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 percent over the existing approaches.
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 percent over the existing approaches.
Accurate workload prediction and advanced resource reservation are indispensably crucial for managing dynamic cloud services. Traditional neural networks and deep learning models frequently encounter challenges with diverse, high-dimensional workloads, especially during sudden resource demand changes, leading to inefficiencies. This issue arises from their limited optimization during training, relying only on parametric (interconnection weights) adjustments using conventional algorithms. To address this issue, this work proposes a novel comprehensively adaptive architectural optimization-based variable quantum neural network (CA-QNN), which combines the efficiency of quantum computing with complete structural and qubit vector parametric learning. The model converts workload data into qubits, processed through qubit neurons with controlled not-gated activation functions for intuitive pattern recognition. In addition, a comprehensive architecture optimization algorithm for networks is introduced to facilitate the learning and propagation of the structure and parametric values in variable-sized quantum neural networks (VQNNs). This algorithm incorporates quantum adaptive modulation (QAM) and size-adaptive recombination during the training process. The performance of the CA-QNN model is thoroughly investigated against seven state-of-the-art methods across four benchmark datasets of heterogeneous cloud workloads. The proposed model demonstrates superior prediction accuracy, reducing prediction errors by up to 93.40% and 91.27% compared to existing deep learning and QNN-based approaches.
The key challenges in cloud computing encompass dynamic resource scaling, load balancing, and power consumption. Accurate workload prediction is identified as a crucial strategy to address these challenges. Despite numerous methods proposed to tackle this issue, existing approaches fall short of capturing the high-variance nature of volatile and dynamic cloud workloads. Consequently, this paper introduces a novel model aimed at addressing this limitation. This paper presents a novel Multiple Controlled Toffoli-driven Adaptive Quantum Neural Network (MCT-AQNN) model to establish an empirical solution to complex, elastic as well as challenging workload prediction problems by optimizing the exploration, adaption, and exploitation proficiencies through quantum learning. The computational adaptability of quantum computing is ingrained with machine learning algorithms to derive more precise correlations from dynamic and complex workloads. The furnished input data point and hatched neural weights are refitted in the form of qubits while the controlling effects of Multiple Controlled Toffoli (MCT) gates are operated at the hidden and output layers of Quantum Neural Network (QNN) for enhancing learning capabilities. Complimentarily, a Uniformly Adaptive Quantum Machine Learning (UAQL) algorithm has evolved to functionally and effectually train the QNN. The extensive experiments are conducted and the comparisons are performed with state-of-the-art methods using four real-world benchmark datasets. Experimental results evince that MCT-AQNN has up to 32%-96% higher accuracy than the existing approaches.
Accurate workload prediction and proactive resource reservation are crucial for industry clouds. However, the conventional machine learning (CML) models with limited learning capabilities often fail to predict diverse, high-dimensional workloads with sudden changes in resource demand, leading to excessive power consumption and resource management issues. In this context, this article proposes a novel Hadamard neural network with quantum blackhole (QB-HNN) optimization. This model combines the computational efficiency of quantum mechanics with the persuasive learning capability of neural networks (NNs). The workload information is transformed into qubits and propagated via a deep network of qubit neurons comprising a Hadamard-gated activation function to fetch superposition within the QB-HNN model for intuitive pattern learning. Furthermore, a novel quantum blackhole biphase optimization (QB-BiO) algorithm is introduced to train and optimize qubit neural weights. The performance of the proposed model is comprehensively evaluated and compared with five state-of-the-art approaches using six benchmark datasets of three heterogeneous varieties of cloud workloads. The prediction accuracy achieved for an extensive range of workloads confirms its influential performance by minimizing the prediction error up to 36.36% and 22.83% over existing LSTM-and EQNN-based prediction approaches, respectively.
This research introduces a novel Quantum Capsule Network (QCapsNet) framework designed to enhance the accuracy and efficiency of spatio-temporal cloud workload prediction. Leveraging the capabilities of quantum parallelism and dynamic routing mechanisms inherent in Capsule Networks, our proposed model effectively captures the hierarchical and temporal structures within cloud workloads. Through simulations based on real-world cloud trace data, the QCapsNet demonstrates significant performance improvements in both workload classification and resource demand forecasting compared to traditional and classical quantum machine learning models.
Quantum computing has been combined with deep learning, and this has created new opportunities to accelerate dataintensive applications and add more computational intelligence. Quantum-Assisted Deep Learning (QADL) uses the capability of quantum parallelism to address the limits to training and scaling of conventional neural networks. But applied to the high-performance cloud infrastructures (HPCIs), QADL experiences some challenges, including quantum-classical synchronization delays, inefficient resource usage, and weak scalability to dynamic workloads. In the attempt to overcome these issues, it is proposed in this paper (QADROT), Quantum-Assisted Deep Reinforcement Optimization of Intelligent Training, a new optimization model of effective hybrid computing. The Q-ADROIT uses a quantum-controlled reinforcement learning to allocate computational tasks in both quantum and classical computers dynamically. It combines an adaptive entanglement-based scheduler to reduce the data transfer latency and a hyperparameter tuner that is self-optimizing and uses quantum sampling to enhance convergence and accuracy. Empirical investigations using benchmark data demonstrate that Q-ADROIT outperforms the current models by 41 percent improvement in training speed, a 29 percent decrease in latency and predictive accuracy. In general, Q-ADROIT can greatly enhance the efficiency, scalability and energy optimization of the QADL systems in cloud infrastructures.
Abstract Quantum Cloud Computing (QCC) is a practice of setting up the cloud platform for delivering computing assets over the internet via a pay-as-you-go model with the help of Quantum Computing (QC) paradigm. Real-time applications have scrupulous compliance regarding performance requirements due to the low-speed of traditional computers. Estimating cloud data center asset usage is a challenging task due to its dynamic nature. It employs a contemporary model to precisely estimate data center CPU utilization and applies an effective resource controller for optimized resource allocation using quantum computers. The proposed design ensures efficient resource estimation, scaling up or down based on predictions. An efficient dynamic resource controller is crucial to solving the scaling process with quantum computing support. Existing systems use a Reinforcement-Based resource controller with a Markov decision process that decides based on the current state of the environment, leading to long scaling and processing times. Our proposed model, the Prediction-Based Offline Reinforcement Learning (PB-ORL) Model, enhances this by considering historical information for prediction-based decisions. This approach achieves accurate and high-performance prediction, optimizing resource allocation proactively and dynamically. The model is analyzed using a real cloud data set with quantum cloud and machine learning approaches, which reduces latency and bandwidth traffic. Empirical results show that the proposed quantum computer-based machine learning approach outperforms previous methods, achieving 30–50% improved accuracy in CPU resource utilization and reducing time complexity by 33–42% in resource allocation.