Skip to main content
SpringerLink
Log in

Combining task scheduling and data replication for SLA compliance and enhancement of provider profit in clouds

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Task scheduling and data replication are highly coupled resource management techniques that are widely used by cloud providers to improve the overall system performance and ensure service level agreement (SLA) compliance while preserving their own economic profit. However, balancing the trade-off between system performance and provider profit is very challenging. In this paper, we propose a novel scheduling algorithm called Bottleneck and Cost Value Scheduling (BCVS) algorithm coupled with a novel dynamic data replication strategy called Correlation and Economic Model-based Replication (CEMR). The main goal is to improve data access effectiveness in order to meet service level objectives in terms of response time SLORT and minimum availability SLOMA, while preserving the provider profit. The BCVS algorithm focuses on reducing system bottleneck situations caused by data transfer when the CEMR focuses on preventing future SLA violations and guaranteeing a minimum availability. An economic model is also proposed to estimate the cloud provider profit. Simulation results indicate that the proposed combination of scheduling and replication algorithms offers higher monetary profit for the cloud provider by up to 30% compared to existing strategies. Moreover, it allows better performance.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Agarwal S (2020) An approach of SLA violation prediction and QoS optimization using regression machine learning techniques. Ph.D. thesis, University of Windsor (Canada)

  2. Al-Fares M, Loukissas A, Vahdat A (2008) A scalable, commodity data center network architecture. In: Proceedings of the ACM SIGCOMM 2008 conference on applications, technologies architectures, and protocols for computer communications, pp 63–74

  3. Alghamdi M, Tang B, Chen Y (2017) Profit-based file replication in data intensive cloud data centers. In: 2017 IEEE International conference on communications (ICC), pp 1–7

  4. Arunarani AR, Manjula D, Sugumaran V (2019) Task scheduling techniques in cloud computing: A literature survey. Futur Gener Comput Syst 91:407–415

    Article  Google Scholar 

  5. Azari L, Rahmani AM, Daniel HA, Qader NN (2018) A data replication algorithm for groups of files in data grids. J Parallel Distrib Comput 113:115–126

    Article  Google Scholar 

  6. Bai X, Jin H, Liao X, Shi X, Shao Z (2013) RTRM: A response time-based replica management strategy for cloud storage system. In: International conference on grid and pervasive computing, pp 124–133

  7. Barroso LA, Hölzle U, Ranganathan P (2018) The datacenter as a computer: Designing warehouse-scale machines. Morgan & Claypool Publishers, San Mateo

    Google Scholar 

  8. Bhoi U, Ramanuj PN, et al. (2013) Enhanced max-min task scheduling algorithm in cloud computing. Int J Appl Innov Eng Manag (IJAIEM) 2(4):259–264

    Google Scholar 

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

    Article  Google Scholar 

  10. Bui D, Hussain S, Huh E, Lee S (2016) Adaptive replication management in HDFS based on supervised learning. IEEE Trans Knowl Data Eng 28(6):1369–1382

    Article  Google Scholar 

  11. 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 algorithms. Softw Pract Experience 41(1):23–50

    Article  Google Scholar 

  12. Chen H, Wang F, Helian N, Akanmu G (2013) User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing. In: 2013 National conference on parallel computing technologies (PARCOMPTECH). IEEE, pp 1–8

  13. Chen K, Hu C, Zhang X, Zheng K, Chen Y, Vasilakos AV (2011) Survey on routing in data centers: insights and future directions. IEEE Netw 25(4):6–10

    Article  Google Scholar 

  14. Dabas C, Aggarwal J (2019) Delayed replication algorithm with dynamic threshold for cloud datacenters. In: Applications of computing, automation and wireless systems in electrical engineering, pp 625–637

  15. Derouiche R, Brahmi Z, Gammoudi MM (2019) FCA-based energy aware-data placement strategy for intensive workflow in cloud computing. In: Knowledge-based and intelligent information & engineering systems: Proceedings of the 23rd international conference KES-2019. Vol 159 of Procedia Computer Science, pp 387–397

  16. Djebbar EI, Belalem G, Benadda M (2016) Task scheduling strategy based on data replication in scientific cloud workflows. Multiagent Grid Syst 12(1):55–67

    Article  Google Scholar 

  17. Edwin EB, Umamaheswari P, Thanka MR (2019) An efficient and improved multi-objective optimized replication management with dynamic and cost aware strategies in cloud computing data center. Clust Comput 22(5):11119–11128

    Article  Google Scholar 

  18. Ghemawat S, Gobioff H, Leung ST (2003) The Google file system. In: Proceedings of the 19th ACM symposium on operating systems principles, pp 29–43

