Skip to main content
Log in

A review of data replication based on meta-heuristics approach in cloud computing and data grid

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Heterogeneous distributed computing environments are emerging for developing data-intensive (big data) applications that require to access huge data files. Therefore, effective data management like efficient access and data availability has become critical requirement in these systems. Data replication is an essential technique applied to achieve these goals through storing multiple replicas in a wisely manner. There are replication algorithms that address some metrics such as reliability, availability, bandwidth consumption, storage usage, response time. In this paper, we present different issues involved in data replication and discuss the key points of the recent algorithms with a tabular representation of all those features. The focus of the review is the existing algorithms of data replication that are based on the meta-heuristic techniques. This review will enable the readers to see that previous studies contributed response time to the data replication, but the contribution of the energy consumption and security improvement has not been considerable well. Moreover, the impact of meta-heuristic algorithms on data replication performance is investigated in a simulation study. Finally, open issues and future challenges have been presented in this research work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  • Abdi S, Mohamadi S (2010) Two level job scheduling and data replication in data grid. Int J Grid Comput Appl (IJGCA) 1:23–37

    Google Scholar 

  • Ahmed Almezeini N, Hafez A (2017) Task scheduling in cloud computing using lion optimization algorithm. Int J Adv Comput Sci Appl 8(11):77–83

    Google Scholar 

  • Al Jadaan O, Abdulal W, Abdul Hameed M, Jabas A (2010) Enhancing data selection using genetic algorithm. In: International conference on computational intelligence and communication networks

  • Alami Milani B, Navimipour N (2016) A comprehensive review of the data replication techniques in the cloud environments: major trends and future directions. J Netw Comput Appl 64:229–238

    Google Scholar 

  • Alghamdi M, Tang B, Chen Y (2017) Profit-based file replication in data intensive cloud data centers. In: IEEE international conference on communications

  • Ali M, Kashif B, Khan U, Bhardwaj V, Keqin L, Albert Z (2018a) DROPS: division and replication of data in cloud for optimal performance and security. IEEE Trans Cloud Comput 6:303–315

    Google Scholar 

  • Ali M, Bilal K, Khan SU, Veeravalli B, Li K, Zomaya AY (2018b) DROPS: division and replication of data in cloud for optimal performance and security. IEEE Trans Cloud Comput 6(2):3030–3315

    Google Scholar 

  • Aljoumah E, Al-Mousawi F, Ahmad I, Al-Shammri M, Al-Jady Z (2015) SLA in cloud computing architectures: a comprehensive study. Int J Grid Distrib Comput 8(5):7–32

    Google Scholar 

  • Almomani O, Madi M (2014) A GA-based replica placement mechanism for data grid. Int J Adv Comput Sci Appl 5(10):1–6

    Google Scholar 

  • Amjad T, Sher M, Daud A (2012) A survey of dynamic replication strategies for improving data availability in data grids. Future Gener Comput Syst 28:337–349

    Google Scholar 

  • Anjum A, McClatchey R, Ali A, Willers I (2006) Bulk scheduling with the DIANA scheduler. IEEE Trans Nucl Sci 53:18–29

    Google Scholar 

  • Aznoli F, Jafari Navimipour N (2017) Cloud services recommendation: reviewing the recent advances and suggesting the future research directions. J Netw Comput Appl 77:73–86

    Google Scholar 

  • Bai X, Jin H, Liao X, Shi X, Shao Z (2013) RTRM: a response time-based replica management strategy for cloud storage system. In: Park JJ et al (eds) Grid and pervasive computing. Springer, Berlin, pp 124–133

    Google Scholar 

  • Basturk B, Karaboga D (2006) An artificial bee colony (ABC) algorithm for numeric function optimization. IEEE Swarm Intell Symp 8:687–697

    MATH  Google Scholar 

  • Bell WH, Cameron DG, Capozza L, Millar AP, Stockinger K, Zini F (2003) Optorsim: a grid simulator for studying dynamic data replication strategies. Int J High Perform Comput Appl 17(4):403–416

