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SECURE AND ENERGY AWARE TASK SCHEDULING IN CLOUD USING DEEP LEARNING AND CRYPTOGRAPHIC TECHNIQUES


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1 Tiruppur Kumaran College for Women, India
 

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Cloud Computing is one amid emerging technology greatly necessitated aiding computing on demand services by letting users for subsequent pay-per-use-on-demand scheme. The service cloud providers have non insignificant impacts on ideal resources exploitation and cost benefit in case of Energy aware task scheduling in cloud. Presently Minimum Migration Time (MMT) policy was employed for Virtual Machines migration and offering an energy proficient cloud service. Nonetheless prevailing methodologies never concentrated on any security for cloud. The confidential data protection is highly demanded now-a-days due to increasing users for cloud computing. Hence robust security system for cloud computing is greatly demanded by various cloud researchers. An enhanced approach is presented for mitigating these concerns in which Artificial Bee Colony Optimization (ABC) is deployed for queuing all incoming tasks into multi-level. Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms are checked initially. The SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms integration is done for evading job starva¬tion probability in SJF-MMBF. The over utilized host detection is achieved through Adaptive Neuro Fuzzy Inference System (ANFIS) and Virtual Machines (VMs) migration is attained via Minimum Migration Time (MMT) policy from over-utilized hosts to other hosts for energy consumption reduction. Also, security in cloud is greatly achieved by presenting a novel cryptographic technique. There are several advantages such as sharing hardware, software and losing data fear deficiency and due to which current demand for cloud computing is greatly necessitated. The significant information on cloud is maintained by business person, hence data security is vital concern as there is hacking and unauthorized access probability. Here cloud data encryption is attained through elliptic curve cryptography, hence successful and secure storage on cloud is accomplished thereby. The authorized user might access cloud data via key in the suggested system.
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  • T. Zhao, S. Zhou, X. Guo and Z. Niu, “Tasks Scheduling and Resource Allocation in Heterogeneous Cloud for DelayBounded Mobile Edge Computing”, Proceedings of IEEE International Conference on Communications, pp. 1-7, 2017.

  • X.L. Zheng and L. Wang, “A Pareto based Fruit Fly Optimization Algorithm for Task Scheduling and Resource Allocation in Cloud Computing Environment”, Proceedings of IEEE Congress on Evolutionary Computation, pp. 3393- 3400, 2016.

  • H. Cui, Y. Li, X. Liu, N. Ansari and Y. Liu, “Cloud Service Reliability Modelling and Optimal Task Scheduling”, IET Communications, Vol. 11, No. 2, pp.161-167, 2017.

  • K.M. Baalamurugan and S.V. Bhanu, “An Efficient Clustering Scheme for Cloud Computing Problems using Metaheuristic Algorithms”, Cluster Computing, Vol. 22, No. 5, pp. 12917-12927, 2019.

  • M. Chen, Y. Hao, C.F. Lai, D. Wu, Y. Li and K. Hwang, “Opportunistic Task Scheduling Over Co-Located Clouds in Mobile Environment”, IEEE Transactions on Services Computing, Vol. 11, No. 3, pp. 549-561, 2016.

  • R.S.V. Venkatesh, P.K. Reejeesh, S. Balamurugan and Charanyaa, S. “Future Trends of Cloud Computing Security: An Extensive Investigation”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, No. 1, pp. 246-253, 2015.

  • K.M. Baalamurugan and S.V. Bhanu, “Analysis of Cloud Storage Issues in Distributed Cloud Data Centres by Parameter Improved Particle Swarm Optimization (PIPSO) Algorithm”, International Journal on Future Revolution in Computer Science and Communication Engineering, Vol. 4, pp. 303-307, 2018.

  • Z. Chkirbene, A. Erbad and R. Hamila “A Combined Decision for Secure Cloud Computing based on Machine Learning and Past Information”, Proceedings of IEEE International Conference on Wireless Communications and Networking, pp. 1-6, 2019.

  • K. El Makkaoui, A. Ezzati, A. Beni-Hssane and C. Motamed, “Cloud Security and Privacy Model for Providing Secure Cloud Services”, Proceedings of International Conference on Cloud Computing Technologies and Applications, pp. 81-86, 2016.

  • S. Wang, X. Wang and Y. Zhang, “A Secure Cloud Storage Framework with Access Control based on Blockchain”, IEEE Access, Vol. 6, pp.112713-112725, 2019.

  • Q. Zhang, L.T. Yang, Z. Chen and P. Li, “PPHOPCM: Privacy-Preserving High-Order Possibilistic C-Means Algorithm for Big Data Clustering with Cloud Computing”, IEEE Transactions on Big Data, Vol. 9, No. 2, pp. 1-14, 2017.

  • M. Marwan, A. Kartit and H. Ouahmane, “Secure Cloudbased Medical Image Storage using Secret Share Scheme”, Proceedings of IEEE International Conference on Multimedia Computing and Systems, pp. 366-371, 2016.

  • K. Gu, N. Wu, B. Yin and W. Jia, “Secure Data Query Framework for Cloud and Fog Computing”, IEEE Transactions on Network and Service Management, Vol. 17, No. 1, pp. 332-345, 2019. [14] Y. Wang, J. You, J. Hang, C. Li and L. Cheng, “An Improved Artificial Bee Colony (ABC) Algorithm with Advanced Search Ability”, Proceedings of International Conference on Electronics Information and Emergency Communication, pp. 91-94, 2018.

