• DocumentCode
    1920535
  • Title

    Probability-Based Cloud Storage Providers Selection Algorithms with Maximum Availability

  • Author

    Chang, Chia-Wei ; Liu, Pangfeng ; Wu, Jan-Jan

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan
  • fYear
    2012
  • fDate
    10-13 Sept. 2012
  • Firstpage
    199
  • Lastpage
    208
  • Abstract
    During recent years cloud service providers have successfully provided reliable and flexible resources to cloud users. For example Amazon Elastic Block Store (Amazon EBS) and Simple Storage Service (Amazon S3) provides users storage in the cloud. Despite the tremendous efforts cloud service providers have devoted to the availability of their services, the interruption is still inevitable. Therefore just as an Internet service provider will not count on a single network provider, a cloud user should not depend on a single cloud service provider either. However, cloud service providers provide different levels of services. A more costly service is usually more reliable. As a result it is an important and challenging problem to choose among a set of service providers to fit one´s need, which could be budget, failure probability, or the amount of data that can survive failure. The goal of this paper is to select cloud service providers in order to maximize the benefits with a given budget. The contributions of this paper include a mathematical formulation of the cloud service provider selection problem in which both the object functions and cost measurements are clearly defined, algorithms that selects among cloud storage providers to maximize the data survival probability or the amount of surviving data, subject to a fixed budget, and a series of experiments that demonstrateDuring recent years cloud service providers have successfully provided reliable and flexible resources to cloud users. For example Amazon Elastic Block Store (Amazon EBS) and Simple Storage Service (Amazon S3) provides users storage in the cloud. Despite the tremendous efforts cloud service providers have devoted to the availability of their services, the interruption is still inevitable. Therefore just as an Internet service provider will not count on a single network provider, a cloud user should not depend on a single cloud service provider either. However, cloud service providers provide diffe- ent levels of services. A more costly service is usually more reliable. As a result it is an important and challenging problem to choose among a set of service providers to fit one´s need, which could be budget, failure probability, or the amount of data that can survive failure. The goal of this paper is to select cloud service providers in order to maximize the benefits with a given budget. The contributions of this paper include a mathematical formulation of the cloud service provider selection problem in which both the object functions and cost measurements are clearly defined, algorithms that selects among cloud storage providers to maximize the data survival probability or the amount of surviving data, subject to a fixed budget, and a series of experiments that demonstrate that the proposed algorithms are efficient enough to find optimal solutions in reasonable amount of time, using price and fail probability taken from real cloud providers. that the proposed algorithms are efficient enough to find optimal solutions in reasonable amount of time, using price and fail probability taken from real cloud providers.
  • Keywords
    cloud computing; probability; storage management; Internet service provider; budget; cloud service providers; cloud users; failure probability; mathematical formulation; probability-based cloud storage providers selection algorithms; Availability; Cloud computing; Dynamic programming; Equations; Heuristic algorithms; Cloud Computing; data replication; dynamic programming; failure probability; service provider selection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Parallel Processing (ICPP), 2012 41st International Conference on
  • Conference_Location
    Pittsburgh, PA
  • ISSN
    0190-3918
  • Print_ISBN
    978-1-4673-2508-0
  • Type

    conf

  • DOI
    10.1109/ICPP.2012.51
  • Filename
    6337581