article

Security-aware intermediate data placement strategy in scientific cloud workflows

Authors:

Guo Sun ZengAuthors Info & Claims

Knowledge and Information Systems, Volume 41, Issue 2

Pages 423 - 447

https://doi.org/10.1007/s10115-014-0755-x

Published: 01 November 2014 Publication History

Abstract

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy data-intensive applications without the infrastructure investment, where large application datasets can be stored in the cloud. Based on the pay-as-you-go model, data placement strategies have been developed to cost-effectively store large volumes of generated datasets in the scientific cloud workflows. As promising as it is, this paradigm also introduces many new challenges for data security when the users outsource sensitive data for sharing on the cloud servers, which are not within the same trusted domain as the data owners. This challenge is further complicated by the security constraints on the potential sensitive data for the scientific workflows in the cloud. To effectively address this problem, we propose a security-aware intermediate data placement strategy. First, we build a security overhead model to reasonably measure the security overheads incurred by the sensitive data. Second, we develop a data placement strategy to dynamically place the intermediate data for the scientific workflows. Finally, our experimental results show that our strategy can effectively improve the intermediate data security while ensuring the data transfer time during the execution of scientific workflows.

References

[1]

Costantino Thanos (2012) Global research data infrastructures: towards a 10-year vision for global research data infrastructures. http://www.grdi2020.eu/Repository/FileScaricati/6bdc07fb-b21d-4b90-81d4-d909fdb96b87.pdf

[2]

European Commission High Level Expert Group on Scientific Data (2010) Riding the wave: how Europe can gain from the rising tide of scientific data. http://cordis.europa.eu/fp7/ict/e-infrastructure/docs/hlg-sdi-report.pdf

[3]

Demchenko Y, Grosso P, de Laat C et al. (2013) Addressing big data issues in scientific data infrastructure. In: Proceedings of the international conference on collaboration technologies and systems, pp 48---55

[4]

Sagiroglu S, Sinanc D (2013) Big data: a review. In: Proceedings of the international conference on collaboration technologies and systems, pp 42---47

[5]

Acar UA, Chen Y (2013) Streaming big data with self-adjusting computation. In: Proceedings of the workshop on data driven functional programming, pp 15---18

[6]

Baru C, Bhandarkar M, Nambiar R et al (2013) Setting the direction for big data benchmark standards. In: Nambiar R, Poess M (eds) Selected topics in performance evaluation and benchmarking. Lecture notes in computer science, Springer, Heidelberg, pp 197---208

[7]

Srivastava D, Dong XL (2013) Big data integration. In: Proceedings of the international conference on data engineering, pp 1245---1248

[8]

Fei X, Lu S (2012) A dataflow-based scientific workflow composition framework. IEEE Trans Serv Comput 5(1):45---58

Digital Library

[9]

Szalay A, Gray J (2006) 2020 Computing: science in an exponential world. Nature 440(7083):413---414

[10]

Deelman E, Gannon D, Shields M et al (2009) Workflows and e-Science: an overview of workflow system features and capabilities. Future Gener Comput Syst 25(5):528---540

Digital Library

[11]

Yuan D, Yang Y, Liu X et al (2013) A highly practical approach towards achieving minimum datasets storage cost in the cloud. IEEE Trans Parallel Distrib Syst 24(6):1234---1244

Digital Library

[12]

Yuan D, Yang Y, Liu X et al (2012) A data dependency based strategy for intermediate data storage in scientific cloud workflow systems. Concurr Comput Pract Exp 24(9):956---976

Digital Library

[13]

Bertram L, Ilkay A, Chad B et al (2006) Scientific workflow management and the Kepler system. Concurr Comput Pract Exp 18(10):1039---1065

[14]

Weiss A (2007) Computing in the clouds. ACM Netw 11(4):16---25

Digital Library

[15]

Foster I, Yong Z, Raicu I et al (2008) Cloud computing and grid computing 360-degree compared. In: Proceedings of the grid computing environments workshop, pp 1---10

[16]

Yuan D, Yang Y, Liu X et al (2010) A data placement strategy in scientific cloud workflows. Future Gener Comput Syst 26(8):1200---1214

Digital Library

[17]

Wan C, Wang C, Pei J (2012) A QoS-awared scientific workflow scheduling schema in cloud computing. In: Proceedings of international conference on information science and technology, pp 634---639

[18]

