Please wait a minute...
Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front Comput Sci Chin    0, Vol. Issue () : 353-368    https://doi.org/10.1007/s11704-011-0369-3
Green challenges to system software in data centers
Yuzhong SUN1(), Yiqiang ZHAO1, Ying SONG1, Yajun YANG1,2, Haifeng FANG1,2, Hongyong ZANG1,2, Yaqiong LI1,2, Yunwei GAO1
1. Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 2. Graduate University of Chinese Academy of Sciences, Beijing 100190, China
 Download: PDF(735 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

With the increasing demand and the wide application of high performance commodity multi-core processors, both the quantity and scale of data centers grow dramatically and they bring heavy energy consumption. Researchers and engineers have applied much effort to reducing hardware energy consumption, but software is the true consumer of power and another key in making better use of energy. System software is critical to better energy utilization, because it is not only the manager of hardware but also the bridge and platform between applications and hardware. In this paper, we summarize some trends that can affect the efficiency of data centers. Meanwhile, we investigate the causes of software inefficiency. Based on these studies, major technical challenges and corresponding possible solutions to attain green system software in programmability, scalability, efficiency and software architecture are discussed. Finally, some of our research progress on trusted energy efficient system software is briefly introduced.

Keywords green software      multi-core      data center      power efficient system software     
Corresponding Author(s): SUN Yuzhong,Email:yuzhongsun@ict.ac.cn   
Issue Date: 05 September 2011
 Cite this article:   
Yuzhong SUN,Yiqiang ZHAO,Ying SONG, et al. Green challenges to system software in data centers[J]. Front Comput Sci Chin, 0, (): 353-368.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-011-0369-3
https://academic.hep.com.cn/fcs/EN/Y0/V/I/353
Fig.1  System software’s function
Fig.2  Processor design trend (stated by max transistor integrity and peak performance) and memory (stated by bandwidth) []
End use component2000 /billion kWhTotal/%2006 /billion kWhTotal/%2000-2006 CAGR

Compound annual growth rate

/%
Site infrastructure14.15030.75014
Network equipment1.453.0514
Storage1.143.2520
High-end servers1.141.525
Mid-range servers2.592.24-2
Volume servers8.02920.93417
Total28.261.414
Tab.1  Electricity use by end-use component in United States (2000 to 2006) []
Fig.3  Walls against high energy efficiency
Fig.4  Diagram of idle inefficiency and scaling inefficiency of software []
Fig.5  Brief call graph of Linux read system call
Fig.6  Key technologies of capacity computing
Fig.7  Three tiered scheduling scheme
Fig.8  Brief call graph of Linux read system call
ResIntervalThresholdImpDeg
Ref [32]CPU10sFixed28%41%
TRainbowCPU, mem1s(CPU), 5s(mem)Auto adjusted19%2%
Tab.2  TRainbow vs. Reference []
Fig.9  Workloads offered to (a) the dedicated servers are consolidated to (b) the consolidated servers
InputOutput
λwλdBMN
8502000.4342
12503300.4363
17004000.4384
21005000.43105
Tab.3  Calculated number of dedicated servers () and corresponding consolidated servers ()
Fig.10  Six exclusive servers consolidated to 2/3/4 shared servers
Fig.11  Eight exclusive servers consolidate to 4 shared servers
Fig.12  Overview of the architecture of TRainbow []
1 Poess M, Nambiar R O. Energy cost, the key challenge of today's data centers: a power consumption analysis of TPC-C results. Proceedings of the VLDB Endowment , 2008, 1(2): 1229-1240
2 Wirth N. A plea for lean software. Computer , 1995, 28(2): 64-68
doi: 10.1109/2.348001
3 Owens J D, Luebke D, Govindaraju N, Harris M, Krüger J, Lefohn A E, Purcell T J. A survey of general-purpose computation on graphics hardware. In: Proceedings of 2005 Annual Conference of the European Association for Computer Graphics . 2005, 21-51
4 Foster I, Zhao Y, Raicu I, Lu S. Cloud computing and grid computing 360-degree compared. In: Proceedings of 2008 Grid Computing Environments Workshop . 2008, 1-10
5 Kogge P, Bergman K, Borkar S, Campbell D, Carlson W, Dally W, Denneau M, Franzon P, Harrod W, Hill K, Hiller J, Karp S, Keckler S, Klein D, Lucas R, Richards M, Scarpelli A, Scot S, Snavely A, Sterling T, Williams R S, Yelick K.Exascale computing study: technology challenges in achieving exascale systems. DARPA Report . 2008.
