Date of Award

31-7-2024

Document Type

Thesis

School

School of Computing

Programme

Ph.D.-Doctoral of Philosophy

First Advisor

Dr.V.S.Shankar Sriram

Keywords

Cloud computing, Optimal Destination Selection

Abstract

Cloud computing offers organizations flexibility and cost-efficiency through pay-asyou- go services, allowing them to scale resources according to their needs and reduce expenditures. Cloud as a Service (CaaS) offloads IT management complexities, while Cloud Data Center (CDC) provides infrastructure for on-demand, scalable, and flexible services over the Internet. Virtualization improves operational efficiency by providing simultaneous access to multiple virtual machines, while Live Virtual Machine Migration enhances agility, resilience, resource allocation, and fault tolerance.

However, achieving effective VMM requires forecasting cloud resource utilization, selecting the right target host, and ensuring security. Live VM migration is inevitable for optimizing CDC resource utilization. A Hypergraph-based Convolutional Deep Bi-Directional Long Short-Term Memory (CDB-LSTM) model is developed to predict resource usage. The model uses the Helly characteristic of Hypergraph to extract informative samples and the Savitzky–Golay filter to eliminate noises. The prediction approach uses the correlation coefficient measure to select the appropriate source VM and potential destination servers for migration. The model enhances resource usage prediction accuracy, reduces migrations, and preserves minimal computational costs during VMM.

However, it fails to determine the most secure destination with a lower total migration time. Selecting the optimal destination for live VM migration is a complex task that requires maximizing resource efficiency, minimizing energy consumption, and ensuring security. A framework for CDC uses the Network-aware Dynamic multi-objective Cuckoo Search algorithm (NDCS), a bio-inspired metaheuristic optimization algorithm, and an adaptive step-size approach. The hybrid movement strategy and fitness function resulted in the best-suited physical machine with the shortest migration time and lowest risk score. The proposed approach ensures security with fast convergence compared to existing methods. The system's efficacy was assessed using the Google cluster dataset, showing it outperforms existing methods regarding reduced makespan time, energy consumption, and total migration time. However, the system still needs to ensure the security of the VM to be migrated. The security of VMs during migration is essential to prevent Service Level Agreement violations. A deep learning-based DDoS classification system is developed to detect if the migrating VM is free from DDoS attacks. Cryptographic methods are employed to protect the migrating VM from vulnerabilities.

An Improved Sparrow Search-based Deep Neural Network (ISSA-DNN) is used for DDoS attack classification. Advanced Encryption Standard-Elliptic Curve Cryptography (AES-ECC) is implemented to ensure VM image security. Preprocessing tasks like removing duplicates, adopting a Random Forest for feature selection, and normalizing the CIC-DDoS dataset were performed to improve the model. The DNN classifier outperformed existing methods, and encrypting VMs for migration is a proactive security strategy that protects sensitive data and ensures compliance with regulations. Compared to state-of-the-art techniques, the developed method reduces encryption and decryption time and increases throughput through effective key generation schemes.

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