Design and Development of an Energy Efficient Multimedia Cloud Data Center with Minimal SLA Violation.

Nirmal Kr. Biswas; Sourav Banerjee; Utpal Biswas

doi:10.9781/ijimai.2021.04.004

Authors

Nirmal Kr. Biswas Global Academy of Technology
Sourav Banerjee Indian Institute of Information Technology Kalyani
Utpal Biswas University of Kalyani

DOI:

https://doi.org/10.9781/ijimai.2021.04.004

Keywords:

Energy, Virtual Machine (VM), Cloud Computing, Multimedia Cloud (MC), SLA Violation (SLAV)

Supporting Agencies

We want to offer our sincere gratitude to Dr. Rajkumar Buyya, one of the renowned researchers behind the inventions and progressive research directions towards cloud computing. One of his excellent contributions to cloud simulation is CloudSim, which has helped thousands of researchers to test different cloud computing algorithms very rapidly. His research work has motivated us to develop the MdSSUR VM selection policy.

Abstract

Multimedia computing (MC) is rising as a nascent computing paradigm to process multimedia applications and provide efficient multimedia cloud services with optimal Quality of Service (QoS) to the multimedia cloud users. But, the growing popularity of MC is affecting the climate. Because multimedia cloud data centers consume an enormous amount of energy to provide services, it harms the environment due to carbon dioxide emissions. Virtual machine (VM) migration can effectively address this issue; it reduces the energy consumption of multimedia cloud data centers. Due to the reduction of Energy Consumption (EC), the Service Level Agreement violation (SLAV) may increase. An efficient VM selection plays a crucial role in maintaining the stability between EC and SLAV. This work highlights a novel VM selection policy based on identifying the Maximum value among the differences of the Sum of Squares Utilization Rate (MdSSUR) parameter to reduce the EC of multimedia cloud data centers with minimal SLAV. The proposed MdSSUR VM selection policy has been evaluated using real workload traces in CloudSim. The simulation result of the proposed MdSSUR VM selection policy demonstrates the rate of improvements of the EC, the number of VM migrations, and the SLAV by 28.37%, 89.47%, and 79.14%, respectively.

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References

N. Williams, G. S. Blair, “Distributed multimedia applications: A review,” Computer Communications, vol. 17, no. 2, pp. 119–132, 1994.

M. N. Birje, P. S. Challagidad, R. H. Goudar, M. T. Tapale, “Cloud computing review: concepts, technology, challenges and security,” International Journal of Cloud Computing, vol. 6, no. 1, pp. 32–57, 2017.

W. Zhu, C. Luo, J. Wang, S. Li, “Multimedia cloud computing,” IEEE Signal Processing Magazine, vol. 28, no. 3, pp. 59–69, 2011.

A. Vogel, B. Kerherve, G. von Bochmann, J. Gecsei, “Distributed multimedia and QoS: A survey,” IEEE multimedia, vol. 2, no. 2, pp. 10–19, 1995.

Masanet, Shehabi, Smith, Lei, “Global Data Center Energy Use: Distribution, Composition, and Near-Term Outlook,” Northwestern University: Evanston, IL, USA, 2018.

E. Innovation, “How Much Energy Do Data Centers Really Use?.” https://energyinnovation.org/2020/03/17/how-much-energy-do-data-centersreally-use/, 2020 (accessed on 11th September, 2020).

F. Pearce, “Energy Hogs: Can World’s Huge Data Centers Be Made More Efficient?.” https://e360.yale.edu/features/energy-hogs-can-huge-datacenters-be-made-more-efficient, 2018 (accessed on 18th September, 2020).

ATAG, “Air Transport Action Group: Facts and Figure.” https://atag.org/facts-figures.html, 2019 (accessed on 17th September, 2020).

F. Lombardi, R. Di Pietro, “Secure virtualization for cloud computing,” Journal of network and computer applications, vol. 34, no. 4, pp. 1113–1122, 2011.

Y. Xing, Y. Zhan, “Virtualization and cloud computing,” Future Wireless Networks and Information Systems, pp. 305–312, 2012.

N. Jain, S. Choudhary, “Overview of virtualization in cloud computing,” Symposium on Colossal Data Analysis and Networking, pp. 1–4, 2016.

A. Abdelsamea, E. E. Hemayed, H. Eldeeb, H. Elazhary, “Virtual machine consolidation challenges: A review,” International Journal of Innovation and Applied Studies, vol. 8, no. 4, p. 1504, 2014.

