Abstract
Cloud computing is a technology which provides the capability to use storage resources and computing services via Internet. Performance and resource management are a challenging task in cloud computing due to increase in demand of services by the user in a multitenant environment. Also, data privacy in cloud is a major issue because different organizations use various services of cloud. Security of cloud computing refers to a method used to protect data and various applications of cloud computing. In this paper, we have surveyed various issues in cloud computing related to resource management, load balancing, data privacy, energy consumption, data storage, etc. Moreover, the survey has also identified the solution to the existing problems in the literature to counter various issues in cloud environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
D. Zissis, D. Lekkas, Addressing cloud computing security issues. Future Gener. Comput. Syst. 28(3), 583–5922 (2012)
S. Subashini, V. Kavitha, A survey on security issues in service delivery models of cloud computing. J. Netw. Comput. Appl. 34(1), 1–11 (2011)
P. Mell, T. Grance, The NIST definition of cloud computing, in NIST, special publication 800, No. 145, p. 7 (2011)
L. Tawalbeh, N.S. Darwazeh, R.S. Al-Qassas, F. AlDosari, A secure cloud computing model based on data classification, in International Workshop on Mobile Cloud Computing Systems, Management, and Security (MCSMS) (Elsevier, Amsterdam, 2015)
P.D. Kaur, I. Chana, Cloud based intelligent system for delivering health care as a service. Comput. Methods Programs Biomed. 113, 346–359 (2014)
B. Joshi, K. Rani, Mitigating data segregation and privacy issues in cloud computing, in Proceedings of International Conference on Communication and Networks, Advances in Intelligent Systems and Computing (Springer, Singapore, 2017)
K. Gai, M. Qiu, H. Zhao, L. Tao, Z. Zong, Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. J. Netw. Comput. Appl. 59 (2016) Elsevier
T. Pasquier, J. Bacon, D. Eyers, Data-centric access control for cloud computing, in Proceedings of the 21st ACM on Symposium on Access Control Models and Technologies, ACM, SACMAT (2016)
R. Raju, J. Amudhavel, N. Kannan, M. Monisha, A bio inspired energy-aware multi objective chiropteran algorithm (EAMOCA) for hybrid cloud computing environment, in 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE) (IEEE Explore, 2014)
Y. Li, K. Gai, L. Qiu, M. Qiu, H. Zhao, Intelligent cryptography approach for secure distributed big data storage in cloud computing, in Information Sciences (Elsevier, Amsterdam, 2016)
B.P. Rimal, M. Maier, Workflow scheduling in multi-tenant cloud computing environments, in IEEE Transactions On Parallel and Distributed Systems (2015)
M. Verma, G.R. Gangadharan, N.C. Narendra, R. Vadlamani, V. Inamdar, L. Ramachandran, R.N. Calheiros, R. Buyya, Dynamic Resource Demand Prediction and Allocation in Multi-tenant Service Clouds (Wiley Online Library, 2016)
P. Gongzhuang, H. Wang, H. Zhang, J. Dong, Knowledge-based resource allocation for collaborative simulation development in a multi-tenant cloud computing environment, in IEEE Transactions on Services Computing (2016)
L. Yanghui, X. Xun, Z. Lin, Workload-based multi-task scheduling in cloud manufacturing. Rob. Comput. Integr. Manuf. 45(C) (2017). ACM Digital Library, New York
S. Mittal, A. Katal, An optimized task scheduling algorithm in cloud computing, in 6th International Conference on Advanced Computing (IACC) (IEEE, Bhimavaram, India, 2016)
M. Gamal, R. Rizk, H. Mahdi, B. Elhady, Bio-inspired load balancing algorithm in cloud computing, in Advances in Intelligent Systems and Computing (Springer International Publishing AG, Switzerland, 2018)
Ä°.B. AYDILEK, A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl. Soft Comput. 66 (2018). Elsevier
H. Wang, W. Wang, X. Zhou, H. Sun, J. Zhao, X. Yu, Z. Cui, Firefly algorithm with neighborhood attraction. Inf. Sci. (2016). Elsevier
A. Slowik, H. Kwasnicka, Nature inspired methods and their industry applications—swarm intelligence algorithms, in IEEE Transactions on Industrial Informatics (2017)
M. Adhikari, T. Amgoth, Heuristic-based load-balancing algorithm for IaaS cloud, in Future Generation Computer Systems (Elsevier, Amsterdam, 2017)
A. Gupta, R. Garg, Load balancing based task scheduling with ACO in cloud computing, in International Conference on Computer and Applications (ICCA) (IEEE, USA, 2017)
S. Domanal, R.M.R. Guddeti, R. Buyya, A hybrid bio-inspired algorithm for scheduling and resource management in cloud environment, in IEEE Transactions on Services Computing (2017)
A.N. Singh, S. Prakash, WAMLB: weighted active monitoring load balancing in cloud computing big data analytics, in Advances in Intelligent Systems and Computing (Springer, Singapore, 2018)
