Engineering simulated evolution for integrated power optimization in data centers
- 76 Downloads
Abstract
Cloud computing has evolved as the next-generation platform for hosting applications ranging from engineering to sciences, and from social networking to media content delivery. The numerous data centers, employed to provide cloud services, consume large amounts of electrical power, both for their functioning and their cooling. Improving power efficiency, that is, decreasing the total power consumed, has become an increasingly important task for many data centers for reasons such as cost, infrastructural limits, and mitigating negative environmental impact. Power management is a challenging optimization problem due to the scale of modern data centers. Most published work focuses on power management in computing nodes and the cooling facility in an isolated manner. In this paper, we use a combination of server consolidation and thermal management to optimize the total power consumed by the computing nodes and the cooling facility. We describe the engineering of an evolutionary non-deterministic iterative heuristic known as simulated evolution to find the best location for each virtual machine (VM) in a data center based on computational power and data center heat recirculation model to optimize total power consumption. A “goodness” function which is related to the target objectives of the problem is defined. It guides the moves and helps traverse the search space using artificial intelligence. In the process of evolution, VMs with high goodness value have a smaller probability of getting perturbed, while those with lower goodness value may be reallocated via a compound move. Results are compared with those published in previous studies, and it is found that the proposed approach is efficient both in terms of solution quality and computational time.
Keywords
Cloud computing Power management Resource provisioning Virtual machine assignment Combinatorial optimization Simulated evolution Non-deterministic algorithms NP hard problemsNotes
Acknowledgements
The authors acknowledge King Fahd University of Petroleum and Minerals (KFUPM) for all support. The work was conducted as part of project COE-572132-2.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
References
- Ajiro Y, Tanaka A (2007) Improving packing algorithms for server consolidation. In: International of CMG conference, pp 399–406Google Scholar
- Al-Qawasmeh AM, Pasricha S, Maciejewski A, Siegel HJ et al (2015) Power and thermal-aware workload allocation in heterogeneous data centers. IEEE Trans Comput 64(2):477–491MathSciNetCrossRefMATHGoogle Scholar
- Barham P, Dragovic B, Fraser K, Hand S, Harris T, Ho A, Neugebauer R, Pratt I, Warfield A (2003) Xen and the art of virtualization. ACM SIGOPS Oper Syst Rev 37(5):164–177CrossRefGoogle Scholar
- Barroso LA, Hölzle U (2007) The case for energy-proportional computing. IEEE Comput 40(12):33–37CrossRefGoogle Scholar
- Basmadjian R, Niedermeier F, De Meer H (2012) Modelling and analysing the power consumption of idle servers. In: Sustainable internet and ICT for sustainability (SustainIT). IEEE, pp 1–9Google Scholar
- Bohrer P, Elnozahy EN, Keller T, Kistler M, Lefurgy C, McDowell C, Rajamony R (2002) The case for power management in web servers. In: Power aware computing. Springer, Berlin, pp 261–289Google Scholar
- Brown DJ, Reams C (2010) Toward energy-efficient computing. Commun ACM 53(3):50–58CrossRefGoogle Scholar
- Chase JS, Anderson DC, Thakar PN, Vahdat AM, Doyle RP (2001) Managing energy and server resources in hosting centers. In: ACM SIGOPS operating systems review, vol 35. ACM, pp 103–116Google Scholar
- Doddavula SK, Kaushik M, Jain A (2011) Implementation of a fast vector packing algorithm and its application for server consolidation. In: IEEE third international conference on cloud computing technology and science (CloudCom). IEEE, pp 332–339Google Scholar
- Fan X, Weber WD, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: ACM SIGARCH Computer Architecture News, vol 35. ACM, pp 13–23Google Scholar
- Feng X, Ge R, Cameron KW (2005) Power and energy profiling of scientific applications on distributed systems. In: Parallel and distributed processing symposium, proceedings. 19th IEEE International. IEEE, pp 34–34Google Scholar
- Filani D, He J, Gao S, Rajappa M, Kumar A, Shah P, Nagappan R (2008) Dynamic data center power management: trends, issues, and solutions. Intel Technol J 12(1):59–67CrossRefGoogle Scholar
- Fu Y, Lu C, Wang H (2010) Robust control-theoretic thermal balancing for server clusters. In: IEEE international symposium on parallel & distributed processing (IPDPS). IEEE, pp 1–11Google Scholar
- Gao Y, Guan H, Qi Z, Hou Y, Liu L (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79(8):1230–1242MathSciNetCrossRefMATHGoogle Scholar
- Glover F (1989) Tabu search-part I. ORSA J Comput 1(3):190–206CrossRefMATHGoogle Scholar
- Glover F (1990) Tabu search-part II. ORSA J Comput 2(1):4–32CrossRefMATHGoogle Scholar