M. A. Khan, A. Paplinski, A. M. Khan, M. Murshed, R. Buyya, “Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management: a review,” Sustainable cloud and energy services, pp. 135–165, 2018.

H. Wang, H. Tianfield, “Energy-aware dynamic virtual machine consolidation for cloud datacenters,” IEEE Access, vol. 6, pp. 15259–15273, 2018.

A. Beloglazov, R. Buyya, “System, method and computer program product for energy-efficient and service level agreement (SLA)-based management of data centers for cloud computing,” US Patent 9,363,190, Google Patents, June 7, 2016.

P. G. J. Leelipushpam, J. Sharmila, “Live VM migration techniques in cloud environment–a survey,” IEEE Conference on Information & Communication Technologies, pp. 408–413, 2013.

A. Choudhary, M. C. Govil, G. Singh, L. K. Awasthi, E. S. Pilli, D. Kapil, “A critical survey of live virtual machine migration techniques,” Journal of Cloud Computing, vol. 6, no. 1, p. 23, 2017.

R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, R. Buyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software: Practice and experience, vol. 41, no. 1, pp. 23–50, 2011.

A. Beloglazov, R. Buyya, “Optimal online deterministic algo-rithms 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.

R. Yadav, W. Zhang, H. Chen, T. Guo, “Mums: Energy-aware vm selection scheme for cloud data center,” 28th International Workshop on Database and Expert Systems Applications, IEEE, pp. 132–136, 2017.

N. Akhter, M. Othman, R. K. Naha, “Energy-aware virtual machine selection method for cloud data center resource allocation,” arXiv preprint arXiv:1812.08375, 2018.

A. Beloglazov, R. Buyya, “Energy efficient resource management in virtualized cloud data centers,” 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, IEEE, pp. 826–831, 2010.

Q. Deng, D. Meisner, L. Ramos, T. F. Wenisch, R. Bianchini, “Memscale: active low-power modes for main memory,” ACM SIGPLAN Notices, vol. 46, no. 3, pp. 225–238, 2011.

Q. Deng, D. Meisner, A. Bhattacharjee, T. F. Wenisch, R. Bianchini, “MultiScale: memory system DVFS with multiple memory controllers,” Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design, pp. 297–302, 2012.

J. C. W. Lin, Y. Shao, Y. Djenouri, U. Yun, “ASRNN: A recurrent neural network with an attention model for sequence labeling,” Knowledge-Based Systems, p. 106548, 2020.

R. Mandal, M. K. Mondal, S. Banerjee, U. Biswas, “An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing,” The Journal of Supercomputing, pp. 1–20, 2020.

R. Yadav, W. Zhang, O. Kaiwartya, P. R. Singh, I. A. Elgendy, Y.C. Tian, “Adaptive energy-aware algorithms for minimizing energy consumption and SLA violation in cloud computing,” IEEE Access, vol. 6, pp. 55923–55936, 2018.

J. C. W. Lin, L. Yang, P. Fournier-Viger, J. M.-T. Wu, T. P. Hong, L. S. L. Wang, J. Zhan, “Mining high-utility itemsets based on particle swarm optimization,” Engineering Applications of Artificial Intelligence, vol. 55, pp. 320–330, 2016.

C. Zhang, Y. Wang, Y. Lv, H. Wu, H. Guo, “An Energy and SLA-Aware Resource Management Strategy in Cloud Data Centers,” Scientific Programming, vol. 2019, 2019.

H. Toumi, B. Marzak, A. Talea, A. Eddaoui, M. Talea, “Use Trust Management Framework to Achieve Effective Security Mechanisms in Cloud Environment,” International Journal of Interactive Multimedia & Artificial Intelligence, vol. 4, no. 3, 2017.

Y. Wen, Z. Li, S. Jin, C. Lin, Z. Liu, “Energy-efficient virtual resource dynamic integration method in cloud computing,” IEEE Access, vol. 5, pp. 12214–12223, 2017.

X. Wu, Y. Zeng, G. Lin, “An energy efficient VM migration algorithm in data centers,” 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, IEEE, pp. 27–30, 2017.

J. C. W. Lin, G. Srivastava, Y. Zhang, Y. Djenouri, M. Alo-qaily, “Privacy Preserving Multi-Objective Sanitization Model in 6G IoT Environments,” IEEE Internet of Things Journal, 2020.

