The security of data communication in Smart Grids (SGs) is traditionally reliant on cryptosystems based on the computational difficulty of certain mathematical problems. However, the advent of quantum computers, with their unparalleled computational power, threatens the robustness of classical cryptographic methods. Despite significant research into quantum-secure solutions, limited attention has been given to quantum-based security in SGs. To address these emerging security challenges, this paper proposes a quantum-based multifaceted secure communication model (Qu-MCM) for SGs, offering unconditional security by leveraging quantum mechanical principles. The key contributions include: quantum encryption of power consumption data using truly random keys, a quantum-secure key exchange protocol between power users and utility suppliers, and data authentication and integrity verification through quantum fingerprinting. The model is experimentally evaluated via simulations on classical computer and actual quantum hardware using the IBM Qiskit platform. The experimental results show that the encryption algorithm achieves an accuracy of 81.73%, while the authentication algorithm reaches 87.4% through a probability distribution graph where accuracy signifies the dominance of correct outputs. The results demonstrate the validity of the proposed model, with a comprehensive security analysis confirming its perfect security, where perfect security is defined as the maximally mixed states achieved after encryption that is impossible to break for an eavesdropper equipped with quantum resources.

In today’s digital landscape, high-efficiency video coding (H.265/HEVC) has emerged as the most widely used video coding standard, employing selective encryption schemes to protect the privacy of video content while maintaining efficient compression performance. However, existing coefficient scrambling methods impose a significant computational load, leading to increased bit rate overhead due to encryption, longer execution times, and insufficient safety measures. To address these issues, a new coefficient scrambling scheme based onchaotic mapsis proposed. This approach leverages the pseudorandomness, ergodicity, and sensitivity to initial conditions inherent in chaotic maps to generate highly unpredictable coefficient distributions, thereby strengthening security while preserving low complexity. Unlike conventional scrambling, chaotic maps ensure minimal correlation between encrypted coefficients, enhancing resistance against statistical and differential attacks. Additionally, the scrambling conditions are specifically designed to minimize the impact on the bit rate overhead. Furthermore, when combined with syntax element encryption (SEC), which includes motion vector difference (MVD), quantized transform coefficients (QTC), and luma intraprediction mode (Luma IPM), this method effectively distorts video content. The proposed scheme operates synchronously with slices, ensuring that the decryption of video content remains intact even if some slices are lost. Additionally, a random sequence generated by AES-CTR is incorporated with the H.265 encoded stream to protect against chosen-plaintext attacks. The experimental results indicate that this scheme features high security, compliance with format standards, fast execution times, synchronous updates with slices, and resilience against common attacks, all while achieving a reduced bit rate overhead of 45.13% with a lowered average execution time overhead of 1.91%.

The intermittent communication of power usage information in smart grids (SGs) is highly susceptible to potential cyberattacks, which is protected by classical public-key cryptosystems. However, recent advancements in the field of quantum computing have rendered these cryptosystems trivially insecure, triggering the immediate requirement for a highly resilient data communication scheme. This paper addresses the above-mentioned issue by designing a Quantum Encryption and Quantum Fourier Transformation (QFT) based secure data communication and aggregation model (QC-EAM) for SG environments. QC-EAM enables the power consumption data of the users to be encrypted using truly random encryption keys, ensuring unconditional security, while group homomorphism of Clifford Gates is utilized to achieve homomorphic evaluation of the encrypted quantum states. Moreover, a quantum-based data aggregation circuit is developed that performs QFT over the encrypted quantum states, yielding a secure and aggregated state. Furthermore, entanglement swaps-based quantum networking is considered as an enabling infrastructure for the proposed scenario, ensuring preservation of highly susceptible quantum states. The experimental evaluation of QC-EAM is achieved through classical computer-based simulations and tested on actual quantum system, provisioned through the IBM Qiskit platform. Statistical measurements of quantum circuits yield significantly accurate results, proving the validity of QC-EAM. Moreover, a thorough security analysis is also provided that confirms the perfect security achieved through maximally mixed states.

Cloud-based digital twin (DT) platforms enable real-time monitoring, simulation, and collaborative decision-making across distributed clients. However, ensuring secure and trustworthy communication remains a critical challenge due to heterogeneous client behavior, resource contention, and evolving adversarial threats. This article proposes themultifactor trust-driven secure communication(MT-SeCom) framework to enforce resilient and intelligent collaboration in DT-enabled cloud environments. MT-SeCom operates through four coordinated phases: First,multifactor trust monitoring, capturing temporal, contextual, and federated trust signals; second,adaptive trust evaluation, adjusting trust weights based on network dynamics and threat intensity; third,Transformer-based trusted client classification, combining anomaly detection with supervised learning to accurately identify malicious or unreliable nodes; and finally,resilient communication management, optimizing routing, isolating compromised clients, and ensuring service continuity. A real-world testbed and comprehensive experiments demonstrate that MT-SeCom significantly enhances secure communication, mitigates cascading adversarial effects, and maintains high resilience under fluctuating attack conditions. MT-SeCom achieves an average18.7%improvement in threat detection accuracy and a24.3%reduction in anomaly occurrences compared to existing methods, confirming its robustness, scalability, and practical suitability for heterogeneous cloud-based DT ecosystems.

