Abstract
Cloud computing is an emerging area of research and is useful for all level of users from end users to top business companies. There are several research areas of cloud computing including load balancing, cost management, workflow scheduling etc., which has been the current research interest of researchers. To deal with such problems, some conventional methods are developed, which are not so effective. Since, last decade the use of nature inspired optimization in cloud computing is a major area of concern. In this chapter, a detailed (yet brief) survey report on the applicability of nature inspired algorithms in various cloud computing problems is highlighted. The chapter aims at providing a detailed knowledge about nature inspired optimization algorithms and their use in the above mentioned problems of cloud computing. Some future research directions of cloud computing and other application areas are also discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
J. Kennedy, Russell Eberhart, Particle swarm optimization. Proc. IEEE Int. Conf. Neural Networks 4, 1942–1948 (1995)
X.S. Yang, S. Deb, Cuckoo search via Lvy flights, in World Congress on Nature and Biologically Inspired Computing (NaBIC 2009) (IEEE, 2009), pp. 210–214
X.S. Yang, A new meta-heuristic bat-inspired algorithm, Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), p. 6574
K.M. Passino, Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)
D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007)
D. Teodorovi, M. DellOrco, Bee colony optimizationa cooperative learning approach to complex transportation problems, in Advanced OR and AI Methods in Transportation: Proceedings of 16th MiniEURO Conference and 10th Meeting of EWGT (13–16 September 2005) Poznan: Publishing House of the Polish Operational and System Research (2005), pp. 51–60
R. Tang, S. Fong, X.S. Yang, S. Deb, Wolf search algorithm with ephemeral memory, in IEEE Seventh International Conference on Digital Information Management (ICDIM) (2012), pp. 165–172
S.C. Chu, P.W. Tsai, J.S. Pan, Cat swarm optimization, in PRICAI 2006: Trends in artificial intelligence (Springer Berlin Heidelberg, 2006), pp. 854–858
X.S. Yang, Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)
A. Mucherino, O. Seref, Monkey search: a novel metaheuristic search for global optimization. Data Min. Syst. Anal. Optim. Biomed. 953(1), 162–173, AIP Publishing (2007)
G.W. Yan, Z.J. Hao, A novel optimization algorithm based on atmosphere clouds model. Int. J. Comput. Intell. Appl. 1(2 (01)) (2013)
D. Simon, Biogeography-based optimization. IEEE Trans. Evolut. Comput. 12(6), 702–713 (2008)
Y. Shi, An optimization algorithm based on brainstorming process, Emerging Research on Swarm Intelligence and Algorithm Optimization (2014)
R. Storn, K. Price, Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
H. Hernndez, C. Blum, Distributed graph coloring: an approach based on the calling behavior of Japanese tree frogs. Swarm Intell. 6(2), 117–150 (2012)
X.S. Yang, Flower pollination algorithm for global optimization, in Unconventional computation and natural computation (Springer Berlin Heidelberg, 2012), pp. 240–249
A. Mozaffari, A. Fathi, S. Behzadipour, The great salmon run: a novel bio-inspired algorithm for artificial system design and optimization. Int. J. Bio-Inspired Comput. 4(5), 286–301 (2012)
S. He, Q.H. Wu, J.R. Saunders, Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans. Evolut. Comput. 13(5), 973–990 (2009)
L.M. Zhang, C. Dahlmann, Y. Zhang, Human-inspired algorithms for continuous function optimization, in IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009, Vol. 1 (2009), pp. 318–321