6 Moore G E. Progress in digital integrated electronics. In: Proceedings of IEEE Digital Integrated Electronic Device Meeting . 1975, 11-13
7 Kish L B. End of Moore’s law: thermal (noise) death of integration in micro and nano electronics. Physics Letters A , 2002, 305(3-4): 144-149
doi: 10.1016/S0375-9601(02)01365-8
8 Lloyd S. Ultimate physical limits to computation. Nature , 2000, 406(6799): 1047-1054
doi: 10.1038/35023282 pmid:10984064
9 Manferdelli J. Supercomputing and mass market desktops. ACM Super Computing , 2007
10 Seiler L, Carmean D, Sprangle E, Forsyth T, Abrash M, Dubey P, Junkins S, Lake A, Suqerman J, Cavin R, Espasa R, Grochowski E, Juan T, Hanrahan P. Larrabee: a many-core x86 architecture for visual computing. ACM Transactions on Graphics , 2008, 27(3): 1-15
doi: 10.1145/1360612.1360617
11 Geer D. Chip makers turn to multicore processors. Computer , 2005, 38(5): 11-13
doi: 10.1109/MC.2005.160
12 Environmental Protection Agency. EPA report to Congress on server and data center energy efficiency. 2007, http://www.energystar.gov/ia/partners/prod_development/downloads/EPA_Datacenter_Report_Congress_Final1.pdf
13 Brown D J, Reams C. Toward energy-efficient computing. Communications of the ACM , 2010, 53(3): 50-58
doi: 10.1145/1666420.1666438
14 Kant K. Data center evolution: a tutorial on state of the art, issues, and challenges. Computer Networks , 2009, 53(17): 2939-2965
doi: 10.1016/j.comnet.2009.10.004
15 Dally W J, Balfour J, Black-Shaffer D, Chen J, Harting R C, Parikh V, Park J, Sheffield D. Efficient embedded computing. Computer , 2008, 41(7): 27-32
doi: 10.1109/MC.2008.224
16 Chu S. The energy problem and Lawrence Berkeley National Laboratory. Talk given to the California Air Resources Board . 2008
17 Brown D, Furber S. A conversation with Steve Furber. ACM Queue: Tomorrow's Computing Today , 2010, 8(2): 1-8
doi: 10.1145/1716383.1716385
18 Saxe E. Power-efficient software. Communications of the ACM , 2010, 53(2): 44-48
doi: 10.1145/1646353.1646370
19 Chamberlain B L, Callahan D, Zima H P. Parallel programmability and the Chapel language. International Journal of High Performance Computing Applications , 2007, 21(3): 291-312
doi: 10.1177/1094342007078442
20 Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters. Communications of the ACM , 2008, 51(1): 107-113
doi: 10.1145/1327452.1327492
21 Fatahalian K, Horn D R, Knight T J, Leem L, Houston M, Park J Y, Erez M, Ren M, Aiken A, Dally W J, Hanrahan P. Sequoia: programming the memory hierarchy. In: Proceedings of 2006 ACM/IEEE Conference on Supercomputing . 2006
22 Hoisie A, Getov V. Extreme-scale computing-where ‘just more of the same’ does not work. Computer , 2009, 42(11): 24-26
doi: 10.1109/MC.2009.354
23 Torrellas J. Architectures for extreme-scale computing. Computer , 2009, 42(11): 28-35
doi: 10.1109/MC.2009.341
24 Barker K J, Davis K, Hoisie A, Kerbyson D J, Lang M, Pakin S, Sancho J C. Using performance modeling to design large-scale systems. Computer , 2009, 42(11): 42-49
doi: 10.1109/MC.2009.372
25 Chase J S, Anderson D C, Thakar P N, Vahdat A M, Doyle R P. Managing energy and server resources in hosting centers. In: Proceedings of 18th ACM Symposium on Operating Systems Principles . 2001, 103-116