Wei L, Zhu H, Cao Z et al (2014) Security and privacy for storage and computation in cloud computing. Inf Sci 258(2):371---386

Digital Library

[19]

Chu CK, Zhu WT, Han J et al (2013) Security concerns in popular cloud storage services. IEEE Pervasive Comput 12(4):50---57

Digital Library

[20]

Kalloniatis C, Mouratidis H, Islam S (2013) Evaluating cloud deployment scenarios based on security and privacy requirements. Requir Eng 18(4):299---319

Digital Library

[21]

Xiong L, Goryczka S, Sunderam V (2011) Adaptive, secure, and scalable distributed data outsourcing: a vision paper. In: Proceedings of workshop on dynamic distributed data-intensive applications, pp 1---6

[22]

Mohamed EM, Abdelkader HS, El-Etriby S (2012) Enhanced data security model for cloud computing. In: Proceedings of 8th international conference on informatics and systems, pp 12---17

[23]

Kaufman LM (2009) Data security in the world of cloud computing. IEEE Secur Priv 7(4):61---64

Digital Library

[24]

Armbrust M, Fox A, Griffith R et al (2010) A view of cloud computing. Commun ACM 53(4):50---58

Digital Library

[25]

Saritha S (2010) Google File System. Dissertation, Cochin University of Science and Technology

[26]

Hadoop (2011) http://hadoop.apache.org/

[27]

Natarajan A (2013) User-oriented modeling of scientific workflows for high frequency event data analysis. In: Proceedings of the 29th IEEE international conference on data engineering workshops, pp 306---309

[28]

Guo L, He Z, Zhao S et al (2012) Multi-objective optimization for data placement strategy in cloud computing. In: Liu C, Wang L, Yang A (eds) Information computing and applications. Communications in computer and information science. Springer, Heidelberg, pp 119---126

[29]

Guo L, Zhao S, Shen S et al (2012) A particle swarm optimization for data placement strategy in cloud computing. In: Zhu R, Ma Y (eds) Information engineering and applications. Lecture notes in electrical engineering, vol 154. Springer, London, pp 946---953

[30]

Ma F, Yang Y, Li T (2012) A data placement method based on Bayesian network for data-intensive scientific workflows. In: Proceedings of the international conference on computer science and service system, pp 1811---1814

[31]

Er-Dum Z, Yong-Qiang Q, Xing-Xing X et al (2012) A data placement strategy based on genetic algorithm for scientific workflows. In: Proceedings of the 8th international conference on computational intelligence and security, pp 146---149

[32]

Liu S-W, Kong L-M, Ren K-J et al (2011) A two-step data placement and task scheduling strategy for optimizing scientific workflow performance on cloud computing platform. Chin J Comput 34(11):2121---2130

[33]

Xi R, Lin N, Chen Y et al (2011) Compression and aggregation of Bayesian estimates for data intensive computing. Knowl Inf Syst 33(1):191---212

[34]

Peng Z, Guiling W, Xu X (2013) A data placement approach for workflow in cloud. J Comput Res Dev 50(3):636---647

[35]

Zeng P, Cui L-Z, Wang H-Y et al (2010) A data placement strategy for data-intensive applications in cloud. Chin J Comput 33(8):1472---1480

[36]

Xie T, Qin X (2006) Scheduling security-critical real-time applications on clusters. IEEE Trans Comput 55(7):864---879

Digital Library

[37]

Bishop M (2003) What is computer security? IEEE Secur Priv 1(1):67---69

Digital Library

[38]

Xie T, Qin X (2007) Performance evaluation of a new scheduling algorithm for distributed systems with security heterogeneity. J Parallel Distrib Comput 67(10):1067---1081

Digital Library

[39]

Zhu X, Lu P (2009) A two-phase scheduling strategy for real-time applications with security requirements on heterogeneous clusters. Comput Electr Eng 35(6):980---993

Digital Library

[40]

Zhu X, Qin X, Qiu M (2011) QoS-aware fault-tolerant scheduling for real-time tasks on heterogeneous clusters. IEEE Trans Comput 60(6):800---812

Digital Library

[41]

Stutzle T, Dorigo M (2002) A short convergence proof for a class of ant colony optimization algorithms. IEEE Trans Evol Comput 6(4):358---365

Digital Library

[42]

Calheiros RN, Ranjan R, Beloglazov A et al (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23---50