The rapid growth of geographically distributed cloud data centers has intensified the demand for secure, privacy-preserving, and resource-efficient collaboration among mutually untrusted cloud environments. This paper presents BlockFed, a blockchain-empowered federated learning framework designed to enable cyber-threat-resilient collaboration across geographically distributed data centers. In BlockFed, each data center independently trains local models using private workload data and shares only the computed gradients rather than raw data, with a centralized aggregation server through a secure blockchain layer. A Proof-of-Work consensus mechanism is employed to validate gradient transactions and maintain an immutable, tamper-resistant ledger, ensuring trust and integrity among participating entities. The centralized server aggregates blockchain-verified updates to construct a global model, which is iteratively redistributed to support collaborative learning. This integrated learning process enhances task-level resource demand prediction while simultaneously improving system security and operational efficiency across all participating data centers. Extensive simulations on the Google Cluster Dataset show that BlockFed outperforms state-of-the-art baselines, including ISTM, ETP-WE, First-Fit, Best-Fit, and Random-Fit, in resource utilization, power consumption, and active server reduction. BlockFed achieves up to 7-81% higher resource utilization, 41-58% lower power consumption, and 31-65% fewer active servers, while attaining a cyber-threat estimation accuracy of 93.19%.

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.

Smart microgrid (SMG) communication networks face significant challenges in maintaining high Quality of Service (QoS) due to dynamic load variations, fluctuating network conditions, and potential component faults, which can increase latency, reduce throughput, and compromise fault recovery. The growing integration of distributed renewable energy resources demands adaptive and intelligent routing mechanisms capable of operating efficiently under such diverse and fault-prone conditions. This paper presents a Q-Reinforcement Learning-based Multi-Agent Bellman Routing (QRL-MABR) algorithm, which enhances the traditional MABR approach by embedding a Q-learning module within each network agent. Agents dynamically learn optimal routing policies, balance exploration and exploitation action selection with adaptive temperature scaling, and jointly optimize latency, throughput, jitter, convergence speed, and fault resilience. Simulations on IEEE 9, 14, 34, 39, and 57 bus SMG testbeds demonstrate that QRL-MABR significantly outperforms conventional routing protocols (MABR, RIP, OLSR, OSPFv2) and advanced RL-based algorithms (SN-MAPPO, DDQL, MDDPG, SARSA- λ , TD3), achieving 16%–28% delay reduction, 14%–16% throughput gains, 17%–21% jitter improvement, and superior fault recovery. Thus, QRL-MABR provides a robust, scalable, and intelligent framework for next-generation smart microgrids.

Digital healthcare is essential to facilitate consumers to access and disseminate their medical data easily for enhanced medical care services. However, the significant concern with digitalization across healthcare systems necessitates for a prompt, productive, and secure storage facility along with a vigorous communication strategy, to stimulate sensitive digital healthcare data sharing and proactive estimation of malicious entities. In this context, this paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management. It equips quantum encryption for the secured storage and dispersal of healthcare data over the shared cloud platform by employing quantum encryption. Also, the framework furnishes a quantum feed-forward neural network unit to examine the intention behind the data request before granting access, for proactive estimation of potential data breach. In this way, the proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning to safeguard the data storage, access, and prediction of malicious entities in an automated manner. Thus, the proposed IQ-HDM leads to more cooperative and effective healthcare delivery and empowers individuals with adequate custody of their health data. The experimental evaluation and comparison of the proposed IQ-HDM framework with state-of-the-art methods outline a considerable improvement up to 67.6%, in tackling cyber threats related to healthcare data security. Note to Practitioners—This paper aims to address the issue of digital healthcare data access, which requires both ease and security. Existing research either focuses solely on safe access or on high security, which often comes with high computational challenges. In this paper, we present a comprehensive approach that takes into account various challenges such as secure data storage, efficient data communication, and the prediction of malicious entities. We have developed a mathematical system to portray the overall management of healthcare data. All techniques proposed in this paper have been implemented using quantum computing and have been tested on four healthcare datasets. Initial experimental results suggest that the proposed approach is feasible. Our techniques can be applied to discover malicious entities and understand the behavior of real-life users in healthcare processes.