V. K. Solanki, M. Venkatesan, S. Katiyar, “Conceptual Model for Smart Cities: Irrigation and Highway Lamps using IoT,” International Journal of Interactive Multimedia & Artificial Intelligence, vol. 4, no. 3, pp. 28–33, 2017.

S. B. Melhem, A. Agarwal, N. Goel, M. Zaman, “Minimizing biased VM selection in live VM migration,” 3rd International Conference of Cloud Computing Technologies and Applications, IEEE, pp. 1–7, 2017.

S. M. Moghaddam, M. O'Sullivan, C. Walker, S. F. Piraghaj, C. P. Unsworth, “Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers,” Future Generation Computer Systems, vol. 106, pp. 221–233, 2020.

H. Peng, L. Liu, L. Ma, W. Zhao, H. Ma, L. Yuntao, “Approximate Error Estimation based Incremental Word Representation Learning,” Data Science and Pattern Recognition, vol. 4, 2020.

S. A. Makhlouf, B. Yagoubi, “Data-Aware Scheduling Strategy for Scientific Workflow Applications in IaaS Cloud Computing,” International Journal of Interactive Multimedia & Artificial Intelligence, vol. 5, no. 4, 2019.

J. S. Pan, X. Wang, S.-C. Chu, T. Nguyen, “A multi-group grasshopper optimisation algorithm for application in capacitated vehicle routing problem,” Data Science and Pattern Recognition, vol. 4, no. 1, pp. 41–56, 2020.

K. W. Huang, C. C. Lin, Y.-M. Lee, Z.-X. Wu, “A deep learn-ing and image recognition system for image recognition,” Data Science and Pattern Recognition, vol. 3, no. 2, pp. 1–11, 2019.

M. El Ghazouani, E. Kiram, M. Ahmed, L. Er-Rajy, Y. El Khanboubi, “Efficient Method Based on Blockchain Ensuring Data Integrity Auditing with Deduplication in Cloud,” International Journal of Interactive Multimedia & Artificial Intelligence, vol. 6, no. 3, 2020.

P. Xu, G. He, Z. Li, Z. Zhang, “An efficient load balancing algorithm for virtual machine allocation based on ant colony optimization,” International Journal of Distributed Sensor Networks, vol. 14, no. 12, p. 1550147718793799, 2018.

M. Dorigo, M. Birattari, T. Stutzle, “Ant colony optimization,” IEEE computational intelligence magazine, vol. 1, no. 4, pp. 28–39, 2006.

S. B. S. Yadav, M. Kalra, “Energy-Aware VM Migration in Cloud Computing,” Proceedings of International Conference on IoT Inclusive Life, NITTTR Chandigarh, India, Springer, pp. 353–364, 2020.

J. Thaman, M. Singh, “SLA conscious VM migration for host consolidation in cloud framework,” International Journal of Communication Networks and Distributed Systems, vol. 19, no. 1, pp. 46–64, 2017.

W. S. Cleveland, “Robust locally weighted regression and smoothing scatterplots,” Journal of the American statistical association, vol. 74, no. 368, pp. 829–836, 1979.

W. S. Cleveland, Visualizing data. Hobart Press, 1993.

R. Buyya, R. Ranjan, R. N. Calheiros, “Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities,” International conference on high performance computing & simulation, IEEE, pp. 1–11, 2009.

T. Goyal, A. Singh, A. Agrawal, “Cloudsim: simulator for cloud computing infrastructure and modeling,” Procedia Engineering, vol. 38, pp. 3566–3572, 2012.

A. Beloglazov, Energy-efficient management of virtual machines in data centers for cloud computing. PhD dissertation, 2013.

A. EC, “Amazon EC2 instance types.” https://aws.amazon.com/ec2/instance-types/, 2019 (accessed on 21st September 2020).

L. Peterson, A. Bavier, M. E. Fiuczynski, S. Muir, “Experiences building planetlab,” Proceedings of the 7th symposium on Operating systems design and implementation, pp. 351–366, 2006.

K. Park, V. S. Pai, “CoMon: a mostly-scalable monitoring system for PlanetLab,” ACM Special Interest Group in Operating Systems Review, vol. 40, no. 1, pp. 65–74, 2006.

R. Sahal, M. H. Khafagy, F. A. Omara, “A survey on SLA management for cloud computing and cloud-hosted big data analytic applications,” International Journal of Database Theory and Application, vol. 9, no. 4, pp. 107–118, 2016.