ABSTRACT Coronary heart disease is a leading cause of mortality worldwide. Although no cure exists for this condition, appropriate treatment and timely intervention can effectively manage its symptoms and reduce the risk of complications such as heart attacks. Prior studies have mostly relied on a limited dataset from the UC Irvine Machine Learning Repository, predominantly focusing on Machine Learning (ML) models without incorporating Explainable Artificial Intelligence (XAI) or Generative Artificial Intelligence (GAI) techniques for dataset enhancement. While some research has explored cloud‐based deployments, the implementation of edge AI in this domain remains largely under‐explored. Therefore, this paper proposes HealthEdgeAI, a sustainable approach to heart disease prediction that enhances XAI through GAI‐driven data augmentation. In our research, we assessed multiple AI models by evaluating accuracy, precision, recall, F1‐score, and area under the curve (AUC). We also developed a web application using Streamlit to demonstrate our XAI methods and employed FastAPI to serve the optimal model as an API. Additionally, we examined the performance of these models in cloud computing and edge AI settings by comparing key Quality of Service (QoS) parameters, such as average response rate and throughput. To highlight the potential of sustainable edge AI and cloud computing, we tested edge devices with both low‐ and high‐end configurations to illustrate differences in QoS. Ultimately, this study identifies current limitations and outlines prospective directions for future research in AI‐based cloud and edge computing environments.

The massive amounts of data generated by the Industrial Internet of Things (IIoT) require considerable processing power, which increases carbon emissions and energy usage, and we need sustainable solutions to enable flexible manufacturing. Serverless computing shows potential for meeting this requirement by scaling idle containers to zero energy-efficiency and cost, but this will lead to a cold start delay. Most solutions rely on idle containers, which necessitates dynamic request time forecasting and container execution monitoring. Furthermore, Artificial Intelligence of Things (AIoT) can provide autonomous and sustainable solutions by combining IIoT with artificial intelligence (AI) to solve this problem. Therefore, we develop a new testbed, CAPTAIN, to facilitate AI-based co-simulation of scalable and flexible serverless computing in IIoT environments. The AI module in the CAPTAIN framework employs random forest (RF) and light gradient-boosting machine (LightGBM) models to optimize cold start frequency and prevent cold starts based on their prediction results. The proxy module additionally monitors the client-server network and constantly updates the AI module training dataset via a message queue. Finally, we evaluated the proxy module’s performance using a predictive maintenance-based real-world IIoT application and the AI module’s performance in a realistic serverless environment using a Microsoft Azure dataset. The AI module of the CAPTAIN outperforms baselines in terms of cold start frequency, computational time with 0.5 ms, energy consumption with 1161.0 joules, and CO2 emissions with 32.25e-05 gCO2. The CAPTAIN testbed provides a co-simulation of sustainable and scalable serverless computing environments for AIoT-enabled predictive maintenance in Industry 4.0.

Abstract The escalating threat of easily transmitted diseases poses a huge challenge to government institutions and health systems worldwide. Advancements in information and communication technology offer a promising approach to effectively controlling infectious diseases. This article introduces a comprehensive framework for predicting and preventing zoonotic virus infections by leveraging the capabilities of artificial intelligence and the Internet of Things. The proposed framework employs IoT‐enabled smart devices for data acquisition and applies a fog‐enabled model for user authentication at the fog layer. Further, the user classification is performed using the proposed ensemble model, with cloud computing enabling efficient information analysis and sharing. The novel aspect of the proposed system involves utilizing the temporal graph matrix method to illustrate dependencies among users infected with the zoonotic flu and provide a nuanced understanding of user interactions. The implemented system demonstrates a classification accuracy of around 91% for around 5000 instances and reliability of around 93%. The presented framework not only aids uninfected citizens in avoiding regional exposure but also empowers government agencies to address the problem more effectively. Moreover, temporal mining results also reveal the efficacy of the proposed system in dealing with zoonotic cases.

Microservice architecture has transformed traditional monolithic applications into lightweight components. Scaling these lightweight microservices is more efficient than scaling servers. However, scaling microservices still faces the challenges resulting from the unexpected spikes or bursts of requests, which are difficult to detect and can degrade performance instantaneously. To address this challenge and ensure the performance of microservice-based applications, we propose a status-aware and elastic scaling framework called StatuScale , which is based on load status detector that can select appropriate elastic scaling strategies for differentiated resource scheduling in vertical scaling. Additionally, StatuScale employs a horizontal scaling controller that utilizes comprehensive evaluation and resource reduction to manage the number of replicas for each microservice. We also present a novel metric named correlation factor to evaluate the resource usage efficiency. Finally, we use Kubernetes, an open source container orchestration and management platform, and realistic traces from Alibaba to validate our approach. The experimental results have demonstrated that the proposed framework can reduce the average response time in the Sock-Shop application by 8.59% to 12.34% and in the Hotel-Reservation application by 7.30% to 11.97%, decrease service level objective violations, and offer better performance in resource usage compared to baselines.