Diversity of workload demands lays a critical impact on efficient resource allocation and management of cloud services. The existing literature has either weakly considered or overlooked the heterogeneous feature of job requests received from wide range of internet services users. To address this context, the proposed approach namedReliable andEnergyEfficientTrafficManagement (REE-TM) has exploited the diversity of internet traffic in terms of variation in resource demands and expected complexity. Specifically, REE-TM incorporates categorization of heterogeneous job requests and executes them by selecting the most admissiblevirtual node(a software-defined instance such as a virtual machine or container) andphysical node(an actual hardware server or compute host) within the cloud infrastructure. To deal with resource-contention-based resource failures and performance degradation, a novel workload estimator ‘Toffoli Gate-based Quantum Neural Network’ (TG-QNN) is proposed, wherein learning process or interconnection weights optimization is achieved using Quantum version of BlackHole (QBHO) algorithm. The proactively estimated workload is used to compute entropy of the upcoming internet traffic with various traffic states analysis for detection of probable resource-congestion. REE-TM is extensively evaluated through simulations using a benchmark dataset and compared with optimal and without REE-TM versions. The performance evaluation and comparison of REE-TM with measured significant metrics reveal its effectiveness in assuring higher reliability by up to 30.25% and energy-efficiency by up to 23% as compared without REE-TM.

The increasing dependence on cloud-based data processing for industrial applications, smart devices, and CyberPhysical Systems (CPS) emphasizes the necessity to address the inherent vulnerabilities of multi-tenant cloud environments. The existing research have limited focus at the simultaneous handling of security and reliability in cloud workload management. This paper proposes a novel Quantum Toffoli Learning-based Service Management (QTL-SM) model to concurrently enhance both security and reliability during cloud applications processing. The model comprises two main components: (1) a reliability management unit composed of a novel Toffoli gate-embedded Quantum Adam neural network and (2) a security management unit for detecting and mitigating malicious virtual nodes. The former unit proactively estimates resource contention-based failures of physical nodes and manages them by analyzing reliability scores. It then allocates physical nodes to maximize these scores before executing client requests. The latter unit calculates vulnerability scores for each physical node by assessing multiple risk factors to identify potential malicious activities, mitigating their impact by preemptively terminating compromised nodes and connections. This integral approach ensures client requests are allocated to the most reliable and secure computation nodes, optimizing performance and service management. The QTL-SM model was implemented and evaluated using two real-world workloads. The comparative analysis with different model versions and state-ofthe-art methods demonstrated its effectiveness in failure analysis and management, resulting in a 59.7% improvement in reliability and a 51.4% reduction in malicious activities compared to models without QTL-SM.

Industrial cross-cloud networks (ICCNs) combine services from multiple providers to support critical operations, but this diversity also creates major security challenges. Because each provider follows different protocols and safeguards, responses to cyber threats are often inconsistent and delayed, reducing the effectiveness of existing Cyber Threat Intelligence (CTI) systems. To address this gap, we propose a three-layered CTI framework that unifies learning and adaptation across heterogeneous clouds. The first layer implements a two-tier federated learning (FL) strategy, developing two global models:MLP-SimiFedandMLP-Non-SimiFed, within each industry cloud to manage scalability and heterogeneity. The second layer aggregates these models across distributed clouds into a Unified Global Model, strengthening collaborative defense. The third layer leverages transfer learning to produce theMeta Unified Global Cyber Threat Intelligence(MUG-CTI) model, which enables swift adaptation to emerging threats. Through test-bed simulation, MUG-CTI demonstrates superior threat management, achieving up to 20.7% and 43.06% higher accuracy, and reducing hamming loss by up to 73.8% and 98.3% compared to Federated Learning and SVM-driven methods, respectively.

Digital twins (DTs), integral to cloud platforms, bridge physical and virtual worlds, fostering collaboration among stakeholders in manufacturing and processing. However, the cloud platforms face challenges such as service outages, vulnerabilities, and resource contention, hindering critical DT application development. The existing research works have limited focus on reliability and fault tolerance in DT processing. In this context, this article proposed a novel self-healing and fault-tolerant cloud-based digital twin processing management (SF-DTM) model. It employs collaborative DT tasks resource requirement estimation unit that utilizes newly devised federated learning with cosine similarity integration. Furthermore, SF-DTM incorporates a self-healing fault-tolerance strategy employing a frequent sequence fault-prone pattern analytics unit for deciding the most admissible virtual machine (VM) allocation. The implementation and evaluation of the SF-DTM model using real traces demonstrates its effectiveness and resilience, revealing improved availability, higher mean time between failure, and lower mean time to repair compared with non-SF-DTM approaches, enhancing collaborative DT application management. SF-DTM improved the services availability up to 13.2% over non-SF-DTM-based DT processing.

