Scalable load balancing in cluster storage systems

  • Authors:

  • Gae-won You;Seung-won Hwang;Navendu Jain

  • Affiliations:

  • Pohang University of Science and Technology, Republic of Korea;Pohang University of Science and Technology, Republic of Korea;Microsoft Research Redmond

  • Venue:
  • Proceedings of the 12th International Middleware Conference
  • Year:
  • 2011

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Abstract

Enterprise and cloud data centers are comprised of tens of thousands of servers providing petabytes of storage to a large number of users and applications. At such a scale, these storage systems face two key challenges: (a) hot-spots due to the dynamic popularity of stored objects and (b) high reconfiguration costs of data migration due to bandwidth oversubscription in the data center network. Existing storage solutions, however, are unsuitable to address these challenges because of the large number of servers and data objects. This paper describes the design, implementation, and evaluation of Ursa, which scales to a large number of storage nodes and objects and aims to minimize latency and bandwidth costs during system reconfiguration. Toward this goal, Ursa formulates an optimization problem that selects a subset of objects from hot-spot servers and performs topology-aware migration to minimize reconfiguration costs. As exact optimization is computationally expensive, we devise scalable approximation techniques for node selection and efficient divide-and-conquer computation. Our evaluation shows Ursa achieves cost-effective load balancing while scaling to large systems and is time-responsive in computing placement decisions, e.g., about two minutes for 10K nodes and 10M objects.