Energy-Efficient Cloud Servers: an Overview of Solutions and Architectures


Department of Computer Engineering, Sahneh Branch, Islamic Azad University, Sahneh, Iran


Because of the changing from traditional paper-based systems to a digital systems and the evolution of online storage and cloud computing, datacenters are becoming fundamental to almost every sector of the economy and the main energy consumers in the universe. With the acceptance of High Performance Computing (HPC) and cloud computing, the area and number of cloud datacenter grow quickly; hence, it has become significant to optimize datacenter energy consumption. With modern energy efficient design in cloud datacenter infrastructure and cooling devices, active items like servers and cooling devices consume most of the power. In many researches, it was shown that cloud datacenters consume enormous energy; therefore researchers are looking for metrics of energy efficiency. The goal of energy efficient researches is to sufficiently take benefit of reachable resources such as processors and network devices, or to reduce thermal cooling expenses and energy consumption. In this paper, we discuss the state of the art researches and provide an overview of energy efficient solutions and architectures for cloud servers in processor design, power distribution unit, and server cooling management.


[1] Yang, T., Pen, H., Li, W., Yuan, D., & Zomaya, A. Y.(2017). An energy-efficient storage strategy for cloud
datacenters based on variable K-coverage of a hypergraph. IEEE Transactions on Parallel and Distributed Systems, 28(12), 3344-3355.
[2] Gu, C., Li, Z., Huang, H., & Jia, X. (2018). Energy Efficient Scheduling of Servers with Multi-Sleep
Modes for Cloud Data Center. IEEE Transactions on Cloud Computing.
[3] Zeadally, S., Khan, S. U., & Chilamkurti, N. (2012).Energy-efficient networking: past, present, and
future. The Journal of Supercomputing, 62(3), 1093-1118.
[4] Brown, R. (2008). Report to congress on server and data center energy efficiency: Public law 109-431.
[5] Koomey, J. (2011). Growth in data center electricity use 2005 to 2010. A report by Analytical Press, completed
at the request of The New York Times, 9.
[6] Meijer, G. I. (2010). Cooling energy-hungry data centers. Science, 328(5976), 318-319.
[7] G. Group, Forecast: Data centers, worldwide, 2010–2015, Accessed March 2013. [Online]. Available:
[8] Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 1(1), 7-18.
[9] Nasri, A., Fathy, M., & Broumandnia, A. (2018). An energy-efficient 3D-stacked STT-RAM cache
architecture for cloud processors: the effect on emerging scale-out workloads. The Journal of
Supercomputing, 74(4), 1547-1561. [10] Rong, H.,Zhang, H., Xiao, S., Li, C., & Hu, C. (2016).
Optimizing energy consumption for data centers. Renewable and Sustainable Energy Reviews, 58, 674-691.
[11] Johnson, P., & Marker, T. (2009). Data centre energy efficiency product profile. Pitt & Sherry, report to
equipment energy efficiency committee (E3) of The Australian Government Department of the
Environment, Water, Heritage and the Arts (DEWHA).
[12] Rong, H., Zhang, H., Xiao, S., Li, C., & Hu, C. (2016). Optimizing energy consumption for data
centers. Renewable and Sustainable Energy Reviews, 58, 674-691.
[13] Chabarek, J., Sommers, J., Barford, P., Estan, C.,Tsiang, D., & Wright, S. (2008, April). Power
awareness in network design and routing.In INFOCOM 2008. The 27th Conference on Computer
Communications. IEEE (pp. 457-465). IEEE.
[14] Fischer, A., Botero, J. F., Beck, M. T., De Meer, H., & Hesselbach, X. (2013). Virtual network embedding: A
survey. IEEE Communications Surveys & Tutorials, 15(4), 1888-1906.
[15] Mahadevan, P., Banerjee, S., Sharma, P., Shah, A., & Ranganathan, P. (2011). On energy efficiency for
enterprise and data center networks. IEEE Communications Magazine, 49(8).
[16] Wang, X., Yao, Y., Wang, X., Lu, K., & Cao, Q. (2012,March). Carpo: Correlation-aware power optimization
in data center networks. In INFOCOM, 2012 Proceedings IEEE (pp. 1125-1133). IEEE.
