Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center

By Benjamin Hindman, Andy Konwinski, Matei A. Zaharia, A. Ghodsi, Anthony D. Joseph, Randy Howard Katz, Scott Shenker, Ion Stoica. 2011

2,075 citations recorded by the source corpus.

Subfields: ML Systems & Hardware

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

In ML Systems & Hardware

Citations in the corpus

TensorFlow: A system for large-scale machine learning (2016, neutral): “It is desirable to be able to train a model using non-dedicated resources, for example using a cluster manager, like Mesos [28] or Borg [72], that does not guarantee availability of the same resources for the duration of the training process.”
Live Video Analytics at Scale with Approximation and Delay-Tolerance (2017, background): “Cluster managers such as Yarn [3], Apollo [31] and Mesos [49] commonly divide resources among multiple queries based on resource fairness.”
Decentralized task-aware scheduling for data center networks (2014, background): “There is a large body of work on centralized cluster schedulers and resource managers [16, 23, 11, 1].”
Efficient coflow scheduling with Varys (2014, positive): “Finally, Varys supports coﬂows from multiple frameworks like Mesos [24] handles non-network resources.”
Optimus: an efficient dynamic resource scheduler for deep learning clusters (2018, background): “Static resource allocation . jobs, e.g. , Mesos [40] in a TensorFlow cluster [23], Yarn [61] for the owner of a training job specifies resource requirements, e.g. ,”
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update (2016, methodology): “Once packaged, the Docker container is deployed onto a 16-node cluster managed using Apache Mesos (35).”
Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads (2012, background): “There is ongoing research to achieve well-behaved multiplexing between different frameworks [32].”
Big data analytics on Apache Spark (2016, background): “Spark core runs over diverse clustermanagers including Hadoop YARN [76], ApacheMesos [39], Amazon EC2 and Spark’s built-in cluster manager (i.”
PACMan: Coordinated Memory Caching for Parallel Jobs (2012, background): “Recently reported research on framework managers [10] show that the number of requests handled by the centralized job managers of Hadoop is significantly less (3, 200 requests/second).”
Hopper: Decentralized Speculation-aware Cluster Scheduling at Scale (2015, background): “CCS Concepts •Networks → Cloud computing; •Computer systems organization → Distributed architectures; Keywords speculation; decentralized scheduling; straggler; fairness”
Learning scheduling algorithms for data processing clusters (2018, background): “More advanced cluster schedulers, such as YARN [57] or Mesos [29], allow jobs to specify their tasks’ resource requirements and create appropriately-sized executors.”
Proposal Scaling Distributed Machine Learning with System and Algorithm Co-design (2016, neutral): “Integration with a cluster resource manager such as Yarn [52] or Mesos [67] is left for future work.”