In the era of crowd delivery vehicle routing and traffic management in smart cities, a complex challenge appears indistinctly, affecting both developed and developing nations worldwide. This challenging problem involves optimizing multi-depot routes while addressing various hurdles: minimizing travel time, distance, fuel consumption, and carbon emissions, all while navigating dynamic traffic congestion across diverse pathways. Existing approaches often focus on isolated aspects like shortest paths, carbon emissions, or traffic prediction, leaving the comprehensive multi-depot traffic management problem unaddressed. In response, this research work proposes an Intelligent Multi-Depot Vehicle Routing and Management (IM-VRM) model which provides a comprehensive and holistic solution. It employs a Graph Neural Network (GNN) learning-based routing with a greedy optimization to establish initial optimal pathways for multi-depot journeys. Subsequently, the IM-VRM model integrates traffic congestion prediction with green parameter computation, engaging the Dijkstra algorithm to select the most admissible routes. This consecutive steps-based travel route guidance process optimizes routing for heterogeneous vehicles, including both heavy-duty and light-duty types. It accounts for load-dependent fuel consumption, velocity, and carbon emissions. By doing so, it simplifies the complexities of multi-depot traffic routing and management. The proposed model has been rigorously evaluated using a real-world multi-depot traffic dataset, demonstrating its practical viability. Notably, IM-VRM model achieves a remarkable improvement in fuel savings, reduced carbon emissions, and shorter travel time outperforming previous state-of-the-art methods in both efficiency and precision.

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.

To empower the management of smart meters’ demand load within a smart grid environment, this paper presents a Feed-forward Neural Network with ADaptive Evolutionary Learning Approach (ADELA). In this model, the load forecasting information is propagated via neurons of input and multiple hidden layers and the final estimated output is achieved with the help of the sigmoid activation function. An improved evolutionary algorithm is proposed for training and adjusting the interconnecting weights among the layers of the intended neural network. This model is capable of addressing the critical challenges of high volatility, uncertainty, missing smart meters data, and sudden upsurge and plunge in electricity demand. The proposed algorithm is able to learn the best suitable evolutionary operators from a given pool of operators and the probabilities associated with them. The proposed load forecasting approach is simulated over three real-world smart meter datasets, including the Australian Smart Grid Smart City project, the Irish Commission for Energy Regulation, and UMass Smart. The performance evaluation and comparison of the proposed approach with the existing state-of-the-art approaches revealed a relative improvement of up to 46.93%, 5.05%, and 2.20% in forecast accuracy over the Smart Grid Smart City, UMass Smart and the Irish Commission for Energy Regulation datasets, respectively.

Secure resource management (SRM) within a cloud computing environment is a critical yet infrequently studied research topic. This article provides a comprehensive survey and comparative performance evaluation of potential cyber threat countermeasure strategies that address security challenges during cloud workload execution and resource management. Cybersecurity is explored specifically in the context of cloud resource management, with an emphasis on identifying the associated challenges. The cyber threat countermeasure methods are categorized into three classes: defensive strategies, mitigating strategies, and hybrid strategies. The existing countermeasure strategies belonging to each class are thoroughly discussed and compared. In addition to conceptual and theoretical analysis, the leading countermeasure strategies within these categories are implemented on a common platform and examined using two real-world virtual machine (VM) data traces. Based on this comprehensive study and performance evaluation, this article discusses the tradeoffs among these countermeasure strategies and their utility, providing imperative concluding remarks on the holistic study of cloud cyber threat countermeasures and SRM. Furthermore, the study suggests future methodologies that could effectively address the emerging challenges of secure cloud resource management.

Workload pattern learning-based resource management is crucial for cloud computing environments for achieving higher performance, sustainability, fault-tolerance, and quality of service. The existing literature lacks a comprehensive discussion and meta-analysis of workload pattern learning centered cloud resource management. In this context, this paper presents a first comprehensive study about five pattern learning and analysis-driven techniques applied for achieving higher efficiency and performance during multi-constrained cloud resource management. The paper manifests utility and significance of workload pattern learning-based resource management as compared with traditional resource management. The five principle techniques are thoroughly discussed with coherent depiction of intended concept alongwith numerical illustration. The most prominent state-of-the-art models belonging to each technique are further distinguished based on distinct objectives conferring an extensive survey and comparison. Besides, conceptual and theoretical analysis, the leading models underlying the major resource management techniques are implemented on a common platform and thoroughly examined using real-world Google Cluster workload traces. Based on the all-inclusive study and performance evaluation, trade-off discussion among these techniques are capsuled to put forward imperative concluding remarks with concrete open issues and insightful future research directions.

