Cura: A Cost-Optimized Model for MapReduce in a Cloud
Parallel & Distributed Processing(2013)
摘要
We propose a new MapReduce cloud service model, Cura, for data analytics in the cloud. We argue that performing MapReduce analytics in existing cloud service models - either using a generic compute cloud or a dedicated MapReduce cloud - is inadequate and inefficient for production workloads. Existing services require users to select a number of complex cluster and job parameters while simultaneously forcing the cloud provider to use those potentially sub-optimal configurations resulting in poor resource utilization and higher cost. In contrast Cura leverages MapReduce profiling to automatically create the best cluster configuration for the jobs so as to obtain a global resource optimization from the provider perspective. Secondly, to better serve modern MapReduce workloads which constitute a large proportion of interactive real-time jobs, Cura uses a unique instant VM allocation technique that reduces response times by up to 65%. Thirdly, our system introduces deadline-awareness which, by delaying execution of certain jobs, allows the cloud provider to optimize its global resource allocation and reduce costs further. Cura also benefits from a number of additional performance enhancements including cost-aware resource provisioning, VMaware scheduling and online virtual machine reconfiguration. Our experimental results using Facebook-like workload traces show that along with response time improvements, our techniques lead to more than 80% reduction in the compute infrastructure cost of the cloud data center.
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关键词
mapreduce analytics,new mapreduce cloud service,cloud service model,dedicated mapreduce cloud,cloud provider,contrast cura leverages mapreduce,cloud data center,global resource allocation,cost-optimized model,cost-aware resource,modern mapreduce workloads,virtual machines,optimization,production,resource management,data analytics,computational modeling,schedules,resource utilization,resource allocation,cloud computing
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