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.
By Xiaobin Zhang, Lefei Shen, Mouxiang Chen, Zhuo Li, Hongkai Li, Han Fu, Jianling Sun, Xiaoxue Ren, Chenghao Liu
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.
By Mehryar Majd, Feng Cheng, Ali Pahlevan
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.
By Tiangang Li, Shi Ying, Xiangbo Tian, Chuan Shi, Ding Xiao
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.
By Yifan Wu, Yuhan Li, Zhenhua Wang, Ke Chen, Lidan Shou, Zonghao Chen, Liang Lin, Huan Li, Gang Chen
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.
By Ashir Javeed, Anton Borg, H{\aa}kan Grahn, Lars Lundberg, Dhyey Patel, Sogand Shirinbab
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.
By Abd Elghani Meliani, Arora Sagar, Adlen Ksentini, Raymond Knopp