Containerized Edge computing offers lightweight, reliable, and quick solutions to latency-critical Machine Learning (ML) and Deep Learning (DL) applications. Existing solutions considering multiple Quality of Service (QoS) parameters either overlook the intricate relation of QoS parameters or pose significant scheduling overheads. Furthermore, reactive decisionmaking can damage Edge servers at peak load, incurring escalated costs and wasted computations. Resource provisioning, scheduling, and ML model selection substantially influence energy consumption, user-perceived accuracy, and delayoriented Service Level Agreement (SLA) violations. Addressing contrasting objectives and QoS simultaneously while avoiding server faults is highly challenging in the exposed heterogeneous and resource-constrained Edge continuum. In this work, we propose the EdgeAIBus framework that offers a novel joint container management and ML model selection algorithm based on Importance Weighted Actor-Learner Architecture to optimize energy, accuracy, SLA violations, and avoid server faults. Firstly, Patch Time Series Transformer (PatchTST) is utilized for CPU usage predictions of Edge servers for its 8.51% Root Mean Squared Error and 5.62% Mean Absolute Error. Leveraging pipelined predictions, EdgeAIBus conducts consolidation, resource oversubscription, and ML/DL model switching with possible migrations to conserve energy, maximize utilization and user-perceived accuracy, and reduce SLA violations. Simulation results show EdgeAIBus oversubscribed 110% cluster-wide CPU with real usage up to 70%, conserved 14 CPU cores, incurred less than 1% SLA violations with 2.54% drop in inference accuracy against industry-led Model Switching Balanced load and Google Kubernetes Optimized schedulers. Google Kubernetes Engine experiments demonstrate 80% oversubscription, 14 CPU cores conservation, 1% SLA violations, and 3.81% accuracy loss against the counterparts. Finally, constrained setting experiment analysis shows that PatchTST and EdgeAIBus can produce decisions within 100ms in a 1-core and 1 GB memory device.

Summary Internet of Things (IoT) based sensor networks have been established as a pillar in intelligent communication systems for efficiently handling roadside congestion and accidents. These IoT networks sense, collect, and process data on a real‐time basis. However, IoT based sensor network clustering has various energy constraints such as inefficient routing due to long‐haul transmission, hot spot problem, network overhead, and unstable network whenever deployed along with the roadside that affect their architecture. In such networks, clustering techniques play a crucial role in extending the lifespan and optimizing the routes by integrating sensor devices through clusters. Therefore, a meta‐heuristic algorithm for clustering in IoT sensor networks for an intelligent transportation system is proposed. In this work, the seagull optimization algorithm is applied for clustering by considering residual and average energy, node spacing, and distance fitness parameters. Moreover, this work also considers the dynamic communication range of the cluster heads for increasing the stability period and lifetime of the proposed networks. The experiment results demonstrate that the proposed Seagull optimization algorithm for clustering in IoT networks (SOAC‐IoTNs) and Seagull optimization algorithm for clustering in IoT networks with dynamic communication range (SOAC‐IoTNs‐DR) achieve a significant increase in the stability period and network lifetime, with percentage increments of 55.68% and 71.47%, and 10.03% and 88.66% respectively, compared to the existing optimized genetic algorithm for cluster head selection with single static sink (OptiGACHS‐StSS).

The vision for the sixth-generation (6G) network involves the integration of communication and sensing capabilities in internet of everything (IoE), towards enabling broader interconnection in the devices of distributed wireless sensor networks (WSN). Moreover, the merging of SDN policies in 6G IoE-based WSNs i.e. SDN-enable WSN improves the network’s reliability and scalability via integration of sensing and communication (ISAC). It consists of multiple controllers to deploy the control services closer to the data plane for a speedy response through control messages. However, controller placement and load balancing are the major challenges in SDN-enabled WSNs due to the dynamic nature of data plane devices. To address the controller placement problem, an optimal number of controllers is identified using the articulation point method. Furthermore, a nature-inspired cheetah optimization algorithm is proposed for the efficient placement of controllers by considering the latency and synchronization overhead. Moreover, a load-sharing based control node migration (LS-CNM) method is proposed to address the challenges of controller load balancing dynamically. The LS-CNM identifies the overloaded controller and corresponding assistant controller with low utilization. Then, a suitable control node is chosen for partial migration in accordance with the load of the assistant controller. Subsequently, LS-CNM ensures dynamic load balancing by considering threshold loads, intelligent assistant controller selection, and real-time monitoring for effective partial load migration. The proposed LS-CNM scheme is executed on the open network operating system (ONOS) controller and the whole network is simulated in ns-3 simulator. The simulation results of the proposed LS-CNM outperform the state of the art in terms of frequency of controller overload, load variation of each controller, round trip time, and average delay.

