Deep Insights into Data Analysis in Multi-Core Active Flash Arrays

Authors

  • P. J. R. Shalem Raju Department of C.S.E, Shri Vishnu Engineering College for Women, Bhimavaram, India.
  • Prasad M. Department of C.S.E, Shri Vishnu Engineering College for Women, Bhimavaram, India.
  • Kiran Sree Pokkuluri Department of C.S.E, Shri Vishnu Engineering College for Women, Bhimavaram, India.
  • Ramesh Babu G. Department of C.S.E, Shri Vishnu Engineering College for Women, Bhimavaram, India.
  • Ch. Phanendra Varma Department of C.S.E, Shri Vishnu Engineering College for Women, Bhimavaram, India.
  • K. Satish Kumar Department of C.S.E, Shri Vishnu Engineering College for Women, Bhimavaram, India.
  • Raja Rao P. B. V. Department of C.S.E, Shri Vishnu Engineering College for Women, Bhimavaram, India.

Keywords:

MCAFA, traditional, data analytics, technology, abstract, highlighting

Abstract

Data analysis has become increasingly vital in the modern digital landscape, with organizations constantly seeking ways to extract valuable insights from their vast repositories of data. One emerging technology that has gained prominence is Multi-Core Active Flash Arrays (MCAFA), which combine the speed and parallel processing capabilities of flash storage with multiple processing cores to accelerate data-intensive workloads. This abstract provides an overview of the role of data analysis in MCAFA systems, highlighting the benefits and challenges associated with this innovative approach. Multi-Core Active Flash Arrays leverage a combination of high-performance flash storage and multiple CPU cores to deliver impressive computational power for data analysis tasks. These systems offer significant advantages over traditional storage arrays by reducing data access latency and increasing overall system throughput. As a result, they are well-suited for applications that demand real-time data processing and analysis, such as data analytics, machine learning, and scientific simulations.

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References

Sim, Hyogi, et al. "An Analysis Workflow-Aware Storage System for Multi-Core Active Flash Arrays." IEEE Transactions on Parallel and Distributed Systems 30.2 (2018): 271-285.

Raju, PJR Shalem, K. V. D. Kiran, and Pokkuluri Kiran Sree. "Digital image watermarking based on hybrid FRT-HD-DWT domain and flamingo search optimisation." International Journal of Computational Vision and Robotics 13, no. 6 (2023): 573-598.

Tiwari, Devesh, et al. "Active Flash: Towards {Energy-Efficient},{In-Situ} Data Analytics on {Extreme-Scale} Machines." 11th USENIX Conference on File and Storage Technologies (FAST 13). 2013.

Sree, P. Kiran, I. Ramesh Babu, and NSSSN Usha Devi. "Investigating an Artificial Immune System to strengthen protein structure prediction and protein coding region identification using the Cellular Automata classifier." International journal of bioinformatics research and applications 5, no. 6 (2009): 647-662.

Bjørling, M., Axboe, J., Nellans, D., & Bonnet, P. (2013, June). Linux block IO: introducing multi-queue SSD access on multi-core systems. In Proceedings of the 6th international systems and storage conference (pp. 1-10).

Huang, J., Qin, W., Wang, X., & Chen, W. (2020). Survey of external memory large-scale graph processing on a multi-core system. The Journal of Supercomputing, 76, 549-579.

Pokkuluri, Kiran Sree, and SSSN Usha Devi Nedunuri. "A novel cellular automata classifier for covid-19 prediction." Journal of Health Sciences 10, no. 1 (2020): 34-38.

Lee, E., Kim, Y., & Bahn, H. (2014, May). QoS Management of real-time applications in NVRAM-Based multi-core smartphones. In 2014 International Conference on Information Science & Applications (ICISA) (pp. 1-4). IEEE.

El Salloum, C., Elshuber, M., Höftberger, O., Isakovic, H., & Wasicek, A. (2013). The ACROSS MPSoC–A new generation of multi-core processors designed for safety–critical embedded systems. Microprocessors and Microsystems, 37(8), 1020-1032.

Bortolotti, D., Mangia, M., Bartolini, A., Rovatti, R., Setti, G., & Benini, L. (2014, October). Rakeness-based compressed sensing on ultra-low power multi-core biomedicai processors. In Proceedings of the 2014 Conference on Design and Architectures for Signal and Image Processing (pp. 1-8). IEEE.

Xu, T. C., Liljeberg, P., Plosila, J., & Tenhunen, H. (2013, June). MMSoC: a multi-layer multi-core storage-on-chip design for systems with high integration. In Proceedings of the 14th International Conference on Computer Systems and Technologies (pp. 67-74).

Kim, D., Yoo, S., & Lee, S. (2015). Hybrid main memory for high bandwidth multi-core system. IEEE Transactions on Multi-Scale Computing Systems, 1(3), 138-149.

Rodríguez-Vázquez, A., Domínguez-Castro, R., Jiménez-Garrido, F., Morillas, S., García, A., Utrera, C., ... & Romay, R. (2009). A CMOS vision system on-chip with multi-core, cellular sensory-processing front-end. In Cellular nanoscale sensory wave computing (pp. 129-146). Boston, MA: Springer US.

Pokkuluri, Kiran Sree, SSSN Usha Devi Nedunuri, and Usha Devi. "Crop Disease Prediction with Convolution Neural Network (CNN) Augmented With Cellular Automata." INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY 19, no. 5 (2022): 765-773.

Prathapan, S., Golpayegani, N., Wyatt, B., Halem, M., Dorband, J., Trantham, J., & Markey, C. (2020, May). Astor: A compute framework for scalable distributed big data processing. In Big Data II: Learning, Analytics, and Applications (Vol. 11395, pp. 80-96). SPIE.

Sree, Pokkuluri Kiran, Phaneendra Varma Chintalapati, M. Prasad, Gurujukota Ramesh Babu, and PBV Raja Rao. "Waste Management Detection Using Deep Learning." In 2023 3rd International Conference on Computing and Information Technology (ICCIT), pp. 50-54. IEEE, 2023.

Pokkuluri, Kiran Sree, and SSSN Usha Devi Nedunuri. "A novel cellular automata classifier for covid-19 prediction." Journal of Health Sciences 10, no. 1 (2020): 34-38.

Josphineleela, R., Raja Rao, P.B.V., shaikh, A. et al. A Multi-Stage Faster RCNN-Based iSPLInception for Skin Disease Classification Using Novel Optimization. J Digit Imaging 36, 2210–2226 (2023).

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Published

24.03.2024

How to Cite

Raju, P. J. R. S. ., M., P. ., Pokkuluri, K. S. ., Babu G., R. ., Varma, C. P. ., Kumar, K. S. ., & P. B. V., R. R. . (2024). Deep Insights into Data Analysis in Multi-Core Active Flash Arrays. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 633–637. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5011

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

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