[17] Zheng, K., Wang, X., Li, L., & Wang, X. (2014, April).Joint power optimization of data center network and
servers with correlation analysis. In INFOCOM, 2014 Proceedings IEEE(pp. 2598-2606). IEEE.
[18] Gao, Y., Guan, H., Qi, Z., Wang, B., & Liu, L. (2013).Quality of service aware power management for
virtualized data centers. Journal of Systems
Architecture, 59(4-5), 245-259.
[19] Jeffers, J., & Reinders, J. (2013). Intel Xeon Phi coprocessor high performance programming. Newnes.
[20] Ellison, B., & Minas, L. (2009). The Problem of Power Consumption in Servers. Energy Efficiency for
Information Technology, 1-17.
[21] Moreno, I. S., & Xu, J. (2012, April). Neural networkbased overallocation for improved energy-efficiency in
real-time cloud environments.In Object/Component/Service-Oriented Real-Time
Distributed Computing (ISORC), 2012 IEEE 15th International Symposium on (pp. 119-126). IEEE.
[22] Isci, C., & Martonosi, M. (2003, December). Runtime power monitoring in high-end processors: Methodology and empirical data. In Proceedings of the 36th annual IEEE/ACM International Symposium on
Microarchitecture (p. 93). IEEE Computer Society.[23] Joseph, R., & Martonosi, M. (2001, August). Run-time
power estimation in high performance microprocessors.In Proceedings of the 2001 international symposium on Low power electronics and design (pp. 135-140).ACM.
[24] Hamilton, J. (2009, January). Cooperative expendable micro-slice servers (CEMS): low cost, low power
servers for internet-scale services. In Conference on Innovative Data Systems Research (CIDR’09)(January
[25] Kgil, T., Saidi, A., Binkert, N., Reinhardt, S., Flautner,K., & Mudge, T. (2008). PicoServer: Using 3D
stacking technology to build energy efficient servers. ACM Journal on Emerging Technologies in Computing Systems (JETC), 4(4), 16.
[26] Ranganathan, P., Leech, P., Irwin, D., & Chase, J.(2006, June). Ensemble-level power management for
dense blade servers. In ACM SIGARCH Computer Architecture News (Vol. 34, No. 2, pp. 66-77). IEEE
Computer Society. [27] Rountree, B., Lowenthal, D. K., Funk, S., Freeh, V. W., De Supinski, B. R., &
Schulz, M. (2007, November). Bounding energy consumption in large-scale MPI programs.In Proceedings of the 2007 ACM/IEEE conference on Supercomputing (p. 49). ACM.
[28] Aroca, R. V., & Gonçalves, L. M. G. (2012). Towards green data centers: A comparison of x86 and ARM
architectures power efficiency. Journal of Parallel and Distributed Computing, 72(12), 1770-1780.
[29] Huang, S., & Feng, W. (2009, May). Energy-efficient cluster computing via accurate workload
characterization. In Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster
Computing and the Grid (pp. 68-75). IEEE Computer Society.
[30] Lim, M. Y., Freeh, V. W., & Lowenthal, D. K. (2011).Adaptive, transparent CPU scaling algorithms
leveraging inter-node MPI communication regions. Parallel Computing, 37(10-11), 667-683.
[31] Springer, R., Lowenthal, D. K., Rountree, B., & Freeh,V. W. (2006, March). Minimizing execution time in
MPI programs on an energy-constrained, powerscalable cluster. In Proceedings of the eleventh ACM
SIGPLAN symposium on Principles and practice of parallel programming (pp. 230-238). ACM.
[32] Rizvandi, N. B., Taheri, J., & Zomaya, A. Y. (2011).Some observations on optimal frequency selection in
DVFS-based energy consumptionminimization. Journal of Parallel and Distributed Computing, 71(8), 1154-1164.
[33] Etinski, M., Corbalán, J., Labarta, J., & Valero, M.(2012). Understanding the future of energyperformance
trade-off via DVFS in HPC environments. Journal of Parallel and Distributed Computing, 72(4), 579-590.
[34] Wang, L., Khan, S. U., Chen, D., KoƂOdziej, J., Ranjan,R., Xu, C. Z., & Zomaya, A. (2013). Energy-aware
parallel task scheduling in a cluster. Future Generation Computer Systems, 29(7), 1661-1670.
