Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 1/2021

15.01.2021

A Survey of Thermal Management in Cloud Data Centre: Techniques and Open Issues

verfasst von: Rama Rani, Ritu Garg

Erschienen in: Wireless Personal Communications | Ausgabe 1/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In recent years, there has been a great increase in usage of cloud data centers which leads the energy consumption growth by about 10% a year continuously. Further, due to the increase in temperature of cloud data center, the hardware failure rate increases and maintenance cost is also increased. Hotspots and an irregular temperature distribution are the major research issues. Therefore, accurate and reliable thermal management of the data center is a challenging task. In order to select appropriate and efficient thermal management approach, this survey presents a state of art review on the development in this field, discusses the classification of thermal management approaches and thermal mapping models. This review also reveals the thermal management and heat management strategies at data center level to make the data centre more energy efficient. Further, it discusses various thermal aware task scheduling strategies. In thermal management, computing equipments are scheduled with the objective to minimize the hotspot and cooling cost. Finally, evaluation metrics for measuring thermal efficiency and open research issues in this field are provided. Both researchers and academicians find this review useful since it presents the significant research in the field of thermal management.
Vorheriger Artikel Development and Performance Investigation of a Single-Channel 160 Gbps Free Space Optics Transmission Link Using Higher Order Modulation Scheme
Nächster Artikel The Smart Fall Detection Mechanism for Healthcare Under Free-Living Conditions