26 Zeng H, Ellis C S, Lebeck A R, Vahdat A. ECOSystem: managing energy as a first class operating system resource. In: Proceedings of 10th International Conference on Architectural Support for Programming Languages and Operating Systems . 2002, 123-132
27 Song Y, Zhang Y W, Sun Y Z, Shi W S. Utility analysis for internet-oriented server consolidation in VM-based data centers. In: Proceedings of 2009 IEEE International Conference on Cluster Computing . 2009, 1-10
28 Padala P, Hou K Y, Shin K G, Zhu X, Uysal M, Wang Z, Singhal S, Merchant A. Automated control of multiple virtualized resources. In: Proceedings of 4th ACM European conference on Computer systems . 2009, 13-26
29 Elnozahy M, Kistler M, Rajamony R. Energy conservation policies for web servers. In: Proceedings of the 4th USENIX Symposium on Internet Technologies and Systems . 2003, 99-112
30 Song Y, Wang H, Li Y Q, Feng B Q, Sun Y Z. Multi-tiered on-demand resource scheduling for VM-based data center. In: Proceedings of 9th IEEE/ACM International Symposium on Cluster Computing and the Grid . 2009, 148-155
31 Song Y, Li Y Q, Wang H, Zhang Y F, Feng B Q, Zang H Y, Sun Y Z. A service-oriented priority-based resource scheduling scheme for virtualized utility computing. In: Proceedings of 15th International Conference on High Performance Computing . 2008, 220-231
32 Padala P, Shin K, Zhu X, Uysal M, Wang Z, Singhal S, Merchant A, Salem K. Adaptive control of virtualized resources in utility computing environments. In: Proceedings of 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems . 2007, 289-302
33 Sun Y Z, Fang H F, Song Y, Du L, Zhang K, Zang H Y, Li Y Q, Yang Y J, Ao R, Huang Y B, Gao Y W. TRainbow: a new trusted virtual machine based platform. Frontiers of Computer Science in China , 2010, 4(1): 47-64
doi: 10.1007/s11704-009-0076-5
[1] Yudong QIN, Deke GUO, Lailong LUO, Geyao CHENG, Zeliu DING. Design and optimization of VLC based small-world data centers[J]. Front. Comput. Sci., 2019, 13(5): 1034-1047.
[2] Hui DOU, Yong QI. An online electricity cost budgeting algorithm for maximizing green energy usage across data centers[J]. Front. Comput. Sci., 2017, 11(4): 661-674.
[3] Jian LIU,Huanqing DONG,Junwei ZHANG,Zhenjun LIU,Lu XU. HWM: a hybrid workload migration mechanism of metadata server cluster in data center[J]. Front. Comput. Sci., 2017, 11(1): 75-87.
[4] Ruihong LIN,Yuhui DENG. Allocating workload to minimize the power consumption of data centers[J]. Front. Comput. Sci., 2017, 11(1): 105-118.
[5] Chuang LIN,Min YAO,Yin LI. Joint study on VMs deployment, assignment and migration in geographically distributed data centers[J]. Front. Comput. Sci., 2016, 10(3): 559-573.
[6] Bing YU,Yanni HAN,Hanning YUAN,Xu ZHOU,Zhen XU. A cost-effective scheme supporting adaptive service migration in cloud data center[J]. Front. Comput. Sci., 2015, 9(6): 875-886.
[7] Lailong LUO,Deke GUO,Wenxin LI,Tian ZHANG,Junjie XIE,Xiaolei ZHOU. Compound graph based hybrid data center topologies[J]. Front. Comput. Sci., 2015, 9(6): 860-874.
[8] Kaishun WU, Jiang XIAO, Lionel M. NI. Rethinking the architecture design of data center networks[J]. Front Comput Sci, 2012, 6(5): 596-603.
[9] Zhenyong CHEN, Wei FAN, Zhang XIONG, Pingan ZHANG, Lixin LUO, . Visual data security and management for smart cities[J]. Front. Comput. Sci., 2010, 4(3): 386-393.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed