xiaole Li
王
王桦 (Hua Wang) *
S
Shuai Liu
C
Chuanqi Jiang
DOI:10.1109/ACCESS.2019.2909084delete
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摘要

摘要

En 中文
Backup datacenters provide massive data storage and access services, and their failure may result in huge economic losses. So their location selection requires low damage risk and high evacuation capability simultaneously. But previous works on backup datacenter placement have not jointly considered these two factors from the viewpoint of traffic engineering and might result in the unnecessary loss in case of disaster. In this paper, with the global view of network resources in the software defined network scenarios, we propose a new disaster-and-evacuation-aware backup datacenter placement strategy. To reduce backup loss risk and apply rapid post-disaster evacuation, we jointly consider expected disaster loss and evacuation latency and formulate a new disaster-and-evacuation-aware facility location problem (NP-hard) which is multi-objective. To obtain the solution according to the disaster situation assessment, we propose a disaster-and-evacuation-aware multi-objective optimization algorithm. We optimize multiple objectives owning different coefficients in different disaster situations. We introduce location-output-capability, backup-evacuation-latency, Pareto-recommendation-degree, and node-damage-loss to guide solution searching. We prune the external set according to fitness-deviation-ratio to improve convergence speed and computation efficiency of the algorithm. Through extensive simulations, we demonstrate that our algorithm is efficient and promising with less expected disaster loss and higher evacuation capability simultaneously.
Keyword:
Disaster-and-evacuation-aware facility location
multi-objective optimization
expected disaster loss
evacuation capability
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IEEE Access
IF:
3.6
论文数:
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被引数:
29.4W

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Beihang University
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5.2W
论文数: 4.1W
被引数: 37
S
shandong university
学者数:
9.5W
论文数: 6.4W
被引数: 94
S
shandong normal university
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1.0W
论文数: 8.2K
被引数: 3
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