Adaptive data replication strategy in cloud computing for performance improvement

Abstract

Cloud computing is becoming a very popular word in industry and is receiving a large amount of attention from the research community. Replica management is one of the most important issues in the cloud, which can offer fast data access time, high data availability and reliability. By keeping all replicas active, the replicas may enhance system task successful execution rate if the replicas and requests are reasonably distributed. However, appropriate replica placement in a large-scale, dynamically scalable and totally virtualized data centers is much more complicated. To provide cost-effective availability, minimize the response time of applications and make load balancing for cloud storage, a new replica placement is proposed. The replica placement is based on five important parameters: mean service time, failure probability, load variance, latency and storage usage. However, replication should be used wisely because the storage size of each site is limited. Thus, the site must keep only the important replicas.We also present a new replica replacement strategy based on the availability of the file, the last time the replica was requested, number of access, and size of replica. We evaluate our algorithm using the CloudSim simulator and find that it offers better performance in comparison with other algorithms in terms of mean response time, effective network usage, load balancing, replication frequency, and storage usage.

Keywords

cloud computing / CloudSim / replica placement / replica replacement

References

[1] Mi H B, Wang H M, Zhou Y F, Rung-Tsong Lyu M, Cai H, Yin G. An online service-oriented performance profiling tool for cloud computing systems. Frontiers of Computer Science, 2013, 7(3): 431–445

[2] Fu X, Zhou C. Virtual machine selection and placement for dynamic consolidation in Cloud computing environment. Frontiers of Computer Science, 2015, 9(2): 322–330

[3] Chen T, Bahsoon R, Tawil A R. Scalable service-oriented replication with flexible consistency guarantee in the cloud. Information Sciences, 2014, 264: 349–370

[4] Wu H, Zhang W B, Zhang J H, Wei J, Huang T. A benefit-aware on-demand provisioning approach for multi-tier applications in cloud computing. Frontiers of Computer Science, 2013, 7(4): 459–474

[5] Al-Fares M, Loukissas A, Vahdat A. A scalable, commodity data center network architecture. Computer Communication Review, 2008, 38: 63–74

[6] Amazon-S3.Amazon simple storage service (Amazon s3). 2009

[7] Ghemawat S, Gobioff H, Leung S. The Google file system. In: Proceedings of the 19th ACM Symposium on Operating Systems Principles. 2003

[8] Calheiros R N, Ranjan R, Beloglazov A, Rose C, Buyya R. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 2011, 41(1): 23–50

[9] Qiu L L, Padmanabhan V N, Voelker G M. On the placement of Web server replicas. In: Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies. 2001, 1587–1596

[10] Aazami A, Ghandeharizadeh S, Helmi T. Near optimal number of replicas for continuous media in ad-hoc networks of wireless devices. In: Proceedings of the 10th International Workshop on Multimedia Information Systems. 2004

[11] Intanagonwiwat C, Govindan R, Estrin D. Directed diffusion: a scalable and robust communication paradigm for sensor networks. In: Proceedings of the 6th Annual International Conference on Mobile Computing and Networking. 2000

[12] Tang B, Das S R, Gupta H. Benefit-based data caching in ad hoc networks. IEEE Transactions on Mobile Computing, 2008, 7(3): 289–304

[13] Jin S D, Wang L M. Content and service replication strategies in multihop wireless mesh networks. In: Proceedings of ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. 2005

[14] Dabrowski C. Reliability in grid computing systems. Concurrency Practice and Experience, 2009, 21(8): 927–959

[15] Bonvin N, Papaioannou T G, Aberer K. Dynamic cost-efficient replication in data clouds. In: Proceedings of the 1stWorkshop on Automated Control for Datacenters and Clouds. 2009, 49–56

[16] Milani B A, Navimipour N J. A comprehensive review of the data replication techniques in the cloud environments: major trends and future directions. Journal of Network and Computer Applications, 2016, 64: 229–238

[17] Bonvin N, Papaioannou T G, Aberer K. A self-organized, fault tolerant and scalable replication scheme for cloud storage. In: Proceedings of the 1st ACM Symposium on Cloud Computing. 2010, 205–216