Large-scale computing systems in the modern era distribute tasks into smaller units that can be executed simultaneously to speed up job completion and decrease energy usage. However, cloud computing systems encounter a significant challenge called the Long Tail problem, where a small subset of slow-performing tasks hinders the overall progress of parallel job execution. This behavior leads to longer service response times and reduced system efficiency. This paper introduces a novel approach called Stochastic Gradient Descent with Momentum-driven Neural Network to analyze and classify heterogeneous tasks as either stragglers or non-stragglers. The straggler tasks are further categorized into Resource Hunter and Long-Tail stragglers based on their specific resource requirements. A traffic management policy is implemented to schedule and assign resources among user job requests, considering the task category, to achieve parallelism and improve sustainability within the cloud infrastructure. Extensive simulations are conducted using the Google Cluster Dataset (GCD) to assess the effectiveness of the proposed framework. The results obtained from these simulations are then compared to state-of-the-art techniques. The experimental findings demonstrate significant reductions in power consumption, carbon emissions, active servers, conflicting servers, and VM migration up to 55.16%, 49.76%, 35%, 25.7%, and 87.29%, respectively. Moreover, there has been an enhancement in resource utilization by up to 78.31%, accompanied by a decrease in execution time of up to 67.74%.

The heterogeneous and multi-structured data within datalakes environment stored at distributed geographical locations raises the difficulty of user query processing. Caching is a technique that can help reduce the latency by facilitating the required web pages within cache memory for faster query processing. The dynamic demands of heterogeneous web content are entangled with critical challenges of adjusting cache size dynamically and scaling processing requirements with elastic resource capacity. However, the existing caching schemes based on duration of web page stay within cache are insufficient to provision dynamic caching of heterogeneous web content automatically. In this context, this paper proposes a novel dynamic and self-adaptive cache management model named latency aware dynamic caching (LAD-Caching). It incorporates deep learning capability of Long Short-term Memory (LSTM) algorithm for proactive estimation of caching contents with an automated cache admission and eviction scheme. The innovative idea of engaging two different LSTM-based units for dynamic cache size prediction and cache content eviction is proposed for the first time which captures all-inclusive dynamic cache management in diverse datalake environment. The simulation and performance evaluation of the proposed LAD-Caching using a benchmark dataset confirms its efficiency in terms of reduced average data access time up to 100.826 nsec as compared with optimal case and minimizing average delay up to 99% over without LAD-Caching. Further, the number of cache hits are improved up to 52.7% and 51.2% over existing caching.

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.

The inefficient sharing of industrial cloud resour-ces among multiple users and vulnerabilities of virtual machines (VM)s and servers prompt unauthorized access to users’ sensitive data along with excess consumption of power and resource wastage. To address these entangled issues, this paper proposes a novelEmerging VMThreatPrediction and DynamicWorkloadEstimation based Resource Allocation (ETP-WE) framework that predicts VM threats and resource usage proactively in real-time. The proposed framework contributes by introducing a Risk-Score Matrix that analyses multiple risks for each VM; utilizing knowledge of proposed security and workload analyzers for efficient VM Placement (VMP), and estimating resource utilization by developing an ensemble predictor for prior mitigation of over-/under-load on servers. ETP-WE framework collaborates machine-learning-based security and workload analysis for secure and resource-efficient VMP, thereby reducing the number of security threats, optimizing resource utilization, power-consumption, and adapting to the changes in application demands. The performance of the proposed framework is evaluated using two benchmark datasets OpenNebula and Google Cluster. The simulation-based comparison with state-of-the-arts validates the efficacy of ETP-WE in terms of reduction of security threats, power consumption, and number of active servers up to 86.9%, 66.67% and 30%-80%, respectively with an improved resource utilization up to 60%-75% over existing approachesNote to Practitioners—Industry clouds serve the precise needs and provide the service features and tools as per the industry’s needs to help organizations meet their workloads processing and storage demands. For instance, healthcare and financial organizations have to comply with extended security to meet specific service requirements. To this context, we have proposed a novel ETP-WE framework for prediction and mitigation of cyberthreats on virtual resources in real-time for secure execution of industrial applications on third-party servers. ETP-WE collaborates machine-learning based security and workload analysis for secure and resource efficient VM allocation, thereby reducing number of security threats, optimizing resource utilization, power-consumption and adaptating to the changes in application demands. During the processing of any sensitive transaction such as medical data, bank transactions, ETP-WE framework will induce improved data protection by mitigating potential data breaches. ETP-WE framework will be deployed at Resource Scheduler to boost security performance by estimating the multiple risks score status of VMs engaged in execution of industrial transactions or workloads. It will help to predict and analyze the probable security threats or breaches proactively and facilitate their mitigation. The performance evaluation and comparison with state-of-the-arts validate potency of ETP-WE in terms of reduction of cybersecurity threats and number of active servers with an improved resource utilization over existing approaches.

