[1]
L. A. Barroso, U. Hölzle, and P. Ranganathan, The Datacenter as A Computer: Designing Warehouse-Scale Machines. 3rd ed. Switzerland: Springer, 2019, p. 189.
[3]
X. Fan, W. D. Weber, and L. A. Barroso, Power provisioning for a warehouse-sized computer, in Proc. 34th Ann. Int. Symp. Computer Architecture, San Diego, CA, USA, 2007, pp. 13–23.
[4]
M. A. Islam, S. Ren, and A. Wierman, Exploiting a thermal side channel for power attacks in multi-tenant data centers, in Proc. 2017 ACM SIGSAC Conf. on Computer and Communications Security, Dallas, TX, USA, 2017, pp. 1079–1094.
[5]
C. Li, Z. Wang, X. Hou, H. Chen, X. Liang, and M. Guo, Power attack defense: Securing battery-backed data centers, in Proc. 2016 ACM/IEEE 43rd Int. Symp. Computer Architecture, Seoul, Republic of Korea, 2016, pp. 493–505.
[6]
X. Hou, C. Li, J. Yang, W. Zheng, X. Liang, and M. Guo, Integrated power anomaly defense: Towards oversubscription-safe data centers, IEEE Trans. Cloud Comput., vol. 10, no. 3, pp. 1875–1887, 2022.
[8]
B. Rountree, D. H. Ahn, B. R. De Supinski, D. K. Lowenthal, and M. Schulz, Beyond DVFS: A first look at performance under a hardware-enforced power bound, in Proc. 2012 IEEE 26th Int. Parallel and Distributed Processing Symp. Workshops & PhD Forum, Shanghai, China, 2012, pp. 947–953.
[9]
S. Li, X. Wang, X. Zhang, V. Kontorinis, S. Kodakara, D. Lo, and P. Ranganathan, Thunderbolt: Throughput- optimized, quality-of-service-aware power capping at scale, in Proc. 14th USENIX Conf. Operating Systems Design and Implementation (OSDI 20), Vritual Event, 2020, pp. 1241–1255.
[10]
S. Malla and K. Christensen, Reducing power use and enabling oversubscription in multi-tenant data centers using local price, in Proc. 2017 IEEE Int. Conf. Autonomic Computing (ICAC), Columbus, OH, USA, 2017, pp. 161–166.
[11]
S. Govindan, A. Sivasubramaniam, and B. Urgaonkar, Benefits and limitations of tapping into stored energy for datacenters, in Proc. 2011 38th Ann. Int. Symp. Computer Architecture (ISCA), San Jose, CA, USA, 2011, pp. 341–351.
[12]
S. Govindan, D. Wang, A. Sivasubramaniam, and B. Urgaonkar, Leveraging stored energy for handling power emergencies in aggressively provisioned datacenters, in Proc. Seventeenth Int. Conf. Architectural Support for Programming Languages and Operating Systems, London, UK, 2012, pp. 75–86.
[13]
V. Kontorinis, L. E. Zhang, B. Aksanli, J. Sampson, H. Homayoun, E. Pettis, D. M. Tullsen, and T. S. Rosing, Managing distributed ups energy for effective power capping in data centers, in Proc. 2012 39th Ann. Int. Symp. Computer Architecture (ISCA), Portland, OR, USA, 2012, pp. 488–499.
[14]
B. Aksanli, E. Pettis, and T. Rosing, Architecting efficient peak power shaving using batteries in data centers, in Proc. 2013 IEEE 21st Int. Symp. Modelling, Analysis and Simulation of Computer and Telecommunication Systems, San Francisco, CA, USA, 2013, pp. 242–253.
[15]
S. Malla, Q. Deng, Z. Ebrahimzadeh, J. Gasperetti, S. Jain, P. Kondety, T. Ortiz, and D. Vieira, Coordinated priority-aware charging of distributed batteries in oversubscribed data centers, in Proc. 2020 53rd Ann. IEEE/ACM Int. Symp. Microarchitecture (MICRO), Athens, Greece, 2020, pp. 839–851.
[16]
G. Wang, S. Wang, B. Luo, W. Shi, Y. Zhu, W. Yang, D. Hu, L. Huang, X. Jin, and W. Xu, Increasing large-scale data center capacity by statistical power control, in Proc. Eleventh European Conf. Computer Systems, London, UK, 2016, p. 8.
[17]
A. G. Kumbhare, R. Azimi, I. Manousakis, A. Bonde, F. V. Frujeri, N. Mahalingam, P. A. Misra, S. A. Javadi, B. Schroeder, M. Fontoura, and R. Bianchini, Prediction-based power oversubscription in cloud platforms, in Proc. 2021 USENIX Ann. Technical Conf., USENIX ATC 21, Virtual Event, 2021, pp. 473–487.