[35] Vishnu, A., Song, S., Marquez, A., Barker, K.,Kerbyson, D., Cameron, K., & Balaji, P. (2013).
Designing energy efficient communication runtime systems: a view from PGAS models. The Journal of
Supercomputing, 63(3), 691-709.
[36] Banerjee, A., Mukherjee, T., Varsamopoulos, G., & Gupta, S. K. (2010, August). Cooling-aware and
thermal-aware workload placement for green HPC data centers. In Green Computing Conference, 2010
International (pp. 245-256). IEEE.
[37] Bash, C., & Forman, G. (2007, June). Cool Job Allocation: Measuring the Power Savings of Placing
Jobs at Cooling-Efficient Locations in the Data Center.In USENIX Annual Technical Conference (Vol. 138, p.
[38] Merkel, A., & Bellosa, F. (2006, April). Balancing power consumption in multiprocessor systems. In ACM
SIGOPS Operating Systems Review (Vol. 40, No. 4, pp.403-414). ACM.
[39]Tang, Q., Gupta, S. K., Stanzione, D., & Cayton, P.(2006, September). Thermal-aware task scheduling to
minimize energy usage of blade server based datacenters. In Dependable, Autonomic and Secure
Computing, 2nd IEEE International Symposium on (pp.195-202). IEEE.
[40] Tang, Q., Gupta, S. K. S., & Varsamopoulos, G.(2008). Energy-efficient thermal-aware task scheduling
for homogeneous high-performance computing data centers: A cyber-physical approach. IEEE Transactions
on Parallel and Distributed Systems, 19(11), 1458-1472.
[41] Wang, L., von Laszewski, G., Dayal, J., & Furlani, T.R. (2009, December). Thermal aware workload
scheduling with backfilling for green data centers.In Performance Computing and Communications
Conference (IPCCC), 2009 IEEE 28th International (pp. 289-296). IEEE.
[42] Wang, L., Von Laszewski, G., Dayal, J., He, X.,Younge, A. J., & Furlani, T. R. (2009, December).
Towards thermal aware workload scheduling in a data center. In Pervasive Systems, Algorithms, and Networks
(ISPAN), 2009 10th International Symposium on (pp.116-122). IEEE.
[43] Valentini, G. L., Lassonde, W., Khan, S. U., Min-Allah, N., Madani, S. A., Li, J., ... & Li, H. (2013). An
overview of energy efficiency techniques in cluster computing systems. Cluster Computing, 16(1), 3-15.
[44] Wen, G., Hong, J., Xu, C., Balaji, P., Feng, S., & Jiang,P. (2011, December). Energy-aware hierarchical
scheduling of applications in large scale data centers.In Cloud and Service Computing (CSC), 2011 International Conference on (pp. 158-165). IEEE.
[45] Markovic, D., Wang, C. C., Alarcon, L. P., Liu, T. T.,& Rabaey, J. M. (2010). Ultralow-power design in
near-threshold region. Proceedings of the IEEE, 98(2),237-252.
[46] Dreslinski, R. G., Wieckowski, M., Blaauw, D.,Sylvester, D., & Mudge, T. (2010). Near-threshold
computing: Reclaiming moore's law through energy efficient integrated circuits. Proceedings of the
IEEE, 98(2), 253-266.
[47] Jain, S., Khare, S., Yada, S., Ambili, V., Salihundam,P., Ramani, S., ... & Ramanarayanan, R. (2012,February). A 280mV-to-1.2 V wide-operating-range IA-32 processor in 32nm CMOS. In Solid-State Circuits Conference Digest of Technical Papers (ISSCC), 2012 IEEE International (pp. 66-68). IEEE.
[48] Dreslinski, R. G., Fick, D., Giridhar, B., Kim, G., Seo,S., Fojtik, M., ... & Wieckowski, M. (2013). Centip3de:
A 64-core, 3d stacked near-threshold system. IEEE Micro, 33(2), 8-16.
