Embedding Virtual Machines in Cloud Computing Based on Big Bang–Big Crunch AlgorithmResearch Areas : Cloud computing
Afshin Mahdavi 2
Keywords: Cloud computing, , Virtual machine, , Big Bang–Big Crunch algorithm, , Energy, , Service level agreement, ,
Cloud computing is becoming an important and adoptable technology for many of the organization which requires a large amount of physical tools. In this technology, services are provided and presented according to users’ requests. Due to the presence of a large number of data centers in cloud computing, power consumption has recently become an important issue. However, data centers hosting Cloud applications consume huge amounts of electrical energy and contributing to high operational costs to the environment. Therefore, we need Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact. Live migration of virtual machines and their scheduling and embedding lead to enhanced efficiency of dynamic resources. The guarantee of service quality and service reliability is an indispensable and irrevocable requirement with respect to service level agreement. Hence, providing a method for reducing costs of power consumption, data transmission, bandwidth and, also, for enhancing quality of service (QoS) in cloud computing is critical. In this paper, a Big Bang–Big Crunch (BB-BC) based algorithm for embedding virtual machines in cloud computing was proposed. We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. Simulation results indicate that the proposed method not only enhances service quality, thanks to the reduction of agreement violation, but also reduces power consumption.
 M. H. Ghahramani, M. Zhou, and C. T. Hon, "Toward cloud computing QoS architecture: Analysis of cloud systems and cloud services," IEEE/CAA Journal of Automatica Sinica, vol. 4, no. 1, pp. 6-18, 2017.
 B. Varghese and R. Buyya, "Next generation cloud computing: New trends and research directions," Future Generation Computer Systems, vol. 79, pp. 849-861, 2018.
 M. Noshy, A. Ibrahim, and H. A. Ali, "Optimization of live virtual machine migration in cloud computing: A survey and future directions," Journal of Network and Computer Applications, vol. 110, pp. 1-10, 2018.
 A. Ghaffari, "Designing a wireless sensor network for ocean status notification system," Indian Journal of Science and Technology, vol. 7, no. 6, p. 809, 2014.
 A. Ghaffari and A. Rahmani, "Fault tolerant model for data dissemination in wireless sensor networks," in 2008 International Symposium on Information Technology, 2008, vol. 4: IEEE, pp. 1-8.
 D. KeyKhosravi, A. Ghaffari, A. Hosseinalipour, and B. A. Khasragi, "New Clustering Protocol to Decrease Probability Failure Nodes and Increasing the Lifetime in WSNs," Int. J. Adv. Comp. Techn., vol. 2, no. 2, pp. 117-121, 2010.
 A. Ghaffari, "Vulnerability and security of mobile ad hoc networks," in Proceedings of the 6th WSEAS international conference on simulation, modelling and optimization, 2006: World Scientific and Engineering Academy and Society (WSEAS), pp. 124-129.
 A. Ghaffari, "Real-time routing algorithm for mobile ad hoc networks using reinforcement learning and heuristic algorithms," Wireless Networks, vol. 23, no. 3, pp. 703-714, 2017.
 R. Mohammadi and A. Ghaffari, "Optimizing reliability through network coding in wireless multimedia sensor networks," Indian Journal of Science and Technology, vol. 8, no. 9, p. 834, 2015.
 W. Shu, W. Wang, and Y. Wang, "A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing," EURASIP Journal on Wireless Communications and Networking, vol. 2014, no. 1, p. 64, 2014.
 M. Gahlawat and P. Sharma, "Survey of virtual machine placement in federated clouds," in 2014 IEEE International Advance Computing Conference (IACC), 2014: IEEE, pp. 735-738.
 Z. Xiao, W. Song, and Q. Chen, "Dynamic resource allocation using virtual machines for cloud computing environment," IEEE transactions on parallel and distributed systems, vol. 24, no. 6, pp. 1107-1117, 2012.
 M. Masdari, S. S. Nabavi, and V. Ahmadi, "An overview of virtual machine placement schemes in cloud computing," Journal of Network and Computer Applications, vol. 66, pp. 106-127, 2016.
 F. López-Pires, B. Barán, L. Benítez, S. Zalimben, and A. Amarilla, "Virtual machine placement for elastic infrastructures in overbooked cloud computing datacenters under uncertainty," Future Generation Computer Systems, vol. 79, pp. 830-848, 2018.
 S. Sotiriadis, N. Bessis, and R. Buyya, "Self managed virtual machine scheduling in Cloud systems," Information Sciences, vol. 433, pp. 381-400, 2018.
A. Kamalinia and A. Ghaffari, "Hybrid task scheduling method for cloud computing by genetic and PSO algorithms," J. Inf. Syst. Telecommun, vol. 4, pp. 271-281, 2016.
A. Kamalinia and A. Ghaffari, "Hybrid task scheduling method for cloud computing by genetic and DE algorithms," Wireless Personal Communications, vol. 97, no. 4, pp. 6301-6323, 2017.
 V. Priya and C. N. K. Babu, "Moving average fuzzy resource scheduling for virtualized cloud data services," Computer Standards & Interfaces, vol. 50, pp. 251-257, 2017.
