arXiv:2606. 31470v1 Announce Type: new Abstract: Cloud virtual machines are often overprovisioned, creating avoidable cost and operational inefficiency.
arXiv:2607. 24773v1 Announce Type: new Abstract: Managing cloud infrastructure efficiently, especially in environments of large cloud providers or hyperscalers, requires optimizing the use of physical resources to minimize costs and maximize performance.
arXiv:2606. 13513v1 Announce Type: new Abstract: Driven by conservative over-provisioning to guarantee service reliability, resource utilization in cloud data centers remains at low levels.
A two-stage forecasting system is introduced for predicting CPU workload in private clouds. The model first forecasts customer service requests in Transactions Per Second (TPS) and then estimates future CPU usage from the TPS forecast, both stages using XGBoost within a cascaded architecture. Experiments on real private‑cloud traces show SMAPE below 7% for most applications, with the best case achieving an MAE of 0.7372 and an R² of 0.9185, and stable error accumulation over a 60‑step horizon.
arXiv:2608. 07945v1 Announce Type: cross Abstract: Cloud-native serverless data warehouses achieve fine-grained elasticity by decoupling storage from compute, yet determining the optimal resource allocation for highly heterogeneous ad-hoc queries remains a formidable industrial challenge.
arXiv:2606. 07565v1 Announce Type: new Abstract: Intelligent scaling of microservices in cloud platforms is crucial for mitigating escalating compute costs while avoiding service disruptions.
arXiv:2606. 09787v1 Announce Type: new Abstract: The Cloud-Edge Continuum (CEC) enables latency-critical applications by distributing resources to the far edge, but its extreme volatility makes proactive Zero Touch Management via time-series forecasting essential.
Atlas is a framework that optimizes the deployment of compound AI workflows on heterogeneous clusters by selecting execution plans that satisfy service level objectives (SLOs). It introduces MAP, a Markovian Accuracy Predictor, which estimates configuration accuracy using local conditional accuracy transitions between adjacent workflow stages, avoiding exhaustive end‑to‑end profiling. Atlas formulates plan selection as a mixed‑integer linear program, achieving near‑oracle accuracy while reducing deployment cost by up to 42% and profiling cost by up to 2.6×.
arXiv:2606. 14707v1 Announce Type: cross Abstract: AI training and deployment consume substantial electricity, but carbon outcomes remain weakly integrated into routine model development decisions.
arXiv:2609.14762v1 Announce Type: cross Abstract: Cloud-hosted large language models (LLMs) are increasingly used for root cause analysis (RCA) in AIOps pipelines, but they introduce data privacy ris...
arXiv:2606. 01162v1 Announce Type: new Abstract: Workflow scheduling in cloud computing demands the intelligent allocation of dynamically arriving, graph-structured workflows with varying deadlines onto ever-changing virtual machine resources.
arXiv:2608. 03071v1 Announce Type: new Abstract: Large language model agents derive much of their capability from tool use.
arXiv:2609.13840v1 Announce Type: new Abstract: A contract-logistics spare-parts operator is paid on order-level service: an order counts only if every requested line is fulfilled, yet forecasters ar...