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
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
Sharing the data center network
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Dominant resource fairness: fair allocation of multiple resource types
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Managing data transfers in computer clusters with orchestra
Proceedings of the ACM SIGCOMM 2011 conference
Towards predictable datacenter networks
Proceedings of the ACM SIGCOMM 2011 conference
FairCloud: sharing the network in cloud computing
Proceedings of the 10th ACM Workshop on Hot Topics in Networks
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
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We consider the problem of allocating network resources across applications in a private cluster running data-parallel frameworks. Our primary observation is that these applications have different communication requirements and thus require different support from the network to effectively parallelize. We argue that network resources should be shared in a performance-centric fashion that aids parallelism and allows developers to reason about the overall performance of their applications. This paper tries to address the question of whether/how fairness-centric proposals relate to a performance-centric approach for different communication patterns common in these frameworks and engages in a quest for a unified mechanism to share the network in such settings.