Enterprise and RAN-Aware Data and Analytics Platforms for Mission-Critical and Low-Latency Digital Services
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P120Keywords:
Enterprise Data Platforms, Distributed Analytics Systems, Cloud-Native Data Architectures, Big Data Processing Frameworks, Streaming Data Pipelines, Low-Latency Data Processing, Mission-Critical Systems, Fault-Tolerant Distributed Systems, RAN-Aware System Design, Network-Aware Platform Design, Edge Integrated RAN, Operational Analytics for Live NetworksAbstract
Enterprise digital service commercial applications are progressing to become constrained by restrictive performance and reliability and latency demands motivated by mission critical work cases like industrial automation, telemedicine, autonomous systems, and real-time operational intelligence. Traditional cloud-based analytics models are typically scalable, but are not typically capable of meeting the requirements of ultra-low-latency and deterministic services introduced by distributed, radio access network (RAN) applications. The paper will include an extensive architectural and an analytical model of Enterprise and RAN-Aware-based Data and Analytics Platforms aimed to serve mission-critical and low-latency digital services. The framework suggested incorporates cloud-native data systems, distributed analytics, streaming data pipelines, and network-aware strategies of computing with a clear understanding of the RAN dynamics. In contrast to conventional enterprise-level data platforms, the RAN-aware model uses radio conditions, network variability, edge resource constraints and latency budgets as first-order design parameters. This study explains why there should be close integration between enterprise analytics layers and live network telemetry to allow placement of adaptive computation, smart workload orchestration, and fault-tolerant distributed processing. The paper presents a multitiered system network with edge computing nodes, regional consolidation planes, and centralized cloud management control planes. The frameworks of data processing utilizes the streaming architecture and low-latency pipelines that are able to perform real-time inferences and decisions. The important innovations are latency adaptive processing models, network aware scheduling functions, and reliability optimized replication strategies. The estimation of latency and workload distribution that aims at describing the system behavior under network uncertainties is provided by mathematical models. Through experimental analysis, we show the improvement of service responsiveness, throughput stability, and fault resilience over cloud-only baselines. The quantitative analysis shows that the application of RAN-aware mechanisms dramatically decreases the tail latency and improves the yield of reliable mission-critical workflows. The findings confirm the usefulness of distributed analytics together with edge-aware RAN intelligence.bThis paper forms part of an emerging effort in the interface between enterprise data engineering, network-aware computing and low-latency distributed systems. The results offer architectural directions on the next-generation enterprise platform which can support the digital services running at scale, in uncertainty, and in heterogeneous network landscapes.
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References
[1] Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
[2] Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. In 2nd USENIX workshop on hot topics in cloud computing (HotCloud 10).
[3] White, T. (2012). Hadoop: The definitive guide. "O'Reilly Media, Inc.".
[4] Ghemawat, S., Gobioff, H., & Leung, S. T. (2003, October). The Google file system. In Proceedings of the nineteenth ACM symposium on Operating systems principles (pp. 29-43).
[5] Kreps, J., Narkhede, N., & Rao, J. (2011, June). Kafka: A distributed messaging system for log processing. In Proceedings of the NetDB (Vol. 11, No. 2011, pp. 1-7).
[6] Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S., & Tzoumas, K. (2015). Apache flink: Stream and batch processing in a single engine. The Bulletin of the Technical Committee on Data Engineering, 38(4).
[7] Akidau, T., Bradshaw, R., Chambers, C., Chernyak, S., Fernández-Moctezuma, R. J., Lax, R., ... & Whittle, S. (2015). The dataflow model: a practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing. Proceedings of the VLDB Endowment, 8(12), 1792-1803.
[8] Brewer, E. (2012). CAP twelve years later: How the" rules" have changed. Computer, 45(2), 23-29.
[9] Lamport, L. (2019). Time, clocks, and the ordering of events in a distributed system. In Concurrency: the Works of Leslie Lamport (pp. 179-196).
[10] Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016). Borg, omega, and kubernetes. Communications of the ACM, 59(5), 50-57.
[11] Alvaro, P., Conway, N., Hellerstein, J. M., & Marczak, W. R. (2011, January). Consistency Analysis in Bloom: a CALM and Collected Approach. In CIDR (pp. 249-260).
[12] DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, S. S. A., ... & Vogels, W. (2007). Dynamo: Amazon’s Highly Available Key-Value Store. SOSP, 2007.
[13] Newman, S. (2021). Building microservices: designing fine-grained systems. " O'Reilly Media, Inc.".
[14] Gupta, H., Vahid Dastjerdi, A., Ghosh, S. K., & Buyya, R. (2017). iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments. Software: practice and experience, 47(9), 1275-1296.
[15] Kottursamy, K., Khan, A. U. R., Sadayappillai, B., & Raja, G. (2022). Optimized D-RAN aware data retrieval for 5G information centric networks. Wireless Personal Communications, 124(2), 1011-1032.
[16] Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., ... & Stoica, I. (2016). Apache spark: a unified engine for big data processing. Communications of the ACM, 59(11), 56-65.
[17] Hawick, K. A., Coddington, P. D., & James, H. A. (2003). Distributed frameworks and parallel algorithms for processing large-scale geographic data. Parallel Computing, 29(10), 1297-1333.
[18] Laszewski, T., Arora, K., Farr, E., & Zonooz, P. (2018). Cloud Native Architectures: Design high-availability and cost-effective applications for the cloud. Packt Publishing Ltd.
[19] Veluru, S. P. (2022). Streaming Data Pipelines for AI at the Edge: Architecting for Real-Time Intelligence. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 60-68.
[20] Din, S., Paul, A., & Rehman, A. (2019). 5G-enabled Hierarchical architecture for software-defined intelligent transportation system. Computer Networks, 150, 81-89.
[21] Elbamby, M. S., Perfecto, C., Liu, C. F., Park, J., Samarakoon, S., Chen, X., & Bennis, M. (2019). Wireless edge computing with latency and reliability guarantees. Proceedings of the IEEE, 107(8), 1717-1737.
