Publications

Found 13 results
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Zaharia, M., Borthakur D., Sarma J. Sen, Elmeleegy K., Shenker S. J., & Stoica I. (2010).  Delay Scheduling: A Simple Technique for Achieving Locality and Fairness in Cluster Scheduling. 265-278.
Zaharia, M., Hindman B., Konwinski A., Ghodsi A., Joseph A. D., Katz R. H., et al. (2011).  The Datacenter Needs an Operating System. 1-5.
Zaharia, M., Chowdhury M., Das T., Dave A., Ma J., McCauley M., et al. (2011).  Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing.
Zaharia, M., Bolosky W. J., Curtis K., Fox A., Patterson D., Shenker S. J., et al. (2011).  Faster and More Accurate Sequence Alignment with SNAP.
Zaharia, M., Chowdhury M., Das T., Dave A., Ma J., McCauley M., et al. (2012).  Fast and Interactive Analytics Over Hadoop Data with Spark. USENIX ;login:. 34(4), 45-51.
Zaharia, M., Das T., Li H., Shenker S. J., & Stoica I. (2012).  Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters. 1-6.
Zaharia, M., Chowdhury M., Das T., Dave A., Ma J., McCauley M., et al. (2012).  Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. 1-14.
Zaharia, M., Katti S., Grier C., Paxson V., Shenker S. J., Stoica I., et al. (2012).  Hypervisors as a Foothold for Personal Computer Security: An Agenda for the Research Community.
Zaharia, M., Chowdhury M., Franklin M. J., Shenker S. J., & Stoica I. (2010).  Spark: Cluster Computing with Working Sets. 1-7.
Zaharia, M., Borthakur D., Sarma J. Sen, Elmeleegy K., Shenker S. J., & Stoica I. (2009).  Job Scheduling for Multi-User MapReduce Clusters.
Zaharia, M., Chowdhury M., Franklin M. J., Shenker S. J., & Stoica I. (2010).  Spark: Cluster Computing with Working Sets.
Zaharia, M., Das T., Li H., Hunter T., Shenker S. J., & Stoica I. (2012).  Discretized Streams: A Fault-Tolerant Model for Scalable Stream Processing.
Zaharia, M., Xin R. S., Wendell P., Das T., Armbrust M., Dave A., et al. (2016).  Apache Spark: a unified engine for big data processing. Communications of the ACM. 59(11), 56-65.