A case for redundant arrays of inexpensive disks (RAID)
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
DiskReduce: RAID for data-intensive scalable computing
Proceedings of the 4th Annual Workshop on Petascale Data Storage
Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling
Proceedings of the 5th European conference on Computer systems
Cloud analytics: do we really need to reinvent the storage stack?
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Spark: cluster computing with working sets
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
Disk-locality in datacenter computing considered irrelevant
HotOS'13 Proceedings of the 13th USENIX conference on Hot topics in operating systems
Design implications for enterprise storage systems via multi-dimensional trace analysis
SOSP '11 Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles
On the duality of data-intensive file system design: reconciling HDFS and PVFS
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Energy efficiency for large-scale MapReduce workloads with significant interactive analysis
Proceedings of the 7th ACM european conference on Computer Systems
PACMan: coordinated memory caching for parallel jobs
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Systems research and innovation in data ONTAP
ACM SIGOPS Operating Systems Review
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Data analytics and enterprise applications have very different storage functionality requirements. For this reason, enterprise deployments of data analytics are on a separate storage silo. This may generate additional costs and inefficiencies in data management, e.g., whenever data needs to be archived, copied, or migrated across silos. We introduce MixApart, a scalable data processing framework for shared enterprise storage systems. With MixApart, a single consolidated storage back-end manages enterprise data and services all types of workloads, thereby lowering hardware costs and simplifying data management. In addition, MixApart enables the local storage performance required by analytics through an integrated data caching and scheduling solution. Our preliminary evaluation shows that MixApart can be 45% faster than the traditional ingest-then-compute workflow used in enterprise IT analytics, while requiring one third of storage capacity when compared to HDFS.