• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2020, Volume: 13, Issue: 24, Pages: 2418-2435

Systematic Review

Current trends in cloud computing

Received Date:02 May 2020, Accepted Date:10 June 2020, Published Date:08 July 2020


Objectives: This work reviewed the latest, state-of-the-art works in the area of Cloud Computing to help researchers, developers and stakeholders in decisionmaking. Method: The reviewed works are filtered after the rigorous process by using renowned indexing database of ACM and IEEE along with the subject based journals on Cloud Computing of international repute. These papers are further filtered by selecting papers published in last 4 years only. Our initial findings lead our reviews to five major areas of Cloud Computing including Load balancing, resource scheduling, resource allocation, resource sharing, and job scheduling. In this work we have limited ourselves to only technical aspects of cloud computing while excluding areas of security, privacy and economics (for example CapEx). We have presented our findings in the form of tables and graphs showing trends in Cloud Computing towards research community on the basis of five aspects as mentioned above. Findings: Our findings show that researchers are working in the area of Job Scheduling while low attention has been given in Resource Scheduling. Moreover, an open source robust framework for research community is needed covering all the aspects shown above for running experiments. Currently these features are available in commercial and proprietary frameworks including Amazon Web Service, Microsoft Azure, and Google Cloud Platform.

Keywords: Load balancing; resource scheduling; resource allocation; cloud computing; resource sharing; job scheduling


