Abstract
Big data and cloud are the common keystones of this data science era. The various social data contributes to the overall data traffic of the universe. The data management policies have gained much interest and research community is moving towards it. Cloud storage is one of such methodology used to manage big data elements. Due to centralized issues in job scheduling, the VMs are used to schedule the jobs. Waiting for service reply from a specific VM or delivering to a mass community from a far apart VM improves the cost of data traffic and completion time. We have proposed LoSM and GLoSM to satisfy the service request from ‘n’ number of hosts with respect to proximity and service activity. The test results from CloudSim and HDFS show that the proposed system outperforms with minimal completion time, reduced data traffic and less energy consumption.
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References
Gantz, J., Reinsel, D.: The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. In: Proceedings of the IDC iView, IDC Anal. Future, (2012)
Laney, D.: 3D data management: controlling data volume, velocity, and variety. META Group Research Note, Storm Lake (2001)
Chen, Z., Wen, Y., Cao, J., Zheng, W., Chang, J., Wu, Y., Ma, G., Hakmaoui, M., Peng, G.: A survey of bitmap index compression algorithms for big data. Tsinghua Sci. Technol. 20, 100–115 (2015)
Zhang, H., Chen, G., Chin, B.O., Tan, K.L., Zhang, M.: In-memory big data management and processing: a survey. IEEE Trans. Knowl. Data Eng. 27, 1920–1948 (2015)
Jiang, H., Wang, K., Wang, Y., Gao, M., Zhang, Y.: Energy big data: a survey. IEEE Access 4, 3844–3861 (2016)
Eugster, P., Jayalath, C., Kogan, K., Stephen, J.: Big data analytics beyond the single datacenter. IEEE Comput. Soc. 50, 60–68 (2017)
Gessert, F., Wingerath, W., Friedrich, S., Ritter, N.: NoSQL database systems: a survey and decision guidance. Comput. Sci. Res. Dev. 32, 353–365 (2017)
Trelewicz, J.Q.: Big data and big money the role of data in the financial sector. IEEE Comput. Soc. 19, 8–10 (2017)
Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I.A., Siddiqa, A., Yaqoob, I.: Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5, 5347–5366 (2017)
Nathuji, R., Kansal, A., Ghaf farkhah, A.: Q-Clouds: managing performance interference effects for QoS-aware clouds. In: Proceedings of the 5th European conference on Computer systems. pp. 237–25 (2010)
Delimitrou, C., Kozyrakis, C.: Paragon: QoS-aware scheduling for heterogeneous datacenters. In: Proceedings of the ASPLOS. pp. 77–88 (2013)
Chiang, R.C., Hwang, J., Huang, H., Wood, T.: Matrix: achieving predictable virtual machine performance in the clouds. In: Proceedings of the USENIX ATC. pp. 1–12 (2014)
Yi, X., Liu, F., Liu, J., Jin, H.: Building a network highway for big data: architecture and challenges. IEEE Netw. Mag. 28(4), 5–13 (2014)
Soltesz, S., Potzl, H., Fiuczynski, M.E., Bavier, A., Peterson, L.: Container-based operating system virtualization: a scalable, high- performance alternative to hypervisors. SIGOPS Oper. Syst. Rev. 41, 275–287 (2007)
Matthews, J.N., Hu, W., Hapuarachchi, M., Deshane, T., Dimatos, D., Hamilton, G., McCabe, M., Owens, J.: Quantifying the performance isolation properties of virtualization systems. In: Proceedings of the Workshop on Experimental Computer Science (2007)
Xu, F., Liu, F., Jin, H., Vasilakos, A.V.: Managing performance overhead of virtual machines in cloud computing: a survey, state of the art, and future directions. IEEE Proc. 102, 11–31 (2014)
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. 24, 1397–1420 (2012)
Rampersaud, S., Grosu, D.: Sharing-aware online virtual machine packing in heterogeneous resource clouds. IEEE Trans. Parallel Distrib. Syst. 28, 2046–2059 (2017)
Liang, H., Li, M., Jian, X., Wenying, H., Pei, X., Jia, X., Song, Y.: vmOS: a virtualization-based, secure desktop system. Comput. Secur. 65, 329–343 (2017)
Barve, Y., Prithviraj, P., Bhattacharjee, A., Gokhale, A.: Pads: design and implementation of a cloud-based, immersive learning environment for distributed systems algorithms. IEEE Trans. Emerg. Topics Comput. 99, 1 (2017)
Jiankang, D., Hongbo, W., Shiduan, C.: Energy-performance tradeoffs in IaaS cloud with virtual machine scheduling. IEEE China Commun. 12, 155 (2015)
Xu, F., Liu, F., Jin, H.: Heterogeneity and interference-aware virtual machine provisioning for predictable performance in the cloud. IEEE Trans. Comput. 65, 2470–2483 (2016)
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Kalai Arasan, K., AnandhaKumar, P. A new GLoSM embedded virtual machine model for big data services in cloud storage systems. Cluster Comput 22 (Suppl 1), 399–405 (2019). https://doi.org/10.1007/s10586-018-2109-z
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DOI: https://doi.org/10.1007/s10586-018-2109-z