[18]
S. Malla and K. Christensen, The effect of server energy proportionality on data center power oversubscription, Future Generat. Computer Syst., vol. 104, pp. 119–130, 2020.
[19]
X. Fu, X. Wang, and C. Lefurgy, How much power oversubscription is safe and allowed in data centers, in Proc. 8th ACM Int. Conf. Autonomic Computing, Karlsruhe, Germany, 2011, pp. 21–30.
[20]
Q. Wu, Q. Deng, L. Ganesh, C. H. Hsu, Y. Jin, S. Kumar, B. Li, J. Meza, and Y. J. Song, Dynamo: Facebook’s data center-wide power management system, in Proc. 2016 ACM/IEEE 43rd Ann. Int. Symp. Computer Architecture, Seoul, Republic of Korea, 2016, pp. 469–480.
[21]
Y. Li, C. R. Lefurgy, K. Rajamani, M. S. Allen-Ware, G. J. Silva, D. D. Heimsoth, S. Ghose, and O. Mutlu, A scalable priority-aware approach to managing data center server power, in Proc. 2019 IEEE Int. Symp. High Performance Computer Architecture (HPCA), Washington, DC, USA, 2019, pp. 701–714.
[22]
Y. Jiang, Z. Huang, and D. H. K. Tsang, On power-peak-aware scheduling for large-scale shared clusters, IEEE Trans. Big Data, vol. 6, no. 2, pp. 412–426, 2020.
[23]
C. Zhang, A. G. Kumbhare, I. Manousakis, D. Zhang, P. A. Misra, R. Assis, K. Woolcock, N. Mahalingam, B. Warrier, D. Gauthier, L. Kunnath, S. Solomon, O. Morales, M. Fontoura, and R. Bianchini, Flex: High-availability datacenters with zero reserved power, in Proc. 2021 ACM/IEEE 48th Ann. Int. Symp. Computer Architecture (ISCA), Valencia, Spain, 2021, pp. 319–332.
[24]
H. Sun, P. Stolf, J. M. Pierson, and G. Da Costa, Multi-objective scheduling for heterogeneous server systems with machine placement, in Proc. 2014 14th IEEE/ACM Int. Symp. Cluster, Cloud and Grid Computing, Chicago, IL, USA, 2014, pp. 334–343.
[25]
M. H. Jamal, M. T. Chaudhry, U. Tahir, F. Rustam, S. Hur, and I. Ashraf, Hotspot-aware workload scheduling and server placement for heterogeneous cloud data centers, Energies, vol. 15, no. 7, p. 2541, 2022.
[26]
C. H. Hsu, Q. Deng, J. Mars, and L. Tang, SmoothOperator: Reducing power fragmentation and improving power utilization in large-scale datacenters, in Proc. Twenty-Third Int. Conf. Architectural Support for Programming Languages and Operating Systems, Williamsburg, VA, USA, 2018, pp. 535–548.
[27]
L. Yan, W. Liu, and D. Bai, Temperature and power aware server placement optimization for enterprise data center, in Proc. 2018 IEEE 24th Int. Conf. Parallel and Distributed Systems (ICPADS), Singapore, 2018, pp. 433–440.
[30]
X. F. Liu, Z. H. Zhan, J. D. Deng, Y. Li, T. Gu, and J. Zhang, An energy efficient ant colony system for virtual machine placement in cloud computing, IEEE Trans. Evolut. Comput., vol. 22, no. 1, pp. 113–128, 2018.
[31]
H. Tabrizchi and M. K. Rafsanjani, Energy refining balance with ant colony system for cloud placement machines, J. Grid Comput., vol. 19, no. 1, p. 7, 2021.
[33]
W. Lin, G. Wu, X. Wang, and K. Li, An artificial neural network approach to power consumption model construction for servers in cloud data centers, IEEE Trans. Sustainable Comput., vol. 5, no. 3, pp. 329–340, 2020.
[34]
R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software Pract. Exper., vol. 41, no. 1, pp. 23–50, 2011.
[35]
M. Tighe, G. Keller, M. Bauer, and H. Lutfiyya, DCSim: A data centre simulation tool for evaluating dynamic virtualized resource management, in Proc. 2012 8th Int. Conf. Network and Service Management (CNSM) and 2012 Workshop on Systems Virtualiztion Management (SVM), Las Vegas, NV, USA, 2012, pp. 385–392.
[36]
A. Alahmadi, A. Alnowiser, M. M. Zhu, D. R. Che, and P. Ghodous, Enhanced first-fit decreasing algorithm for energy-aware job scheduling in cloud, in Proc. 2014 Int. Conf. Computational Science and Computational Intelligence, Las Vegas, NV, USA, 2014, pp. 69–74.