A.R. Mehrabian, C. Lucas, A novel numerical optimization algorithm inspired from weed colonization. Ecolo. Inf. 1(4), 355–366 (2006)
U. Premaratne, J. Samarabandu, T. Sidhu, A new biologically inspired optimization algorithm, in International Conference on Industrial and Information Systems (ICIIS) (IEEE, 2009), pp. 279–284
S.H. Jung, Queen-bee evolution for genetic algorithms. Electron. Lett. 39(6) (2003)
R. Hedayatzadeh, F.A. Salmassi, M. Keshtgari, R. Akbari, K. Ziarati, Termite colony optimization: A novel approach for optimizing continuous problems, in 18th Iranian Conference on In Electrical Engineering (ICEE) (IEEE, 2010), pp. 553–558
I. FisterJr, X.S. Yang, I. Fister, J. Brest, D. Fister, A brief review of nature-inspired algorithms for optimization (2013). arXiv preprint arXiv:1307.4186
R. Buyya, C.S. Yeo, S. Venugopal, J. Broberg, I. Brandic, Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)
I. Foster, Y. Zhao, I. Raicu, S. Lu. Cloud computing and grid computing 360-degree compared. in Grid Computing Environments Workshop, IEEE, GCE’08 (2008), pp. 1–10
Q. Zhang, L. Cheng, R. Boutaba, Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)
M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R. Katz, A. Konwinski, G. Lee et al., A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)
P. Mell, T. Grance, The NIST definition of cloud computing. National Institute of Standards and Technology, Information Technology Laboratory, Technical Report Version 15, (2009)
H. Das, G.S. Panda, B. Muduli, P.K. Rath. The complex network analysis of power grid: a case study of the West Bengal power network. in Intelligent Computing, Networking, and Informatics (Springer, New Delhi, 2014), pp. 17–29
Das, Himansu, S.K. Mishra, D.S. Roy, The topological structure of the Odisha power grid: a complex network analysis. IJMCA 1(1), 012–016 (2013)
Das, Himansu, D.S. Roy, A grid computing service for power system monitoring. Int. J. Comput. Appl. 62(20) (2013)
H. Das, A.K. Jena, P.K. Rath, B. Muduli, S.R. Das. Grid computing-based performance analysis of power system: a graph theoretic approach. in Intelligent Computing, Communication and Devices (Springer, New Delhi, 2015), pp. 259–266
I. Kar, R.N Ramakant Parida, H. Das, Energy aware scheduling using genetic algorithm in cloud data centers. in IEEE International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (2016), pp. 3545–3550
Sarkhel, Preeta, H. Das, L.K. Vashishtha. Task-Scheduling Algorithms in Cloud Environment. in Computational Intelligence in Data Mining (Springer, Singapore, 2017), pp. 553–562
C.R. Panigrahi, M. Tiwary, B. Pati, H. Das, Big Data and Cyber Foraging: Future Scope and Challenges. in Techniques and Environments for Big Data Analysis (Springer International Publishing, 2016), pp. 75–100
X. Xin, H. Yu, A game theory approach to fair and efficient resource allocation in cloud computing. Math. Probl. Eng. 2014, 1–14 (2014)
L. Mashayekhy, M.M. Nejad, D. Grosu, Cloud federations in the sky: Formation game and mechanism. IEEE Trans. Cloud Comput. 3(1), 14–27 (2015)
A. Corradi, M. Fanelli, L. Foschini, VM consolidation: A real case based on openstack cloud. Future Gener. Comput. Syst. 32, 118–127 (2014)
S. Nedevschi, L. Popa, G. Iannaccone, S. Ratnasamy, D. Wetherall, Reducing network energy consumption via sleeping and rate-adaptation, in NSDI, Vol. 8 (2008), p. 323336
P. Arroba et al., Server power modeling for run-time energy optimization of cloud computing facilities. Energy Procedia 62, 401–410 (2014)
M.A. Sharkh, A. Shami, An evergreen cloud: Optimizing energy efficiency in heterogeneous cloud computing architectures. Veh. Commun. 9(11), 199–210 (2017)