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Qian, L., Luo, Z., Du, Y., & Guo, L. (2009). Cloud computing: An overview. In IEEE international conference on cloud computing (pp. 626–631). Springer, Berlin.
2.
3.
JoSEP, A. D., & KAtz, R., KonWinSKi, A., Gunho, L. E. E., PAttERSon, D., & RABKin, A. . (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
4.
Cloud Computing—The Business Perspective-02-09-02.pdf.
5.
6.
Gartner report, financial times, 2007.
7.
Kaplan, J. Forrest, W., & Kindler, N. (2008). Revolutionizing data center energy efficiency. Technical Report (p. 15), McKinsey Company.
8.
9.
Barroso, L. A., & Hölzle, U. (2007). The case for energy-proportional computing. Computer, 40(12), 33–37.
10.
Liu, J., Zhao, F., Liu, X., & He, W. (2009). Challenges towards elastic power management in internet data centers. In 2009 29th ieee international conference on distributed computing systems workshops (pp. 65–72). IEEE.
11.
Moore, J. D., Chase, J. S., Ranganathan, P., & Sharma, R. K. (2005). Making scheduling “cool”: temperature-aware workload placement in data centers. In USENIX annual technical conference, general track (pp. 61–75).
12.
Donald, J., & Martonosi, M. (2006). Techniques for multicore thermal management: Classification and new exploration. ACM SIGARCH Computer Architecture News, 34(2), 78–88.
13.
Wang, L., Khan, S. U., & Dayal, J. (2012). Thermal aware workload placement with task-temperature profiles in a data center. The Journal of Supercomputing, 61(3), 780–803.
14.
Kong, J., Chung, S. W., & Skadron, K. (2012). Recent thermal management techniques for microprocessors. ACM Computing Surveys (CSUR), 44(3), 1–42.
15.
Sharma, Y., Javadi, B., Si, W., & Sun, D. (2016). Reliability and energy efficiency in cloud computing systems: Survey and taxonomy. Journal of Network and Computer Applications, 74, 66–85.
16.
Lee, E. K., Kulkarni, I., Pompili, D., & Parashar, M. (2012). Proactive thermal management in green datacenters. The Journal of Supercomputing, 60(2), 165–195.
17.
Sheikh, H. F., Ahmad, I., Wang, Z., & Ranka, S. (2012). An overview and classification of thermal-aware scheduling techniques for multi-core processing systems. Sustainable Computing: Informatics and Systems, 2(3), 151–169.
18.
Arghode, V. K., Kang, T., Joshi, Y., Phelps, W., & Michaels, M. (2017). Measurement of air flow rate through perforated floor tiles in a raised floor data center. Journal of Electronic Packaging, 139(1), 011007-1-011007–8.
19.
Masdari, M., Nabavi, S. S., & Ahmadi, V. (2016). An overview of virtual machine placement schemes in cloud computing. Journal of Network and Computer Applications, 66, 106–127.
20.
Singh, A. K., Shafique, M., Kumar, A., & Henkel, J. (2013). Mapping on multi/many-core systems: Survey of current and emerging trends. In 2013 50th ACM/EDAC/IEEE design automation conference (DAC) (pp. 1–10). IEEE.
21.
Lee, E. K., Viswanathan, H., & Pompili, D. (2015). Proactive thermal-aware resource management in virtualized HPC cloud datacenters. IEEE Transactions on Cloud Computing, 5(2), 234–248.
22.
Bobroff, N., Kochut, A., & Beaty, K. (2007). Dynamic placement of virtual machines for managing SLA violations. In 2007 10th IFIP/IEEE international symposium on integrated network management (pp. 119–128). IEEE.
23.
Beloglazov, A., & Buyya, R. (2010). Energy efficient resource management in virtualized cloud data centers. In 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing (pp. 826–831). IEEE.
24.
Beloglazov, A., & Buyya, R. (2012). Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Transactions on Parallel and Distributed Systems, 24(7), 1366–1379.
25.
Beloglazov, A., & Buyya, R. (2010). Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. MGC @ Middleware, 4, 1890799–1890803.
26.
Song, W., Xiao, Z., Chen, Q., & Luo, H. (2013). Adaptive resource provisioning for the cloud using online bin packing. IEEE Transactions on Computers, 63(11), 2647–2660.MathSciNetMATH
27.
Song, Y., Sun, Y., Wang, H., & Song, X. (2007). An adaptive resource flowing scheme amongst VMs in a VM-based utility computing. In 7th IEEE international conference on computer and information technology (CIT 2007) (pp. 1053–1058). IEEE.
28.
Rodero, I., Viswanathan, H., Lee, E. K., Gamell, M., Pompili, D., & Parashar, M. (2012). Energy-efficient thermal-aware autonomic management of virtualized HPC cloud infrastructure. Journal of Grid Computing, 10(3), 447–473.
29.
