SPANStore: Cost-Effective Geo-Replicated Storage Spanning Multiple Cloud Services

被引:128
|
作者
Wu, Zhe [1 ]
Butkiewicz, Michael [1 ]
Perkins, Dorian [1 ]
Katz-Bassett, Ethan [2 ]
Madhyastha, Harsha, V [1 ]
机构
[1] UC Riverside, Riverside, CA 92521 USA
[2] USC, Los Angeles, CA USA
基金
美国国家科学基金会;
关键词
D O I
10.1145/2517349.2522730
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
By offering storage services in several geographically distributed data centers, cloud computing platforms enable applications to offer low latency access to user data. However, application developers are left to deal with the complexities associated with choosing the storage services at which any object is replicated and maintaining consistency across these replicas. In this paper, we present SPANStore, a key-value store that exports a unified view of storage services in geographically distributed data centers. To minimize an application provider's cost, we combine three key principles. First, SPANStore spans multiple cloud providers to increase the geographical density of data centers and to minimize cost by exploiting pricing discrepancies across providers. Second, by estimating application workload at the right granularity, SPANStore judiciously trades off greater geo-distributed replication necessary to satisfy latency goals with the higher storage and data propagation costs this entails in order to satisfy fault tolerance and consistency requirements. Finally, SPANStore minimizes the use of compute resources to implement tasks such as two-phase locking and data propagation, which are necessary to offer a global view of the storage services that it builds upon. Our evaluation of SPANStore shows that it can lower costs by over 10x in several scenarios, in comparison with alternative solutions that either use a single storage provider or replicate every object to every data center from which it is accessed.
引用
收藏
页码:292 / 308
页数:17
相关论文
共 50 条
  • [41] Scalable and cost-effective NGS genotyping in the cloud
    Souilmi, Yassine
    Lancaster, Alex K.
    Jung, Jae-Yoon
    Rizzo, Ettore
    Hawkins, Jared B.
    Powles, Ryan
    Amzazi, Saaid
    Ghazal, Hassan
    Tonellato, Peter J.
    Wall, Dennis P.
    [J]. BMC MEDICAL GENOMICS, 2015, 8
  • [42] Reimbursement and cost-effective services in cervical cytology
    Frankel, K
    [J]. AMERICAN JOURNAL OF CLINICAL PATHOLOGY, 1997, 108 (05) : 604 - 604
  • [43] COST-EFFECTIVE SCHOOL NURSE PRACTITIONER SERVICES
    SOBOLEWSKI, SD
    [J]. JOURNAL OF SCHOOL HEALTH, 1981, 51 (09) : 585 - 588
  • [44] QUALITY SERVICES AND COST-EFFECTIVE USE OF RESOURCES
    HYNNIMAN, CE
    [J]. AMERICAN JOURNAL OF HOSPITAL PHARMACY, 1985, 42 (08): : 1768 - 1770
  • [45] Toward cost-effective storage provisioning for DBMSs
    Zhang, Ning
    Tatemura, Junichi
    Patel, Jignesh M.
    Hacigumus, Hakan
    [J]. VLDB JOURNAL, 2014, 23 (02): : 329 - 354
  • [46] Toward cost-effective storage provisioning for DBMSs
    Ning Zhang
    Junichi Tatemura
    Jignesh M. Patel
    Hakan Hacigumus
    [J]. The VLDB Journal, 2014, 23 : 329 - 354
  • [47] Towards Cost-Effective Storage Provisioning for DBMSs
    Zhang, Ning
    Tatemura, Junichi
    Patel, Jignesh M.
    Haciguemues, Hakan
    [J]. PROCEEDINGS OF THE VLDB ENDOWMENT, 2011, 5 (04): : 274 - 285
  • [48] Design and Implementation of an Online and Cost-Effective Attendance Management System Using Smartphones and Cloud Services
    Khan, M. Fahim Ferdous
    Yamazaki, Taisei
    Sakamura, Ken
    [J]. MOBILE AND UBIQUITOUS SYSTEMS: COMPUTING, NETWORKING AND SERVICES, 2022, 419 : 650 - 664
  • [49] Approximate Code: A Cost-Effective Erasure Coding Framework for Tiered Video Storage in Cloud Systems
    Jin, Huayi
    Wu, Chentao
    Xie, Xin
    Li, Jie
    Guo, Minyi
    Lin, Hao
    Zhang, Jianfeng
    [J]. PROCEEDINGS OF THE 48TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING (ICPP 2019), 2019,
  • [50] A Popularity-aware Cost-effective Replication Scheme for High Data Durability in Cloud Storage
    Liu, Jinwei
    Shen, Haiying
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2016, : 384 - 389