High-Performance Data Grids for Enterprise Applications

  • Authors

    • Kavya Muppaneni Senior Software Engineer at HCL Global Systems, USA. Author

    DOI:

    https://doi.org/10.67228/30715628/ICIoTCAA-2026P104

    Published 08-31-2026

  • High-Performance Computing, Data Grids, Distributed Caching, Enterprise Applications, Scalability, Fault Tolerance, In-Memory Computing, Cloud Architecture, Data Replication, Performance Optimization

    Issue

    ICIoTCAA26 - Conference Proceeding

    Section

    Articles

  • Abstract

    High-performances data grids, in particular, are essentially the foundation of modern enterprise computing that is aimed at meeting the rising demand for live data access, in-memory processing, and horizontal scalability in distributed systems. Large-scale enterprise environments where applications are expected to handle millions of transactions per second are a terrain where traditional databases are often found wanting in terms of latency and bottlenecks. Data grids do away with these kinds of situations by making the data and computation available to clusters of servers, thus allowing parallel execution and high throughput. Nevertheless, there are still issues of keeping scalability, fault tolerance, and data consistency while at the same time balancing performance and cost efficiency. This paper identifies these issues and presents a high-performance data grid architecture that would not only optimize data distribution, caching, and synchronization but also would be effective in dynamic enterprise workloads. The research is centered on how we can improve the locality of data, make the load balancing adaptive, and use replication strategies that are intelligent to achieve performance and at the same time not compromise reliability. The proposed architecture, through the implementation of event-driven processing and AI-assisted resource allocation concepts, is able to achieve scalability and latency reduction in applications that are mission-critical, such as those in the financial sector, e-commerce, and IoT data analytics. The results reveal the potential of data grids to transform from mere caching frameworks into intelligent, self-optimizing data platforms that enable enterprises to rapidly make informed decisions based on data. This paper extends the knowledge of distributed data systems and can be seen as a theoretical insight as well as a practical design principles guide for the creation of enterprise infrastructures that are resilient and fast.

  • References

    [1] Rabl, Tilmann, et al. "Solving big data challenges for enterprise application performance management." arXiv preprint arXiv:1208.4167 (2012).

    [2] Natarajan, Ramesh, Radu Sion, and Thomas Phan. "A grid-based approach for enterprise-scale data mining." Future Generation Computer Systems 23.1 (2007): 48-54.

    [3] Allcock, Bill, et al. "Data management and transfer in high-performance computational grid environments." Parallel computing 28.5 (2002): 749-771.

    [4] Johnston, William E., et al. "Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering." CAS. 2000.

    [5] Farooq, Umar, Shikharesh Majumdar, and Eric W. Parsons. "Engineering grid applications and middleware for high performance." Proceedings of the 6th international workshop on Software and performance. 2007.

    [6] Chu, Xingchen, et al. "Aneka: Next-generation enterprise grid platform for e-science and e-business applications." Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007). IEEE, 2007.

    [7] Merelli, Ivan. "Infrastructure for high-performance computing: grids and grid computing." Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics 230 (2018): 235.

    [8] Chien, Andrew, et al. "Entropia: architecture and performance of an enterprise desktop grid system." Journal of Parallel and Distributed Computing 63.5 (2003): 597-610.

    [9] Lyu, Huahui, et al. "High-availability deployment for large enterprises." 2016 International Conference on Progress in Informatics and Computing (PIC). IEEE, 2016.

    [10] Garlasu, Dan, et al. "A big data implementation based on Grid computing." 2013 11th RoEduNet International Conference. IEEE, 2013.

    [11] Stankovski, Vlado, et al. "Grid-enabling data mining applications with DataMiningGrid: An architectural perspective." Future Generation Computer Systems 24.4 (2008): 259-279.

    [12] Bhattarai, Bishnu P., et al. "Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions." IET Smart Grid 2.2 (2019): 141-154.

    [13] Darema, Frederica. "Grid computing and beyond: The context of dynamic data driven applications systems." Proceedings of the IEEE 93.3 (2005): 692-697.

    [14] Parashar, Manish, and James C. Browne. "Systems engineering for high performance computing software: The HDDA/DAGH infrastructure for implementation of parallel structured adaptive mesh." Structured adaptive mesh refinement (SAMR) grid methods. New York, NY: Springer New York, 2000. 1-18.

    [15] Baker, Mark, Rajkumar Buyya, and Domenico Laforenza. "Grids and Grid technologies for wide‐area distributed computing." Software: Practice and Experience 32.15 (2002): 1437-1466.

  • Downloads

Similar Articles

11-20 of 114

You may also start an advanced similarity search for this article.