No abstract

No abstract

No abstract

No abstract

In a cloud system, a malicious user initiates the side channel attacks to steal the private information of other users that share the computing resources through virtualisation. In this Letter, the authors propose a multi-objective load balancing framework to minimise the likelihood of these attacks by reducing the number of shared servers along with efficient use of computing resources and power. The proposed framework significantly improves the security by reducing the shared servers up to 55.83% and improves the resource and power usage up to 96.54 and 21.97%, respectively, over best fit, first fit and random heuristic schemes.

No abstract

This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload prediction model for Cloud datacenter. It exploits the computational efficiency of quantum computing by encoding workload information into qubits and propagating this information through the network to estimate the workload or resource demands with enhanced accuracy proactively. The rotation and reverse rotation effects of the Controlled-NOT (C-NOT) gate serve activation function at the hidden and output layers to adjust the qubit weights. In addition, a Self Balanced Adaptive Differential Evolution (SB-ADE) algorithm is developed to optimize qubit network weights. The accuracy of the EQNN prediction model is extensively evaluated and compared with seven state-of-the-art methods using eight real world benchmark datasets of three different categories. Experimental results reveal that the use of the quantum approach to evolutionary neural network substantially improves the prediction accuracy up to 91.6% over the existing approaches.

No abstract