    MATH  Google Scholar 

  • Bielik N, Ahmad I (2012) Cooperative versus non-cooperative game theoretical techniques for energy aware task scheduling. In: International green computing conference

  • Bilal K, Khan SU, Zhang L, Li H, Hayat K, Madani SA, Min-Allah N, Wang L, Chen D, Iqbal M, Xu CZ, Zomaya AY (2013) Quantitative comparisons of the state of the art data center architectures. Concurr Comput Pract Exp 25(12):1771–1783

    Google Scholar 

  • Boru D, Kliazovich D, Granelli F, Bouvry P, Zomaya AY (2015) Energy-efficient data replication in cloud computing datacenters. Cluster Comput 18(1):385–402

    Google Scholar 

  • Bsoul M, Al-Khasawneh A, Abdallah E, Kilani Y (2011) Enhanced fast spread replication strategy for data grid. J Netw Comput Appl 34:575–580

    Google Scholar 

  • Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithm. Softw Pract Exp 41(1):23–50

    Google Scholar 

  • Chunlin L, Ping WY, Hengliang T, Youlong L (2019) Dynamic multi-objective optimized replica placement and migration strategies for SaaS applications in edge cloud. Future Gener Comput Syst 100:921–937

    Google Scholar 

  • Cui L, Zhang J, Yue L, Shi Y, Li H, Yuan D (2018) A genetic algorithm based data replica placement strategy for scientific applications in clouds. IEEE Trans Serv Comput 11(4):727–739

    Google Scholar 

  • Dinesh Reddy V, Gangadharan GR, Subrahmanya G, Rao VRK (2019) Energy-aware virtual machine allocation and selection in cloud data centers. Soft Comput 23(6):1917–1932

    Google Scholar 

  • Dokeroglu T, Sevinc E, Kucukyilmaz T, Cosar A (2019) A survey on new generation metaheuristic algorithms. Comput Ind Eng 137:106040

    Google Scholar 

  • Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy

  • Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 4:28–39

    Google Scholar 

  • Ebadi Y, Jafari Navimipour N (2018) An energy-aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm. Concurr Comput Pract Exp 31:e4757

    Google Scholar 

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science (MHS’95), pp 39–43

  • Ebrahimzade H, Khayati GR, Schaffie M (2018) A novel predictive model for estimation of cobalt leaching from waste Li-ion batteries: application of genetic programming for design. J Environ Chem Eng 6(4):3999–4007

    Google Scholar 

  • Ebrahimzade H, Khayati GR, Schaffie M (2020) PSO–ANN-based prediction of cobalt leaching rate from waste lithium–ion batteries. J Mater Cycles Waste Manag 22(1):228–239

    Google Scholar 

  • El-Henawy I, Abdelmegeed NA (2018) Meta-heuristics algorithms: a survey. Int J Comput Appl 179(22):45–54

    Google Scholar 

  • Farzampour A, Khatibinia M, Mansouri I (2019) Shape optimization of butterfly-shaped shear links using grey wolf algorithm. Ingegneria Sismica 36(1):27–41

    Google Scholar 

  • Foster I, Zhao Y, Raicu I, Lu S (2008) Cloud computing and grid computing 360-degree compared. In: Grid computing environments workshop, pp 1–10

  • Gill NK, Singh S (2016) A dynamic, cost-aware, optimized data replication strategy for heterogeneous cloud data centers. Future Gener Comput Syst 65:10–32

    Google Scholar 

  • Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99

    Google Scholar 

  • Goyal T, Singh A, Agrawal A (2012) Cloudsim: simulator for cloud computing infrastructure and modeling. Procedia Eng 38:3566–3572

    Google Scholar 

  • Grace K, Rajkuma M, Sumeetha S, Selvanayaki P (2014) GA based replica selection in data grid. In: International conference on advances in engineering and technology

  • Hamrouni T, Slimani S, Ben Charrada F (2016) A survey of dynamic replication and replica selection strategies based on data mining techniques in data grids. Eng Appl Artif Intell 48:140–158