This article proposed a novel industry cloud resource management framework that exploits resource oversubscription and heterogeneous service pricing models to maximize profitability and operational efficiency for industry cloud providers. The framework proposes an adaptive ensemble machine learning driven prediction model for proactive estimation of resource utilization of Virtual Machines (VM)s-based on previous resource utilization of respective users’ VMs to minimize resource wastage due to oversubscription by them. Accordingly, the VMs having similar predicted resource usage are grouped using Fuzzy C-means clustering. This helps to determine the required number of VMs with specific configuration to be deployed before executing user requests. Concurrently, the framework incorporates two distinct categories of cloud service pricing models, namely theDelay Sensitive Modeland theBest-Effort Model. Accordingly, the user requests are classified and executed by selecting the most suitable VMs, with the goal of maximizing revenue and reducing electricity costs in cloud data centers ($\mathbb{C}\mathbb{D}\mathbb{C}$s). Experimental simulation and comparison against state-of-the-art methods, using two benchmark VM traces, validates the performance of proposed framework. It significantly reduces electricity bills by 55.56%, power consumption and active servers by up to 60.7% and 51%, respectively, while improving resource utilization and profits by up to 60% and 51.18%, respectively.

The precise estimation of resource usage is a complex and challenging issue due to the high variability and dimensionality of heterogeneous service types and dynamic workloads. Over the last few years, the prediction of resource usage and traffic has received ample attention from the research community. Many machine learning-based workload forecasting models have been developed by exploiting their computational power and learning capabilities. This paper presents the first systematic survey cum performance analysis-based comparative study of diversified machine learning-driven cloud workload prediction models. The discussion initiates with the significance of predictive resource management followed by a schematic description, operational design, motivation, and challenges concerning these workload prediction models. Classification and taxonomy of different prediction approaches into five distinct categories are presented focusing on the theoretical concepts and mathematical functioning of the existing state-of-the-art workload prediction methods. The most prominent prediction approaches belonging to a distinct class of machine learning models are thoroughly surveyed and compared. All five classified machine learning-based workload prediction models are implemented on a common platform for systematic investigation and comparison using three distinct benchmark cloud workload traces via experimental analysis. The essential key performance indicators of state-of-the-art approaches are evaluated for comparison and the paper is concluded by discussing the trade-offs and notable remarks.

Massive upsurge in cloud resource usage stave off service availability resulting into outages, resource contention, and excessive power-consumption. The existing approaches have addressed this challenge by providing multi-cloud, VM migration, and running multiple replicas of each VM which accounts for high expenses of cloud data centre (CDC). In this context, a novel VM Significance Ranking and Resource Estimation based High Availability Management (SRE-HM) Model is proposed to enhance service availability for users with optimized cost for CDC. The model estimates resource contention based server failure and organises needed resources beforehand for maintaining desired level of service availability. A significance ranking parameter is introduced and computed for each VM, executing critical or non-critical tasks followed by the selection of an admissible High Availability (HA) strategy respective to its significance and user specified constraints. It enables cost optimization for CDC by rendering failure tolerance strategies for significant VMs only instead of all the VMs. The proposed model is evaluated and compared against state-of-the-arts by executing experiments using Google Cluster dataset. SRE-HM improved the services availability up to 19.56% and scales down the number of active servers and power-consumption up to 26.67% and 19.1%, respectively over HA without SRE-HM.

Cloud virtualization technology, ingrained with physical resource sharing, prompts cybersecurity threats on users’ virtual machines (VMs) due to the presence of inevitable vulnerabilities on the offsite servers. Contrary to the existing works which concentrated on reducing resource sharing and encryption/decryption of data before transfer for improving cybersecurity which raises computational cost overhead, the proposed model operates diversely for efficiently serving the same purpose. This article proposes a novel multiple risks analysis-based VM threat prediction model (MR-TPM) to secure computational data and minimize adversary breaches by proactively estimating the VMs threats. It considers multiple cybersecurity risk factors associated with the configuration and management of VMs, along with analysis of users’ behavior. All these threat factors are quantified for the generation of respective risk score values and fed as input into a machine learning-based classifier to estimate the probability of threat for each VM. The performance of MR-TPM is evaluated using benchmark Google Cluster and OpenNebula VM threat traces. The experimental results demonstrate that the proposed model efficiently computes the cybersecurity risks and learns the VM threat patterns from historical and live data samples. The deployment of MR-TPM with existing VM allocation policies reduces cybersecurity threats up to 88.9%.

Cloud computing has multiple benefits in terms of minimum cost, maximum efficiency, and high scalability, which prompts shifting a large amount of data from the local machine to the cloud environment for storage, computation, and data sharing among various parties stakeholders. However, owners do not fully trust the cloud platform operated by a third party. Therefore, security and privacy emerge as critical issues while sharing data among different parties. In this paper, a novel privacy-preserving model is proposed by utilizing encryption, differential privacy, and machine learning approaches. It facilitates data owners to share their data securely in the cloud environment. The model defines access policy and communication protocol among the involved untrusted parties for data processing and privacy preservation. The proposed model is evaluated by executing experiments using distinct datasets. The achieved results reveal that the proposed model provides high accuracy, precision, recall, and f1-score up to 98%, 98%, 97%, and 97%, respectively, over the state of the art methods.

