As cloud computing is becoming increasingly popular, cloud storage services attract more attentions for their high security and availability with a low cost. One way to provide availability and scalability is by the use of replication, which places multiple copies of the data in different servers in order to survive to server failures(availability) and to distribute the request load (scalability). On the other hand, the existence of multiple data replicas introduces a challenge which is how to maintain replicas consistent. Initially we proposed in this paper a model based on an optimistic approach using versions of vectors between the replicas to manage the consistency of replicas. In a second step we improved the approach proposed by the integration of a convergence mechanism and conflict resolution between the replicas that suits the Clouds Computing. This mechanism is based on the different forms of negotiation between Local Consistency Service (LCS). We made the modeling and formulation of the proposed model. We also evaluated the performance of this model. The results obtained by the simulation show the effectiveness of our approach in term of execution time and masking divergence and incompatibility situations.
ZhangS., ChenX., ZhangS. and HuoX., The comparison between cloud computing and grid computing, Proc International Conference on Computer Application and System Modeling (ICCASM), Taiyuan 11 (2010), 72-75.
2.
PengJ., ZhangX., LeiZ., ZhangB., ZhangW. and LiQ., Comparison of several cloud computing platforms, Proceedings of the 2009 Second International Symposium on Information Science and Engineering, Washington, DC, USA, (2009), 23-27.
3.
GongC., LiuJ., ZhangQ., ChenH. and GongZ., The characteristic of cloud computing, IEEE 39th International Conference on Parallel Processing Workshops, San Diego, CA, (2010), 275-279.
4.
ChihoubH.-E., Managing consistency for big data applications: Tradeoffs and self-adaptiveness, Ph.D. Dissertation, École normale supérieure de Cachan-Antenne de Bretagne, 2013.
5.
BelalemG., BenotmaneZ. and BenhallouK., Self adjustable negotiation mechanism for convergence and conflict resolution of replicas in data grids, International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)3(1) (2009), 95-110.
6.
PuccianiG., The replica consistency problem in data grids, Ph.D. Dissertation, University of Pisa, 2008.
7.
NandiB.B., PaulH.S., BanerjeeA. and GhoshS.C., Fault tolerance as a service, IEEE Sixth International Conference on Cloud Computing, Santa Clara, CA, (2013), 446-453.
8.
ChihoubH.-E., IbrahimS., AntoniuG. and PérezM., Harmony: Towards automated self-adaptive consistency in cloud storage, IEEE International Conference on Cluster Computing, Beijing, China, (Sep 2012).
9.
ZouariM. and RodriguezI.B., Towards automated deployment of distributed adaptation systems, 7th European Conference on Software Architecture (ECSA), Montpellier, France, (1-5 July 2013), 336-339.
10.
LimamS. and BelalemG., A migration approach for fault tolerance in cloud computing, International Journal of Grid and High Performance Computing (IJGHPC)6(2) (2014), 24-37.
11.
BelalemG. and SlimaniY., A hybrid approach for consistency management in large scale systems, International Conference on Networking and Services (ICNS'06), Silicon Valey, CA, USA, (16-19 July 2006), 71-71.
12.
KawellL.J., BeckhardtS., HalvorsenT., OzzieR. and GreifI., Replicated document management in a group communication system, in: Second Conference on Computer-Supported Cooperative Work, Portland, Oregon, (1988), 226-235.
13.
BernsteinP.A., HadzilacosV. and GoodmanN., Concurrency control and recovery in database systems, Addison-Wesley, 1987.
14.
BelalemG. and YagoubiB., Negotiation mechanisms for resolution conflicts among replicas in data grid, International Journal of Hybrid Information Technology1(3) (July 2008), 47-60.
15.
ReiherP., HeidemannJ., RatnerD., SkinnerG. and PopekG., Resolving file conflicts in the Ficus file system, in: Proceedings of the USENIX Summer 1994 Technical Conference, Boston, Massachusetts, USA, (June 1994), 183-195.
16.
KumarP. and SatyanarayananM., Flexible and safe resolution of file conflicts, Proceedings of the USENIX Winter 1995 Technical Conference on UNIX and Advanced Computing Systems, New Orleans, Louisiana, USA, (1995), 95-106.
