Renewable-aware geographical load balancing of web applications for sustainable data centers

被引:70
作者
Toosi, Adel Nadjaran [1 ]
Qu, Chenhao [1 ]
de Assuncao, Marcos Dias [2 ]
Buyya, Rajkumar [1 ]
机构
[1] Univ Melbourne, Sch Comp & Informat Syst, Cloud Comp & Distributed Comp Lab, Melbourne, Vic 3010, Australia
[2] ENS Lyon, Inria, Lyon, France
基金
澳大利亚研究理事会;
关键词
Geographical load balancing; Web applications; Renewable energy; Green computing; Wikipedia; Auto-scaling; Brown energy; Green Energy; Cost saving; System prototype; CLOUD DATA CENTERS; ENERGY; ELECTRICITY; MANAGEMENT; SCALE;
D O I
10.1016/j.jnca.2017.01.036
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The ever-increasing demand for web applications deployed across multiple data centers results in large electricity costs for service providers and significant impact on the environment. This has motivated service providers to move towards more sustainable data centers powered by renewable or green sources of energy, such as solar or wind. However, efficient utilization of green energy to service web applications is a challenging problem due to intermittency and unpredictability of both application workload and renewable energy availability. One possible solution to reduce cost and increase renewable energy utilization is to exploit the spatio-temporal variations in on-site power and grid power prices by balancing the load among multiple data centers geographically distributed. In this paper, we propose a framework for reactive load balancing of web application requests among Geo-distributed sustainable data centers based on the availability of renewable energy sources on each site. A system prototype is developed, its underlying design and algorithms are described, and experiments are conducted with it using real infrastructure (Grid'5000 in France) and workload traces (real traffic to English Wikipedia). The experimental results demonstrate that our approach can reduce cost and brown energy usage with efficient utilization of green energy and without a priori knowledge of future workload, availability of renewable energy, and grid electricity prices.
引用
收藏
页码:155 / 168
页数:14
相关论文
共 42 条
[1]   Online server and workload management for joint optimization of electricity cost and carbon footprint across data centers [J].
Abbasi, Zahra ;
Pore, Madhurima ;
Gupta, Sandeep K. S. .
2014 IEEE 28TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM, 2014,
[2]  
Adnan M. A., 2012, 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), P188, DOI 10.1109/CLOUD.2012.45
[3]  
[Anonymous], 2011, P ACM SIGMETRICS JOI
[4]  
[Anonymous], 2011, P 2011 INT C HIGH PE
[5]  
[Anonymous], 2012, EuroSys, DOI [DOI 10.1145/2168836.2168843, 10.1145/2168836, DOI 10.1145/2168836]
[6]  
[Anonymous], 2013, SIGPLAN Not., DOI [DOI 10.1145/2499368.2451123, DOI 10.1145/2490301.2451123]
[7]  
[Anonymous], 2009, THESIS
[8]  
[Anonymous], P INT GREEN COMP C I
[9]  
[Anonymous], INT C TESTB RES INFR
[10]  
[Anonymous], ANTH SCAL EN EFF DAT