The elasticity of cloud resources allows cloud clients to expand and shrink their demand for resources dynamically over time. However, fluctuations in the resource demands and pre-defined size of virtual machines (VMs) lead to lack of resource utilization, load imbalance, and excessive power consumption. To address these issues and to improve the performance of data center, an efficient resource management framework is proposed, which anticipates resource utilization of the servers and balances the load accordingly. It facilitates power saving, by minimizing the number of active servers, VM migrations, and maximizing the resource utilization. An online resource prediction system, is developed and deployed at each VM to minimize the risk of Service Level Agreement (SLA) violations and performance degradation due to under/overloaded servers. In addition, multi-objective VM placement and migration algorithms are proposed to reduce the network traffic and power consumption within data center. The proposed framework is evaluated by executing experiments on three real world workload datasets namely, Google Cluster dataset, Planet Lab, and Bitbrains VM traces. The comparison of proposed framework with the state-of-the-art approaches reveals its superiority in terms of different performance metrics. The improvement in power saving achieved by OP-MLB framework is upto 85.3 percent over the Best-Fit approach.

The growing congruence of gadgets today resulted in a numerous type of cyber attacks. A similar trend occurs with the industrial Internet of Things (IIoT), wherein increasing data created by connected equipment offers up new opportunities for enhancing service quality for new applications. But, security has become a major design priority for smart devices since the introduction of the IIoT. However, data providers have significant challenges in exchanging their data between different cyber physical systems due to privacy and security concerns (e.g., data imbalance and leakage). In this article, we developed a safe data sharing architecture for various IIoT devices using federated learning (FL). The proposed architecture incorporates FL into the edge computing consensus process, allowing the consensus computing activity to be used for federated training as well. The proposed framework achieves high efficiency, and better security, according to numerical findings generated by experimenting deep learning models. More precisely, the proposed framework named as SecureIIoT, is able to achieve 99.79% accuracy by detecting attacks as a binary classification problem.

Cloud computing has become inevitable for every digital service which has exponentially increased its usage. However, a tremendous surge in cloud resource demand stave off service availability resulting into outages, performance degradation, load imbalance, and excessive power-consumption. The existing approaches mainly attempt to address the problem by using multi-cloud and running multiple replicas of a virtual machine (VM) which accounts for high operational-cost. This paper proposes a Fault Tolerant Elastic Resource Management (FT-ERM) framework that addresses aforementioned problem from a different perspective by inducing high-availability in servers and VMs. Specifically,(1)an online failure predictor is developed to anticipate failure-prone VMs based on predicted resource contention;(2)the operational status of server is monitored with the help of power analyser, resource estimator and thermal analyser to identify any failure due to overloading and overheating of servers proactively; and(3)failure-prone VMs are assigned to proposed fault-tolerance unit composed of decision matrix and safe box to trigger VM migration and handle any outage beforehand while maintaining desired level of availability for cloud users. The proposed framework is evaluated and compared against state-of-the-arts by executing experiments using two real-world datasets. FT-ERM improved the availability of the services up to 34.47% and scales down VM-migration and power-consumption up to 88.6% and 62.4%, respectively over without FT-ERM approach.

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.

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.

Data leakage has become a critical challenge for any organisation, especially when the third party is involved with legitimate permissions to access its confidential data. This study provides a statistical evaluation‐based leaker identification (SELI) scheme to identify the malicious entity when the data is leaked by any agent at some unauthorised place. A new distribution strategy for object and agent selection which enriches the guilty agent recognition is introduced. The three algorithms named SELI–first come first serve, SELI–round Robin (SELI–RR) and SELI–shortest request first (SELI–SRF) are presented for the agent selection. Furthermore, the spurious data objects are added while distributing the data sets among the agents to uniquely identify the leaker. The SELI scheme estimates the guilty entity based on the allocated data among the agents using bigraph. The proposed solution achieves an improvement of up to 25.37 and 58.18% for average probability and average success rate respectively in case of SELI–SRF and 163.68% in case of SELI–RR for detection rate compared to the prior work. In addition to this, the SELI scheme proved its efficiency by securing up to 99.82% accuracy, 99.92% precision, 99.4% recall and 99.97% specificity.

Data sharing across multiple different entities is on‐demand to upgrade an enterprise's performance. However, some malicious entity can reveal this data to an unauthorised third party that may result in heavy loss to the enterprises in terms of finance, reputation, and long‐term stability. This study presents a novel model GUIM‐SMD for the identification of the guilty entity which is responsible for the data leakage to the unauthorised party in the shared environment. An effective distribution strategy to allocate the data among the users based on the access control mechanism is proposed in this model. The approach introduces the summation matrix which is computed using D‐score and U‐score that are assigned to the classified data and user, correspondingly. Furthermore, D‐score and U‐score are based on the data sensitivity and user guilty record relatively; and their values vary between 0 and 1. The evaluated summation matrix is used for data distribution among various users. The results show improvement up to 98.74, 236.38, and 252.39% for average probability, average success rate, and detection rate, respectively, as compared to the prior work.

