Dynamic Management of CPU Resources Towards Energy Efficient and Profitable Datacentre Operation
Authors: 
Christos Kalogirou, Christos D. Antonopoulos, Spyros Lalis, Nikolaos BellasAuthors Info & Claims
Christos Kalogirou, Christos D. Antonopoulos, Spyros Lalis, Nikolaos BellasAuthors Info & ClaimsPages 131 - 151
Abstract
Energy reduction has become a necessity for modern datacentres, with CPU being a key contributor to the energy consumption of nodes. Increasing the utilization of CPU resources on active nodes is a key step towards energy efficiency. However, this is a challenging undertaking, as the workload can vary significantly among the nodes and over time, exposing operators to the risk of overcommitting the CPU. In this paper, we explore the trade-off between energy efficiency and node overloads, to drive virtual machine (VM) consolidation in a cost-aware manner. We introduce a model that uses runtime information to estimate the target utilization of the nodes to control their load, identifying and considering correlated behavior among collocated workloads. Moreover, we introduce a VM allocation and node management policy that exploits the model to increase the profit of datacentre operators considering the trade-off between energy reduction and potential SLA violation costs. We evaluate our work through simulations using node profiles derived from real machines and workloads from real datacentre traces. The results show that our policy adapts the nodes’ target utilization in a highly effective way, converging to a target utilization that is statically optimal for the workload at hand. Moreover, we show that our policy closely matches, or even outperforms two state-of-the-art policies that combine VM consolidation with VFS – the second one, also operating the CPU at reduced voltage margins – even when these are configured to use a static, workload- and architecture-specific target utilization derived through offline characterization of the workload.
References
[1]
Amazon EC2 pricing. https://aws.amazon.com/ec2/pricing/
[2]
Eletric Power Monthly. https://www.eia.gov/electricity/monthly/
[3]
Arroba P, Moya JM, Ayala JL, and Buyya R Dynamic Voltage and Frequency Scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers Concurrency Comput. Pract. Experience 2017 29 10 e4067
[4]
Beloglazov A, Abawajy J, and Buyya R Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing Future Gener. Comput. Syst. 2012 28 5 755-768
[5]
Beloglazov A and Buyya R Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers Concurrency Comput. Pract. Experience 2012 24 13 1397-1420
[6]
Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, and Buyya R CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms Softw. Pract. Exp. 2011 41 1 23-50
[7]
Cao Z and Dong S An energy-aware heuristic framework for virtual machine consolidation in cloud computing J. Supercomput. 2014 69 1 429-451
[8]
Dayarathna M, Wen Y, and Fan R Data center energy consumption modeling: a survey IEEE Commun. Surv. Tutorials 2016 18 1 732-794
[9]
Engbers, N., Taen, E.: Green Data Net. Report to IT Room INFRA. European Commission. FP7 ICT 2013.6.2;2014 (2016)
[10]
Farahnakian, F., Liljeberg, P., Plosila, J.: Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. In: 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 500–507, February 2014.
[11]
Garg SK, Gopalaiyengar SK, and Buyya R Xiang Y, Cuzzocrea A, Hobbs M, and Zhou W SLA-based resource provisioning for heterogeneous workloads in a virtualized cloud datacenter Algorithms and Architectures for Parallel Processing 2011 Heidelberg Springer 371-384
[12]
Ghribi, C., Hadji, M., Zeghlache, D.: Energy efficient VM scheduling for cloud data centers: exact allocation and migration algorithms. In: 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, pp. 671–678, May 2013.
[13]
Herbert, S., Marculescu, D.: Analysis of dynamic voltage/frequency scaling in chip-multiprocessors. In: 2007 ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), pp. 38–43, August 2007.
[14]
Iqbal W, Dailey MN, Carrera D, and Janecek P Adaptive resource provisioning for read intensive multi-tier applications in the cloud Future Gener. Comput. Syst. 2011 27 6 871-879
[15]
Kalogirou, C., et al.: Exploiting CPU voltage margins to increase the profit of cloud infrastructure providers. In: 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 302–311. IEEE (2019)
[16]
von Laszewski, G., Wang, L., Younge, A.J., He, X.: Power-aware scheduling of virtual machines in DVFS-enabled clusters. In: 2009 IEEE International Conference on Cluster Computing and Workshops, pp. 1–10, August 2009.
[17]
Lee YC and Zomaya AY Energy efficient utilization of resources in cloud computing systems J. Supercomput. 2012 60 2 268-280
[18]
Liu W, Du W, Chen J, Wang W, and Zeng G Adaptive energy-efficient scheduling algorithm for parallel tasks on homogeneous clusters J. Netw. Comput. Appl. 2014 41 101-113
[19]
Reiss, C., Wilkes, J., Hellerstein, J.L.: Google cluster-usage traces: format + schema. Technical report, Google Inc., Mountain View, CA, USA, November 2011. revised 2014–11-17 for version 2.1. Posted at https://github.com/google/cluster-data
[20]
Salimian L, Esfahani FS, and Nadimi-Shahraki MH An adaptive fuzzy threshold-based approach for energy and performance efficient consolidation of virtual machines Computing 2016 98 6 641-660
[21]
Yadav R, Zhang W, Kaiwartya O, Singh PR, Elgendy IA, and Tian YC Adaptive energy-aware algorithms for minimizing energy consumption and SLA violation in cloud computing IEEE Access 2018 6 55923-55936
[22]
Zhou Z et al. Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms Future Gener. Comput. Syst. 2018 86 836-850
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Information
Published In
Jun 2022
266 pages
ISBN:978-3-031-22697-7
DOI:10.1007/978-3-031-22698-4
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 12 January 2023
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