Resource Provisioning Framework for MapReduce Jobs with Performance Goals

  • Abhishek Verma
  • Ludmila Cherkasova
  • Roy H. Campbell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7049)

Abstract

Many companies are increasingly using MapReduce for efficient large scale data processing such as personalized advertising, spam detection, and different data mining tasks. Cloud computing offers an attractive option for businesses to rent a suitable size Hadoop cluster, consume resources as a service, and pay only for resources that were utilized. One of the open questions in such environments is the amount of resources that a user should lease from the service provider. Often, a user targets specific performance goals and the application needs to complete data processing by a certain time deadline. However, currently, the task of estimating required resources to meet application performance goals is solely the users’ responsibility. In this work, we introduce a novel framework and technique to address this problem and to offer a new resource sizing and provisioning service in MapReduce environments. For a MapReduce job that needs to be completed within a certain time, the job profile is built from the job past executions or by executing the application on a smaller data set using an automated profiling tool. Then, by applying scaling rules combined with a fast and efficient capacity planning model, we generate a set of resource provisioning options. Moreover, we design a model for estimating the impact of node failures on a job completion time to evaluate worst case scenarios. We validate the accuracy of our models using a set of realistic applications. The predicted completion times of generated resource provisioning options are within 10% of the measured times in our 66-node Hadoop cluster.

Keywords

Completion Time Performance Goal Input Dataset Intermediate Data Hadoop Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Abhishek Verma
    • 1
  • Ludmila Cherkasova
    • 2
  • Roy H. Campbell
    • 1
  1. 1.University of Illinois at Urbana-ChampaignUSA
  2. 2.HP LabsPalo Alto

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