    Google Scholar 

  • Hashemi SM, Khatibi Bardsiri A (2012) Cloud computing vs. grid computing. ARPN J Syst Softw 2(5):188–194

    Google Scholar 

  • Henry Holland J (1992) Adaptation in natural and artificial systems, 2nd edn. MIT Press, Cambridge

    Google Scholar 

  • Huang X, Wu F (2018) A cost-effective data replica placement strategy based on hybrid genetic algorithm for cloud services. In: International conference on research and practical issues of enterprise information systems, pp 43–56

  • Hussain K, Najib Mohd Salleh M, Cheng S, Shi Y (2019) Metaheuristic research: a comprehensive survey. Artif Intell Rev 52(4):2191–2233

    Google Scholar 

  • Jafari Navimipour N, Alami Milani B (2016) Replica selection in the cloud environments using an ant colony algorithm. In: Third international conference on digital information processing, data mining, and wireless communications, pp 105–110

  • Jayasree P, Saravanan V (2018) Apsdrdo: adaptive particle swarm division and replication of data optimization for security in cloud computing. IOSR J Eng

  • Junfeng T, Weiping L (2016) Pheromone-based genetic algorithm adaptive selection algorithm in cloud storage. Int J Grid Distrib Comput 9(6):269–278

    Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Engineering Faculty, Computer Engineering Department, Erciyes University

  • Khalili Azimi S (2019) A bee colony (beehive) based approach for data replication in cloud environments. In: Kouhsari SM (ed) Fundamental research in electrical engineering. Springer, Singapore, pp 1039–1052

    Google Scholar 

  • Khojand M, Fatan Serj M, Ashrafi S, Namaki V (2018) Predicting dynamic replication based on fuzzy system in data grid. arXiv:1804.02963

  • Kingsy Grace R, Manimegalai R (2014) Dynamic replica placement and selection strategies in data grids—a comprehensive survey. J Parallel Distrib Comput 74:2099–2108

    Google Scholar 

  • Kliazovich D, Bouvry P, Khan SU (2012) GreenCloud: a packet-level simulator of energy-aware cloud computing data centers. J Supercomput 62:1263–1283

    Google Scholar 

  • Kumar M, Sharma SC, Goel A, Singh SP (2019) Comprehensive survey for scheduling techniques in cloud computing. J Netw Comput Appl 143:1–33

    Google Scholar 

  • Li R, Hu Y, Lee P (2017) Enabling efficient and reliable transition from replication to erasure coding for clustered file systems. IEEE Trans Parallel Distrib Syst 28(9):2500–2513

    Google Scholar 

  • Limam S, Mokadem R, Belalem G (2019) Data replication strategy with satisfaction of availability, performance and tenant budget requirements. Cluster Comput 22(4):1199–1210

    Google Scholar 

  • Liu L, Yang Y, Wang H, Tan Z, Li C (2017) A group based genetic algorithm data replica placement strategy for scientific workflow. In: 16th international conference on computer and information science, pp 459–464

  • Liu J, Shen H, Narman HS, Lin Z, Li Z (2018) Popularity-aware multi-failure resilient and cost-effective replication for high data durability in cloud storage. Trans Parallel Distrib Syst 30:2355–2369

    Google Scholar 

  • Long SQ, Zhao YL, Chen W (2014) MORM: a multi-objective optimized replication management strategy for cloud storage cluster. J Syst Architect 60(2):234–244

    Google Scholar 

  • Ma K, Yang B (2017) Stream-based live data replication approach of in-memory cache. Concurr Comput Pract Exp 29(11):1–9

    Google Scholar 

  • Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441–453

    Google Scholar 

  • Mansouri N (2014) Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments. Front Comput Sci 8(3):391–408

    MathSciNet  Google Scholar 

  • Mansouri N (2016) Adaptive data replication strategy in cloud computing for performance improvement. Front Comput Sci 10(5):925–935

    Google Scholar 

  • Mansouri Y, Buyya R (2018) Dynamic replication and migration of data objects with hot-spot and cold-spot statuses across storage data centers. J Parallel Distrib Comput 126:121–133