Tang, Q., Gupta, S. K., & Varsamopoulos, G. (2007). Thermal-aware task scheduling for data centers through minimizing heat recirculation. In 2007 ieee international conference on cluster computing (pp. 129–138). IEEE.
30.
Huang, W., Allen-Ware, M., Carter, J. B., Elnozahy, E., Hamann, H., Keller, T., et al. (2011). TAPO: Thermal-aware power optimization techniques for servers and data centers. In 2011 International green computing conference and workshops (pp. 1–8). IEEE.
31.
Zhu, H., Wang, J., Song, M., & Fang, Q. (2015). Thermal-aware load provisioning for server clusters by using model predictive control. In 2015 ieee conference on control applications (CCA) (pp. 336–340). IEEE.
32.
Tang, Q., Mukherjee, T., Gupta, S. K., & Cayton, P. (2006). Sensor-based fast thermal evaluation model for energy efficient high-performance datacenters. In 2006 Fourth international conference on intelligent sensing and information processing (pp. 203–208). IEEE.
33.
Bash, C., & Forman, G. (2007). 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. 140).
34.
Sun, G., Liao, D., Anand, V., Zhao, D., & Yu, H. (2016). A new technique for efficient live migration of multiple virtual machines. Future Generation Computer Systems, 55, 74–86.
35.
Sarker, T. K., & Tang, M. (2013). Performance-driven live migration of multiple virtual machines in datacenters. In 2013 IEEE international conference on granular computing (GrC) (pp. 253–258). IEEE.
36.
Liu, H., Jin, H., Xu, C. Z., & Liao, X. (2013). Performance and energy modeling for live migration of virtual machines. Cluster Computing, 16(2), 249–264.
37.
Goudarzi, H., Ghasemazar, M., & Pedram, M. (2012). SLA-based optimization of power and migration cost in cloud computing. In 2012 12th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGRID 2012) (pp. 172–179). IEEE.
38.
Callegati, F., & Cerroni, W. (2013). Live migration of virtualized edge networks: Analytical modeling and performance evaluation. In 2013 IEEE SDN for future networks and services (SDN4FNS) (pp. 1–6). IEEE.
39.
Zhang, W., Lam, K. T., & Wang, C. L. (2014). Adaptive live VM migration over a wan: Modeling and implementation. In 2014 IEEE 7th international conference on cloud computing (pp. 368–375). IEEE.
40.
Clark, C., Fraser, K., Hand, S., Hansen, J. G., Jul, E., Limpach, C., et al. (2005). Live migration of virtual machines. In Proceedings of the 2nd conference on symposium on networked systems design and implementation (vol. 2, pp. 273–286).
41.
Jin, H., Deng, L., Wu, S., Shi, X., & Pan, X. (2009). Live virtual machine migration with adaptive, memory compression. In 2009 ieee international conference on cluster computing and workshops (pp. 1–10). IEEE.
42.
Rao, L., Liu, X., Xie, L., & Liu, W. (2010). Minimizing electricity cost: optimization of distributed internet data centers in a multi-electricity-market environment. In 2010 Proceedings IEEE INFOCOM (pp. 1–9). IEEE.
43.
Qureshi, A., Weber, R., Balakrishnan, H., Guttag, J., & Maggs, B. (2009). Cutting the electric bill for internet-scale systems. In Proceedings of the ACM SIGCOMM 2009 conference on data communication (pp. 123–134).
44.
Fang, Q., Wang, J., Gong, Q., & Song, M. (2017). Thermal-aware energy management of an HPC data center via two-time-scale control. IEEE Transactions on Industrial Informatics, 13(5), 2260–2269.
45.
Ranganathan, P., Leech, P., Irwin, D., & Chase, J. (2006). Ensemble-level power management for dense blade servers. ACM SIGARCH Computer Architecture News, 34(2), 66–77.
46.
Von Laszewski, G., Wang, L., Younge, A. J., & He, X. (2009). Power-aware scheduling of virtual machines in DVFS-enabled clusters. In 2009 IEEE international conference on cluster computing and workshops (pp. 1–10). IEEE.
47.
Meisner, Q. D. D., Bhattacharjee, A., Wenisch, T. F., & Bianchini, R. (2012). MultiScale: memory system DVFS with multiple memory controllers.
48.
Lee, S., & Kim, J. (2010). Using dynamic voltage scaling for energy-efficient flash-based storage devices. In 2010 international SoC design conference (pp. 63–66). IEEE.
49.
Xia, H., & Zhang, Y. (2013). A dynamic temperature controlling method for processors in constrained sealed spaces. Journal of Computers, 8(12), 3066–3072.
50.
Steinder, M., Whalley, I., Carrera, D., Gaweda, I., & Chess, D. (2007). Server virtualization in autonomic management of heterogeneous workloads. In 2007 10th IFIP/IEEE international symposium on integrated network management (pp. 139–148). IEEE.
51.
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.