  19. Gkatzikis L, Sourlas V, Fischione C, Koutsopoulos I (2017) Low complexity content replication through clustering in content-delivery networks. Comput Netw 121:137–151

    Article  Google Scholar 

  20. Hamdeni C, Hamrouni T, Ben Charrada F (2016) Data popularity measurements in distributed systems: Survey and design directions. J Netw Comput Appl 72:150–161

    Article  Google Scholar 

  21. Hamrouni T, Slimani S, Ben Charrada F (2015) A data mining correlated patterns-based periodic decentralized replication strategy for data grids. J Syst Softw 110:10–27

    Article  Google Scholar 

  22. Hao F, Park DS, Min G, Jeong YS, Park JH (2016) k-Cliques mining in dynamic social networks based on triadic formal concept analysis. Neurocomputing 209:57–66

    Article  Google Scholar 

  23. Hao F, Park DS, Sim DS, Kim MJ, Jeong YS, Park JH, Seo HS (2018) An efficient approach to understanding social evolution of location-focused online communities in location-based services. Soft Comput 22(13):4169–4174

    Article  Google Scholar 

  24. Hu C, Deng Y (2019) Aggregating correlated cold data to minimize the performance degradation and power consumption of cold storage nodes. J Supercomput 75(2):662–687

    Article  Google Scholar 

  25. Hussein MK, Mousa MH (2012) A light-weight data replication for cloud data centers environment. Int J Eng Innov Technol 1(6):169–175

    Google Scholar 

  26. Islam MT, Srirama SN, Karunasekera S, Buyya R (2020) Cost-efficient dynamic scheduling of big data applications in Apache Spark on cloud. J Syst Softw 162:110515

    Article  Google Scholar 

  27. Jabbarifar M, Shameli-Sendi A, Kemme B (2019) A scalable network-aware framework for cloud monitoring orchestration. J Netw Comput Appl 133:1–14

    Article  Google Scholar 

  28. Jaschke R, Hotho A, Schmitz C, Ganter B, Stumme G (2006) TRIAS–An algorithm for mining iceberg tri-lattices. In: Sixth international conference on data mining (ICDM 2006), pp 907–911

  29. Jia R, Yang Y, Grundy J, Keung J, Li H (2019) A highly efficient data locality aware task scheduler for cloud-based systems. In: 2019 IEEE 12th International conference on cloud computing (CLOUD), pp 496–498

  30. Kathidjiotis Y, Kolomvatsos K, Anagnostopoulos C (2020) Predictive intelligence of reliable analytics in distributed computing environments. Appl Intell 50:3219–3238

    Article  Google Scholar 

  31. Kaytoue M, Kuznetsov SO, Macko J, Napoli A (2014) Biclustering meets triadic concept analysis. Ann Math Artif Intell 70(1-2):55–79

    Article  MathSciNet  MATH  Google Scholar 

  32. Khelifa A, Hamrouni T, Mokadem R, Ben Charrada F (2020) Cloud provider profit-aware and triadic concept analysis-based data replication strategy for tenant performance improvement. Int J High Perform Comput Netw 16(2-3):67–86

    Article  Google Scholar 

  33. Kumar A, Bawa S (2020) A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services. Soft Comput 24(6):3909–3922

    Article  Google Scholar 

  34. Kumar AS, Venkatesan M (2019) Multi-objective task scheduling using hybrid genetic-ant colony optimization algorithm in cloud environment. Wirel Pers Commun 107(4):1835–1848

    Article  Google Scholar 

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

    Article  Google Scholar 

  36. Lavanya M, Shanthi B, Saravanan S (2020) Multi objective task scheduling algorithm based on SLA and processing time suitable for cloud environment. Comput Commun 151:183–195

    Article  Google Scholar 

  37. Lehmann F, Wille R (1995) A triadic approach to formal concept analysis. In: International conference on conceptual structures, pp 32–43

  38. Li C, Zhang J, Tang H (2019) Replica-aware task scheduling and load balanced cache placement for delay reduction in multi-cloud environment. J Supercomput 75(5):2805–2836

    Article  Google Scholar 

  39. Li X, Wang L, Abawajy JH, Qin X (2018) Data-centric task scheduling algorithm for hybrid tasks in cloud data centers. Int Conf Algorithm Archit Parallel Process 11335:630–644

    Article  Google Scholar 

  40. Li Z, Zhang Z, Wang LM (2017) Research on text classification algorithm based on triadic concept analysis. Comput Sci 44(8):207–215

    Google Scholar 

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

    Article  Google Scholar 

  42. Ma J, Li W, Fu T, Yan L, Hu G (2016) A novel dynamic task scheduling algorithm based on improved genetic algorithm in cloud computing. In: Wireless communications networking and applications, pp 829–835

