Data Analytics for Energy-Efficient Code Refactoring in Large-Scale Distributed Systems
DOI:
https://doi.org/10.18034/apjee.v8i2.771Keywords:
Data Analytics, Energy Efficiency, Code Refactoring, Distributed Systems, Large-Scale Systems, Software Optimization, Green ComputingAbstract
It examines how data analytics improves energy efficiency in large-scale distributed systems via code reworking. The primary goal is to study how data-driven techniques maximize resource allocation, energy usage, and system performance. Secondary data-based reviews of energy-efficient data analytics case studies from Google, Facebook, AWS, and Microsoft are used in the process. Significant results show that performance profiling, real-time monitoring, predictive modeling, and energy-aware resource management reduce energy use and ensure system scalability and performance. Energy savings were realized utilizing dynamic resource allocation, job scheduling, load balancing, and predictive analytics using machine learning. Energy consumption is also reduced by managing network traffic and data storage. However, integrating contemporary analytics tools into older systems and handling their massive data sets remain substantial obstacles. The paper recommends uniform legislation to promote energy-efficient practices, incentives for sustainable computing research, and industry best practices. This work emphasizes energy efficiency in large-scale distributed systems and advances sustainable computing research.
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Abadi, M., Keidar-Barner, S., Pidan, D., Veksler, T. (2019). Verifying Parallel Code after Refactoring Using Equivalence Checking. International Journal of Parallel Programming, 47(1), 59-73. https://doi.org/10.1007/s10766-017-0548-4 DOI: https://doi.org/10.1007/s10766-017-0548-4
Aljuhani, A., Benedicenti, L., Alshehri, S. (2017). A Multiple-Criteria Decision Making Model for Ranking Refactoring Patterns. International Journal of Advanced Computer Science and Applications, 8(11). https://doi.org/10.14569/IJACSA.2017.081101 DOI: https://doi.org/10.14569/IJACSA.2017.081101
Allam, A. R. (2020). Integrating Convolutional Neural Networks and Reinforcement Learning for Robotics Autonomy. NEXG AI Review of America, 1(1), 101-118.
Boinapalli, N. R. (2020). Digital Transformation in U.S. Industries: AI as a Catalyst for Sustainable Growth. NEXG AI Review of America, 1(1), 70-84.
Chu, P-h., Hsueh, N-l., Chen, H-h., Liu, C-h. (2012). A Test Case Refactoring Approach for Pattern-based Software Development. Software Quality Journal, 20(1), 43-75. https://doi.org/10.1007/s11219-011-9143-x DOI: https://doi.org/10.1007/s11219-011-9143-x
Cruz, L., Abreu, R. (2019). Improving Energy Efficiency through Automatic Refactoring. Journal of Software Engineering Research and Development, 7, 2:1- 2:9. https://doi.org/10.5753/jserd.2019.17 DOI: https://doi.org/10.5753/jserd.2019.17
Devarapu, K., Rahman, K., Kamisetty, A., & Narsina, D. (2019). MLOps-Driven Solutions for Real-Time Monitoring of Obesity and Its Impact on Heart Disease Risk: Enhancing Predictive Accuracy in Healthcare. International Journal of Reciprocal Symmetry and Theoretical Physics, 6, 43-55. https://upright.pub/index.php/ijrstp/article/view/160
Gummadi, J. C. S., Narsina, D., Karanam, R. K., Kamisetty, A., Talla, R. R., & Rodriguez, M. (2020). Corporate Governance in the Age of Artificial Intelligence: Balancing Innovation with Ethical Responsibility. Technology & Management Review, 5, 66-79. https://upright.pub/index.php/tmr/article/view/157
Hermans, F., Pinzger, M., van Deursen, A. (2015). Detecting and Refactoring Code Smells in Spreadsheet Formulas. Empirical Software Engineering, 20(2), 549-575. https://doi.org/10.1007/s10664-013-9296-2 DOI: https://doi.org/10.1007/s10664-013-9296-2
