Dynamic estimation of CPU demand of web traffic
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
A scalable application placement controller for enterprise data centers
Proceedings of the 16th international conference on World Wide Web
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
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Quincy: fair scheduling for distributed computing clusters
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Data warehousing and analytics infrastructure at facebook
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Improving MapReduce performance in heterogeneous environments
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Reining in the outliers in map-reduce clusters using Mantri
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
ARIA: automatic resource inference and allocation for mapreduce environments
Proceedings of the 8th ACM international conference on Autonomic computing
FLEX: a slot allocation scheduling optimizer for MapReduce workloads
Proceedings of the ACM/IFIP/USENIX 11th International Conference on Middleware
Building and scaling virtual clusters with residual resources from interactive clouds
Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
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We present a resource-aware scheduling technique for MapReduce multi-job workloads that aims at improving resource utilization across machines while observing completion time goals. Existing MapReduce schedulers define a static number of slots to represent the capacity of a cluster, creating a fixed number of execution slots per machine. This abstraction works for homogeneous workloads, but fails to capture the different resource requirements of individual jobs in multi-user environments. Our technique leverages job profiling information to dynamically adjust the number of slots on each machine, as well as workload placement across them, to maximize the resource utilization of the cluster. In addition, our technique is guided by user-provided completion time goals for each job. Source code of our prototype is available at [1].