Observability-Driven SLO Enforcement in High-Throughput Data Infrastructure
Article Sidebar
Main Article Content
Abstract
High-throughput data infrastructures underpin mission-critical financial, healthcare, and e-commerce applications that require stringent service-level objectives (SLOs) to ensure both performance and reliability. Despite significant advancements in observability platforms, existing SLO enforcement mechanisms remain primarily static and based on pre-determined thresholds and coarse-grained telemetry that fail to account for the high-level dynamism of data workloads. This contribution fills the research gap identified by devising an observability-driven SLO enforcement framework that is specifically tailored for dynamic, high-volume data pipelines. Leverage real-time metrics such as per-stream latency distributions, adaptive throughput metrics, and fine-grained resource consumption traces, our framework continuously optimizes enforcement policies using feedback loops that map observed behavior to user-centric objectives. We present a hierarchical control architecture that integrates lightweight instrumentation agents with data-nodes and a centralized policy engine, thus allowing for both local corrective measures and global adjustments without excessively high overhead. Leverage a combination of simulation and real-world deployment in an open-source streaming platform, we demonstrate that our framework reduces SLO violations by up to 60% compared to static enforcement, all at sub-millisecond decision latency. We also elaborate on implications of our design on scalability, fault tolerance, and multi-tenant fairness, and how observability-derived insights can inform predictive scaling and proactive resource allocation. The results unveil the potential of observability-driven enforcement, setting the stage for self-adaptive data infrastructures that can uphold service commitments under varying load conditions.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Articles are published open access under the Creative Commons Attribution 4.0 International licence (CC BY 4.0). Authors keep the copyright of their work. Published by ResaGate Global.
References
• Chen, L. Y., & Bahsoon, R. (2017). Self-adaptive and sensitivity-aware QoS modeling for the cloud. IEEE Transactions on Software Engineering, 43(5), 453–475. https://doi.org/10.1109/TSE.2016.2608823
• Sudhakar Tiwari. (2023). Biometric Authentication in the Face of Spoofing Threats: Detection and Defense Innovations. Innovative Research Thoughts, 9(5), 402–420. https://doi.org/10.36676/irt.v9.i5.1583
• Cortez, E., Morrey, C., Moschetti, G., & Budiu, M. (2017). Resource central: Understanding enterprise resource management in large-scale cloud platforms. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI '16), 153–168. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/cortez
• Gambi, A., Toffetti, G., Pezze, M., & Dustdar, S. (2020). Kriging-based self-adaptive cloud autoscaling for cost-efficient and SLO-aware resource provisioning. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 14(4), 1–28. https://doi.org/10.1145/3342192
• Gan, Y., Delimitrou, C., & Kozyrakis, C. (2019). The case for machine learning-based autoscaling in cloud platforms. Proceedings of the 2019 Symposium on Cloud Computing (SoCC '19), 103–116. https://doi.org/10.1145/3357223.3362707
• Mao, M., & Humphrey, M. (2016). A performance study on the VM startup time in the cloud. 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), 423–430. https://doi.org/10.1109/CLOUD.2012.103
• Rasmi, A., & Bahsoon, R. (2021). Auto-tuning and self-learning control strategies for cloud elasticity: A reinforcement learning approach. Future Generation Computer Systems, 124, 249–265. https://doi.org/10.1016/j.future.2021.05.007
• Tang, C., Lin, Z., & Wang, Q. (2020). Observability in distributed systems: A survey. ACM Computing Surveys (CSUR), 53(6), 1–35. https://doi.org/10.1145/3417983
• Tirmazi, A., Ousterhout, K., Shen, A., Ghodsi, A., & Zaharia, M. (2020). Borg: The next generation. ACM Queue, 18(3), 30–39. https://queue.acm.org/detail.cfm?id=3399814
• Yadwadkar, N., & Katz, R. (2018). Learning autoscaling policies for cloud applications. Proceedings of the 2018 ACM Symposium on Cloud Computing (SoCC '18), 359–373. https://doi.org/10.1145/3267809.3275399
• Zhang, H., Liu, X., Pu, Q., & Lan, Z. (2019). DADS: A real-time deep anomaly detection system for streaming data. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1875–1883. https://doi.org/10.1145/3292500.3330690
• Google Cluster Data v2. (2019). Retrieved from https://github.com/google/cluster-data
• Alibaba Cluster Trace Program. (2018). Retrieved from https://github.com/alibaba/clusterdata
• OpenTelemetry Project. (2021). OpenTelemetry documentation. Retrieved from https://opentelemetry.io/docs/
• Prometheus Authors. (2022). Prometheus monitoring system documentation. Retrieved from https://prometheus.io/docs/introduction/overview/
• RedHat Developers. (2021). Introduction to eBPF and observability. Retrieved from https://www.redhat.com/en/topics/linux/what-is-ebpf
• Jaeger Tracing. (2021). Jaeger documentation for distributed tracing. Retrieved from https://www.jaegertracing.io/docs/
• Sloth SLO Framework. (2023). GitHub repository for SLOs as code. Retrieved from https://github.com/slok/sloth
• Reinforcement Learning with RLlib. (2020). RLlib documentation from Ray.io. Retrieved from https://docs.ray.io/en/latest/rllib/
• Munn, R., & Goh, J. (2024). Toward automated SLO-based orchestration in cloud-native systems. Journal of Cloud Computing, 13(1), 55–72. https://doi.org/10.1186/s13677-024-00356-4
• Agrawal, S., & Tripathi, A. (2023). Distributed observability and adaptive enforcement policies in service mesh architectures. IEEE Access, 11, 45523–45539. https://doi.org/10.1109/ACCESS.2023.3265509
