This work outlines AutoSLO, a latency-SLO-aware work-load management framework for multi-cluster cloud data ware-houses that includes a periodic Policy Tuner that proactively plans resources using workload fore-casts, an SLO-aware reactive Autoscaler that adjusts the active cluster set based on the observed workload, and an online Query Router that reacts to concurrent query load when routing.
This work proposes and develops an open-source policy simulation framework, LoadStar, which forms a reusable benchmark pipeline for validating policies for resource-centric NoSQL workloads, and defines a resource optimization problem for placing Cosmos DB replicas onto VM nodes, and develops the Luna model for forecast...
Gunika Verma, V. AashutoshA, P. Srinivas et al.· Proceedings of the VLDB Endo...· 0 citations
Seron is presented, a smart query routing system customized for multi-primary cloud-native databases with shared storage that generates workload-aware logical routing rules at different granularities using a two-stage framework, and actively detects and handles physical data collisions through judicious conflict modeli...
Yu-Wei Huang, Guo-Liang Li, Jin-Yu Zhang· Proceedings of the VLDB Endo...· 0 citations
Modern web services are required to meet critical non-functional requirements, including availability, responsiveness, scalability, and reliability, which are formalized through Service Level Agreements (SLAs). SLAs define Service Level Objectives (SLOs), such as latency, throughput, and uptime, that ensure consistent...
Angelo Marchese, O. Tomarchio· International Conference on...· 0 citations
The paper argues that multi-tenancy must be treated not merely as a virtualization problem but as a data-governance, workload-management, and responsible-AI problem, and provides a foundation for scalable AI data lakes while identifying limitations related to resource interference, governance complexity, data heterogen...
Arjun Mehta, Priya Sharma· International Journal of Adv...· 0 citations
Results demonstrate that HPA effectively responds to workload increases by provisioning additional pods, maintaining system stability and throughput during high-demand periods, with CPU usage and RPS exhibiting predictable scaling behavior aligned with the 15-second Metrics Server scraping interval.
Shamsuddeen Rabiu, Sani Muhammad Tanko, Eli Adama Jiya· Journal of Basics and Applie...· 0 citations
A dynamic priority scheduling framework at the kernel level that enhances the CPU allocation to latency-sensitive containers running in Kubernetes environments and reveals a significant improvement in terms of latency reduction, enhanced throughput, efficient utilization of CPU resources, and stable performance of sche...
T. Rajkumar, Nishanth D., P. M et al.· Journal of Soft Computing Pa...· 0 citations
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