M. Vanitha, P. Marikkannu, Effective resource utilization in cloud environment through a dynamic well-organized load balancing algorithm for virtual machines, in Computers and Electrical Engineering (Elsevier, Amsterdam, 2016)
B. Raj, P. Ranjan, N. Rizvi, P. Pranav, S. Paul, Improvised bat algorithm for load balancing-based task scheduling, in Advances in Intelligent Systems and Computing, vol. 518 (Springer, Singapore, 2018)
L.P. Tizzei, M.A.S. Netto, S. Tao, Optimizing multi-tenant cloud resource pools via allocation of reusable time slots, in International Conference on Grid Economics and Business Models (Springer, Switzerland 2016)
N. Vurukonda, B.T. Rao, A study on data storage security issues in cloud computing, in 2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC). Procedia Comput. Sci. 92, 128–135 (2016). Elsevier
R.W. Ahmad, A. Gani, S. Hafizah, A. Hamid, M. Shiraz, A. Yousafzai, F. Xia, A survey on virtual machine migration and server consolidation frameworks for cloud datacenters. J. Netw. Comput. Appl. 52, 11–25 (2015). Elsevier
H.T. Dinh, C. Lee, D. Niyato, P. Wang, A survey of mobile cloud computing: architecture, applications, and approaches, in Wireless Communications and Mobile Computing (Wiley, Hoboken, 2016), pp. 1587–1611
D. Maharana, B. Sahoo, S. Sethi, Energy-efficient real-time tasks scheduling in cloud data centers, in National Conference on Next Generation Computing and Its Applications in Science & Technology, (NGCAST)-IGIT (2016)
H. Liu, B. He, f2c: enabling fair and fine-grained resource sharing in multi-tenant Iaas clouds, in IEEE Transactions on Parallel and Distributed Systems (IEEE, USA, 2015)
J.P. Molina, J.M.L. Vega, J.M.L. Soler, A. Corradi, L. Foschini, DARGOS: a highly adaptable and scalable monitoring architecture for multi-tenant clouds, in Future Generation Computer Systems (Elsevier, Amsterdam, 2013)
C.M. Wu, R.S. Chang, H.Y. Chan, A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters, in Future Generation Computer Systems (Elsevier, Amsterdam, 2013)
A. Djenna, M. Batouche, Security problems in cloud infrastructures, in The 2014 International Symposium on Networks Computers and Communications, pp. 1–6 (2014)
D.S. Terzi, R. Terzi, S. Sagiroglu, a survey on security and privacy issues in big data, in The 10th International Conference for Internet Technology and Secured Transactions (ICITST) (IEEE, USA, 2015)
P. Ruth, A. Mandal, C. Castillo, R. Fowler, J. Tilson, I. Baldin, Y. Xin, Achieving Performance Isolation on Multi-Tenant Networked Clouds Using Advanced Block Storage Mechanisms (ScienceCloud, Portland, Oregon, USA, 2015)
S.T. Selvi, C. Valliyammai, V. Neelaya Dhatchayani, Resource allocation issues and challenges in cloud computing, in International Conference on Recent Trends in Information Technology (IEEE, USA, 2014)
J. Bharath, V.S.S. Sriram, Genetically modified ant colony optimization based trust evaluation in cloud computing. Indian J. Sci. Technol. 9(48) (2016)
J.J. Yang, J.Q. Li, Y. Niu, A hybrid solution for privacy preserving medical data sharing in the cloud environment, in Future Generation Computer Systems (2014)
W. Wang, L. Chen, Q. Zhang, Outsourcing high-dimensional healthcare data to cloud with personalized privacy preservation. Comput. Netw. Elsevier 88, 136–148 (2015)
A. S. Anakath, S. Rajakumar, S. Ambika, Privacy preserving multi factor authentication using trust Management, in Cluster Computing Springer (2017)
D. Vatsalan, Z. Sehili, P. Christen, E. Rahm, Privacy-preserving Record Linkage for Big Data: Current Approaches and Research Challenges (Springer International Publishing AG, Switzerland, 2017)
L. Wei, H. Zhu, Z. Cao, X. Dong, W. Jia, Y. Chen, A.V. Vasilakos, Security and privacy for storage and computation in cloud computing. Inf. Sci. 258, 371–386 (2014)
Z. Xiao, Y. Xiao, Security and privacy in cloud computing. IEEE Commun. Surv. Tutorials 15(2) (2013)
M. Ezzarii, H.E. Ghazi, T. Sadiki, Performance Analysis of a Two Stage Security Approach in Cloud Computing IEEE 978-1-4673-8149-9/15 (2015)
C.W. Liu, W.F. Hsien, C. Yang, M.S. Hwang, A survey of attribute-based access control with user revocation in cloud data storage. Int. J. Netw. Secur. 18(5), 900–916 (2016)
D.A.B. Fernandes, L.F.B. Soares, J.V. Gomes, M.M. Freire, P.R. Inácio, Security issues in cloud environments: a survey. Int. J. Inf. Secur. 13(2), 113–170 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Siddiqui, S., Darbari, M., Yagyasen, D. (2019). A Comprehensive Study of Challenges and Issues in Cloud Computing. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 900. Springer, Singapore. https://doi.org/10.1007/978-981-13-3600-3_31
Download citation
DOI: https://doi.org/10.1007/978-981-13-3600-3_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3599-0
Online ISBN: 978-981-13-3600-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)