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HAIL: Prediction-Calibrated Hierarchical Resource Isolation for Multi-Tenant Cloud Platforms

Lana Kapoor, Lena Huang, Serena Arora
Journal of Computing and Electronic Information Management, (2026), Vol.23, No.1, pp.39-45
Published: October 28, 2026
DOI: 10.54097/2jvszb05
PDF: Download Full Text PDF
Abstract

Multi-tenant cloud platforms must reserve capacity for protected workloads while avoiding needless loss of best-effort service. This paper presents HAIL, a hierarchical adaptive isolation-and-lending controller operating across CPU, memory, network, and storage. It maps a three-layer isolation taxonomy to physical-capacity constraints, host-local quota control, and application-level demand coverage. Four gradient-boosted predictors forecast next-interval demand; independent validation residuals supply one-sided protection margins, and bounded online error feedback increases reservations for persistently underpredicted protected tenants. Unallocated priority capacity is made available to batch tenants through a deterministic feasible projection. We conduct reproducible experiments with synthetic but temporally correlated and bursty tenant traces, twelve disjoint test seeds, five comparison controllers, an oracle upper bound, and targeted ablations. With causal historical profiles, HAIL reduces modeled protected shortfalls from 24.11% for a hierarchical profiling-and-lending baseline to 8.86% (paired 95% interval: -19.47 to -11.04 percentage points); utilization decreases from 87.15% to 86.44%. Stress tests reveal that strict priority can starve best-effort demand under severe scarcity. These are simulated resource-coverage results, not measured request latencies. The released experiment package preserves code, generated demands, seed splits, and per-episode results.

Keywords: Multi-tenancy, Resource isolation, Cloud scheduling, Calibrated prediction, Quality of service, Capacity lending
APA Citation: Lana Kapoor, Lena Huang, Serena Arora (2026). HAIL: Prediction-Calibrated Hierarchical Resource Isolation for Multi-Tenant Cloud Platforms. Journal of Computing and Electronic Information Management, 23(1), 39-45. https://doi.org/10.54097/2jvszb05

