Exokernel: an operating system architecture for application-level resource management
SOSP '95 Proceedings of the fifteenth ACM symposium on Operating systems principles
Cilk: an efficient multithreaded runtime system
Journal of Parallel and Distributed Computing - Special issue on multithreading for multiprocessors
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Interpreting the data: Parallel analysis with Sawzall
Scientific Programming - Dynamic Grids and Worldwide Computing
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
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Clustera: an integrated computation and data management system
Proceedings of the VLDB Endowment
Intel threading building blocks
Intel threading building blocks
Evolution of a virtual machine subsystem
IBM Systems Journal
Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling
Proceedings of the 5th European conference on Computer systems
Manimal: relational optimization for data-intensive programs
Procceedings of the 13th International Workshop on the Web and Databases
Spark: cluster computing with working sets
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
Proceedings of the 2nd ACM Symposium on Cloud Computing
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Proceedings of the VLDB Endowment
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
The family of mapreduce and large-scale data processing systems
ACM Computing Surveys (CSUR)
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The success of MapReduce has sparked many efforts to design cluster computing frameworks. We argue that no single framework will be optimal for all applications, and that we should instead enable organizations to run multiple frameworks effciently in the same cloud. Furthermore, to ease development of new frameworks, it is critical to identify common abstractions and modularize their architectures. To achieve these goals, we propose Nexus, a low-level substrate that provides isolation and efficient resource sharing across frameworks running on the same cluster, while giving each framework freedom to implement its own programming model and fully control the execution of its jobs. Nexus fosters innovation in the cloud by letting organizations run new frameworks alongside existing ones and by letting framework developers focus on specific applications rather than building one-size-fits-all frameworks.