TripS: Automated multi-tiered data placement in a geo-distributed cloud environment

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Exploiting the cloud storage hierarchy both within and across data-centers of different cloud providers empowers Internet applications to choose data centers (DCs) and storage services based on storage needs. However, using multiple storage services across multiple data centers brings a complex data placement problem that depends on a large number of factors including, e.g., desired goals, storage and network characteristics, and pricing policies. In addition, dynamics e.g., changing user locations and access patterns, make it impossible to determine the best data placement statically. In this paper, we present TripS, a lightweight system that considers both data center locations and storage tiers to determine the data placement for geo-distributed storage systems. Such systems make use of TripS by providing inputs including SLA, consistency model, fault tolerance, latency information, and cost information. With given inputs, TripS models and solves the data placement problem using mixed integer linear programming (MILP) to determine data placement. In addition, to adapt quickly to dynamics, we introduce the notion of Target Locale List (TLL), a pro-active approach to avoid expensive re-evaluation of the optimal placement. The TripS prototype is running on Wiera, a policy driven geo-distributed storage system, to show how a storage system can easily utilize TripS for data placement. We evaluate TripS/Wiera on multiple data centers of AWS and Azure. The results show that TripS/Wiera can reduce cost 14.96% ∼ 98.1% based on workloads in comparison with other works' approaches and can handle both short- and long-term dynamics to avoid SLA violations.

Original languageEnglish (US)
Title of host publicationSYSTOR 2017 - Proceedings of the 10th ACM International Systems and Storage Conference
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450350358
DOIs
StatePublished - May 22 2017
Event10th ACM International Systems and Storage Conference, SYSTOR 2017 - Haifa, Israel
Duration: May 22 2017May 24 2017

Publication series

NameSYSTOR 2017 - Proceedings of the 10th ACM International Systems and Storage Conference

Other

Other10th ACM International Systems and Storage Conference, SYSTOR 2017
CountryIsrael
CityHaifa
Period5/22/175/24/17

Keywords

  • Data placement
  • Multi-DC storage
  • Multi-tiered storage

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    Oh, K., Chandra, A., & Weissman, J. (2017). TripS: Automated multi-tiered data placement in a geo-distributed cloud environment. In SYSTOR 2017 - Proceedings of the 10th ACM International Systems and Storage Conference [12] (SYSTOR 2017 - Proceedings of the 10th ACM International Systems and Storage Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3078468.3078485