  1. Terefe MB, Lee H, Heo N, Fox GC, Oh S. Energy-efficient multisite offloading policy using Markov decision process for mobile cloud computing. Pervasive and Mobile Computing. 2016;27:75–89. Available from: https://dx.doi.org/10.1016/j.pmcj.2015.10.008
  2. Paya A, Marinescu DC. Energy-Aware Load Balancing and Application Scaling for the Cloud Ecosystem. IEEE Transactions on Cloud Computing. 2017;5(1):15–27. doi: 10.1109/tcc.2015.2396059
  3. Bölöni L, Turgut D. Value of information based scheduling of cloud computing resources. Future Generation Computer Systems. 2017;71:212–220. Available from: https://dx.doi.org/10.1016/j.future.2016.10.024
  4. Gai K, Qiu M, Zhao H. Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing. IEEE Transactions on Cloud Computing. 2016;(99) 1. Available from: https://dx.doi.org/10.1109/tcc.2016.2594172
  5. Dai W, Ibrahim I, Bassiouni M. Improving Load Balance for Data-Intensive Computing on Cloud Platforms. IEEE International Conference on Smart Cloud (SmartCloud). 2016.
  6. Chauhan SS, Pilli ES, Joshi RC, Singh G, Govil MC. Brokering in interconnected cloud computing environments: A survey. Journal of Parallel and Distributed Computing. 2019;133:193–209. doi: 10.1016/j.jpdc.2018.08.001
  7. Wikipedia. Load Balancing (Computing) [Web Page]. (accessed ) Available from: https://en.wikipedia.org/wiki/Load_balancing_(computing
  8. Alkhanak EN, Lee SP, Khan SUR. Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities. Future Generation Computer Systems. 2015;50:3–21. Available from: https://dx.doi.org/10.1016/j.future.2015.01.007
  9. Masdari M, ValiKardan S, Shahi Z, Azar SI. Towards workflow scheduling in cloud computing: A comprehensive analysis. Journal of Network and Computer Applications. 2016;66:64–82. Available from: https://dx.doi.org/10.1016/j.jnca.2016.01.018
  10. Rodriguez MA, Buyya R. A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments. Concurrency and Computation: Practice and Experience. 2017;29(8). Available from: https://dx.doi.org/10.1002/cpe.4041
  11. Wu F, Wu Q, Tan Y. Workflow scheduling in cloud: a survey. The Journal of Supercomputing. 2015;71(9):3373–418.
  12. Chen W, Xie G, Li R, Bai Y, Fan C, Li K. Efficient task scheduling for budget constrained parallel applications on heterogeneous cloud computing systems. Future Generation Computer Systems. 2017;74:1–11. Available from: https://dx.doi.org/10.1016/j.future.2017.03.008
  13. Ghomi EJ, Rahmani AM, Qader NN. Load-balancing algorithms in cloud computing: A survey. Journal of Network and Computer Applications. 2017;88:50–71. Available from: https://dx.doi.org/10.1016/j.jnca.2017.04.007
  14. Singh S, Chana I. A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges. Journal of Grid Computing. 2016;14(2):217–264. Available from: https://dx.doi.org/10.1007/s10723-015-9359-2
  15. Wu J, Guo S, Li J, Zeng D. Big Data Meet Green Challenges: Big Data Toward Green Applications. IEEE Systems Journal. 2016;10(3):888–900. Available from: https://dx.doi.org/10.1109/jsyst.2016.2550530
  16. Wu J, Guo S, Li J, Zeng D. Big Data Meet Green Challenges: Greening Big Data. IEEE Systems Journal. 2016;10(3):873–887. Available from: https://dx.doi.org/10.1109/jsyst.2016.2550538
  17. Gai K, Qiu M, Zhao H, Sun X. Resource Management in Sustainable Cyber-Physical Systems Using Heterogeneous Cloud Computing. IEEE Transactions on Sustainable Computing. 2018;3(2):60–72. Available from: https://dx.doi.org/10.1109/tsusc.2017.2723954
  18. Wu J, Guo S, Huang H, Liu W, Xiang Y. Information and Communications Technologies for Sustainable Development Goals: State-of-the-Art, Needs and Perspectives. IEEE Communications Surveys & Tutorials. 2018;20(3):2389–2406. Available from: https://dx.doi.org/10.1109/comst.2018.2812301
  19. Atat R, Liu L, Wu J, Li G, Ye C, Yang Y. Big Data Meet Cyber-Physical Systems: A Panoramic Survey. IEEE Access. 2018;6:73603–73636. Available from: https://dx.doi.org/10.1109/access.2018.2878681
  20. Goundar S, Bhardwaj A. Efficient Fault Tolerance on Cloud Environments. International Journal of Cloud Applications and Computing. 2018;8(3):20–31. Available from: https://dx.doi.org/10.4018/ijcac.2018070102
  21. Stergiou C, Psannis KE, Kim BG, Gupta B. Secure integration of IoT and Cloud Computing. Future Generation Computer Systems. 2018;78:964–975. Available from: https://dx.doi.org/10.1016/j.future.2016.11.031
  22. Ouaguid A, Abghour N, Ouzzif M. A Novel Security Framework for Managing Android Permissions Using Blockchain Technology. International Journal of Cloud Applications and Computing. 2018;8(1):55–79. Available from: https://dx.doi.org/10.4018/ijcac.2018010103
  23. Dahiya A, Gupta BB. 2019.
  24. Gupta BB, Agrawal DP. 2019.
  25. Han Y, Chan J, Alpcan T, Leckie C. Using Virtual Machine Allocation Policies to Defend against Co-Resident Attacks in Cloud Computing. IEEE Transactions on Dependable and Secure Computing. 2017;14(1):95–108.
  26. Wei W, Fan X, Song H, Fan X, Yang J. Imperfect Information Dynamic Stackelberg Game Based Resource Allocation Using Hidden Markov for Cloud Computing. IEEE Transactions on Services Computing. 2018;11(1):78–89. Available from: https://dx.doi.org/10.1109/tsc.2016.2528246
  27. Pillai PS, Rao S. Resource Allocation in Cloud Computing Using the Uncertainty Principle of Game Theory. IEEE Systems Journal. 2016;10(2):637–648. Available from: https://dx.doi.org/10.1109/jsyst.2014.2314861