52.
Mukherjee, T., Banerjee, A., Varsamopoulos, G., Gupta, S. K., & Rungta, S. (2009). Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers. Computer Networks, 53(17), 2888–2904.MATH
53.
Choi, J., Kim, Y., Sivasubramaniam, A., Srebric, J., Wang, Q., & Lee, J. (2008). A CFD-based tool for studying temperature in rack-mounted servers. IEEE Transactions on Computers, 57(8), 1129–1142.MathSciNetMATH
54.
Moore, J., Chase, J. S., & Ranganathan, P. (2006). Weatherman: Automated, online and predictive thermal mapping and management for data centers. In 2006 IEEE international conference on autonomic computing (pp. 155–164). IEEE.
55.
Zong, Z., Manzanares, A., Ruan, X., & Qin, X. (2010). EAD and PEBD: Two energy-aware duplication scheduling algorithms for parallel tasks on homogeneous clusters. IEEE Transactions on Computers, 60(3), 360–374.MathSciNetMATH
56.
Garg, R., & Singh, A. K. (2016). Energy-aware workflow scheduling in grid under QoS constraints. Arabian Journal for Science and Engineering, 41(2), 495–511.
57.
Wu, C. M., Chang, R. S., & Chan, H. Y. (2014). A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Future Generation Computer Systems, 37, 141–147.
58.
Garg, S. K., Yeo, C. S., Anandasivam, A., & Buyya, R. (2011). Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers. Journal of Parallel and Distributed Computing, 71(6), 732–749.MATH
59.
Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28(5), 755–768.
60.
Lee, Y. C., & Zomaya, A. Y. (2012). Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, 60(2), 268–280.
61.
Ma, K., Li, X., Chen, W., Zhang, C., & Wang, X. (2012). Greengpu: A holistic approach to energy efficiency in GPU–CPU heterogeneous architectures. In 2012 41st international conference on parallel processing (pp. 48–57). IEEE.
62.
Sharkh, M. A., & Shami, A. (2017). An evergreen cloud: Optimizing energy efficiency in heterogeneous cloud computing architectures. Vehicular Communications, 9, 199–210.
63.
Ding, Y., Qin, X., Liu, L., & Wang, T. (2015). Energy efficient scheduling of virtual machines in cloud with deadline constraint. Future Generation Computer Systems, 50, 62–74.
64.
Goh, L. K., Veeravalli, B., & Viswanathan, S. (2008). Design of fast and efficient energy-aware gradient-based scheduling algorithms heterogeneous embedded multiprocessor systems. IEEE Transactions on Parallel and Distributed Systems, 20(1), 1–12.
65.
Garg, R., & Mittal, M. (2019). Reliability and energy efficient workflow scheduling in cloud environment. Cluster Computing, 22(4), 1283–1297.
66.
Maurya, A. K., Modi, K., Kumar, V., Naik, N. S., & Tripathi, A. K. (2020). Energy-aware scheduling using slack reclamation for cluster systems. Cluster Computing, 23(2), 911–923.
67.
Mishra, A., & Khare, N. (2015). Analysis of DVFS techniques for improving the gpu energy efficiency. Open Journal of Energy Efficiency, 4(04), 77.
68.
Mochocki, B., Hu, X. S., & Quan, G. (2005). Practical on-line DVS scheduling for fixed-priority real-time systems. In 11th IEEE real time and embedded technology and applications symposium (pp. 224–233). IEEE.
69.
Zhuo, J., & Chakrabarti, C. (2005). System-level energy-efficient dynamic task scheduling. In Proceedings of the 42nd annual design automation conference (pp. 628–631).
70.
Chen, J. J., Kuo, T. W., & Shih, C. S. (2005). 1 + ε approximation clock rate assignment for periodic real-time tasks on a voltage-scaling processor. In Proceedings of the 5th ACM international conference on embedded software (pp. 247–250).
71.
Xie, F., Martonosi, M., & Malik, S. (2005). Bounds on power savings using runtime dynamic voltage scaling: An exact algorithm and a linear-time heuristic approximation. In Proceedings of the 2005 international symposium on low power electronics and design (pp. 287–292).
72.
Zhong, X., & Xu, C. Z. (2008). System-wide energy minimization for real-time tasks: Lower bound and approximation. ACM Transactions on Embedded Computing Systems (TECS), 7(3), 1–24.
73.
Qin, Y., Zeng, G., Kurachi, R., Li, Y., Matsubara, Y., & Takada, H. (2019). Energy-efficient intra-task dvfs scheduling using linear programming formulation. IEEE Access, 7, 30536–30547.
74.
Yeo, I., Liu, C. C., & Kim, E. J. (2008). Predictive dynamic thermal management for multicore systems. In Proceedings of the 45th annual design automation conference (pp. 734–739).
75.
Dhiman, G., & Rosing, T. S. (2006). Dynamic power management using machine learning. In Proceedings of the 2006 IEEE/ACM international conference on computer-aided design (pp. 747–754).