Abstract Microservices is a structural approach, where multiple small set of services are composed and processed independently with lightweight communication mechanism. To accomplish the end‐user demand in minimum delay and cost without violating the service level agreement (SLA) constraints and overhead is a challenging issue in cloud computing. In addition, existing framework tries to deploy the microservice over the best computing resource for latency‐sensitive applications, but long boot‐time, and low resource utilization still remains a challenging task. To find the solution for aforementioned issues, we propose a Quality of Service (QoS) aware resource allocation model based on a Fine‐tuned Sunflower Whale Optimization Algorithm (FSWOA) that find the best resources for microservice deployment and fulfill the objectives of users as well as service provider. The proposed technique deploys the container‐based services over the physical machine based upon the capacity, to execute the micro services by utilizing the CPU and memory maximally. The proposed work aims is to distribute the workload in efficient manner and avoid the wastage of resources that leads to optimize the QoS parameters. The experimental results conducted in simulation environment demonstrates that proposed approach perform superior over baseline approaches and reduces the time, memory consumption, CPU consumption, and service cost up to 4.26%, 11.29%, 17.07% and 24.22% compared to SFWAO, GA, PSO and ACO.

Abstract Federated learning (FL) is widely used in edge‐cloud collaborative training due to its distributed architecture and privacy‐preserving properties without sharing local data. FLTrust, the most state‐of‐the‐art FL defense method, is a federated learning defense system with trust guidance. However, we found that FLTrust is not very robust. Therefore, in the edge collaboration scenario, we mainly study the poisoning attack on the FLTrust defense system. Due to the aggregation rule, FLTrust, with trust guidance, the model updates of participants with a significant deviation from the root gradient direction will be eliminated, which makes the poisoning effect on the global model not obvious. To solve this problem, under the premise of not being deleted by the FLTrust aggregation rules, we construct malicious model updates that deviate from the trust gradient to the greatest extent to achieve model poisoning attacks. First, we utilize the rotation of high‐dimensional vectors around axes to construct malicious vectors with fixed orientations. Second, the malicious vector is constructed by the gradient inversion method to achieve an efficient and fast attack. Finally, a method of optimizing random noise is used to construct a malicious vector with a fixed direction. Experimental results show that our attack method reduces the model accuracy by 20%, severely undermining the usability of the model. Attacks are also successful hundreds of times faster than the FLTrust adaptive attack method.

Yellow fever is a vigorous, phlebotomic, vector-borne disease that poses a significant public health threat in regions with high mosquito density and inadequate vaccination coverage. The disease's toxic phase is lethal, making prompt identification and control measures crucial. The emergence of the latest technologies and data analytics techniques, such as edge-cloud computing, data analytics, and machine learning/deep learning, has played a pivotal role in revolutionizing remote healthcare services. Henceforth, applying the abovementioned technologies leads to improvements in the response time, service quality, and location awareness of healthcare systems. Relative to this context, we propose an intelligent fuzzy-centric fog–cloud-assisted healthcare framework to identify and control yellow fever epidemics. Initially, at the fog layer, singular value decomposition is used for data dimensionality reduction analysis and the Fuzzy-C mean clustering (FCM) algorithm is leveraged to get rigorous results. Moreover, for better results and to focus on time-series patterns, the deep interval type 2 fuzzy Bi-LSTM model is proposed at the cloud layer to generate a yellow fever severity index and visualize each yellow fever region based on self-organized maps. In addition, we propose an alert generation mechanism to facilitate real-time decision-making. Finally, results show that the proposed system yields significant efficacy, compared with other state-of-the-art methodologies.

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove that this technique holds promise for enhancing the capabilities of IoT and e-health systems in the future for predicting the manifestation of TB in the lungs. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures based on performance metrics such as test binary accuracy, test loss, intersection over union, precision, recall, and F1 score. The findings hint at the potential of integrating these advanced Machine Learning (ML) models within IoT and e-health frameworks, thereby paving the way for more comprehensive and data-driven approaches to enable smart healthcare. The best-performing model, VGG-19, is selected for different deployment strategies using server and serless-based environments. We used JMeter to measure the performance of the deployed model, including the average response rate, throughput, and error rate. This study provides valuable insights into the selection and deployment of ML models in healthcare, highlighting the advantages and challenges of different deployment options. Furthermore, it also allows future studies to integrate such models into IoT and e-health systems, which could enhance healthcare outcomes through more informed and timely treatments.

A wide variety of Mobile Devices (MDs) are adopted in Internet of Things (IoT) environments, resulting in a dramatic increase in the volume of task data and greenhouse gas emissions. However, due to the limited battery power and computing resources of MD, it is critical to process more data with less energy. This paper studies the Wireless Power Transfer-based Mobile Edge Computing (WPT-MEC) network system assisted by Intelligent Reflective Surface (IRS) to enhance communication performance while improving the battery life of MD. In order to maximize the Computation Energy Efficiency (CEE) of the system and reduce the carbon footprint of the MEC server, we jointly optimize the CPU frequencies of MDs and MEC server, the transmit power of Power Beacon (PB), the processing time of MEC server, the offloading time and the energy harvesting time of MDs, the local processing time and the offloading power of MD and the phase shift coefficient matrix of Intelligent Reflecting Surface (IRS). Moreover, we transform this joint optimization problem into a fractional programming problem. We then propose the Dinkelbach Iterative Algorithm with Gradient Updates (DIA-GU) to solve this problem effectively. With the help of convex optimization theory, we can obtain closed-form solutions, revealing the correlation between different variables. Compared to other algorithms, the DIA-GU algorithm not only exhibits superior performance in enhancing the system's CEE but also demonstrates significant reductions in carbon emissions.

