Abstract
As the sizes of cloud infrastructures continue to grow, the complexity of the cloud is becoming more and more difficult to manage. Currently, centralised management schemes dominate and there are already signs that these are no longer fit for purpose. The CloudLightning project takes a novel route, making use of self-organisation techniques to address the problems emerging from the confluence of issues in the emerging cloud: rising complexity and energy costs, problems of management and efficiency of use, the need to efficiently deploy services to a growing community of non-specialist users and the need to facilitate solutions based on heterogeneous components. CloudLightning efficiently addresses three main challenges in the domain of heterogeneous cloud computing: energy efficiency, improved accessibility to cloud and support for heterogeneity. The chapter provides an overview of the CloudLightning system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
OpenStack Nova: http://docs.openstack.org/developer/nova/.
- 2.
Kubernetes: http://kubernetes.io/.
- 3.
Apache Mesos: http://mesos.apache.org/.
- 4.
Docker Swarm: https://github.com/docker/swarm/.
- 5.
OpenStack Ironic: http://docs.openstack.org/developer/ironic/deploy/user-guide.html.
- 6.
SPECpower: https://www.spec.org/power_ssj2008/.
- 7.
EEMBC: http://www.eembc.org/.
References
Ahuja M, Chen CC, Gottapu R, Hallmann J, Hasan W, Johnson R, Kozyrczak M, Pabbati R, Pandit N, Pokuri S et al (2009) Peta-scale data warehousing at Yahoo! In: Proceedings of the 2009 ACM SIGMOD international conference on management of data. ACM, pp 855–862
Barroso LA, Clidaras J, Hölzle U (2013) The datacenter as a computer: an introduction to the design of warehouse-scale machines. In: Synthesis lectures on computer architecture, vol 8, no 3, pp 1–154
Beloglazov A, Buyya R, Lee YC, Zomaya A et al (2011) A taxonomy and survey of energy-efficient data centers and cloud computing systems. Adv Comput 82(2):47–111
Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768
Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exper 41(1):23–50. https://doi.org/10.1002/spe.995
Dong D, Herbert J (2013) Energy efficient VM placement supported by data analytic service. In: 2013 13th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid). IEEE, pp 648–655
Etro F (2009) The economic impact of cloud computing on business creation, employment and output in europe. Rev Bus Econ 54(2):179–208
Filelis-Papadopoulos C, Xiong H, Spataru A, Castane G, Dong D, Gravvanis G, Morrison JP (2017) A generic framework supporting self-organisation and self-management in hierarchical systems. In: The 16th international symposium on parallel and distributed computing (ISPDC 2017), Paper accepted
Hauswald J, Laurenzano MA, Zhang Y, Li C, Rovinski A, Khurana A, Dreslinski RG, Mudge T, Petrucci V, Tang L et al (2015) Sirius: an open end-to-end voice and vision personal assistant and its implications for future warehouse scale computers. In: Proceedings of the twentieth international conference on architectural support for programming languages and operating systems. ACM, pp 223–238
Hindman B, Konwinski A, Zaharia M, Ghodsi A, Joseph AD, Katz R, Shenker S, Stoica I (2011) Mesos: a platform for fine-grained resource sharing in the data center. In: Proceedings of the 8th USENIX conference on networked systems design and implementation (NSDI 2011), pp 295–308
Joseph E, Conway S, Dekate C, Cohen L (2014) IDC HPC update at ISC14
Marinescu DC (2016) Complex systems and clouds: a self-organization and self-management perspective. Morgan Kaufmann
Núñez A, Vázquez-Poletti JL, Caminero AC, Castañé GG, Carretero J, Llorente IM (2012) iCanCloud: a flexible and scalable cloud infrastructure simulator. J Grid Comput 10(1):185–209
Schubert L, Jeffery K, Neidecker-Lutz B (2010) The future of cloud computing: opportunities for European cloud computing beyond 2010. Expert Group report, public version 1
Sohrabi S, Moser I (2015) A survey on energy-aware cloud. Eur J Adv Eng Technol 2(2):80–91
Sverdlik Y (2014) Survey: industry average data center PUE stays nearly flat over four years. Data Center Knowl 2(06)
Tang L, Mars J, Zhang X, Hagmann R, Hundt R, Tune E (2013) Optimizing Google’s warehouse scale computers: the NUMA experience. In: 2013 IEEE 19th international symposium on high performance computer architecture (HPCA2013). IEEE, pp 188–197
Tian W, Xu M, Chen A, Li G, Wang X, Chen Y (2015) Open-source simulators for cloud computing: comparative study and challenging issues. Simul Model Pract Theory (special issue on Cloud Simulation) 58(Part 2):239–254
Whitney J, Delforge P (2014) Data center efficiency assessment. National Resources Defense Council, New York
Acknowledgements
This work is funded by the European Union’s Horizon 2020 Research and Innovation Programme through the CloudLightning project under Grant Agreement Number 643946.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Xiong, H., Filelis-Papadopoulos, C., Dong, D., Castañé, G.G., Meyer, S., Morrison, J.P. (2019). Energy-Efficient Servers and Cloud. In: Kachris, C., Falsafi, B., Soudris, D. (eds) Hardware Accelerators in Data Centers. Springer, Cham. https://doi.org/10.1007/978-3-319-92792-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-92792-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92791-6
Online ISBN: 978-3-319-92792-3
eBook Packages: EngineeringEngineering (R0)