- Hamilton J (2009) Cooperative expendable micro-slice servers (CEMS): low cost, low power servers for internet-scale services. In: Conference on innovative data systems research (CIDR)Google Scholar
- Hartmann AK, Weigt M (2006) Phase transitions in combinatorial optimization problems: basics, algorithms and statistical mechanics. Wiley, New YorkMATHGoogle Scholar
- Helion Eucalyptus Docs Team (2015) Eucalyptus Scheduling Policies. https://www.eucalyptus.com/docs/eucalyptus/4.0.2/install-guide/sched_pol.html. Accessed 5 Nov 2015
- Ingber L (1993) Simulated annealing: practice versus theory. Math Comput Model 18(11):29–57MathSciNetCrossRefMATHGoogle Scholar
- Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: Evolutionary computation. IEEE International conference on. IEEE, pp 303–308Google Scholar
- Khan JA, Sait SM, Minhas MR (2002) Fuzzy biasless simulated evolution for multiobjective vlsi placement. In: Evolutionary computation. CEC’02. Proceedings of the 2002 congress on, vol 2. IEEE, pp 1642–1647Google Scholar
- Kling RM, Banerjee P (1987) Esp: a new standard cell placement package using simulated evolution. In: Proceedings of the 24th ACM/IEEE design automation conference. ACM, pp 60–66Google Scholar
- Kramer HH, Petrucci V, Subramanian A, Uchoa E (2012) A column generation approach for power-aware optimization of virtualized heterogeneous server clusters. Comput Ind Eng 63(3):652–662CrossRefGoogle Scholar
- Moore JD, Chase JS, Ranganathan P, Sharma RK (2005) Making scheduling “cool”: temperature-aware workload placement in data centers. In: USENIX annual technical conference, General Track, pp 61–75Google Scholar
- Mukherjee T, Tang Q, Ziesman C, Gupta SK, Cayton P (2007) Software architecture for dynamic thermal management in datacenters. In: 2nd International conference on communication systems software and middleware. IEEE, pp 1–11Google Scholar
- Nahar S, Sahni S, Shragowitz E (1989) Simulated annealing and combinatorial optimization. Int J Comput-Aid VLSI Des 1(1):1–23Google Scholar
- OpenNebula (2015) OpenNebula Scheduling Policies. http://archives.opennebula.org/documentation:rel4.4:schg. Accessed 5 Nov 2015
- Patel CD, Bash CE, Sharma R, Beitelmal M, Friedrich R (2003) Smart cooling of data centers. In: ASME 2003 international electronic packaging technical conference and exhibition. American Society of Mechanical Engineers, pp 129–137Google Scholar
- Pinheiro E, Bianchini R, Carrera EV, Heath T (2001) Load balancing and unbalancing for power and performance in cluster-based systems. In: Workshop on compilers and operating systems for low power, vol 180. Barcelona, pp 182–195Google Scholar
- Sait SM, Bala A, El-Maleh AH (2016) Cuckoo search based resource optimization of datacenters. Appl Intell. doi: 10.1007/s10489-015-0710-x Google Scholar
- Sait SM, Shahid KS (2015) Engineering simulated evolution for virtual machine assignment problem. Appl Intell 43(2):1–12CrossRefGoogle Scholar
- Sait SM, Youssef H (1994) VLSI physical design automation: theory and practice. McGraw-Hill Inc, New YorkGoogle Scholar
- Sait SM, Youssef H (1999) Iterative computer algorithms with applications in engineering: solving combinatorial optimization problems. IEEE Computer Society Press, Los Alamitos, CA, USAGoogle Scholar
- Sullivan RF (2000) Alternating cold and hot aisles provides more reliable cooling for server farms. Uptime Institute, Santa Fe, New MexicoGoogle Scholar
- Systems IT Governance Research Team (2008) In: Green IT: reality, benefits and best practices. IT Governance, Ely,UKGoogle Scholar
- Tang Q, Gupta SK, Varsamopoulos G (2008) Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: a cyber-physical approach. IEEE Trans Parallel Distrib Syst 19(11):1458–1472CrossRefGoogle Scholar
- Tang Q, Mukherjee T, Gupta SK, Cayton P (2006) Sensor-based fast thermal evaluation model for energy efficient high-performance datacenters. In: Fourth international conference on intelligent sensing and information processing (ICISIP). IEEE, pp 203–208Google Scholar
- The Climate Group on behalf of the Global eSustainability Initiative (GeSI) (2008) SMART 2020: enabling the low carbon economy in the information age. Climate GroupGoogle Scholar
- Wang L, von Laszewski G, Dayal J, Furlani TR (2009) Thermal aware workload scheduling with backfilling for green data centers. In: Performance computing and communications conference (IPCCC), IEEE 28th International. IEEE, pp 289–296Google Scholar
- Wang X, Wang Y, Cui Y (2016) An energy-aware bi-level optimization model for multi-job scheduling problems under cloud computing. Soft Comput 20(1):303–317CrossRefGoogle Scholar
- Xu B, Peng Z, Xiao F, Gates AM, Yu JP (2015) Dynamic deployment of virtual machines in cloud computing using multi-objective optimization. Soft Comput 19(8):2265–2273CrossRefGoogle Scholar
- Youssef H, Sait SM, Khan SA (2002) Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm. Eng Appl Artif Intell 15(3):327–340CrossRefGoogle Scholar
- Zheng F, Zecchin AC, Simpson AR (2015) Investigating the run-time searching behavior of the differential evolution algorithm applied to water distribution system optimization. Environ Model Softw 69:292–307CrossRefGoogle Scholar