    Google Scholar 

  • Mansouri N, Dastghaibyfard GH (2013) Enhanced dynamic hierarchical replication and weighted scheduling strategy in data grid. J Parallel Distrib Comput 73:534–543

    Google Scholar 

  • Mansouri N, Javidi MM (2018a) An efficient data replication strategy in large-scale data grid environments based on availability and popularity. AUT J Model Simul 50(1):39–50

    Google Scholar 

  • Mansouri N, Javidi MM (2018b) A new prefetching-aware data replication to decrease access latency in cloud environment. J Syst Softw 144:197–215

    Google Scholar 

  • Mansouri N, Javidi MM (2018c) A hybrid data replication strategy with fuzzy-based deletion for heterogeneous cloud data centers. J Supercomput 74(10):5349–5372

    Google Scholar 

  • Mansouri N, Javidi MM (2019) Cost-based job scheduling strategy in cloud computing environments. Distrib Parallel Databases. https://doi.org/10.1007/s10619-019-07273-y

    Article  Google Scholar 

  • Mansouri N, Dastghaibyfard GH, Horri A (2011) A novel job scheduling algorithm for improving data grid’s performance. In: International conference on P2P, parallel, grid, cloud and internet computing

  • Mansouri N, Kuchaki Rafsanjani M, Javidi MM (2017) DPRS: a dynamic popularity aware replication strategy with parallel download scheme in cloud environments. Simul Model Pract Theory 77:177–196

    Google Scholar 

  • Mansouri N, Javidi MM, Mohammad Hasani Zade B (2019) Using data mining techniques to improve replica management in cloud environment. Soft Comput. https://doi.org/10.1007/s00500-019-04357-w

    Article  Google Scholar 

  • Mansouri N, Mohammad Hasani Zade B, Javidi MM (2019b) Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory. Comput Ind Eng 130:597–633

    Google Scholar 

  • Masdari M, Salehi F, Jalali M, Bidaki M (2016) A survey of PSO-based scheduling algorithms in cloud computing. J Netw Syst Manag 25(1):122–158

    Google Scholar 

  • Michael MA, Linton A, Michael F, Sebastien G (2010) Autonomic clouds on the grid. J Grid Comput 8:1–18

    Google Scholar 

  • Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  • Mirzai NM, Zahrai SM, Bozorgi F (2017) Proposing optimum parameters of TMDs using GSA and PSO algorithms for drift reduction and uniformity. Struct Eng Mech 63(2):147–160

    Google Scholar 

  • Mohammad Khanli L, Isazadeh A, Shishavan TN (2011) PHFS: a dynamic replication method, to decrease access latency in the multi-tier data grid. Future Gen Comput Syst 27(3):233–244

    Google Scholar 

  • Mokadem R, Hameurlain A (2020) Data replication strategy with tenant performance and provider economic profit guarantees in cloud data centers. J Syst Softw 159:110447

    Google Scholar 

  • Moura J, Hutchison D (2016) Review and analysis of networking challenges in cloud computing. J Netw Comput Appl 60:113–129

    Google Scholar 

  • Muñoz VM, Carballeira FG (2006) PSO-LRU algorithm for data grid replication service. In: International conference on high performance computing for computational science, pp 656–669

  • Nadh Singh BR, Raja Srinivasa Reddy B (2017) A review on big data mining in cloud computing. In: Saini H, Sayal R, Rawat S (eds) Innovations in computer science and engineering. Springer, Singapore, pp 131–142

    Google Scholar 

  • Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1–18

    Google Scholar 

  • Natesan G, Chokkalingam A (2019) Optimal task scheduling in the cloud environment using a mean grey wolf optimization algorithm. Int J Technol 10(1):126–136

    Google Scholar 

  • Park AM, Kim JH, Go YB, Yoon WS (2003) Dynamic grid replication strategy based on internet hierarchy. In: International workshop on grid and cooperative computing, vol 1001, pp 1324–1331

