The growing reliance on big data frameworks such as Hadoop has revolutionised data processing across various domains, enabling large-scale storage and distributed computation. Hadoop is widely employed in real-world applications such as high-performance computation tasks, e-commerce and data analysis in healthcare. However, the efficiency of Hadoop systems is often hampered by faults and anomalies, with stragglers emerging as one of the most prevalent issues. Stragglers disrupt workflows, waste resources and degrade system performance. While existing anomaly detection models employ methods like median analysis or static thresholds, they often struggle with issues such as high false positives, lack of adaptability and poor handling of complex heterogeneous environments. To address these challenges, this paper presents Plabs , a flexible stragglers detection framework for Hadoop. The framework comprises two core components: (1) a Monitoring Module providing real-time tracking of cluster resources and task progress and (2) a Pluggable AI-based straggler detection module, designed for precise straggler task identification. By leveraging advanced monitoring and AI-driven analysis, Plabs offers an automated, flexible and scalable solution for detecting stragglers at run-time in Hadoop clusters. We evaluated Plabs exhaustively with three Machine Learning (ML), two Deep Learning (DL) and two Large Language Models (LLMs) on five different applications in a real testbed environment. Our experiment evaluation shows that DL models outperform others in identifying Hadoop stragglers, achieving superior accuracy and reliability for all the applications.

In recent years, the frequent leakage of sensitive information in natural language texts has presented significant challenges in text distribution, sharing, and migration. Existing methods for text protection and watermarking are limited, capable of either hide sensitive data or prevent unauthorized copying and tampering, but not both simultaneously. To address these limitations, this paper proposes a masking-watermarking cooperative framework designed to hide text-sensitive information, prevent unintentional data leakage, and ensure content ownership verification and tampering prevention. The framework introduces three novel techniques: a variable autoencoder to ensure diverse watermark generation, an improved transformer to enhance the adaptability of dynamic masks, and a dual discriminator for joint verification of text and watermarks. A comprehensive evaluation was conducted, covering text similarity, masking flexibility, and the imperceptibility of text masking, as well as watermark classification recognition and robustness against various attacks. The proposed framework achieved a score of 0.98 on the SBERT metric, demonstrating its effectiveness in achieving imperceptible text masking and robust watermark embedding.

With the growing reliance on cloud computing for both personal and enterprise-level applications, the need for robust data security has become essential. Confidential Computing Environments (CCEs) offer a promising solution by providing hardware-based isolation to protect sensitive data and computations. It encrypts the main memory and creates an isolated execution environment for executing the workload. Among the leading CCE technologies, Intel's Trusted Domain Extensions (TDX) and AMD's Secure Encrypted Virtualisation with Secure Nested Paging (SEV-SNP) have emerged as significant solutions. However, it becomes essential to understand their performance impact across a range of applications. The challenge lies in balancing security and performance, especially for organisations deploying resource-intensive workloads in the cloud. This study is motivated by the need to offer a comprehensive, applicationlevel performance comparison of Intel TDX and AMD SEVSNP in a real-world cloud environment. By evaluating both CCEs for micro benchmarks and application microservices on the Microsoft Azure cloud environment, this paper aims to provide insights into how these technologies handle different types of workloads, thus enabling cloud users to make informed decisions based on their security and performance requirements.

Smart home systems, which integrate diverse IoT devices for automation and energy efficiency, are increasingly targeted by cyber threats such as Man-in-the-Middle (MitM) and Denial-of-Service (DoS)/Distributed DoS (DDoS) attacks. Traditional security mechanisms often lack the adaptability and responsiveness needed for real-time protection, leaving devices vulnerable to data breaches and manipulation. This paper proposes a behavioural fingerprinting-based security framework that combines threshold-based detection with machine learning (ML) classification to identify anomalies in device activity patterns, including round-trip time (RTT) and Address Resolution Protocol (ARP) behaviour. The proposed approach is evaluated in a real smart home testbed environment using an IP camera, Raspberry Pis, and an ASUS router. Experimental results demonstrate a high detection accuracy across varied attack scenarios. The results show that the proposed system can detect attacks in near real-time, while maintaining compatibility with third-party applications, making it a practical and effective solution for enhancing smart home security.