Serverless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications like autonomous cars. Most existing approaches need containers to idle and use extra computing resources. Edge devices have fewer resources than cloud-based systems, requiring new sustainable solutions. Therefore, we propose an AI-powered, sustainable resource management framework called ATOM for serverless edge computing. ATOM utilizes a deep reinforcement learning model to predict exactly when cold start latency will happen. We create a cold start dataset using a heart disease risk scenario and deploy using Google Cloud Functions. To demonstrate the superiority of ATOM, its performance is compared with two different baselines, which use the warm-start containers and a two-layer adaptive approach. The experimental results showed that although the ATOM required more calculation time of 118.76 seconds, it performed better in predicting cold start than baseline models with an RMSE ratio of 148.76. Additionally, the energy consumption and$CO_{2}$emission amount of these models are evaluated and compared for the training and prediction phases.

Summary The cloud services demand has increased exponentially in the last decade due to its plethora of services. It becomes a significant platform to compute large and diverse applications over the internet. On the contrary, on‐demand resource allocation to a variety of applications becomes a serious issue due to dynamic workload conditions and uncertainty in the cloud environment. Several existing state of art techniques often fails to allocate the optimal resources to forthcoming demands, leading to an imbalance workload over cloud platform, degrading the performance. This article introduces a secure and self‐adaptive resource allocation framework that addressed the mentioned issues and allocates the most suitable resources to users' applications while ensuring the deadline constraints. Further, the proposed framework is integrated with a metaheuristic algorithm named enhanced spider monkey optimization algorithm that is based on the intelligent foraging behavior of spider monkeys. The proposed algorithm finds an optimal resource for the user's application using the fission‐fusion approach and improves multiple influential parameters like time, cost, degree of load balancing, energy consumption, task rejection ratio and so on. The experimental CloudSim based results verified that the proposed framework performs superior to state of art approaches like PSO, GSA, ABC, and IMMLB.

Heart disease is one of the leading causes of death worldwide, and with early detection, mortality rates can be reduced. Well-known studies have shown that the latest artificial intelligence (AI) can be used to determine the risk of heart disease. However, existing studies did not consider dynamic scalability to get the best performance from these AI models in case of an increasing number of users. To solve this problem, we proposed an AI-powered smart healthcare framework called HealthFaaS, using the Internet of Things (IoT) and a Serverless Computing environment to reduce heart disease-related deaths and prevent financial losses by reducing misdiagnoses. HealthFaaS framework collects health data from users via IoT devices and sends it to AI models deployed on a Google Cloud Platform (GCP)-based serverless computing environment due to its advantages, such as dynamic scalability, less operational complexity, and a pay-as-you-go pricing model. The performance of five different AI models for heart disease risk detection is evaluated and compared based on key parameters, such as accuracy, precision, recall,$F$-Score, and AUC. Experimental results demonstrate that the light gradient boosting machine model gives the highest success in detecting heart diseases with an accuracy rate of 91.80%. Further, we have tested the performance of the HealthFaaS framework in terms of Quality-of-Service (QoS) parameters, such as throughput and latency against the increasing number of users and compared it with a non-serverless platform. In addition, we have also evaluated the cold start latency using a serverless platform which determined that the amount of memory and the software language makes a direct impact on the cold start latency.

The assets in Industry 4.0 are categorized into physical, virtual, and human. The innovation and popularization of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc., for asset identification and tracking. The generated data from the Industrial Internet of Things (IIoT) ease information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of the manufacturing equipment. Predictive maintenance enables the business owner to decide, such as repairing or replacing the component before an actual failure that affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimize the task distributions and predictive maintenance model. This article presents the genetic algorithm (GA)-based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost, and energy performance of GA along with MinMin, MaxMin, FCFS, and RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time data sets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost, and energy usage. The execution time is 0.48% faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. The training and testing accuracy of the prediction model is 95.1% and 94.5%, respectively.

Abstract With the advent of the Internet of Things (IoT) paradigm, the cloud model is unable to offer satisfactory services for latency‐sensitive and real‐time applications due to high latency and scalability issues. Hence, an emerging computing paradigm named as fog/edge computing was evolved, to offer services close to the data source and optimize the quality of services (QoS) parameters such as latency, scalability, reliability, energy, privacy, and security of data. This article presents the evolution in the computing paradigm from the client‐server model to edge computing along with their objectives and limitations. A state‐of‐the‐art review of Cloud Computing and Cloud of Things (CoT) is presented that addressed the techniques, constraints, limitations, and research challenges. Further, we have discussed the role and mechanism of fog/edge computing and Fog of Things (FoT), along with necessitating amalgamation with CoT. We reviewed the several architecture, features, applications, and existing research challenges of fog/edge computing. The comprehensive survey of these computing paradigms offers the depth knowledge about the various aspects, trends, motivation, vision, and integrated architectures. In the end, experimental tools and future research directions are discussed with the hope that this study will work as a stepping‐stone in the field of emerging computing paradigms.

