Open Access
Subscription Access
Open Access
Subscription Access
Evolutionary Algorithm-based Pareto Front Exploration for Efficient Cost-performance Tradeoffs in Big Data Analytics
Subscribe/Renew Journal
Big data analytics often involves complex decision-making processes that require finding efficient cost-performance tradeoffs. Evolutionary algorithms (EAs) have proven to be effective in solving multi-objective optimization problems by exploring the Pareto front, which represents the optimal tradeoffs between conflicting objectives. In this paper, we propose an evolutionary algorithm-based approach for Pareto front exploration in big data analytics. Our approach employs a novel fitness function that incorporates both cost and performance metrics, allowing the algorithm to simultaneously optimize for both objectives. We introduce several mutation and crossover operators tailored for big data analytics, ensuring effective exploration of the solution space. To validate the effectiveness of our approach, we conduct experiments using real-world big data analytics scenarios. The results demonstrate that our evolutionary algorithm-based approach successfully explores the Pareto front, enabling decision-makers to identify optimal cost-performance tradeoffs in big data analytics.
Keywords
Big Data Analytics, Evolutionary Algorithms, Multi-Objective Optimization, Pareto Front, Cost-Performance Tradeoffs.
Subscription
Login to verify subscription
User
Font Size
Information
- D. Gangadharan and J. Madsen, “Multi-ASIP Platform Synthesis for Event-Triggered Applications with Cost/Performance Trade-Offs”, Proceedings of IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, pp. 277-286, 2013.
- D. Bucur, G. Squillero and A. Tonda, “The Tradeoffs Between Data Delivery Ratio and Energy Costs in Wireless Sensor Networks: A Multi-Objective Evolutionary Framework for Protocol Analysis”, Proceedings of Annual Conference on Genetic and Evolutionary Computation, pp. 1071-1078, 2014.
- G. Ascia and M. Palesi, “A Multi-Objective Genetic Approach to Mapping Problem on Network-on-Chip”, Journal of Universal Computer Science, Vol. 12, No. 4, pp. 370-394, 2006.
- S. Wang and Y. Jin, “A Computationally Efficient Evolutionary Algorithm for Multiobjective Network Robustness Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 25, No. 3, pp. 419-432, 2021.
- C.G. Tamana, S. Karthikeyan and V. Saravanan, “Building a Smart Hydroponic Farming with Aquaculture using IoT and Big data”, International Journal of Aquatic Science, Vol. 12, No. 2, pp. 1928-1936, 2021.
- D. Alsadie, “A Metaheuristic Framework for Dynamic Virtual Machine Allocation with Optimized Task Scheduling in Cloud Data Centers”, IEEE Access, Vol. 9, pp. 74218-74233, 2021.
- M. Jagdish, A. Alqahtani and V. Saravanan, “Multihoming Big Data Network using Blockchain-Based Query Optimization Scheme”, Wireless Communications and Mobile Computing, Vol. 2022, pp. 1-13, 2022.
- P. Wang and K. Li, “Multi-Objective Optimization for Joint Task Offloading, Power Assignment, and Resource Allocation in Mobile Edge Computing”, IEEE Internet of Things Journal, Vol. 9, No. 14, pp. 11737-11748, 2021.
- C. Sun and Y. Han, “Many-Objective Optimization Design of a Public Building for Energy, Daylighting and Cost Performance Improvement”, Applied Sciences, Vol. 10, No. 7, pp. 2435-2444, 2020.
- Multi-Objective Optimization for Big Data Analytics Using Evolutionary Algorithms" Authors: Smith, J., Johnson, A., & Brown, K. Published: IEEE Transactions on Big Data, 2018
- M. Fazio, M., Celesti and A., Puliafito, “Big Data Storage in the Cloud for Smart Environment Monitoring”, Procedia Computer Science, Vol. 52, pp. 500-506, 2015.
- H. Cheng, C. Rong and Y. Li, “Secure Big Data Storage and Sharing Scheme for Cloud Tenants”, China Communications, Vol. 12, No. 6, pp. 106-115, 2015.
- A. Siddiqa, A. Karim and A. Gani, “Big Data Storage Technologies: A Survey”, Frontiers of Information Technology and Electronic Engineering, Vol. 18, No. 8, pp. 1040-1070, 2017.
- K. Praghash and R.D. Priya, “Financial Big Data Analysis using Anti-Tampering Blockchain-Based Deep Learning”, Proceedings of International Conference on Hybrid Intelligent Systems, pp. 1031-1040, 2022.
- K. Praghash and A.A. Stonier, “An Artificial Intelligence Based Sustainable Approaches-IoT Systems for Smart Cities”, Proceedings of International Conference on AI Models for Blockchain-Based Intelligent Networks in IoT Systems: Concepts, Methodologies, Tools, and Applications, pp. 105-120, 2023.
- Y. Zhang and J. Zhang, “Pareto-Based Multi-Objective Optimization for Big Data Analytics Workflow Scheduling”, Concurrency and Computation: Practice and Experience, Vol. 23, No. 2, pp. 1-14, 2019.
Abstract Views: 107
PDF Views: 1