17.
KistlerJ.J. and SatyanarayananM., Disconnected operation in the coda file system, ACM Transactions on Computer Systems10(1) (February 1992), 3-25.
18.
PetersenK., SpreitzerM., TerryD.B., TheimerM. and DemersA.J., Flexible update propagation for weakly consistent replication, in: Proceedings of the Sixteenth ACM Symposium on Operating Systems Principles, Saint-Malo, France, (1997), 288-301.
19.
DomeniciA., DonnoF., PaschenK., PuccianiG., StockingerH. and StockingerK., Replica consistency in a data grid, in: IX International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT03), Tsukuba, Japan, (December 2003).
20.
AbawajyJ.H. and DerisM.M., Data replication approach with consistency guarantee for data grid, IEEE Transactions on Computers63(12) (December 2014), 2975-2987.
21.
KermarrecA.-M., RowstronA., ShapiroM. and DruschelP., The IceCube approach to the reconciliation of divergent replicas, Proceedings of the Twentieth Annual ACM Symposium on Principles of Distributed Computing, Newport, Rhode Island, USA, (August 2001), 210-218.
22.
DeCandiaG. et al., Dynamo: Amazon's highly available key-value store, in: Proceedings of Twenty-First ACM SIGOPS Symposium on Operating Systems Principles, SOSP '07, Stevenson, Washington, USA, (2007), 205-220.
23.
VogelsW., Eventually consistent, Queue6(6) (December 2008), 14-19.
24.
CooperB.F. et al., PNUTS: Yahoo!'s hosted data serving platform, in: Proc VLDB Endow1 (2008), 1277-1288.
25.
LiC., PortoD., ClementA., GehrkeJ., PreguiçaN. and RodriguesR., Making geo-replicated systems fast as possible, consistent when necessary, in: Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation, OSDI'12, Hollywood, CA, USA: USENIX Association, (2012), 265-278.
26.
BelalemG. and SlimaniY., A hybrid approach for consistency management in large scale systems, Proceedings of the International conference on Networking and Services, IEEE Computer Society, (2006), 71.
27.
BermbachD., KlemsM., TaiS. and MenzelM., MetaStorage: A federated cloud storage system to manage consistency-latency tradeoffs, IEEE International Conference on Cloud Computing (CLOUD), Washington, DC, (4-9 July 2011), 452-459.
28.
ElgedawyI., DCaaS: Data consistency as a service for managing data uncertainty on the clouds, International Journal of Advanced Computer Science and Applications4(5) (2013), 59-68.
29.
ChenT., BahsoonR. and TawilA.-R.H., Scalable service-oriented replication with flexible consistency guarantee in the cloud, Information Sciences: An International Journal264 (April 2014), 349-370.
30.
WangX., YangS., WangS., NiuX. and XuJ., An application-based adaptive replica consistency for cloud storage, Proceedings of the Ninth International Conference on Grid and Cloud Computing (1-5 November 2010), 13-17.
31.
ChihoubH.-E., Self-adaptive cost-efficient consistency management in the cloud, 2013 IEEE 27th International Symposium on Parallel & Distributed Processing Workshops and PhD Forum (IPDPSW), (20-24 May 2013), 2290-2293.
32.
ChihoubH.-E., IbrahimS., AntoniuG. and Pérez-HernandezM.S., Harmony: Towards automated self-adaptive consistency in cloud storage, in: 2012 IEEE International Conference on Cluster Computing (CLUSTER'12), Beijing, China, (2012), 293-301.
33.
ZhouZ., ChenS., RenT. and WuT., File heat-based self-adaptive replica consistency strategy for cloud storage, Journal of Computers9(8) (August 2014), 1928-1933.
34.
KimJ. and HwangS.-T., Adaptive consistency approaches for cloud computing platform, International Journal of Software Engineering and Its Applications9(7) (2015), 127-134.
35.
BelalemG. and LimamS., Towards improving the functioning of CloudSim simulator, in: International Conference on Digital Information Processing and Communications (ICDIPC), Ostrava, Czech Republic, (July 2011), 258-267.
