Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling
Proceedings of the 5th European conference on Computer systems
See spot run: using spot instances for mapreduce workflows
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
Performance Management of Accelerated MapReduce Workloads in Heterogeneous Clusters
ICPP '10 Proceedings of the 2010 39th International Conference on Parallel Processing
Mesos: a platform for fine-grained resource sharing in the data center
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
A reflective platform for highly adaptive multi-cloud systems
Adaptive and Reflective Middleware on Proceedings of the International Workshop
Exploiting hardware heterogeneity within the same instance type of Amazon EC2
HotCloud'12 Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing
AROMA: automated resource allocation and configuration of mapreduce environment in the cloud
Proceedings of the 9th international conference on Autonomic computing
Consolidated cluster systems for data centers in the cloud age: a survey and analysis
Frontiers of Computer Science: Selected Publications from Chinese Universities
PIKACHU: how to rebalance load in optimizing mapreduce on heterogeneous clusters
USENIX ATC'13 Proceedings of the 2013 USENIX conference on Annual Technical Conference
Market mechanisms for managing datacenters with heterogeneous microarchitectures
ACM Transactions on Computer Systems (TOCS)
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Data analytics are key applications running in the cloud computing environment. To improve performance and cost-effectiveness of a data analytics cluster in the cloud, the data analytics system should account for heterogeneity of the environment and workloads. In addition, it also needs to provide fairness among jobs when multiple jobs share the cluster. In this paper, we rethink resource allocation and job scheduling on a data analytics system in the cloud to embrace the heterogeneity of the underlying platforms and workloads. To that end, we suggest an architecture to allocate resources to a data analytics cluster in the cloud, and propose a metric of share in a heterogeneous cluster to realize a scheduling scheme that achieves high performance and fairness.