D.-M. Bui et al., Energy efficiency for cloud computing system based on predictive optimization. J. Parallel Distrib. Comput. 102, 103–114 (2017)
Chun-Han Lin, Pi-Cheng Hsiu, Cheng-Kang Hsieh, Dynamic backlight scaling optimization: a cloud-based energy-saving service for mobile streaming applications. IEEE Trans. Comput. 63(2), 335–348 (2014)
M. Ferrara, E. Fabrizio, J. Virgone, M. Filippi, A simulation-based optimization method for cost-optimal analysis of nearly zero energy buildings. Energy Build. 84, 442–457 (2014)
S. Wang, Z. Liu, Z. Zheng, Q. Sun, F. Yang, Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centers, in IEEE International Conference on Parallel and Distributed Systems (ICPADS) (2013), pp. 102–109
H. Goudarzi, M. Ghasemazar, M. Pedram, IEEE 12th IEEE/ACM International Symposium on SLA-based optimization of power and migration cost in cloud computing (Cloud and Grid Computing (CCGrid), In Cluster, 2012), pp. 172–179
W. Shu, W. Wang, Y. Wang, A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing. EURASIP J. Wirel. Commun. Netw. 1 (2014)
A.P. Xiong, C.-X. Xu, Energy efficient multiresource allocation of virtual machine based on PSO in cloud data center. Math. Probl. Eng. (2014)
X.F. Liu, Z.H. Zhan, K.J. Du, W.N. Chen, Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach. in Proceedings of the 2014 ACM Annual Conference on Genetic and Evolutionary Computation (2014), pp. 41–48
D.L. Eager, E.D. Lazowska, J. Zahorjan, Adaptive load sharing in homogeneous 647 distributed systems. IEEE Trans. Softw. Eng. 12(5), 662675
E.J. Ghomi, A.M. Rahmani, N.N. Qader, Load-balancing Algorithms in Cloud Computing: A Survey. J. Netw. Comput. Appl. 88, 50–71 (2017)
A.S. Milani, N.J. Navimipour, Load balancing mechanisms and techniques in the cloud environments: systematic literature review and future trends. J. Netw. Comput. Appl. 71, 8698 (2016)
M. Mesbahi, A.M. Rahmani, Load balancing in cloud computing: a state of the art survey. Int. J. Mod. Educ. Comput. Sci. 8(3) (2016)
V.R.T. Kanakala, V.K. Reddy, Performance analysis of load balancing techniques in cloud computing environment. TELKOMNIKA Indones. J. Electr. Eng. 13(3), 568573 (2015a)
V.R.T. Kanakala, V.K. Reddy, Performance analysis of load balancing techniques in cloud computing environment. TELKOMNIKA Indones. J. Electr. Eng. 13(3), 568573 (2015b)
I.N. Ivanisenko, T.A. Radivilova, Survey of major load balancing algorithms in distributed system. in IEEE Information Technologies in Innovation Business Conference (ITIB) (2015), pp. 89–92
A.A.S. Farrag, S.A. Mahmoud, Intelligent Cloud Algorithms for Load Balancing problems: A Survey. IEEE in Proceedings of the Seventh International Conference on Intelligent Computing and Information Systems (ICICIS ’J 5) (2015), pp. 210–216
L.D. Dhinesh Babu, P. Venkata Krishna, Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13(5), 2292–2303 (2013)
K. Nishant, P. Sharma, V. Krishna, C. Gupta, K.P. Singh, R. Rastogi, Load balancing of nodes in cloud using ant colony optimization. in IEEE UKSim 14th International Conference on Computer Modelling and Simulation (UKSim) (2012), pp. 3–8
J. Gu, J. Hu, T. Zhao, G. Sun, A new resource scheduling strategy based on genetic algorithm in cloud computing environment. JCP J. Comput. 7(1), 42–52 (2012)
F. Ramezani, J. Lu, F.K. Hussain, Task-based system load balancing in cloud computing using particle swarm optimization. Int. J. Parallel Progm. Int. J. Parallel Program. 42(5), 739–754 (2013)
K. Dasgupta, B. Mandai, P. Dutta, J.K. Mandai, S. Dam, A Genetic Algorithm (GA) based load balancing strategy for cloud computing. Int. Conf. Comput. Intell. Model. Tech. Appl. 10, 340–347 (2013)
