Stochastic Models for Optimizing Availability, Cost and Sustainability of Data Center Power Architectures through Genetic Algorithm
DOI:
https://doi.org/10.22456/2175-2745.83498Keywords:
Genetic Algorithm, Energy Flow Model, Sustainability, Stochastic Petri Net, Reliability Block Diagrams, AvailabilityAbstract
In recent years, the growth of information technology has required higher reliability, accessibility, collaboration, availability, and a reduction of costs on data centers due to factors such as social network, cloud computing, and e-commerce. These systems require redundant mechanisms on the data center infrastrucutre to achieve high availability, which may increase the electric energy consumption, impacting in both the sustainability and cost. This work proposes a multi-objective optimization approach, based on Genetic Algorithms, to optimize cost, sustainability and availability of data center power infrastructures. The main goal is to maximize availability and minimize cost and exergy consumed (adopted to estimate the environmental impacts). In order to compute such metrics, this work adopts the energy flow model (EFM), reliability block diagrams (RBD) and stochastic petri nets (SPN). Two case studies are conducted to show the applicability of the proposed strategy: (i) takes into account 5 typical data center architectures that were optimized to conduct the validation process of the proposed strategy; (ii) uses the optimization strategy in two architectures classified by ANSI / TIA-942 (TIER I and II). In both case studies, significant improvements were achieved in the results, which were very close to the optimum one that was obtained by a brute force algorithm that analyzes all the possibilities and returns the optimal solution. It is worth mentioning that the time used to obtain the results using the genetic algorithm approach was significantly lower (6,763,260 times), in comparison with the strategy which combines all the possible combinations to obtain the optimal result.
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CALLOU, G. et al. Estimating sustainability impact of high dependable data centers: A comparative study between brazilian and us energy mixes. Computing, Springer, v. 95, n. 12, p. 1137–1170, 2013.
CALLOU, G. et al. An integrated modeling approach to evaluate and optimize data center sustainability, dependability and cost. Energies, Multidisciplinary Digital Publishing Institute, v. 7, n. 1, p. 238–277, 2014.
FIGUEIREDO, J. et al. Estimating reliability importance and total cost of acquisition for data center power infrastructures. In: Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on. [S.l.: s.n.], 2011. p. 421–426.
SILVA, B. et al. An integrated environment for performance and dependability evaluation of general systems. In: 45th Dependable Systems and Networks Conference. [S.l.: s.n.], 2015. v, p. 1–6.
ASSOCIATION, T. I. Telecommunications Infrastructure Standard for Data centers ANSI/TIA-942. [S.l.]: Telecommunications Industry Association, 2005.
ROSA, R.; ARANDA, M.; ANTONIOLLI, P. Segurança física em datacenters: estudo de caso. Refas-Revista Fatec Zona Sul, v. 3, n. 4, p. 1–22, 2017.
AVIZIENIS, A.; LAPRIE, J.; RANDELL, B. Fundamental Concepts of Dependability. Technical Report Series-University of Newcastle upon Tyne Computing Science, 2001.
MEYER, J. F.; SANDERS, W. H. Specification and construction of performability models. In: Proceedings of the Second International Workshop on Performability Modeling of Computer and Communication Systems. [S.l.: s.n.], 1993. p. 28–30.
AVIZIENIS, A. et al. Basic concepts and taxonomy of dependable and secure computing. Dependable and Secure Computing, IEEE Transactions on, v. 1, n. 1, p. 11 – 33, jan.-march 2004.
PRADHAN, D. K. Fault-tolerant computer system design. [S.l.]: Prentice-Hall, 1996.
MACIEL, P. et al. Performance and dependability in service computing: Concepts, techniques and research directions. In: . [S.l.]: Igi Global, 2011. (Premier Reference Source), cap. Dependability Modeling.
SOUSA, E. Modelagem de desempenho, dependabilidade e custo para o planejamento de infraestruturas de nuvens privadas. Tese (Doutorado em Ciência da Computação) — Centro de Informática, Universidade Federal de Pernambuco, 2015.
ANDRADE, E. et al. Availability modeling and analysisof a disaster-recovery-as-a-service solution. Computing, Springer, V, p. 1–26, 2017.
KOTAS, T. J. The exergy method of thermal plant analysis. Butterworth Publishers, Stoneham, MA, 1985.
SZARGUT, J.; MORRIS, D.; STEWARD, F. Energy analysis of thermal, chemical, and metallurgical processes. Hemisphere Publishing, New York, NY, 1988.
ALVES, G. et al. Supply chain management - applications and simulations. In: . [S.l.]: InTech, 2011. cap. 8 - Business and Environment Performance Evaluation in Supply Chains: a Formal Model-Driven Approach, p. 157–182.