A common performance problem in large-scale cloud systems is dealing with straggler tasks that are slow running instances which increase the overall response time. Such tasks impact the system's QoS and the SLA. There is a need for automatic straggler detection and mitigation mechanisms that execute jobs without violating the SLA. Prior work typically builds reactive models that focus first on detection and then mitigation of straggler tasks, which leads to delays. Other works use prediction based proactive mechanisms, but ignore volatile task characteristics. We propose a Straggler Prediction and Mitigation Technique (START) that is able to predict which tasks might be stragglers and dynamically adapt scheduling to achieve lower response times. START analyzes all tasks and hosts based on compute and network resource consumption using an Encoder LSTM network to predict and mitigate expected straggler tasks. This reduces the SLA violation rate and execution time without compromising QoS. Specifically, we use the CloudSim toolkit to simulate START and compare it with IGRU-SD, SGC, Dolly, GRASS, NearestFit and Wrangler in terms of QoS parameters. Experiments show that START reduces execution time, resource contention, energy and SLA violations by 13%, 11%, 16%, 19%, compared to the state-of-the-art.

Artificial intelligence (AI)-based studies have been carried out recently for the early detection of COVID-19. The goal is to prevent the spread of the disease and the number of fatal cases. In AI-based COVID-19 diagnostic studies, the integrity of the data is critical to obtain reliable results. In this paper, we propose a Blockchain-based framework called AIBLOCK, to offer the data integrity required for applications such as Industry 4.0, healthcare, and online banking. In addition, the proposed framework is integrated with Google Cloud Platform (GCP)-Cloud Functions, a serverless computing platform that automatically manages resources by offering dynamic scalability. The performance of five different machine learning models is evaluated and compared in terms of Accuracy, Precision, Recall, F-Score and Area under the curve (AUC). The experimental results show that decision trees gives the best results in terms of accuracy (98.4 %). Further, it has been identified that utilization of Blockchain technology can increase the load on memory.

Over the last decade, there has been a gradual increase in the number of learners on Computer Science-based programmes, which in turn has led to a situation where educators have been teaching large classes. This is a challenge, as it can be difficult for educators to provide personalised support for each learner. The pandemic has only exasperated this further, given the online delivery of courses. The work in this paper describes the implementation of a pedagogic framework that was deployed during the delivery of a flrst-year web programming module. The motivation behind the development of this framework was driven by the need to facilitate an online learning environment for a large class, which adapted existing pedagogic approaches such as problem and project-based learning with the view to enabling learners to develop their problem-solving skills. In addition to this, an online lab co-ordination system was formulated to measure engagement and provide support to the learners.

As the concept of the Internet of Things (IoT) has gained attraction, we are seeing an increase in the number of smart homes equipped with Internet-capable devices (such as smart door locks, disaster detectors, and sweeping robots). As a result of the volume of data generated by these connected devices, storage space may be limited, and network congestion can impede the regular operation of other devices. While it is possible to rely on cloud computing to meet performance requirements, some critical and deadline-oriented services require low latency (such as healthcare applications) and improves energy efficiency. As a result, fog computing has been proposed to reduce latency and network congestion, by extending the service to the terminal’s edge. To this aim, a high-performance router can be used as a fog computing node by deploying the service on it. In this paper, we have developed a data forwarding application called Router-Distributor for sustainable smart home, which simulates the fog computing environment using the iFogSim toolkit. We evaluate the performance by comparing the cloud with fog in terms of latency, network utilisation and energy consumption, which clearly shows sustainability of fog computing.

Abstract Quantum computing (QC) is an emerging paradigm with the potential to offer significant computational advantage over conventional classical computing by exploiting quantum‐mechanical principles such as entanglement and superposition. It is anticipated that this computational advantage of QC will help to solve many complex and computationally intractable problems in several application domains such as drug design, data science, clean energy, finance, industrial chemical development, secure communications, and quantum chemistry. In recent years, tremendous progress in both quantum hardware development and quantum software/algorithm has brought QC much closer to reality. Indeed, the demonstration of quantum supremacy marks a significant milestone in the Noisy Intermediate Scale Quantum (NISQ) era—the next logical step being the quantum advantage whereby quantum computers solve a real‐world problem much more efficiently than classical computing. As the quantum devices are expected to steadily scale up in the next few years, quantum decoherence and qubit interconnectivity are two of the major challenges to achieve quantum advantage in the NISQ era. QC is a highly topical and fast‐moving field of research with significant ongoing progress in all facets. A systematic review of the existing literature on QC will be invaluable to understand the state‐of‐the‐art of this emerging field and identify open challenges for the QC community to address in the coming years. This article presents a comprehensive review of QC literature and proposes taxonomy of QC. The proposed taxonomy is used to map various related studies to identify the research gaps. A detailed overview of quantum software tools and technologies, post‐quantum cryptography, and quantum computer hardware development captures the current state‐of‐the‐art in the respective areas. The article identifies and highlights various open challenges and promising future directions for research and innovation in QC.