B.K. Ruekaew, W. Kimpan, Virtual machine scheduling management on cloud computing using artificial bee colony, in Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 1 (2014)
H. Yuan, C. Li, M. Du, Optimal virtual machine resources scheduling based on improved particle swarm optimization in cloud computing. JSW J. Softw. 9(3), 705–708 (2014)
M.A. Tawfeek, A. EI-Sisi, A.E. Keshk, F.A. Torkey, Cloud task scheduling based on ant colony optimization, in 2013 8th International Conference on Computer Engineering & Systems (ICCES), vol. 12, no. 2 (2015), pp. 64–69
A. Al - maamari, F.A. Omara, Task scheduling using hybrid algorithm in cloud computing environments. IOSR J. Comput. Eng. 17(3), 96–106 (2015)
S. Dam, G. Mandal, K. Dasgupta, P. Dutta, Genetic algorithm and gravitational emulation based hybrid load balancing strategy in cloud computing. in IEEE Third International Conference on 2015 Computer, Communication, Control and Information Technology (C3it) (2015), pp. 1–7
T.S. Ashwin, S.G. Domanal, R.M. Guddeti, A Novel Bio-Inspired Load Balancing Of Virtual Machines In Cloud Environment. in IEEE International Conference On Cloud Computing In Emerging Markets (Ccem) (2014), pp. 1–4
S. Aslanzadeh, Z. Chaczko, Load balancing optimization in cloud computing: applying endocrine-particle swarm optimization. in 2015 IEEE International Conference On Electro/Information Technology (Eit) (2015), pp. 165–169
T. Wang, Z. Liu, Y. Chen, Y. Xu, X. Dai, Load balancing task scheduling based on genetic algorithm in cloud computing, in IEEE 12th International Conference On Dependable. Autonomic And Secure Computing (Dasc) (2014), pp. 146–152
Z. Zhang, X. Zhang, A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation. in 2010 IEEE 2nd International Conference On 2010 May 30 IEEE Industrial Mechatronics And Automation (Icima), Vol. 2, pp. 240–243
E. Gupta, V. Deshpande, A technique based on ant colony optimization for load balancing in cloud data center. in 2014 IEEE International Conference On Information Technology (Icit) (2014), pp. 12–17
R. Kaur, N. Ghumman, Hybrid improved max min ant algorithm for load balancing in cloud. in International Conference On Communication, Computing and Systems (IEEE Icccs2014)
K. Pan, J. Chen, Load balancing in cloud computing environment based on an improved particle swarm optimization. in 2015 6th IEEE International Conference On Software Engineering And Service Science (Icsess) (2015), pp. 595–598
K.R. Babu, A.A. Joy, P. Samuel, Load balancing of tasks in cloud computing environment based on bee colony algorithm. in 2015 IEEE Fifth International Conference On Advances In Computing And Communications (Icacc) (2015), pp. 89–93
R. Achar, P.S. Thilagam, N. Soans, P.V. Vikyath, S. Rao, A.M. Vijeth, Load balancing in cloud based on live migration of virtual machines. in 2013 Annual IEEE India Conference (Indicon) (2013), pp. 1–5
F. Ramezani, J. Lu, F.K. Hussain, Task-based system load balancing in cloud computing using particle swarm optimization. Int. J. Parallel Program. 42(5), 739–754 (2014)
S. Mohanty, P.K. Patra, S. Mohapatra, M. Ray, MPSO: A Novel Meta-Heuristics for Load Balancing in Cloud Computing. Int. J. Appl. Evolut. Comput. (IJAEC) 8(1), 1–25 (2017)
K.M. Cho, P.W. Tsai, C.W. Tsai, C.S. Yang, A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Comput. Appl. 26(6), 1297–1309 (2015)
D. Gabi, A.S. Ismail, A. Zainal, Z. Zakaria, Solving task scheduling problem in cloud computing environment using orthogonal Taguchi-Cat algorithm. Int. J. Electr. Comput. Eng. (IJECE) 7(3) (2017)
M.G. Avram, Advantages and challenges of adopting cloud computing from an enterprise perspective. Procedia Technol. 12, 529534 (2014)