Digital Library

Cited By

Alam MShahid MMustajab S(2024)Security challenges for workflow allocation model in cloud computing environment: a comprehensive survey, framework, taxonomy, open issues, and future directionsThe Journal of Supercomputing10.1007/s11227-023-05873-180:8(11491-11555)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11227-023-05873-1
Soveizi NTurkmen FKarastoyanova D(2023)Security and privacy concerns in cloud-based scientific and business workflowsFuture Generation Computer Systems10.1016/j.future.2023.05.015148:C(184-200)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.future.2023.05.015
Prity FGazi MUddin K(2023)A review of task scheduling in cloud computing based on nature-inspired optimization algorithmCluster Computing10.1007/s10586-023-04090-y26:5(3037-3067)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04090-y
Show More Cited By

Security-aware intermediate data placement strategy in scientific cloud workflows
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures

Recommendations

Cloud Data Management for Scientific Workflows: Research Issues, Methodologies, and State-of-the-Art
SKG '14: Proceedings of the 2014 10th International Conference on Semantics, Knowledge and Grids

Data-intensive scientific applications are posing many challenges in distributed computing systems. In the scientific field, the application data are expected to double every year over the next decade and further. With this continuing data explosion, ...
A data placement strategy in scientific cloud workflows

In scientific cloud workflows, large amounts of application data need to be stored in distributed data centres. To effectively store these data, a data manager must intelligently select data centres in which these data will reside. This is, however, not ...
A data dependency based strategy for intermediate data storage in scientific cloud workflow systems

Many scientific workflows are data intensive where large volumes of intermediate data are generated during their execution. Some valuable intermediate data need to be stored for sharing or reuse. Traditionally, they are selectively stored according to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 41, Issue 2

November 2014

305 pages

ISSN:0219-1377

Issue’s Table of Contents

Copyright © Copyright © 2014 Springer-Verlag London.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2014

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Alam MShahid MMustajab S(2024)Security challenges for workflow allocation model in cloud computing environment: a comprehensive survey, framework, taxonomy, open issues, and future directionsThe Journal of Supercomputing10.1007/s11227-023-05873-180:8(11491-11555)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11227-023-05873-1
Soveizi NTurkmen FKarastoyanova D(2023)Security and privacy concerns in cloud-based scientific and business workflowsFuture Generation Computer Systems10.1016/j.future.2023.05.015148:C(184-200)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.future.2023.05.015
Prity FGazi MUddin K(2023)A review of task scheduling in cloud computing based on nature-inspired optimization algorithmCluster Computing10.1007/s10586-023-04090-y26:5(3037-3067)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04090-y
Huang BLi YLi ZPan LWang SXu YHu H(2019)Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge ComputingWireless Communications & Mobile Computing10.1155/2019/38162372019Online publication date: 23-Dec-2019
https://dl.acm.org/doi/10.1155/2019/3816237
Huang BLi ZTang PWang SZhao JHu HLi WChang V(2019)Security modeling and efficient computation offloading for service workflow in mobile edge computingFuture Generation Computer Systems10.1016/j.future.2019.03.01197:C(755-774)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1016/j.future.2019.03.011
Kumar RGoyal R(2019)On cloud security requirements, threats, vulnerabilities and countermeasuresComputer Science Review10.1016/j.cosrev.2019.05.00233:C(1-48)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1016/j.cosrev.2019.05.002
Chen HZhu XQiu DLiu LDu Z(2017)Scheduling for Workflows with Security-Sensitive Intermediate Data by Selective Tasks Duplication in CloudsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2017.267850728:9(2674-2688)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1109/TPDS.2017.2678507
Singh PDutta MAggarwal N(2017)A review of task scheduling based on meta-heuristics approach in cloud computingKnowledge and Information Systems10.1007/s10115-017-1044-252:1(1-51)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s10115-017-1044-2
Yousafzai AGani ANoor RSookhak MTalebian HShiraz MKhan M(2017)Cloud resource allocation schemesKnowledge and Information Systems10.1007/s10115-016-0951-y50:2(347-381)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s10115-016-0951-y
Zhu XZha YJiao PChen HWeinbub JBaboulin MThacker WPolok L(2016)Security-aware workflow scheduling with selective task duplication in cloudsProceedings of the 24th High Performance Computing Symposium10.22360/SpringSim.2016.HPC.048(1-8)Online publication date: 3-Apr-2016
https://dl.acm.org/doi/10.22360/SpringSim.2016.HPC.048
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents