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. 13513v1 Announce Type: new Abstract: Driven by conservative over-provisioning to guarantee service reliability, resource utilization in cloud data centers remains at low levels.
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.
MCRL2 is a reinforcement learning framework that enhances microservice scheduling in cloud data centers by integrating multi-resource cross-attention-based representation learning. It introduces MCRL, a representation learning component that captures structured interactions among nodes, resources, and microservices, and couples this with an actor‑critic architecture and a maximum entropy objective. Experiments on real production cluster traces show that MCRL2 outperforms existing baselines in load balancing, scheduling success rate, and average completion time across diverse workloads.
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.
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: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.
arXiv:2607. 19974v1 Announce Type: cross Abstract: The rapid proliferation of data-intensive applications, cloud infrastructure, and IoT ecosystems has made proactive resource provisioning critical for maintaining optimal network performance.
arXiv:2607. 15511v1 Announce Type: cross Abstract: Serverless computing provides automatic resource management and pay-per-use execution, but effective autoscaling remains challenging because of dynamic workloads, cold-start latency, and dependencies among functions.
arXiv:2609.37085v1 Announce Type: cross Abstract: Data-intensive services in the Computing Continuum must balance analytics quality, resource usage, and cost across heterogeneous nodes with limited a...
arXiv:2606. 31470v1 Announce Type: new Abstract: Cloud virtual machines are often overprovisioned, creating avoidable cost and operational inefficiency.
arXiv:2606. 11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features.
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.