We are pleased to present a special issue that focuses on the software systems for managing the impact of the coronavirus disease 19 (COVID-19) pandemic. The COVID-19 pandemic has affected around 192 million people worldwide and has led to ˜4.13 million deaths as of July 22, 2021. Globally, most of the countries have implemented lockdowns to protect their citizens. However, lockdown over an extended period is unsustainable. Hence, it is widely believed that virus testing and tracking is the best approach to ease lockdown measures. There is a need for innovative software systems to manage the impact of the COVID-19 pandemic effectively in many areas such as healthcare system, transport systems, supply-chain system, educational system, government-service delivery, pharmaceutical companies, manufacturing, software industries, and multinational companies. For example, in healthcare, smart-software systems would be able to remotely measure a person's body temperature, heart and respiratory rates, identifying their movements (including sneezing, coughing, shivering, etc.) to identify whether a person is displaying symptoms of COVID-19 or not. An essential aspect associated with these technologies is data privacy, scalability, and quality of service (QoS) in terms of reliability, availability, security, latency, and energy which need to be considered throughout the development of the software systems. In countries like India, UK, Russia, Brazil, and USA, the system would also help to ensure that isolated communities have access to testing, delivered in a fast, accurate, and efficient manner. These software systems would help and support the assessment of public-health strategies and policies such as social distancing and assess further interventions to control the spread of the virus. Innovative software systems can increase stakeholder participation, as cost-effective assistance in the COVID-19 pandemic monitoring is of great interest to many countries. To manage the impact of this pandemic, there is a need to design and develop scalable, reliable, and energy-efficient sustainable software solutions for different COVID-19 scenarios. In consideration of the existing systems and their features, an Internet of Things (IoT)-based system suitable for COVID-19 or pandemic situations associated with other influenza viruses can be developed. Furthermore, these systems can be integrated with artificial-intelligence (AI) processes for effective data-collection, analysis, statistical visualization, sharing, and decision making. Moreover, these systems can be implemented using both simulations and real-time testbeds for COVID-19 operations (sanitization, medication, monitoring, thermal imaging, etc.) to test their performance in terms of scalability, reliability, availability, and energy efficiency. There is a need to use AI methods, such as reinforcement learning, deep learning, and genetic algorithms while developing IoT-based software systems to achieve self-learning, self-adaptation, and autonomous decision-making capabilities in order to improve efficiency of the systems. Meanwhile, a huge voluminous amount of complex data is generated from various sources including World Health Organization (WHO), social networking, edge devices, private and public hospitals, patients and academic institutes, which needs an effective big data analytics mechanism to manage this data proficiently. Furthermore, there is a need to study the impact of system configuration on workload processing at different cloud nodes while maintaining the QoS dynamically. The data are collected in databases, it is subsequently examined and monitored, and it is important to manage data consistency and integrity. In this context, we argue that it is essential to employ decentralized data-gathering approaches, maintaining the privacy of the population as a high priority. This special issue has received articles by researchers and practitioners from both academia and industry to develop innovative software systems for managing the impact of the COVID-19 pandemic. This special issue, therefore, aims to focus the attention of its readers to four research articles carefully selected after multiple rounds of peer-review. The brief contributions of these papers are discussed in the following section: The first paper entitled "An approach to forecast impact of COVID-19 using supervised machine learning model" by Mohan et al.1 proposes a hybrid model to predict the effect of COVID-19 using moving regressive, autoregressive, and ensemble learning model. This work uses two datasets from Worldometer and Ministry of Health & Family Welfare of India to conduct the countrywise predictions across the world and statewise predictions of India, respectively. The second paper entitled "NovidChain: Blockchain-based privacy-preserving platform for COVID-19 test/vaccine certificates" by Abid et al.2 includes various promising ideas such as maintains the immutability and data integrity using Blockchain technology, enhances the privacy by incorporating encryption for personal information and verifies the COVID-19 proof using W3C verifiable credentials standard immediately. The third paper entitled "Software System to Predict the Infection in COVID-19 Patients using Deep Learning and Web of Things" by Singh et al.3 generates synthetic data using various data augmentation techniques. Proposed system uses U-Net and WoT to segment the COVID Medseg and Radiopedia datasets in an autonomic manner. Experimental results show that the system gives better performance in terms of network latency, response time, and server latency. The fourth paper entitled "Advanced Data Integration in Banking, Financial, and Insurance Software in the Age of COVID-19" by Maiti et al.4 contributes to recognize the effect of the COVID-19 pandemic on the global Banking Financial Services and Insurance landscape. Further, a hype cycle has been developed to find out the important software technologies to handle real-world challenges related to corporate. We believe the work that has been approved in this special issue will assist readers of the journal and a broader research community to learn about the topics of software systems and impacts of COVID-19 pandemic, and inspire them to study more in this area. We would like to express our gratitude to the Editor-in-Chief (Prof. Rajkumar Buyya) and editorial board members for allowing us to bring out this special issue and guiding us throughout the process. We also want to express our gratitude to and further acknowledge the administrative staff, reviewers, and especially the authors for their contributions to the success of this issue.