The growing scale and complexity of distributed systems such as Hadoop produce massive volumes of complex log data, making automated anomaly detection and root cause analysis both essential and increasingly challenging. Traditional rule-based approaches, relying on static thresholds or manual heuristics, struggle to scale due to limited adaptability and high maintenance costs. Transformer-based language models have emerged as powerful tools for modelling log sequences by capturing contextual patterns. To enable efficient adaptation with fewer parameters, techniques such as In-Context Learning (ICL) and Low-Rank Adaptation (LoRA) have been proposed. However, applying these methods to log analysis with Small Language Models (SLMs) introduces several challenges, including high memory and computational overhead (in the case of ICL), limited fine-tuning capacity (with LoRA on smaller models), and poor generalisation across heterogeneous environments. To address these limitations, we propose Lineadapter, a parameter-efficient fine-tuning framework tailored for SLMs in log anomaly detection and root cause analysis. Lineadapter extends convolutional adapter principles to sequential data by integrating lightweight 1D convolutional layers within transformer blocks. This design enables SLMs to adapt effectively to log-specific patterns with minimal computational cost, while preserving the backbone model's representational power. We evaluate LINEADAPTER on multiple real-world system log datasets, comparing its performance with ICL, LoRA, and rule-based baselines. Results show that Lineadapter achieves higher F1-scores and better precision-recall trade-offs, particularly on medium-scale models, establishing it as a scalable, robust, and practical solution for log-based anomaly detection.

Ensuring Data Quality (DQ) is crucial for the reliability of environmental monitoring systems in the smart city environment. In this work, we present an automated framework, Data Quality Detector (DQD), for detecting, classifying, and analyzing DQ issues. We leveraged the Internet of Data Fault Taxonomy (IoDFT) to categorise faults based on various metrics, including type, duration, and pitfalls, providing a structured approach to identifying anomalies. By integrating these fault characteristics with key DQ dimensions-completeness, timeliness, and consis-tency- DQD enables a more granular classification of anomalies. Instead of simply marking missing faults, DQD differentiates between general and contextual missing faults, reducing false positives and improving decision-making accuracy. The DQD framework has been evaluated on real data from Newcastle Urban Observatory (NUO) to demonstrate its ability to enhance fault detection, minimise misclassification, and distinguish normal variations from actual quality issues, ultimately improving the trustworthiness and effectiveness of Air Quality (AQ) monitoring systems.

With the unprecedented demand for cloud computing, ensuring trust in the underlying environment is challenging. Applications executing in the cloud are prone to attacks of different types including malware, network and data manipulation. These attacks may remain undetected for a significant length of time thus causing a lack of trust. Untrusted cloud services can also lead to business losses in many cases and therefore need urgent attention. In this paper, we presentTrusted Public Cloud(TPC), a generic framework ensuring theZero-trustsecurity of client machine. It tracks the system state, alerting the user of unexpected changes in the machine’s state, thus increasing the run-time detection of security vulnerabilities. We validatedTPCon Microsoft Azure with Local, Software Trusted Platform Module (SWTPM) and Software Guard Extension (SGX)-enabled SWTPM security providers. We also evaluated the scalability ofTPCon Amazon Web Services (AWS) with a varying number of client machines executing in a concurrent environment. The execution results show the effectiveness ofTPCas it takes a maximum of 35.6 seconds to recognise the system state when there are 128 client machines attached.

Docker Swarm facilitates the deployment of modular applications in an independent yet interconnected manner. Applications within the Swarm communicate through Docker's internal network, ensuring rapid computation and enhanced security. Despite these advantages, the complex nature of software development often leads to the occurrence of various faults, including memory leaks, application failures, and network outages. To systematically identify and address such issues, chaos engineering has emerged as a powerful approach within both development and production environments. However, conducting chaos experiments within a Docker Swarm cluster in a multi-cloud environment proves to be challenging. In this paper, we present Swarm Storm, an automated framework designed for orchestrating chaos engineering experiments within Docker Swarm-based clusters in a cloud-agnostic environment with comprehensive testing features. We validate Swarm Storm using a simple Java benchmark application, demonstrating its effectiveness in addressing faults within the system.

Geo-distributed web-applications (GWA) can be deployed across multiple geographically separated datacenters to reduce the latency of access for users. Finding a suitable deployment for a GWA is challenging due to the requirement to consider a number of different parameters, such as host configurations across a federated infrastructure. The ability to evaluate multiple deployment configurations enables an efficient outcome to be determined, balancing resource usage while satisfying user requirements. We proposeGeoDeploy, a framework designed for finding a deployment solution for GWA. We evaluateGeoDeployusing both a formal algorithmic model and a practical cloud-based deployment. We also compare our approach with other existing techniques.

Personalised health and fitness provide users with information regarding their wellbeing and an opportunity to inform healthcare services for better patient outcomes. Underpinning this industry sector is the need to establish human activity recognition (HAR) in a ubiquitous manner. For example, through the use of smartwatches and/or mobile phones gathering information such as heart rates, movement, and steps of a user. The engineering challenge is providing accurate, informative, and timely data without rapidly depleting the mobile device's battery life. This problem is compounded as a number of algorithms used to process such data require substantial, cloud-based resources, to achieve higher accuracy. Therefore, a balance is required between battery depletion, accuracy of data, and timely delivery of results through a mixture of cloud and local algorithmic execution. In this article, we proposeAE-HAR (Accuracy and Energy Aware-HAR)model that delivers engineered solutions which approach optimal combinations in the consideration of energy consumption, accuracy, and timeliness of results.AE-HARintroduces a “light-weight”machine learningon-device component identifying the probabilistic accuracy of data together with energy consumption identification requirements. A heuristic is then adopted to determine if cloud-enabled calculations are required while including possible performance costs related to the analysis of networking infrastructures. Our model is validated in a real-world environment through experimentation that demonstrates accuracy in excess of 93% and energy consumption savings in excess of 94%.