M.D. Assuno, A. Costanzo, R. Buyya, A cost-benefit analysis of using cloud computing to extend the capacity of clusters. Clust. Comput. 13(3), 335347 (2010)
Y. Zhu, P. Liu, Multi-dimensional constrained cloud computing task scheduling mechanism based on genetic algorithm. Int. J. Online Eng. 9, 1518 (2013)
L. Wu, Y.J. Wang, C.K. Yan, Performance comparison of energy-aware task scheduling with GA and CRO algorithms in cloud environment, in Applied Mechanics and Materials (2014), p. 204208
F. Taoa, L.Z. Ying Fengb, T.W. Liaoc, CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Appl. Soft Comput. 19, 264279 (2014)
K. Li, G. Xu, G. Zhao, Y. Dong, D. Wang, Cloud task scheduling based on load balancing ant colony optimization. in 2011 IEEE Sixth Annual Chinagrid Conference (ChinaGrid) (2011), pp. 3–9
S. Xue et al., An ACO-LB algorithm for task scheduling in the cloud environment. J. Softw. 9(2), 466473 (2014)
S. Xue, J. Zhang, X. Xu, An improved algorithm based on ACO for cloud service PDTs scheduling. Adv. Inf. Sci. Serv. Sci. 4(18), 340348 (2012)
S. Kaur, A. Verma, An efficient approach to genetic algorithm for task scheduling in cloud computing environment. Int. J. Inf. Technol. Comput. Sci. 10, 7479 (2012)
Z. Yang et al., Optimized task scheduling and resource allocation in cloud computing using PSO based fitness function. Inf. Technol. J. 12(23), 70907095 (2013)
S. Zhan, H. Huo, Improved PSO-based task scheduling algorithm in cloud computing. J. Inf. Comput. Sci. 9(13), 38213829 (2012)
K. Kaur, N. Kaur, K. Kaur, A Novel Context and Load-Aware Family Genetic Algorithm Based Task Scheduling in Cloud Computing, Data Engineering and Intelligent Computing (Springer, Singapore, 2018), pp. 521–531
H.B. Alla, S.B. Alla, A. Ezzati, A. Mouhsen, A novel architecture with dynamic queues based on fuzzy logic and particle swarm optimization algorithm for task scheduling in cloud computing. in Advances in Ubiquitous Networking 2 (Springer, Singapore, 2017) pp. 205–217
K.R. Kumari, P. Sengottuvelan, J. Shanthini, A hybrid approach of genetic algorithm and multi objective PSO task scheduling in cloud computing. Asian J. Res. Soc. Sci. Humanit. 7(3), 1260–1271 (2017)
W.-J. Wang, Y.-S. Chang, W.-T. Lo, Y.K. Lee, Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments. J. Supercomput. 66(2), 129 (2013)
E.N. Alkhanak, S.P. Lee, R. Rezaei, R.M. Parizi, Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues. J. Syst. Softw. 113, 1–26 (2016)
W.N. Chen, J. Zhang, A set-based discrete PSO for cloud workflow scheduling with user-defined QoS constraints. in 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2012), pp. 773–778
Z. Wu, Z. Ni, L. Gu, X. Liu, A revised discrete particle swarm optimization for cloud workflow scheduling. in 2010 IEEE International Conference on Computational Intelligence and Security (CIS) (2010), pp. 184–188
S. Pandey, L. Wu, S.M. Guru, R. Buyya, A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. in 2010 24th IEEE international conference on Advanced information networking and applications (AINA) (2010), pp. 400–407
Z. Wu, X. Liu, Z. Ni, D. Yuan, Y. Yang, A market-oriented hierarchical scheduling strategy in cloud workflow systems. J. Supercomput. 1–38 (2013)
C. Wei-Neng, J. Zhang, An ant colony optimization approach to a grid workflow scheduling problem with various QoS requirements. in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 39, No. 1 (2009), pp. 29–43
H. Liu, D. Xu, H.K. Miao, Ant colony optimization based service flow scheduling with various QoS requirements in cloud computing. in IEEE 2011 First ACIS International Symposium on Software and Network Engineering (SSNE), pp. 53–58
J. Yu, R. Buyya, Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci. Program. 14, 217–230 (2006)