  28. Gao C, Wang H, Zhai L, Gao Y, Yi S. An Energy-Aware Ant Colony Algorithm for Network-Aware Virtual Machine Placement in Cloud Computing. IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS). 2016;2016:13–16.
  29. Chase J, Niyato D. Joint Optimization of Resource Provisioning in Cloud Computing. IEEE Transactions on Services Computing. 2017;10(3):396–409. Available from: https://dx.doi.org/10.1109/tsc.2015.2476812
  30. Liu L, Mei H, Xie B. Towards a multi-QoS human-centric cloud computing load balance resource allocation method. The Journal of Supercomputing. 2016;72(7):2488–2501. Available from: https://dx.doi.org/10.1007/s11227-015-1472-2
  31. Li D, Chen C, Guan J, Zhang Y, Zhu J, Dcloud YR. Deadline-Aware Resource Allocation for Cloud Computing Jobs. IEEE Transactions on Parallel and Distributed Systems. 2016;27(8):2248–60.
  32. Lin YK, Chong CS. Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system. Journal of Intelligent Manufacturing. 2017;28(5):1189–201.
  33. Gawali MB, Shinde SK. Task scheduling and resource allocation in cloud computing using a heuristic approach. Journal of Cloud Computing. 2018;7(1). Available from: https://dx.doi.org/10.1186/s13677-018-0105-8
  34. Praveen SP, Rao KT, Janakiramaiah B. Effective Allocation of Resources and Task Scheduling in Cloud Environment using Social Group Optimization. Arabian Journal for Science and Engineering. 2018;43(8):4265–4272. Available from: https://dx.doi.org/10.1007/s13369-017-2926-z
  35. Chen X, Jiao L, Li W, Fu X. Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing. IEEE/ACM Transactions on Networking. 2016;24(5):2795–808.
  36. Cardellini V, Personé VDN, Valerio VD, Facchinei F, Grassi V, Presti FL, et al. A game-theoretic approach to computation offloading in mobile cloud computing. Mathematical Programming. 2016;157(2):421–449. Available from: https://dx.doi.org/10.1007/s10107-015-0881-6
  37. Vázquez-Poletti JL, Moreno-Vozmediano R, Han R, Wang W, Llorente IM. SaaS enabled admission control for MCMC simulation in cloud computing infrastructures. Computer Physics Communications. 2017;211:88–97. Available from: https://dx.doi.org/10.1016/j.cpc.2016.07.004
  38. Tao F, Li C, Liao TW, Laili Y. BGM-BLA: A New Algorithm for Dynamic Migration of Virtual Machines in Cloud Computing. IEEE Transactions on Services Computing. 2016;9(6):910–925. Available from: https://dx.doi.org/10.1109/tsc.2015.2416928
  39. Liu L, Zhang M, Buyya R, Fan Q. Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing. Concurrency and Computation: Practice and Experience. 2017;29:e3942. Available from: https://dx.doi.org/10.1002/cpe.3942
  40. Xu R, Wang Y, Huang W, Yuan D, Xie Y, Yang Y. Near-optimal dynamic priority scheduling strategy for instance-intensive business workflows in cloud computing. Concurrency and Computation: Practice and Experience. 2017;29(18). Available from: https://dx.doi.org/10.1002/cpe.4167
  41. Casas I, Taheri J, Ranjan R, Wang L, Zomaya AY. A balanced scheduler with data reuse and replication for scientific workflows in cloud computing systems. Future Generation Computer Systems. 2017;74:168–178. Available from: https://dx.doi.org/10.1016/j.future.2015.12.005
  42. Priya V, Sathiya Kumar C, Kannan R. Resource scheduling algorithm with load balancing for cloud service provisioning. Applied Soft Computing. 2019;76:416–424. doi: 10.1016/j.asoc.2018.12.021
  43. Li Q, Yin X, Meng S, Liu Y, Ying Z. A security event description of intelligent applications in edge-cloud environment. Journal of Cloud Computing. 2020;9:23.
  44. Wu H, Li X, Deng Y. Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges. Journal of Cloud Computing. 2020;9:21.
  45. Rashid A, Chaturvedi A. Cloud Computing Characteristics and Services A Brief Review. International Journal of Computer Sciences and Engineering. 2019;7(2):421–426. doi: 10.26438/ijcse/v7i2.421426
  46. Mesbahi MR, Hashemi M, Rahmani AM. Performance evaluation and analysis of load balancing algorithms in cloud computing environments. Second International Conference on Web Research (ICWR). 2016;2016:27–28.
  47. Yang J, Jiang B, Lv Z, Choo KK. 2017. Available from: https://doi.org/10.1016/j.future.2017.03.024
  48. Wang X, Wang Y, Cui Y. An energy-aware bi-level optimization model for multi-job scheduling problems under cloud computing. Soft Computing. 2016;20(1):303–320.
  49. Rimal BP, Maier M. Workflow Scheduling in Multi-Tenant Cloud Computing Environments. IEEE Transactions on Parallel and Distributed Systems. 2017;28(1):290–304. doi: 10.1109/tpds.2016.2556668
  50. Zhao T, Zhou S, Guo X, Niu Z. Tasks scheduling and resource allocation in heterogeneous cloud for delay-bounded mobile edge computing. IEEE International Conference on Communications (ICC). 2017.
  51. Conejero J, Corella S, Badia RM, Labarta J. Task-based programming in COMPSs to converge from HPC to big data. The International Journal of High Performance Computing Applications. 2018;32(1):45–60. doi: 10.1177/1094342017701278
  52. Juarez F, Ejarque J, Badia RM. Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Future Generation Computer Systems. 2018;78:257–271. doi: 10.1016/j.future.2016.06.029


© 2020 Munir, Jami. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 
Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.