  • Peraza C, Valdez F, Garcia M, Melin P, Castillo O (2016) A new fuzzy harmony search algorithm using fuzzy logic for dynamic parameter adaptation. Algorithms 9(4):69

    MathSciNet  MATH  Google Scholar 

  • Pitchai R, Babu S, Supraja P, Anjanayya S (2019) Prediction of availability and integrity of cloud data using soft computing technique. Soft Comput 23:8555–8562

    Google Scholar 

  • Qu K, Meng L, Yang Y (2016) A dynamic replica strategy based on Markov model for Hadoop distributed file system, HDFS. In: International conference on cloud computing and intelligence systems, IEEE Computer Society Press, New York, pp 337–342

  • Rahman RM, Barker K, Alhajj R (2008) Replica placement strategies in data grid. J Grid Comput 6(1):103–123

    MATH  Google Scholar 

  • Ranganathan K, Foster I (2001) Identifying dynamic replication strategies for a high performance data grid. In: International workshop on grid computing, pp 75–86

  • Ranganathan K, Foster I (2002) Decoupling computation and data scheduling in distributed data-intensive applications. In: Proceedings of 11th IEEE international symposium on high performance distributed computing (HPDC’02)

  • Rehman UU, Ali A, Anwar Z (2014) secCloudSim: secure cloud simulator. In: 12th international conference on frontiers of information technology, pp 208–213

  • Sadeghzadeh M, Navaezadeh S (2014) Improving replica in data grid by using firefly algorithm. In: International conference on challenges in IT, engineering and technology (ICCIET’2014), pp 17–18

  • Salem R, Salam MA, Abdelkader H, Awad A, Arafa A (2019) An artificial bee colony algorithm for data replication optimization in cloud environments. IEEE Access 7:1–12

    Google Scholar 

  • Sang-Min P, Jair-Hoom K (2003) Chameleon: a resource scheduler in a data grid environment. In: Proceedings of third IEEE international symposium on cluster computing and the grid (CCGRID’03), pp 258–265

  • Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimization algorithm: theory and application. Adv Eng Softw 105:30–47

    Google Scholar 

  • Séguéla M, Mokadem R, Pierson JM (2019) Comparing energy-aware vs. cost-aware data replication strategy. In: Tenth international green and sustainable computing conference (IGSC). IEEE, Alexandria, VA, USA

  • Shijie J, Yi P, Weisheng L, Liyin S (2010) Study on analyzing questionnaire survey by Monte Carlo simulation.  In: International conference on E-business and E-government

  • Shojaatmand A, Saghiri N, Hashemi S, Abbasi Dezfoli M (2011) Improving replica selection in data grid using a dynamic ant algorithm. Int J Inf Stud 3(4):139

    Google Scholar 

  • Shojaiemehr B, Rahmani AM, Nasih Qader N (2018) Cloud computing service negotiation: a systematic review. Comput Stand Interfaces 55:196–206

    Google Scholar 

  • Shvachko K, Hairong K, Radia S, Chansler (2010) The Hadoop distributed file system. In: Proceedings of the 26th symposium on mass storage systems and technologies, pp 1–10

  • Singh Kushwah V, Kumar Goyal S, Sharma A (2018) Meta-heuristic techniques study for fault tolerance in cloud computing environment: a survey work. In: Ray K, Sharma T, Rawat S, Saini R, Bandyopadhyay A (eds) Soft computing: theories and applications. Springer, Singapore, pp 1–11

    Google Scholar 

  • Sun M, Sun J, Lu E, Yu C (2005) Ant algorithm for file replica selection in data grid. In: First international conference on semantics, knowledge and grid

  • Sun DW, Chang GR, Gao S, Jin LZ, Wei Wang X (2012) Modeling a dynamic data replication strategy to increase system availability in cloud computing environments. J Comput Sci Technol 27(2):256–272

    MATH  Google Scholar 

  • Taheri J, Choon Lee Y, Zomaya AY, Jay Siegel H (2013) A bee colony based optimization approach for simultaneous job scheduling and data replication in grid environments. Comput Oper Res 40(6):1564–1578

