Borg, Omega, and Kubernetes

948 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Auto-Meta: Automated Gradient Based Meta Learner Search (2018, methodology): “We apply a Kubernetes system [5] that supports utilities to deploy over clusters of GPUs, in order to achieve the parallelization of training and testing top models in each iteration of block progression.”
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods (2020, background): “Examples include Apache Mesos (Hindman et al. 2011), Kubernetes (Burns et al. 2016), Uber Peloton (Cai and Bansal 2019) and Slurm (Yoo, Jette, and Grondona 2003).”
Pond: CXL-Based Memory Pooling Systems for Cloud Platforms (2022, neutral): “The model uses supervised learning (§5) with core-PMU metrics as features and the slowdown of pool memory relative to NUMA-local memory as labels.”
Elasticity in Cloud Computing: State of the Art and Research Challenges (2018, methodology): “It uses reactive threshold-based rules for CPU utilization metric [190].”
Autopilot: workload autoscaling at Google (2020, background): “Borg [34], Omega [29] and Kubernetes [3] all require their users to set such limits when submitting jobs (Borg, Omega) or pods (Kubernetes).”
Performance analysis of single board computer clusters (2020, neutral): “9 illustrates relative cluster performance as cluster size increases.”