Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (5): 1184-1199.doi: 10.1007/s11390-021-0232-4

Special Issue: Computer Networks and Distributed Computing

• Regular Paper • Previous Articles     Next Articles

Apollo: Rapidly Picking the Optimal Cloud Configurations for Big Data Analytics Using a Data-Driven Approach

Yue-Wen Wu1, Yuan-Jia Xu1, Heng Wu2,*, Member, CCF, ACM, IEEE, Lin-Gang Su1 Wen-Bo Zhang2, Senior Member, CCF, and Hua Zhong2, Senior Member, CCF        

  1. 1 University of Chinese Academy of Sciences, Beijing 100049, China;
    2 State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2020-02-27 Revised:2021-08-05 Online:2021-09-30 Published:2021-09-30
  • About author:Yue-Wen Wu received his M.S. degree in software engineering from Huazhong University of Science and Technology, Wuhan, in 2013. He is a Ph.D. candidate with the Institute of Software, Chinese Academy of Sciences, Beijing. His current research interests include cloud computing and resource provisioning, machine learning and performance modeling.
  • Supported by:
    This work was supported by the National Key Research and Development Program of China under Grant No. 2017YFB1001804.

Big data analytics applications are increasingly deployed on cloud computing infrastructures, and it is still a big challenge to pick the optimal cloud configurations in a cost-effective way. In this paper, we address this problem with a high accuracy and a low overhead. We propose Apollo, a data-driven approach that can rapidly pick the optimal cloud configurations by reusing data from similar workloads. We first classify 12 typical workloads in BigDataBench by characterizing pairwise correlations in our offline benchmarks. When a new workload comes, we run it with several small datasets to rank its key characteristics and get its similar workloads. Based on the rank, we then limit the search space of cloud configurations through a classification mechanism. At last, we leverage a hierarchical regression model to measure which cluster is more suitable and use a local search strategy to pick the optimal cloud configurations in a few extra tests. Our evaluation on 12 typical workloads in HiBench shows that compared with state-of-the-art approaches, Apollo can improve up to 30% search accuracy, while reducing as much as 50% overhead for picking the optimal cloud configurations.

Key words: big data analytics; cloud configuration; data driven;

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