[49] Wang, J., Fu, X., Zhang, W., Zhang, J., Qiu, K., & Li,T. (2017). On the Implication of NTC versus Dark
Silicon on Emerging Scale-Out Workloads: The Multi Core Architecture Perspective. IEEE Transactions on
Parallel and Distributed Systems, 28(8), 2314-2327.
[50] Skadron, K., Stan, M. R., Sankaranarayanan, K.,Huang, W., Velusamy, S., & Tarjan, D. (2004).Temperature-aware microarchitecture: Modeling and implementation. ACM Transactions on Architecture and Code Optimization (TACO), 1(1), 94-125.
[51] Grundy, R. (2005). Recommended data center temperature & humidity. Retirado a, 22.
[52] Chiriac, V. A., & Chiriac, F. (2012, May). Novel energy recovery systems for the efficient cooling of
data centers using absorption chillers and renewable energy resources. In Thermal and Thermomechanical
Phenomena in Electronic Systems (ITherm), 2012 13th IEEE Intersociety Conference on (pp. 814-820). IEEE.
[53] Beitelmal, A. H., & Patel, C. D. (2007). Thermo-fluids provisioning of a high performance high density data
center. Distributed and Parallel Databases, 21(2-3),227-238.
[54] Jiang, N., & Parashar, M. (2009, May). Enabling autonomic power-aware management of instrumented
data centers. In Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed
Processing (pp. 1-8). IEEE Computer Society.
[55] Moore, J. D., Chase, J. S., Ranganathan, P., & Sharma,R. K. (2005, April). Making Scheduling" Cool":
Temperature-Aware Workload Placement in Data Centers. In USENIX annual technical conference,
General Track (pp. 61-75).
[56] Xu, J., & Fortes, J. A. (2010, December). Multiobjective virtual machine placement in virtualized data
center environments. In Green Computing and Communications (GreenCom), 2010 IEEE/ACM Int'l
Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) (pp. 179-188). IEEE.
[57] Wang, Q., Li, N., Shen, L., & Wang, Z. (2019). A statistic approach for power analysis of integrated
GPU. Soft Computing, 23(3), 827-836.
[58] Khalaj, A. H., & Halgamuge, S. K. (2017). A Review on efficient thermal management of air-and liquidcooled data centers: From chip to the cooling system. Applied energy, 205, 1165-1188.
[59] Wang, C. (2017). A new DC UPS for DC power distribution system in data center (Doctoral dissertation).
[60] M. Floyd, S. Ghiasi, T. Keller, K. Rajamani, F.Rawson, J. Rubio, and M. Ware, “System power management support in the IBM POWER6 microprocessor,” IBM J. Res. Develop., vol. 51, no. 6,pp. 733–746, Nov. 2007.
[61] R. Raghavendra, P. Ranganathan, V. Talwar, Z. Wang,and X. Zhu, “No power struggles: Coordinated multilevel power management for the data center,” ACM SIGARCH Comput. Archit. News, vol. 36, no. 1, pp.
48–59, Mar. 2008.
[62] X. Fan, W.-D. Weber, and L. A. Barroso, “Power provisioning for a warehouse-sized computer,” ACM
SIGARCH Comput. Archit. News, vol. 35, no. 2, pp.13–23, May 2007.
[63] S. Pelley, D. Meisner, P. Zandevakili, T. Wenisch, and J. Underwood, “Power routing: Dynamic power
provisioning in the data center,” ACM Sigplan Notices,vol. 45, no. 3, pp. 231–242, Mar. 2010.
[64] Sondur, S., Gross, K., & Li, M. (2018). Data Center Cooling System Integrated with Low-Temperature
Desalination and Intelligent Energy-Aware Control (No. 637). EasyChair.
[65] Khalaj, A. H., & Halgamuge, S. K. (2017). A Review on efficient thermal management of air-and liquidcooled data centers: From chip to the cooling system. Applied energy, 205, 1165-1188.
[66] Khalaj, Ali Habibi, Thomas Scherer, and Saman K.Halgamuge. "Energy, environmental and economical
saving potential of data centers with various economizers across Australia." Applied energy 183(2016): 1528-1549.
Volume 13, Issue 1
June 2020
Pages 33-44
  • Receive Date: 27 April 2020
  • Revise Date: 19 October 2020
  • Accept Date: 07 November 2020
  • First Publish Date: 07 November 2020