In the cloud computing environment, the privacy of the electronic data is a serious issue that requires special considerations. We have presented a state-of-the-art review of the methodologies and approaches that are currently being used to cope with the significant issue of privacy. We have categorized the privacy-preserving approaches into four categories, i.e., privacy by cryptography, privacy by probability, privacy by anonymization and privacy by ranking. Moreover, we have developed taxonomy of the techniques that have been used to preserve the privacy of the governing data. We also presented a comprehensive comparison of the privacy-preserving approaches from the angle of the privacy-preserving requirements’ satisfaction. Therefore, it is highly desirable that the mechanisms should be developed to deploy efficient auditing and accountability mechanisms that anonymously monitor the utilization of data records and track the provenance to ensure the confidentiality of the data.

This article presents an algorithm to determine the relevancy of hanging pages in the link-structure-based ranking algorithms. Recently, hanging pages have become major obstacles in web information retrieval (IR). As an increasing number of meaningful hanging pages appear on the Web, their relevancy has to be determined according to the query term in order to make the search engine result pages fair and relevant. Exclusion of these pages in ranking calculation can give biased/inconsistent results, but inclusion of these pages will reduce the speed significantly. Most of the IR ranking algorithms exclude the hanging pages. But there are relevant and important hanging pages on the Web, and they cannot be ignored. In our proposed method, we use anchor text to determine the hanging relevancy, and we use stability analysis to show that rank results are consistent before and after altering the link structure.

The quantity and quality of seed sets are key factors for the success of propagation-based anti-web spam techniques. This kind of approach is simple yet effective, but the manual evaluation of seed sets is time-consuming. Therefore, a manual evaluation process is vital. In this article, we propose Trust Propagation Rank (TPRank), which automatically propagates trust to demote web spam based on a small number of reputable and spam seeds. Moreover, the proposed algorithm is extended to trust propagation (TP) spam mass in detection of web spam. Experiments were performed on two public available data sets—WEBSPAM-UK2006 and WEBSPAM-UK2007—and the results showed that both TPRank and TP spam mass outperform the state-of-the-art TrustRank in demotion up to 10.623% and spam mass algorithm in detection up to 43.216%.

Design space exploration of a processor system, prior to its hardware implementation, usually involves cycle-accurate simulations. The simulations provide a good measure of performance but require long periods of time even when a small set of design variations are assessed. An alternative is to use empirically-developed models which are much faster than actual simulations. In this paper, we have proposed an NN model for processor performance (IPC) prediction. The model uses a larger set of input parameters (especially the software parameters) than the prior models. For dimension reduction, we found PCA to be a more useful technique than correlation and graphical analysis. For the purpose of training the NNs, we used the data from a large number of simulations of industry-standard SPEC CPU 2000 and SPEC CPU 2006 benchmark suites In order to collect the NN training data in a reasonable period of time, we utilized two well-known techniques, namely, benchmark-subsetting and SPs.

Web spam has become one of the most exciting challenges and threats to web search engines. The relationship between the search systems and those who try to manipulate them came up with the field of adversarial information retrieval. In this article, we set up several experiments to compare HostRank and TrustRank to show how effective it is for TrustRank to combat web spam, and we report a comparison on different link-based web spam detection algorithms.

In this article we first explain the knowledge extraction (KE) process from the World Wide Web (WWW) using search engines. Then we explore the PageRank algorithm of Google search engine (a well-known link-based search engine) with its hidden Markov analysis. We also explore one of the problems of link-based ranking algorithms called hanging pages or dangling pages (pages without any forward links). The presence of these pages affects the ranking of Web pages. Some of the hanging pages may contain important information that cannot be neglected by the search engine during ranking. We propose methodologies to handle the hanging pages and compare the methodologies. We also introduce the TrustRank algorithm (an algorithm to handle the spamming problems in link-based search engines) and include it in our proposed methods so that our methods can combat Web spam. We implemented the PageRank algorithm and TrustRank algorithm and modified those algorithms to implement our proposed methodologies.

This paper presents a technique for representing multiple-output binary and word-level functions in GF(JV) (where N = pm, p is a prime number, and m is a nonzero positive integer) based on decision diagrams (DDs). The presented DD is canonical and can be made minimal with respect to a given variable order. The DD has been tested on benchmarks, including integer multiplier circuits, and the results show that it can produce better node compression (more than an order of magnitude in some cases) compared to shared binary DDs (BDDs). The benchmark results also reflect the effect of varying the input and output field sizes on the number of nodes. Methods of graph-based representation of characteristic and encoded characteristic functions in GF(iV) are also presented. Performance of the proposed representations has been studied in terms of average path lengths and the actual evaluation times with 50,000 randomly generated patterns on many benchmark circuits. All of these results reflect that the proposed technique can outperform existing techniques.