As data of COVID-19 patients is increasing, the new framework is required to secure the data collected from various Internet of Things (IoT) devices and predict the trend of disease to reduce its spreading. This article proposes security- and privacy-based lightweight framework called iFaaSBus, which uses the concept of IoT, machine learning (ML), and function as a service (FaaS) or serverless computing to diagnose the COVID-19 disease and manages resources automatically to enable dynamic scalability. iFaaSBus offers OAuth-2.0 Authorization protocol-based privacy and JSON Web Token & Transport Layer Socket protocol-based security to secure the patient's health data. iFaaSBus outperforms response time compared to nonserverless computing while responding to up to 1100 concurrent requests. Further, the performance of various ML models is evaluated based on accuracy, precision, recall, F-score, and area under the curve (AUC) values, and the K-nearest neighbor model gives the highest accuracy rate of 97.51%.

The emerging trend towards moving from monolithic applications to microservices has raised new performance challenges in cloud computing environments. Compared with traditional monolithic applications, the microservices are lightweight, fine-grained, and must be executed in a shorter time. Efficient scaling approaches are required to ensure microservices’ system performance under diverse workloads with strict Quality of Service (QoS) requirements and optimize resource provisioning. To solve this problem, we investigate the trade-offs between the dominant scaling techniques, including horizontal scaling, vertical scaling, and brownout in terms of execution cost and response time. We first present a prediction algorithm based on gradient recurrent units to accurately predict workloads assisting in scaling to achieve efficient scaling. Further, we propose a multi-faceted scaling approach using reinforcement learning called CoScal to learn the scaling techniques efficiently. The proposed CoScal approach takes full advantage of data-driven decisions and improves the system performance in terms of high communication cost and delay. We validate our proposed solution by implementing a containerized microservice prototype system and evaluated with two microservice applications. The extensive experiments demonstrate that CoScal reduces response time by 19%-29% and decreases the connection time of services by 16% when compared with the state-of-the-art scaling techniques for Sock Shop application. CoScal can also improve the number of successful transactions with 6%-10% for Stan’s Robot Shop application.

Cloud computing has been regarded as a successful paradigm for IT industry by providing benefits for both service providers and customers. In spite of the advantages, cloud computing also suffers from distinct challenges, and one of them is the inefficient resource provisioning for dynamic workloads. Accurate workload predictions for cloud computing can support efficient resource provisioning and avoid resource wastage. However, due to the high-dimensional and high-variable features of cloud workloads, it is difficult to predict the workloads effectively and accurately. The current dominant work for cloud workload prediction is based on regression approaches or recurrent neural networks, which fail to capture the long-term variance of workloads. To address the challenges and overcome the limitations of existing works, we proposed an e fficient supervised learning-based D eep N eural Network ( esDNN ) approach for cloud workload prediction. First, we utilize a sliding window to convert the multivariate data into a supervised learning time series that allows deep learning for processing. Then, we apply a revised Gated Recurrent Unit (GRU) to achieve accurate prediction. To show the effectiveness of esDNN, we also conduct comprehensive experiments based on realistic traces derived from Alibaba and Google cloud data centers. The experimental results demonstrate that esDNN can accurately and efficiently predict cloud workloads. Compared with the state-of-the-art baselines, esDNN can reduce the mean square errors significantly, e.g., 15%. rather than the approach using GRU only. We also apply esDNN for machines auto-scaling, which illustrates that esDNN can reduce the number of active hosts efficiently, thus the costs of service providers can be optimized.

Summary There is an abundant expansion in the race of technology, specifically in the production of data, because of the smart devices, such as mobile phones, smart cards, sensors, and Internet of Things (IoT). Smart phones and devices have undergone an enormous evolution in a way that they can be used. More and more new applications, such as face recognition, augmented reality, online interactive gaming, and natural language processing are emerging and attracting the users. Such applications are generally data intensive or compute intensive, which demands high resource and energy consumption. Mobile devices are known for the resource scarcity, having limited computational power and battery life. The tension between compute/data intensive application and resource constrained mobile devices hinders the successful adaption of emerging paradigms. In the said perspective, the objective of this article is to study the role of computation offloading in mobile cloud computing to supplement mobile platforms ability in executing complex applications. This article proposes a systematic approach (EFFORT) for offload communication in the cloud. The proposed approach provides a promising solution to partially solve energy consumption issue for communication‐intensive applications in a smartphone. The experimental study shows that our proposed approach outperforms its counterparts in terms of energy consumption and fast processing of smartphone devices. The battery consumption was reduced to 19% and the data usage was reduced to 16%.

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidation-based energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided.