  43. Mahmud R, Srirama SN, Ramamohanarao K, Buyya R (2020) Profit-aware application placement for integrated Fog-Cloud computing environments. J Parallel Distrib Comput 135:177–190

    Article  Google Scholar 

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

    Article  Google Scholar 

  45. Mansouri N, Javidi MM (2020) A review of data replication based on meta-heuristics approach in cloud computing and data grid. Soft Comput 24:1–28

    Article  Google Scholar 

  46. Mansouri N, Javidi MM, Zade BMH (2020) Using data mining techniques to improve replica management in cloud environment. Soft Comput 24(10):7335–7360

    Article  Google Scholar 

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

    Article  Google Scholar 

  48. Mapetu JPB, Chen Z, Kong L (2019) Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing. Appl Intell 49(9):3308–3330

    Article  Google Scholar 

  49. Milani BA, Navimipour NJ (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

    Article  Google Scholar 

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

    Article  Google Scholar 

  51. Prassanna J, Venkataraman N (2019) Threshold based multi-objective memetic optimized Round Robin scheduling for resource efficient load balancing in cloud. Mob Netw Appl 24(4):1214–1225

    Article  Google Scholar 

  52. Pries R, Jarschel M, Schlosser D, Klopf M, Tran-Gia P (2011) Power consumption analysis of data center architectures. In: International conference on green communications and networking, vol 51, pp 114–124

  53. Ramezani F, Lu J, Taheri J, Hussain FK (2015) Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments. World Wide Web 18(6):1737–1757

    Article  Google Scholar 

  54. Saadat N, Rahmani AM (2012) PDDRA: A new pre-fetching based dynamic data replication algorithm in data grids. Futur Gener Comput Syst 28(4):666–681

    Article  Google Scholar 

  55. Séguéla M, Mokadem R, Pierson JM (2019) Comparing energy-aware vs. cost-aware data replication strategy. In: International green and sustainable computing conference (IGSC), pp 1–8

  56. Silberschatz A, Galvin PB, Gagne G (2006) Operating system principles. John Wiley & Sons, New York

    Google Scholar 

  57. Simic V, Stojanovic B, Ivanovic M (2019) Optimizing the performance of optimization in the cloud environment–an intelligent auto-scaling approach. Futur Gener Comput Syst 101:909–920

    Article  Google Scholar 

  58. Slimani S, Hamrouni T, Ben Charrada F (2020) Service-oriented replication strategies for improving quality-of-service in cloud computing: a survey. Clust Comput, pp 1–32

  59. Tos U, Mokadem R, Hameurlain A, Ayav T, Bora S (2018) Ensuring performance and provider profit through data replication in cloud systems. Clust Comput 21(3):1479–1492

    Article  Google Scholar 

  60. Wei L, Qian T, Wan Q, Qi J (2018) A research summary about triadic concept analysis. Int J Mach Learn Cybern 9(4):699–712

    Article  Google Scholar 

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

  62. Wong TS, Chan GY, Chua FF (2018) A machine learning model for detection and prediction of cloud quality of service violation. In: International conference on computational science and Its applications. Springer, pp 498–513

  63. Xie F, Yan J, Shen J (2018) A data dependency and access threshold based replication strategy for multi-cloud workflow applications. In: International conference on service-oriented computing, pp 281–293

  64. Xing Y, Zhan Y (2012) Virtualization and cloud computing. In: Future wireless networks and information systems, pp 305–312

  65. Zhao Q, Xiong C, Yu C, Zhang C, Zhao X (2016) A new energy-aware task scheduling method for data-intensive applications in the cloud. J Netw Comput Appl 59:14–27

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LIPAH, Faculty of Sciences of Tunis, Tunis El Manar University, University Campus, Tunis, Tunisia

    Amel Khelifa & Tarek Hamrouni

  2. Institut de Recherche en Informatique de Toulouse (IRIT), Paul Sabatier University, Toulouse, France

    Riad Mokadem

  3. LIMTIC, Faculty of Sciences of Tunis, Tunis El Manar University, University Campus, Tunis, Tunisia

    Faouzi Ben Charrada

Authors
  1. Amel Khelifa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tarek Hamrouni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Riad Mokadem
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Faouzi Ben Charrada
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amel Khelifa.

Additional information

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

Khelifa, A., Hamrouni, T., Mokadem, R. et al. Combining task scheduling and data replication for SLA compliance and enhancement of provider profit in clouds. Appl Intell 51, 7494–7516 (2021). https://doi.org/10.1007/s10489-021-02267-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02267-9

Keywords

Search

Navigation