  • F. Xie, F. Li, C. Lei, J. Yang and Y. Zhang, “Unsupervised Band Selection based on Artificial Bee Colony Algorithm for Hyperspectral Image Classification”, Applied Soft Computing, Vol. 45, No. 1, pp. 428-440, 2019.

  • R.A. Vazquez and B.A. Garro, “Crop Classification using Artificial Bee Colony (ABC) Algorithm”, Proceedings of International Conference on Swarm Intelligence, pp. 171- 178, 2016.

  • A.V. Reddy, C.P. Krishna and P.K. Mallick, “An Image Classification Framework Exploring the Capabilities of Extreme Learning Machines and Artificial Bee Colony”, Neural Computing and Applications, Vol. 14, No. 2, pp. 1- 21, 2019.

  • Banharnsakun, “Hybrid ABC-ANN for Pavement Surface Distress Detection and Classification”, International Journal of Machine Learning and Cybernetics, Vol. 8, No. 2, pp.699-710, 2017.

  • F.H.A. Vieira and A.C.P. De Leon Ferreira, “Deep Learning for Biological Image Classification”, Expert Systems with Applications, Vol. 85, pp. 114-122, 2017.

  • K. Kedarisetti, R. Gamini and V. Thanikaiselvan, “Elliptical Curve Cryptography for Images using Fractal Based Multiple Key Hill Cipher”, Proceedings of International Conference on Electronics, Communication and Aerospace Technology, pp. 643-649, 2018.

  • L. Sujihelen and C. Jayakumar, “Inclusive Elliptical Curve Cryptography (IECC) for Wireless Sensor Network Efficient Operations”, Wireless Personal Communications, Vol. 99, No. 2, pp. 893-914, 2018.

  • S. Selvi, M. Gobi, M. Kanchana and S.F. Mary, “Hyper Elliptic Curve Cryptography in Multi Cloud-Security using DNA (Genetic) Techniques” Proceedings of International Conference on Computing Methodologies and Communication, pp. 934-939, 2017.

  • X. Duan, D. Guo, N. Liu, B. Li, M. Gou and C. Qin, “A New High-Capacity Image Steganography Method Combined with Image Elliptic Curve Cryptography and Deep Neural Network”, IEEE Access, Vol. 8, pp. 25777-25788, 2020.

  • V. Chang, B. Gobinathan, A. Pinagapani and S. Kannan, “Automatic Detection of Cyberbullying using Multi-Feature based Artificial Intelligence with Deep Decision Tree Classification”, Computers and Electrical Engineering, Vol. 92, pp. 1-16, 2021.

  • K.M. Baalamurugan and S.V. Bhanu, “A Multi-Objective Krill Herd Algorithm for Virtual Machine Placement in Cloud Computing”, Journal of Supercomputing, Vol. 76, No. 6, pp. 4525-4542, 2020.

  • S. Eswaran, D. Dominic, J. Natarajan and P. B. Honnavalli, “Augmented Intelligent Water drops Optimisation Model for Virtual Machine Placement in Cloud Environment”, IET Networks, Vol. 9, No. 5, pp. 215-222, 2020.


Abstract Views: 448

PDF Views: 224




  • SECURE AND ENERGY AWARE TASK SCHEDULING IN CLOUD USING DEEP LEARNING AND CRYPTOGRAPHIC TECHNIQUES

Abstract Views: 448  |  PDF Views: 224

Authors

S Rekha
Tiruppur Kumaran College for Women, India
C Kalaiselvi
Tiruppur Kumaran College for Women, India

Abstract


Cloud Computing is one amid emerging technology greatly necessitated aiding computing on demand services by letting users for subsequent pay-per-use-on-demand scheme. The service cloud providers have non insignificant impacts on ideal resources exploitation and cost benefit in case of Energy aware task scheduling in cloud. Presently Minimum Migration Time (MMT) policy was employed for Virtual Machines migration and offering an energy proficient cloud service. Nonetheless prevailing methodologies never concentrated on any security for cloud. The confidential data protection is highly demanded now-a-days due to increasing users for cloud computing. Hence robust security system for cloud computing is greatly demanded by various cloud researchers. An enhanced approach is presented for mitigating these concerns in which Artificial Bee Colony Optimization (ABC) is deployed for queuing all incoming tasks into multi-level. Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms are checked initially. The SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms integration is done for evading job starva¬tion probability in SJF-MMBF. The over utilized host detection is achieved through Adaptive Neuro Fuzzy Inference System (ANFIS) and Virtual Machines (VMs) migration is attained via Minimum Migration Time (MMT) policy from over-utilized hosts to other hosts for energy consumption reduction. Also, security in cloud is greatly achieved by presenting a novel cryptographic technique. There are several advantages such as sharing hardware, software and losing data fear deficiency and due to which current demand for cloud computing is greatly necessitated. The significant information on cloud is maintained by business person, hence data security is vital concern as there is hacking and unauthorized access probability. Here cloud data encryption is attained through elliptic curve cryptography, hence successful and secure storage on cloud is accomplished thereby. The authorized user might access cloud data via key in the suggested system.

References