[18] Nguyen T, Cutway A, Shi W. Differentiated replication strategy in data centers. In: Proceedings of the IFIP International Conference on Network and Parallel Computing. 2010, 277–288

[19] Ahmad N, Fauzi A C, Sidek R M, Zin N M, Beg A H. Lowest data replication storage of binary vote assignment data grid. In: Proceedings of the 2nd International Conference on Networked Digital Technologies. 2010, 466–473

[20] Bin L, Jiong Y, Hua S, Mei N. A QoS-aware dynamic data replica deletion strategy for distributed storage systems under cloud computing environments. In: Proceedings of the 2nd International Conference on Cloud and Green Computing. 2012, 219–225

[21] Shvachko K, Hairong K, Radia S, Chansler R. The Hadoop distributed file system. In: Proceedings of the 26th Symposium on Mass Storage Systems and Technologies. 2010, 1–10

[22] Rahman R M, Barker K, Alhajj R. Replica placement design with static optimality and dynamic maintainability. In: Proceedings of the 6th IEEE International Symposium on Cluster Computing and the Grid. 2006, 434–437

[23] Mansouri N, Dastghaibyfard G H. A dynamic replica management strategy in data grid. Journal of Network and Computer Applications, 2012, 35(4): 1297–1303

[24] Mansouri N, Dastghaibyfard G H. Enhanced dynamic hierarchical replication and weighted scheduling strategy in data grid. Journal of Parallel and Distributed Computing, 2013, 73(4): 534–543

[25] Mansouri N. Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments. Frontiers of Computer Science, 2014, 8(30): 391–408

[26] Dogan A. A study on performance of dynamic file replication algorithms for real-time file access in data grids. Future Generation Computer Systems, 2009, 25(8): 829–839

[27] Hussein M, Mousa M H. A light-weight data replication for cloud data centers environment. International Journal of Engineering and Innovative Technology, 2012, 1(6): 169–175

[28] Rajalakshmi A, Vijayakumar D, Srinivasagan K G. An improved dynamic data replica selection and placement in cloud. In: Proceedings of the 2014 International Conference on Recent Trends in Information Technology. 2014, 1–6

[29] Li B, Song S, Bezakova I, Cameron W. Energy-aware replica selection for data-intensive services in Cloud. In: Proceedings of the 20th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems. 2012, 504–506

[30] Barroso L, Holzle U. The case for energy-proportional computing. Computer, 2007, 40(12): 33–37

[31] Li W H, Yang Y, Yuan D. A novel cost-effective dynamic data replication strategy for reliability in Cloud data centres. In: Proceedings of the 9th IEEE International Conference on Dependable, Autonomic and Secure Computing. 2011, 496–502

[32] Wei Q, Veeravalli B, Gong B, Zeng L, Feng D. CDRM: A cost-effective dynamic replication management scheme for cloud storage cluster. In: Proceedings of the IEEE International Conference on Cluster Computing. 2010, 188–196

[33] Yuan D, Yang Y, Liu X, Chen J J. A data placement strategy in scientific cloud workflows. Future Generation Computer Systems, 2010, 26(8): 1200–1214

[34] McCormick W T, Sehweitzer P J, White T W. Problem decomposition and data reorganization by a clustering technique. Operations Research, 1972, 20(5): 993–1009

[35] Jeffrey D, Sanjay G. MapReduce: simplifed data processing on large clusters. In: Proceedings of the 6th Symposium on Operating System Design and Implementation (OSDI). 2004, 137–150

[36] Kwan T, Mcgrath R, Reed D. NCSAs World Wide Web server design and performance. Computer, 1995, 28(11): 67–74

[37] Xie T. SEA: a striping-based energy-aware strategy for data placement in RAID-structured storage systems. IEEE Transactions on Computers, 2008, 57(6): 748–761

[38] Howell F, Mcnab R. SimJava: a discrete event simulation library for Java. In: Proceedings of the 1st International Conference onWeb-based Modeling and Simulation. 1998

[39] Cameron D G, Carvajal-schiaffino R, Millar A P, Nicholson C, Stockinger K, Zini F. UK Grid Simulation with OptorSim. In: Proceedings of UK e-Science All Hands Meeting. 2003

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg