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

被引:10
|
作者
Li, Xiaole [1 ]
Wang, Hua [2 ]
Yi, Shanwen [1 ]
Liu, Shuai [3 ]
Zhai, Linbo [4 ]
Jiang, Chuanqi [1 ]
机构
[1] Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Shandong, Peoples R China
[2] Shandong Univ, Sch Software, Jinan 250100, Shandong, Peoples R China
[3] Beihang Univ, Sch Comp, Beijing 100083, Peoples R China
[4] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, Peoples R China
基金
中国国家自然科学基金;
关键词
Disaster-and-evacuation-aware facility location; multi-objective optimization; expected disaster loss; evacuation capability; VIRTUAL MACHINE PLACEMENT; CLOUD; NETWORK;
D O I
10.1109/ACCESS.2019.2909084
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:48196 / 48208
页数:13
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