References

  1. Dean, J., & Barroso, L. A. (2013). The tail at scale. Communications of the ACM, 56(2), 74-80. https://doi.org/10.1145/2408776.2408794
  2. Wang, J., Ge, Y., Liu, J., & Jiang, B. (2024). Early warning of liquidity stress in U.S. banks with seasonally aligned frailty hazards and delayed-feedback conformal alarms. Journal of Trends in Finance and Economics, 1(1), 85-100.
  3. Tirmazi, M., Barker, A., Deng, N., Haque, M. E., Qin, Z., Hand, S., Harchol-Balter, M., & Wilkes, J. (2020). Borg: The next generation. In Proceedings of the 15th European Conference on Computer Systems (EuroSys) (Article 30, pp. 1-14). ACM. https://doi.org/10.1145/3342195.3387517
  4. Ding, J., Chen, T., & Qin, Y. (2026). Achieving resource isolation in multi-tenant cloud platforms without sacrificing performance. Journal of Computing and Electronic Information Management, 21(1), 10-18. https://doi.org/10.54097/b5qr1a18
  5. Ghodsi, A., Zaharia, M., Hindman, B., Konwinski, A., Shenker, S., & Stoica, I. (2011). Dominant resource fairness: Fair allocation of multiple resource types. In Proceedings of the 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI) (pp. 323-336). USENIX Association.
  6. Grandl, R., Ananthanarayanan, G., Kandula, S., Rao, S., & Akella, A. (2014). Multi-resource packing for cluster schedulers. In Proceedings of the 2014 ACM Conference on SIGCOMM (pp. 455-466). ACM. https://doi.org/10.1145/2619239.2626334
  7. Zhou, F. (2026). Moment-constrained side-force allocation for wind-assisted merchant ships: Hydrodynamic performance of retractable anti-leeway fins and flap rudders. Innovation and Technology Studies, 3(1), 27-41.
  8. Delimitrou, C., & Kozyrakis, C. (2014). Quasar: Resource-efficient and QoS-aware cluster management. In Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) (pp. 127-144). ACM. https://doi.org/10.1145/2541940.2541941
  9. Lo, D., Cheng, L., Govindaraju, R. K., Ranganathan, P., & Kozyrakis, C. (2015). Heracles: Improving resource efficiency at scale. In Proceedings of the 42nd Annual International Symposium on Computer Architecture (ISCA) (pp. 450-462). ACM. https://doi.org/10.1145/2749469.2749475
  10. Qiu, H., Banerjee, S. S., Jha, S., Kalbarczyk, Z. T., & Iyer, R. K. (2020). FIRM: An intelligent fine-grained resource management framework for SLO-oriented microservices. In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI) (pp. 805-825). USENIX Association.
  11. Shahrad, M., Fonseca, R., Goiri, Í., Chaudhry, G., Batum, P., Cooke, J., Laureano, E., Tresness, C., Russinovich, M., & Bianchini, R. (2020). Serverless in the wild: Characterizing and optimizing the serverless workload at a large cloud provider. In Proceedings of the 2020 USENIX Annual Technical Conference (ATC) (pp. 205-218). USENIX Association.
  12. Hadary, O., Marshall, L., Menache, I., Pan, A., Greeff, E. E., Dion, D., Dorminey, S., Joshi, S., Chen, Y., Russinovich, M., & Moscibroda, T. (2020). Protean: VM allocation service at scale. In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI) (pp. 845-861). USENIX Association.
  13. Ambati, P., Irwin, D., Kaufmann, L., Lantz, P., Rai, A., & Zeryihun, A. (2020). Providing SLOs for resource-harvesting VMs in cloud platforms. In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI) (pp. 735-751). USENIX Association.
  14. Mao, H., Schwarzkopf, M., Venkatakrishnan, S. B., Meng, Z., & Alizadeh, M. (2019). Learning scheduling algorithms for data processing clusters. In Proceedings of the ACM Special Interest Group on Data Communication (SIGCOMM) (pp. 270-288). ACM. https://doi.org/10.1145/3341302.3342080
  15. Yue, X., & Zhu, R. (2024). An explainable hybrid machine learning framework for cash-flow-conditioned multi-horizon liquidity risk early warning: An SME-oriented benchmark study. World Journal of Management Science, 2(1), 63-72.
  16. Cortez, E., Bonde, A., Muzio, A., Russinovich, M., Fontoura, M., & Bianchini, R. (2017). Resource Central: Understanding and predicting workloads for improved resource management in large cloud platforms. In Proceedings of the 26th Symposium on Operating Systems Principles (SOSP) (pp. 153-167). ACM. https://doi.org/10.1145/3132747.3132772
  17. Chen, J., Zhang, X., & Han, X. (2023). Explainable identification of tax-related financial reporting anomalies in U.S. public companies: Cross-fitted tax-consistency residuals and Owen-value Shapley propagation. World Journal of Economics and Business Research, 1(1), 33-45.
  18. Romano, Y., Patterson, E., & Candès, E. J. (2019). Conformalized quantile regression. In Advances in Neural Information Processing Systems 32 (NeurIPS) (pp. 3543-3553).
  19. Rahman, J., & Lama, P. (2019). Predicting the end-to-end tail latency of containerized microservices in the cloud. In Proceedings of the IEEE International Conference on Cloud Engineering (IC2E) (pp. 200-210). IEEE. https://doi.org/10.1109/IC2E.2019.00034
  20. Agache, A., Brooker, M., Iordache, A., Liguori, A., Neugebauer, R., Piwonka, P., & Popa, D.-M. (2020). Firecracker: Lightweight virtualization for serverless applications. In Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI) (pp. 419-434). USENIX Association.
  21. Chen, J., Han, X., Zhang, X., & Yang, Y. (2023). Exact concept-level explanations for early detection of financial misstatements. Eurasia Journal of Science and Technology, 5(2), 34-48.
  22. Wang, Y., Ma, X., Li, Z., & Chen, H. (2021). SmartHarvest: Harvesting idle CPUs safely and efficiently in the cloud. In Proceedings of the 16th European Conference on Computer Systems (EuroSys) (pp. 1-16). ACM. https://doi.org/10.1145/3447786.3456225
  23. Al Maruf, H., Chowdhury, M., & Mahajan, A. (2023). TPP: Transparent page placement for CXL-enabled tiered-memory. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) (Vol. 3, pp. 742-755). ACM. https://doi.org/10.1145/3582016.3582063
  24. Canella, C., Van Bulck, J., Schwarz, M., Lipp, M., von Bergmann, B., Payer, M., & Gruss, D. (2019). A systematic evaluation of transient execution attacks and defenses. In Proceedings of the 28th USENIX Security Symposium (pp. 249-266). USENIX Association.
  25. Wang, Y., Zou, X., Lu, Z., Wei, K., & Rhee, M. (2026). PRESAGE: Uncertainty-aware predictive elastic scheduling for cloud analytics and GenAI inference. IEEE Access, 14, 1-16. https://doi.org/10.1109/ACCESS.2026.3734498
  26. Shang, W., Teng, D., Chen, T., Zhang, F., & Ding, J. (2026). Neuro-Elastic: A unified framework for hardware-aware adaptive quantization and dynamic sparsity in real-time edge intent prediction. IEEE Access, 14, 1-15. https://doi.org/10.1109/ACCESS.2026.3715276
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