76.
Guo, Z., Bhuiyan, A., Saifullah, A., Guan, N., & Xiong, H. (2017). Energy-efficient multi-core scheduling for real-time DAG tasks. In 29th Euromicro conference on real-time systems (ECRTS 2017).
77.
Varsamopoulos, G., Banerjee, A., & Gupta, S. K. (2009). Energy efficiency of thermal-aware job scheduling algorithms under various cooling models. In International conference on contemporary computing (pp. 568–580). Springer, Berlin.
78.
Bash, C. B., Patel, C. D., & Sharma, R. K. (2006). Dynamic thermal management of air cooled data centers. In Thermal and thermomechanical proceedings 10th intersociety conference on phenomena in electronics systems, 2006. ITHERM 2006 (p. 8). IEEE.
79.
Wang, L., Von Laszewski, G., Dayal, J., He, X., Younge, A. J., & Furlani, T. R. (2009). Towards thermal aware workload scheduling in a data center. In 2009 10th international symposium on pervasive systems, algorithms, and networks (pp. 116–122). IEEE.
80.
Wang, L., von Laszewski, G., Dayal, J., & Furlani, T. R. (2009). Thermal aware workload scheduling with backfilling for green data centers. In 2009 IEEE 28th international performance computing and communications conference (pp. 289–296). IEEE.
81.
Garg, R., & Rani, R. (2019). State-of-the-art energy-efficient thermal-aware scheduling in cloud. In Information and communication technology for competitive strategies (pp. 157–164). Springer, Singapore.
82.
Parolini, L., Sinopoli, B., Krogh, B. H., & Wang, Z. (2011). A cyber–physical systems approach to data center modeling and control for energy efficiency. Proceedings of the IEEE, 100(1), 254–268.
83.
Ma, Y., Chantem, T., Dick, R. P., & Hu, X. S. (2017). Improving system-level lifetime reliability of multicore soft real-time systems. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25(6), 1895–1905.
84.
Abdi, A., & Zarandi, H. R. (2018). HYSTERY: A hybrid scheduling and mapping approach to optimize temperature, energy consumption and lifetime reliability of heterogeneous multiprocessor systems. The Journal of Supercomputing, 74(5), 2213–2238.
85.
Huang, L., Yuan, F., & Xu, Q. (2009). Lifetime reliability-aware task allocation and scheduling for MPSoC platforms. In 2009 design, Automation and test in Europe conference and exhibition (pp. 51–56). IEEE.
86.
Oxley, M. A., Jonardi, E., Pasricha, S., Maciejewski, A. A., Siegel, H. J., Burns, P. J., & Koenig, G. A. (2018). Rate-based thermal, power, and co-location aware resource management for heterogeneous data centers. Journal of Parallel and Distributed Computing, 112, 126–139.
87.
Stansberry, M. (2013). 2013 Data Center Industry Survey. Uptime Inst. Data Cent. Ind. Survey (pp. 1–28
88.
Li, S., Abdelzaher, T., & Yuan, M. (2011). Tapa: Temperature aware power allocation in data center with map-reduce. In 2011 international green computing conference and workshops (pp. 1–8). IEEE.
89.
Merkel, A., Bellosa, F., & Weissel, A. (2005). Event-driven thermal management in SMP systems. In Second workshop on temperature-aware computer systems (TACS’05).
90.
Kumar, A., Shang, L., Peh, L. S., & Jha, N. K. (2006). HybDTM: A coordinated hardware–software approach for dynamic thermal management. In 2006 43rd ACM/IEEE design automation conference (pp. 548–553). IEEE.
91.
Naveh, A., Rotem, E., Mendelson, A., Gochman, S., Chabukswar, R., Krishnan, K., & Kumar, A. (2006). Power and thermal management in the intel core duo processor. Intel Technology Journal, 10(2), 109.
92.
Coskun, A. K., Rosing, T. S., & Gross, K. C. (2008). Temperature management in multiprocessor SoCs using online learning. In 2008 45th ACM/IEEE design automation conference (pp. 890–893). IEEE.
93.
Taneja, S., Zhou, Y., Alghamdi, M. I., & Qin, X. (2017). Thermal-aware job scheduling of mapreduce applications on high performance clusters. In 2017 46th International conference on parallel processing workshops (ICPPW) (pp. 261–270). IEEE.
94.
Liu, H., Liu, B., Yang, L. T., Lin, M., Deng, Y., Bilal, K., & Khan, S. U. (2017). Thermal-aware and DVFS-enabled big data task scheduling for data centers. IEEE Transactions on Big Data, 4(2), 177–190.
95.
Zhou, J., & Wei, T. (2015). Stochastic thermal-aware real-time task scheduling with considerations of soft errors. Journal of Systems and Software, 102, 123–133.
96.
Cao, K., Zhou, J., Yin, M., Wei, T., & Chen, M. (2016). Static thermal-aware task assignment and scheduling for makespan minimization in heterogeneous real-time MPSoCs. In 2016 International symposium on system and software reliability (ISSSR) (pp. 111–118). IEEE.