Karanam, R. K., Natakam, V. M., Boinapalli, N. R., Sridharlakshmi, N. R. B., Allam, A. R., Gade, P. K., Venkata, S. G. N., Kommineni, H. P., & Manikyala, A. (2018). Neural Networks in Algorithmic Trading for Financial Markets. Asian Accounting and Auditing Advancement, 9(1), 115–126. https://4ajournal.com/article/view/95
Kim, D., Hong, J-E., Yoon, I., Lee, S-H. (2018). Code Refactoring Techniques for Reducing Energy Consumption in Embedded Computing Environment. Cluster Computing, 21(1), 1079-1095. https://doi.org/10.1007/s10586-016-0691-5 DOI: https://doi.org/10.1007/s10586-016-0691-5
Kommineni, H. P., Fadziso, T., Gade, P. K., Venkata, S. S. M. G. N., & Manikyala, A. (2020). Quantifying Cybersecurity Investment Returns Using Risk Management Indicators. Asian Accounting and Auditing Advancement, 11(1), 117–128. Retrieved from https://4ajournal.com/article/view/97
Kothapalli, S., Manikyala, A., Kommineni, H. P., Venkata, S. G. N., Gade, P. K., Allam, A. R., Sridharlakshmi, N. R. B., Boinapalli, N. R., Onteddu, A. R., & Kundavaram, R. R. (2019). Code Refactoring Strategies for DevOps: Improving Software Maintainability and Scalability. ABC Research Alert, 7(3), 193–204. https://doi.org/10.18034/ra.v7i3.663 DOI: https://doi.org/10.18034/ra.v7i3.663
Kundavaram, R. R., Rahman, K., Devarapu, K., Narsina, D., Kamisetty, A., Gummadi, J. C. S., Talla, R. R., Onteddu, A. R., & Kothapalli, S. (2018). Predictive Analytics and Generative AI for Optimizing Cervical and Breast Cancer Outcomes: A Data-Centric Approach. ABC Research Alert, 6(3), 214-223. https://doi.org/10.18034/ra.v6i3.672 DOI: https://doi.org/10.18034/ra.v6i3.672
Mahmoud, A., Niu, N. (2014). Supporting Requirements to Code Traceability through Refactoring. Requirements Engineering, 19(3), 309-329. https://doi.org/10.1007/s00766-013-0197-0 DOI: https://doi.org/10.1007/s00766-013-0197-0
Mei, X. Y., Liu, J. B. (2012). A Refactoring Framework of Program Model Based on Procedure Blueprint. Applied Mechanics and Materials, 198-199, 490. https://doi.org/10.4028/www.scientific.net/AMM.198-199.490 DOI: https://doi.org/10.4028/www.scientific.net/AMM.198-199.490
Meng, F., Su, X. (2019). WCET Optimization Strategy Based on Source Code Refactoring. Cluster Computing, suppl. 3, 22, 5563-5572. https://doi.org/10.1007/s10586-017-1369-3 DOI: https://doi.org/10.1007/s10586-017-1369-3
Roberts, C., Kundavaram, R. R., Onteddu, A. R., Kothapalli, S., Tuli, F. A., Miah, M. S. (2020). Chatbots and Virtual Assistants in HRM: Exploring Their Role in Employee Engagement and Support. NEXG AI Review of America, 1(1), 16-31.
Rodriguez, M., Mohammed, M. A., Mohammed, R., Pasam, P., Karanam, R. K., Vennapusa, S. C. R., & Boinapalli, N. R. (2019). Oracle EBS and Digital Transformation: Aligning Technology with Business Goals. Technology & Management Review, 4, 49-63. https://upright.pub/index.php/tmr/article/view/151
Rodriguez, M., Sridharlakshmi, N. R. B., Boinapalli, N. R., Allam, A. R., & Devarapu, K. (2020). Applying Convolutional Neural Networks for IoT Image Recognition. International Journal of Reciprocal Symmetry and Theoretical Physics, 7, 32-43. https://upright.pub/index.php/ijrstp/article/view/158
Sridharlakshmi, N. R. B. (2020). The Impact of Machine Learning on Multilingual Communication and Translation Automation. NEXG AI Review of America, 1(1), 85-100.
Thompson, C. R., Talla, R. R., Gummadi, J. C. S., Kamisetty, A. (2019). Reinforcement Learning Techniques for Autonomous Robotics. Asian Journal of Applied Science and Engineering, 8(1), 85-96. https://ajase.net/article/view/94 DOI: https://doi.org/10.18034/ajase.v8i1.94
Zimmermann, O. (2017). Architectural Refactoring for the Cloud: A Decision-centric View on Cloud Migration. Computing. Archives for Informatics and Numerical Computation, 99(2), 129-145. https://doi.org/10.1007/s00607-016-0520-y DOI: https://doi.org/10.1007/s00607-016-0520-y
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Copyright (c) 2021 Narayana Reddy Bommu Sridharlakshmi
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