    MathSciNet  MATH  Google Scholar 

  • Terry DB, Prabhakaran V, Kotla R, Balakrishnan M, Aguilera MK, Abu-Libdeh H (2013) Consistency-based service level agreements for cloud storage. In: Proceedings of the twenty-fourth ACM symposium on operating systems principles

  • Tharani R (2016) Balanced ant colony optimization algorithm for job scheduling in grid computing. Int J Eng Res Technol 4(11):1–6

    Google Scholar 

  • Tos U, Mokadem R, Hameurlain A, Ayav T, Bora S (2015) Dynamic replication strategies in data grid systems: a survey. J Supercomput 71(11):4116–4140

    Google Scholar 

  • Tos U, Mokadem R, Hameurlain A, Ayav T, Bora S (2018) Ensuring performance and provider profit through data replication in cloud systems. Cluster Comput 21:1479–1492

    Google Scholar 

  • Tsai CW, Rodrigues J (2014) Metaheuristic scheduling for cloud: a survey. IEEE Syst J 8(1):279–297

    Google Scholar 

  • Tsai CW, Tsai PW, Pan JS, Chao HC (2015) Metaheuristics for the deployment problem of WSN: a review. Microprocess Microsyst 39(8):1305–1317

    Google Scholar 

  • Tu M, Li P, Yen IL, Thuraisingham BM, Khan L (2010) Secure data objects replication in data grid. IEEE Trans Depend Secure Comput 7(1):50–64

    Google Scholar 

  • Tziritas N, Kolodziej J, Zomaya AY, Madani SA, Min-Allah N, Wang L, Xu CZ, Marwan Malluhi Q, Pecero JE, Balaji P, Vishnu A, Ranjan R, Zeadally S, Li H (2015) Performance analysis of data intensive cloud systems based on data management and replication: a survey. Distrib Parallel Databases 34(2):179–215

    Google Scholar 

  • Wang L, Luo J, Shen J, Dong F (2013) Cost and time aware ant colony algorithm for data replica in alpha magnetic spectrometer experiment. In: IEEE international congress on big data, pp 247–254

  • Wei Q, Veeravalli B, Gong B, Zeng L, Feng D (2010) CDRM: a cost-effective dynamic replication management scheme for cloud storage cluster. In: IEEE international conference on cluster computing, pp 188–196

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Google Scholar 

  • Wu X (2016) Data sets replicas placements strategy from cost-effective view in the cloud. Sci Program 11:1–13

    Google Scholar 

  • Wu X (2017) Combination replicas placements strategy for data sets from cost-effective view in the cloud. Int J Comput Intell Syst 10:521–539

    Google Scholar 

  • Xu Q, Xu Z, Wang T (2015) A data-placement strategy based on genetic algorithm in cloud computing. Int J Intell Sci 5:145–157

    Google Scholar 

  • Yang X-S (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms, pp 169–178

  • Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), pp 65–74

  • Yang XS (2013) Firefly algorithm: recent advances and applications. Int J Swarm Intell 1(1):36–50

    Google Scholar 

  • Yang L, Lin J, Zheng Y (2013) A replica selection strategy on ant-algorithm in data-intensive applications. Int J Online Eng 9:38–41

    Google Scholar 

  • Yang J, Jiang B, Lv Z, Raymond Choo KK (2020) A task scheduling algorithm considering game theory designed for energy management in cloud computing. Future Gen Comput Syst 105:985–992

    Google Scholar 

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

    Google Scholar 

  • Zhang B, Wang X, Huang M (2014) A data replica placement scheme for cloud storage under healthcare IoT environment. Appl Mech Mater 556–562:5511–5517

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Najme Mansouri.

Ethics declarations

Conflict of interest

N. Mansouri and M.M. Javidi declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mansouri, N., Javidi, M.M. A review of data replication based on meta-heuristics approach in cloud computing and data grid. Soft Comput 24, 14503–14530 (2020). https://doi.org/10.1007/s00500-020-04802-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04802-1

Keywords

Navigation