FERREIRA, J. et al. An algorithm to optimize electrical flows. In: IEEE COMPUTER SOCIETY. Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics. [S.l.], 2013. p. 109–114.
MURATA, T. Petri nets: Properties, analysis and applications. Proceedings of the IEEE, IEEE, v. 77, n. 4, p. 541–580, 1989.
MARSAN, M. et al. Modelling with Generalized Stochastic Petri Nets. ACM SIGMETRICS Performance Evaluation Review, ACM Press New York, NY, USA, v. 26, n. 2, 1998.
TRIVEDI, K. Probability and Statistics with Reliability, Queueing, and Computer Science Applications. 2. ed. [S.l.]: Wiley Interscience Publication, 2002.
ARAúJO, C. Avaliação e modelagem de desempenho para planejamento de capacidade do sistema de transferência eletrônica de fundos utilizando tráfego em rajada. Dissertação (Mestrado em Ciência da Computação) — Universidade Federal de Pernambuco, Centro de Informática, 2009.
SILVA, B. A framework for availability, performance and survivability evaluation of disaster tolerant cloud computing systems. Tese (Doutorado) — Centro de Informática, Universidade Federal de Pernambuco, Recife, 2016.
MARSAN, M. A. et al. Modelling with generalized stochastic petri nets. ACM SIGMETRICS Performance Evaluation Review, ACM, v. 26, n. 2, p. 2, 1998.
BALBO, G. Introduction to stochastic petri nets. Lectures on Formal Methods and Performance Analysis: First EEF/Euro Summer School on Trends in Computer Science, Berg en Dal, The Netherlands, July 3-7, 2000: Revised Lectures, Springer, 2001.
ARAúJO, C. et al. Availability evaluation of digital library cloud services. In: Dependable Systems and Networks (DSN), 2014 44th Annual IEEE/IFIP International Conference on. [S.l.: s.n.], 2014. p. 666–671.
EBELING, C. An Introduction to Reliability and Maintainability Engineering. [S.l.]: Waveland Press, 1997.
CALLOU, G. Planning of Sustainable Data Centers with High Availability: An Integrated Modeling Approach to Evaluate and
Optimize Sustainability, Dependability and Cost of Data Center Systems. [S.l.]: Lambert Academic Publishing, 2014.
GOLDBERG, D. E. Genetic algorithms in search, optimization, and machine learning. [S.l.]: Reading: Addison-Wesley, 1989.
VASCONCELLOS, D.; ABRIL, I.; MARTÍNEZ, V. Compensación de potencia reactiva en sistemas desbalanceados utilizando algoritmos genéticos. Ingeniare. Revista chilena de ingeniería, SciELO Chile, v. 20, n. 3, p. 284–292, 2012.
LUCAS, D. Algoritmos Genéticos: uma Introdução. 2002. Universidade Federal do Rio Grande do Sul. Apostila elaborada sob a orientação de Luís Otávio Álvares, para a disciplina de Ferramentas de Inteligência Artificial.
PAPPA, G. Seleção de atributos utilizando Algoritmos Genéticos multiobjetivos. Dissertação (Mestrado) — Programa de Pós Graduação em Informática Aplicada da Pontifícia Universidade Católica do Paraná. Curitiba, 2002.
HINTERDING, R. Representation, mutation and crossover issues in evolutionary computation. In: IEEE. Evolutionary Computation, 2000. Proceedings of the 2000 Congress on. [S.l.], 2000. v. 2, p. 916–923.
KAR, I.; PARIDA, R. N. R.; DAS, H. Energy aware scheduling using genetic algorithm in cloud data centers. In: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). [S.l.: s.n.], 2016. p. 3545–3550.
LUO, L. et al. A resource optimization algorithm of cloud data center based on correlated model of reliability, performance and energy. In: 2016 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). [S.l.: s.n.], 2016. p. 416–417.
BOSSE, S. et al. Introducing greenhouse emissions in cost optimization of fault-tolerant data center design. In: 2016 IEEE 18th Conference on Business Informatics (CBI). [S.l.: s.n.], 2016. v. 01, p. 163–172.
ZIAFAT, H.; BABAMIR, S. M. Towards optimization of availability and cost in selection of geo-distributed clouds datacenter. In: 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT). [S.l.: s.n.], 2016. p. 1–5.
OLIVEIRA, D. M. et al. Advanced stochastic petri net modeling with the mercury scripting language. In: 11th EAI International Conference on Performance Evaluation Methodologies and Tools. [S.l.: s.n.], 2017.
MARWAH, M. et al. Quantifying the sustainability impact of data center availability. SIGMETRICS Performance Evaluation Review, v. 37, p. 64–68, 2010.
IEEE Gold Book 493, Design of Reliable Industrial and Commercial Power Systems. [S.l.]: IEEE, 2007.