A.K. Talukder, M. Kirley, R. Buyya, Multiobjective differential evolution for scheduling workflow applications on global grids. Concurr. Comput. Pract. Exp. 21(13), 1742–1756 (2009)
S. Pandey, L. Wu, S.M. Guru, R. Buyya, A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. in IEEE 2010 24th IEEE international conference on Advanced information networking and applications (AINA) (2010), pp. 400–407
H.B. Alla, S.B. Alla, A. Ezzati, A. Mouhsen, A novel architecture with dynamic queues based on fuzzy logic and particle swarm optimization algorithm for task scheduling in cloud computing. in Advances in Ubiquitous Networking 2 (Springer, Singapore, 2017), pp. 205–217
B. Kumar, M. Kalra, P. Singh, Discrete binary cat swarm optimization for scheduling workflow applications in cloud systems. in 3rd International Conference on Computational Intelligence and Communication Technology (CICT) (IEEE, 2017), pp. 1–6
S. Prathibha, B. Latha, G. Suamthi, Particle swarm optimization based workflow scheduling for medical applications in cloud. Biomed. Res. 1(1) (2017)
D. Gabi, A.S. Ismail, A. Zainal, Z. Zakaria, Solving Task Scheduling Problem in Cloud Computing Environment Using Orthogonal Taguchi-Cat Algorithm. Int. J. Electr. Comput. Eng. (IJECE) 7(3) (2017)
Y. Xu, K. Li, L. He, L. Zhang, K. Li, A hybrid chemical reaction optimization scheme for task scheduling on heterogeneous computing systems. IEEE Trans. parallel Distribut. Syst. 26(12), 3208–3222 (2015)
Y. Jiang, Z. Shao, Y. Guo, A DAG scheduling scheme on heterogeneous computing systems using tuple-based chemical reaction optimization. Sci. World J. (2014)
S.S. Kim, J.H. Byeon, H. Yu, H. Liu, Biogeography-based optimization for optimal job scheduling in cloud computing. Appl. Math. Comput. 247, 266–280 (2014)
V. Kumari, M. Kalra, S. Singh, Independent task scheduling in cloud environment using Big Bang-Big Crunch approach. in 2nd International Conference on Recent Advances in Engineering and Computational Sciences (RAECS) (IEEE, 2015), pp. 1–4
S. Selvarani, G. Sadhasivam, An intelligent water drop algorithm for optimizing task scheduling in grid environment. Int. Arab J. Inf. Technol. 13(6) (2016)
G. Guo-Ning, H. Ting-Lei, Genetic simulated annealing algorithm for task scheduling based on cloud computing environment, in Proceedings of International Conference on Intelligent Computing and Integrated Systems (2010), pp. 60–63
Q. Zhang, L. Cheng, R. Boutaba, Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)
XenSource Inc, Xen, www.xensource.com
F. Farahnakian, A. Ashraf, T. Pahikkala, P. Liljeberg, J. Plosila, I. Porres, H. Tenhunen, Using ant colony system to consolidate VMs for green cloud computing. IEEE Trans. Serv. Comput. 8(2), 187–198 (2015)
S.E. Dashti, A.M. Rahmani, Dynamic VMs placement for energy efficiency by PSO in cloud computing. J. Exp. Theor. Artif. Intell. 28, 97–112 (2016)
F. Farahnakian, A. Ashraf, P. Liljeberg, T. Pahikkala, J. Plosila, I. Porres, H. Tenhunen, Energy-aware dynamic VM consolidation in cloud data centers using ant colony system. in Cloud Computing (CLOUD) (2014), pp. 104–111
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Nayak, J., Naik, B., Jena, A.K., Barik, R.K., Das, H. (2018). Nature Inspired Optimizations in Cloud Computing: Applications and Challenges. In: Mishra, B., Das, H., Dehuri, S., Jagadev, A. (eds) Cloud Computing for Optimization: Foundations, Applications, and Challenges. Studies in Big Data, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-73676-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-73676-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73675-4
Online ISBN: 978-3-319-73676-1
eBook Packages: EngineeringEngineering (R0)