 M. Elhoseny, A. Abdelaziz, A. S. Salama, A. M. Riad, K. Muhammad, and A. K. Sangaiah, "A hybrid model of internet of things and cloud computing to manage big data in health services applications," Future generation computer systems, vol. 86, pp. 1383-1394, 2018.
 A. Satpathy, S. K. Addya, A. K. Turuk, B. Majhi, and G. Sahoo, "Crow search based virtual machine placement strategy in cloud data centers with live migration," Computers & Electrical Engineering, vol. 69, pp. 334-350, 2018.
K. R. Remesh Babu and P. Samuel, "Service‐level agreement–aware scheduling and load balancing of tasks in cloud," Software: Practice and Experience, vol. 49, no. 6, pp. 995-1012, 2019.
 P. R. Theja and S. K. Babu, "Evolutionary computing based on QoS oriented energy efficient VM consolidation scheme for large scale cloud data centers," Cybernetics and Information Technologies, vol. 16, no. 2, pp. 97-112, 2016.
 M. Abdel-Basset, L. Abdle-Fatah, and A. K. Sangaiah, "An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment," Cluster Computing, pp. 1-16, 2018.
 X. Fu, J. Chen, S. Deng, J. Wang, and L. Zhang, "Layered virtual machine migration algorithm for network resource balancing in cloud computing," Frontiers of Computer Science, vol. 12, no. 1, pp. 75-85, 2018.
 Z. Ning, X. Kong, F. Xia, W. Hou, and X. Wang, "Green and sustainable cloud of things: Enabling collaborative edge computing," IEEE Communications Magazine, vol. 57, no. 1, pp. 72-78, 2018.
 H. Wang and H. Tianfield, "Energy-aware dynamic virtual machine consolidation for cloud datacenters," IEEE Access, vol. 6, pp. 15259-15273, 2018.
 M. S. Mekala and P. Viswanathan, "Energy-efficient virtual machine selection based on resource ranking and utilization factor approach in cloud computing for IoT," Computers & Electrical Engineering, vol. 73, pp. 227-244, 2019.
 O. K. Erol and I. Eksin, "A new optimization method: big bang–big crunch," Advances in Engineering Software, vol. 37, no. 2, pp. 106-111, 2006.
 P. Zhang and M. Zhou, "Dynamic cloud task scheduling based on a two-stage strategy," IEEE Transactions on Automation Science and Engineering, vol. 15, no. 2, pp. 772-783, 2017.
 S. Chaisiri, B.-S. Lee, and D. Niyato, "Optimal virtual machine placement across multiple cloud providers," in 2009 IEEE Asia-Pacific Services Computing Conference (APSCC), 2009: IEEE, pp. 103-110.
 J. Gao and G. Tang, "Virtual Machine Placement Strategy Research," in 2013 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2013: IEEE, pp. 294-297.
 M. Hemalatha, "Cluster based BEE algorithm for virtual machine placement in cloud data center," Journal of Theoretical & Applied Information Technology, vol. 57, no. 3, 2013.
 W. Song, Z. Xiao, Q. Chen, and H. Luo, "Adaptive resource provisioning for the cloud using online bin packing," IEEE Transactions on Computers, vol. 63, no. 11, pp. 2647-2660, 2013.
 N. Janani, R. S. Jegan, and P. Prakash, "Optimization of virtual machine placement in cloud environment using genetic algorithm," Research Journal of Applied Sciences, Engineering and Technology, vol. 10, no. 3, pp. 274-287, 2015.
 A. Beloglazov, J. Abawajy, and R. Buyya, "Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing," Future generation computer systems, vol. 28, no. 5, pp. 755-768, 2012.
 T. Shabeera, S. M. Kumar, S. M. Salam, and K. M. Krishnan, "Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm," Engineering Science and Technology, an International Journal, vol. 20, no. 2, pp. 616-628, 2017.
 D. Kesavaraja and A. Shenbagavalli, "QoE enhancement in cloud virtual machine allocation using Eagle strategy of hybrid krill herd optimization," Journal of Parallel and Distributed Computing, vol. 118, pp. 267-279, 2018.
 F. Farhadian, M. M. R. Kashani, J. Rezazadeh, R. Farahbakhsh, and K. Sandrasegaran, "An efficient IoT cloud energy consumption based on genetic algorithm," Digital Communications and Networks, 2019.
 X. Zhang et al., "Energy-aware virtual machine allocation for cloud with resource reservation," Journal of Systems and Software, vol. 147, pp. 147-161, 2019.
 X. Xiao, W. Zheng, Y. Xia, X. Sun, Q. Peng, and Y. Guo, "A workload-aware VM consolidation method based on coalitional game for energy-saving in cloud," IEEE Access, vol. 7, pp. 80421-80430, 2019.
 H. Yuan, J. Bi, and M. Zhou, "Spatial Task Scheduling for Cost Minimization in Distributed Green Cloud Data Centers," IEEE Transactions on Automation Science and Engineering, vol. 16, no. 2, pp. 729-740, 2018.
 K. R. Babu and P. Samuel, "Energy aware clustered load balancing in cloud computing environment," International Journal of Networking and Virtual Organisations, vol. 19, no. 2-4, pp. 305-320, 2018.
 A. Beloglazov and R. Buyya, "Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers," Concurrency and Computation: Practice and Experience, vol. 24, no. 13, pp. 1397-1420, 2012.