Abstract Flying ad hoc networks (FANETs) are a collection of unmanned aerial vehicles that communicate without any predefined infrastructure. FANET, being one of the most researched topics nowadays, finds its scope in many complex applications like drones used for military applications, border surveillance systems and other systems like civil applications in traffic monitoring and disaster management. Quality of service (QoS) performance parameters for routing e.g. delay, packet delivery ratio, jitter and throughput in FANETs are quite difficult to improve. Mobility models play an important role in evaluating the performance of the routing protocols. In this paper, the integration of two selected mobility models, i.e. random waypoint and Gauss–Markov model, is implemented. As a result, the random Gauss integrated model is proposed for evaluating the performance of AODV (ad hoc on-demand distance vector), DSR (dynamic source routing) and DSDV (destination-Sequenced distance vector) routing protocols. The simulation is done with an NS2 simulator for various scenarios by varying the number of nodes and taking low- and high-node speeds of 50 and 500, respectively. The experimental results show that the proposed model improves the QoS performance parameters of AODV, DSR and DSDV protocol.

Abstract Establishing the maturity levels of ‐ Small and Medium Enterprises (SMEs) without Capability Maturity Model Integration (CMMI) certification has always been regarded as an extremely challenging task. Software process improvement (SPI) has targeted to monitor and improve software processes, thereby improving the software business. Although there is scientific interest in SPI, little attention has been specifically given to the exploration of maturity levels for non‐CMMI SMEs. The goal is to explore the effect of time on process maturity and maturity levels achieved informally or unofficially by SMEs that are not otherwise CMMI certified. To find out the maturity levels achieved informally, a CMMI‐DEV v1.3 based survey questionnaire is administered to Indian software SMEs. Time of establishment of SMEs and follow‐up of CMMI‐based processes and practices unofficially are used as two important parameters to decide upon process maturity and achievement of specific CMMI level informally. This paper has been successful in ascertaining the effect of time of establishment of SMEs and follow‐up of CMMI‐based processes on process maturity using proposed RuleML that advocates adoption of more than 70% of CMMI‐DEV v1.3 process area‐specific practices for an SME to be unofficially ready for CMMI‐based SPI initiatives. The findings manifest multidimensional aspects of unofficial readiness of SMEs for CMMI‐based SPI that can be used by relevant authorities to select SMEs for funding for SPI initiatives. Finally, the proposed work has been validated statistically using t‐test for CMMI Level II and Level III.

Summary Cloud computing is becoming a profitable technology because of it offers cost‐effective IT solutions globally. A well‐designed task scheduling algorithm ensures the optimal utilization of clouds resources and reducing execution time dynamically. This research article deals with the task scheduling of inter‐dependent subtasks on unrelated parallel computing machines in a cloud computing environment. This article considers two variants of the problem‐based on two different objective function values. The first variant considers the minimization of the total completion time objective function while the second variant considers the minimization of the makespan objective function. Heuristic and meta‐heuristic (HEART) based algorithms are proposed to solve the task scheduling problems. These algorithms utilize the property of list scheduling algorithm of unrelated parallel machine scheduling problem. A mixed integer linear programming (MILP) formulation has been provided for the two variants of the problem. The optimal solution is obtained by solving MILP formulation using A Mathematical Programming Language (AMPL) software. Extensive numerical experiments have been performed to evaluate the performance of proposed algorithms. The solutions obtained by the proposed algorithms are found to out‐perform the existing algorithms. The proposed algorithms can be used by cloud computing service providers (CCSPs) for enhancing their resources utilization to reduce their operating cost.

Cloud computing has recently emerged as an important service to manage applications efficiently over the Internet. Various cloud providers offer pay per use cloud services that requires Quality of Service (QoS) management to efficiently monitor and measure the delivered services through Internet of Things (IoT) and thus needs to follow Service Level Agreements (SLAs). However, providing dedicated cloud services that ensure user's dynamic QoS requirements by avoiding SLA violations is a big challenge in cloud computing. As dynamism, heterogeneity and complexity of cloud environment is increasing rapidly, it makes cloud systems insecure and unmanageable. To overcome these problems, cloud systems require self-management of services. Therefore, there is a need to develop a resource management technique that automatically manages QoS requirements of cloud users thus helping the cloud providers in achieving the SLAs and avoiding SLA violations. In this paper, we present SLA-aware autonomic resource management technique called STAR which mainly focuses on reducing SLA violation rate for the efficient delivery of cloud services. The performance of the proposed technique has been evaluated through cloud environment. The experimental results demonstrate that STAR is efficient in reducing SLA violation rate and in optimizing other QoS parameters which effect efficient cloud service delivery.

Summary Cloud computing utilizes heterogeneous resources that are located in various datacenters to provide an efficient performance on a pay‐per‐use basis. However, existing mechanisms, frameworks, and techniques for management of resources are inadequate to manage these applications, environments, and the behavior of resources. There is a requirement of a Quality of Service (QoS) based autonomic resource management technique to execute workloads and deliver cost‐efficient and reliable cloud services automatically. In this paper, we present an intelligent and autonomic resource management technique named RADAR. RADAR focuses on two properties of self‐management: firstly, self‐healing that handles unexpected failures and, secondly, self‐configuration of resources and applications. The performance of RADAR is evaluated in the cloud simulation environment and the experimental results show that RADAR delivers better outcomes in terms of execution cost, resource contention, execution time, and SLA violation while it delivers reliable services.