97.
Marcel, A., Cristian, P., Eugen, P., Claudia, P., Cioara, T., Anghel, I., & Ioan, S. (2016). Thermal aware workload consolidation in cloud data centers. In 2016 IEEE 12th international conference on intelligent computer communication and processing (ICCP) (pp. 377–384). IEEE.
98.
Al-Qawasmeh, A. M., Pasricha, S., Maciejewski, A. A., & Siegel, H. J. (2013). Power and thermal-aware workload allocation in heterogeneous data centers. IEEE Transactions on Computers, 64(2), 477–491.MathSciNetMATH
99.
Cupertino, L., Da Costa, G., Oleksiak, A., Pia, W., Pierson, J. M., Salom, J., et al. (2015). Energy-efficient, thermal-aware modeling and simulation of data centers: the CoolEmAll approach and evaluation results. Ad Hoc Networks, 25, 535–553.
100.
Polverini, M., Cianfrani, A., Ren, S., & Vasilakos, A. V. (2013). Thermal-aware scheduling of batch jobs in geographically distributed data centers. IEEE Transactions on Cloud Computing, 2(1), 71–84.
101.
Shamalizadeh, H., Almeida, L., Wan, S., Amaral, P., Fu, S., & Prabh, S. (2013). Optimized thermal-aware workload distribution considering allocation constraints in data centers. In 2013 IEEE international conference on green computing and communications and ieee internet of things and IEEE cyber, physical and social computing (pp. 208–214). IEEE.
102.
Zhou, J., Cao, K., Cong, P., Wei, T., Chen, M., Zhang, G., et al. (2017). Reliability and temperature constrained task scheduling for makespan minimization on heterogeneous multi-core platforms. Journal of Systems and Software, 133, 1–16.
103.
Zhou, J., Wei, T., Chen, M., Yan, J., Hu, X. S., & Ma, Y. (2015). Thermal-aware task scheduling for energy minimization in heterogeneous real-time MPSoC systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 35(8), 1269–1282.
104.
Tyagi, S. K. S., Jain, D. K., Fernandes, S. L., & Muhuri, P. K. (2017). Thermal-aware power-efficient deadline based task allocation in multi-core processor. Journal of Computational Science, 19, 112–120.
105.
Rani, R., & Garg, R. (2020). Power and temperature-aware workflow scheduling considering deadline constraint in cloud. Arabian Journal for Science and Engineering, 45, 10775–10791.
106.
Fu, L., Wan, J., Liu, T., Gui, X., & Zhang, R. (2017). A temperature-aware resource management algorithm for holistic energy minimization in data centers. In 2017 2nd workshop on recent trends in telecommunications research (RTTR) (pp. 1–5). IEEE.
107.
Akbari, A., Khonsari, A., & Ghoreyshi, S. M. (2020). Thermal-aware virtual machine allocation for heterogeneous cloud data centers. Energies, 13(11), 2880.
108.
Salami, B., Baharani, M., & Noori, H. (2014). Proactive task migration with a self-adjusting migration threshold for dynamic thermal management of multi-core processors. The Journal of Supercomputing, 68(3), 1068–1087.
109.
Herrlin, M. K. (2008). Airflow and cooling performance of data centers: Two performance metrics. ASHRAE Transactions, 114(2), 182–187.
110.
Sharma, R., Bash, C., & Patel, C. (2002). Dimensionless parameters for evaluation of thermal design and performance of large-scale data centers. In 8th AIAA/ASME Joint thermophysics and heat transfer conference (p. 3091)
Metadaten
Titel
A Survey of Thermal Management in Cloud Data Centre: Techniques and Open Issues
verfasst von
Rama Rani
Ritu Garg
Publikationsdatum
15.01.2021
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 1/2021
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-08039-x

Weitere Artikel der Ausgabe 1/2021

Wireless Personal Communications 1/2021 Zur Ausgabe

OriginalPaper

Efficient Cross-Correlation Algorithm for Correction of Common Phase Error Employing Preamble for Orthogonal Frequency Division Multiplexing (OFDM) Receivers

OriginalPaper

Learning-Based Security Technique for Selective Forwarding Attack in Clustered WSN

OriginalPaper

An Automated Early Detection of Glaucoma using Support Vector Machine Based Visual Geometry Group 19 (VGG-19) Convolutional Neural Network

OriginalPaper

Opposition Based Artificial Flora Algorithm for Load Balancing in LTE Network

OriginalPaper

IRCTGP: A Novel Intra-node Resource Control Protocol Based on Traffic Priority and Evolutionary Games Theory for Wireless Sensor Networks

OriginalPaper

DTN-Balance: A Forwarding-Capacity and Forwarding-Queue Aware Routing for Self-organizing DTNs

Neuer Inhalt

    Bildnachweise