The emergence of cloud computing allows users to execute their applications in a ubiquitous manner. Public cloud offers various ready-to-use services e.g. AWS EC2, Amazon RDS on a pay-per-use basis. Alongside these advantages, the cloud also brings a number of issues, for example offloading data for storage and computation may lead to privacy and security concerns. Also, it is not easy to guarantee the performance of the underlying system. With the increasing performance and security concerns, it is necessary to continuously monitor and evaluate the system and its performance. This can help us to quickly detect anomalies that can hinder system performance and/or make the system untrusted. In this paper, we present PERSECMON: performance and security-aware monitoring framework for continuous run-time monitoring in the public cloud environment. PERSECMON provides not only the system performance metrics but also the security measurements which can be used to analyse the system state at run-time. It uses the BCC/eBPF (BPF Compiler Collection/ Extended Berkeley Packet Filters) framework to instrument the system. PERSECMON is integrated with the open-source user interface framework, Kibana which provides a clear visualisation of the obtained metrics. To show the efficacy of our proposed work, we have developed a benchmarking case study using Bonnie++, Fibonacci Sequence and Netperf executed on Ubuntu Server 21.04. The results show that PERSECMON successfully captures relevant metrics that can be utilised in real-time to analyse the system performance. These metrics can further be accessed to detect the system state including memory leaks, queuing delay and remote access time which may lead to security or reliability events.

Edge computing has gained momentum in recent years, as complementary to cloud computing, for supporting applications (e.g., industrial control systems) that require time-critical communication guarantees. While edge computing can provide immediate analysis of streaming data from Internet of Things devices, those devices lack computing capabilities to guarantee reasonable performance for time-critical applications. To alleviate this critical problem, the prevalent trend is to offload these data analytic tasks from the edge devices to the cloud. However, existing offloading approaches are static in nature as they are unable to adapt varying workload and network conditions. To handle these issues, we present a novel distributed and quality of services based multilevel queue traffic scheduling system that can undertake semiautomatic bandwidth slicing to process time-critical incoming traffic in the edge-cloud environments. Our developed system shows a great enhancement in latency and throughput as well as reduction in energy consumption for edge-cloud environments.

Summary Containers are gaining popularity over virtual machines as they provide the advantages of virtualization with the performance of near bare metal. The uniformity of support provided by Docker containers across different cloud providers makes them a popular choice for developers. Evolution of microservice architecture allows complex applications to be structured into independent modular components making them easier to manage. High‐performance computing (HPC) applications are one such application to be deployed as microservices, placing significant resource requirements on the container framework. However, there is a possibility of interference between different microservices hosted within the same container (intracontainer) and different containers (intercontainer) on the same physical host. In this paper, we describe an extensive experimental investigation to determine the performance evaluation of Docker containers executing heterogeneous HPC microservices. We are particularly concerned with how intracontainer and intercontainer interference influences the performance. Moreover, we investigate the performance variations in Docker containers when control groups (cgroups) are used for resource limitation. For ease of presentation and reproducibility, we use Cloud Evaluation Experiment Methodology (CEEM) to conduct our comprehensive set of experiments. We expect that the results of evaluation can be used in understanding the behavior of HPC microservices in the interfering containerized environment.

Internet of Things (IoT) allows the creation of virtually endless connections into a global array of distributed intelligence. However, the design, development, and deployment of IoT applications are complex and complicated due to various unwarranted challenges. For instance, addressing the IoT application users' subjective and objective opinions with IoT workflow instances remains a challenge for the design of a more holistic approach. Moreover, the complexity of IoT applications increased exponentially due to the heterogeneous nature of the Edge/Cloud services, utilised with the aim of lowering latency in data transformation and increase re-usability. Hence, in this paper, we present an IoT workflow composition system (IoTWC) to allow IoT users to pipeline their workflows with proposed IoT workflow activity abstract patterns. IoTWC leverages the analytic hierarchy process (AHP) to compose the multi-level IoT workflow that satisfies the requirements of any IoT application. Moreover, the users are befitted with recommended IoT workflow configurations using an AHP based multi-level composition framework. The proposed IoTWC is validated on a user case study to evaluate the coverage of IoT workflow activity abstract patterns and a real-world scenario for smart buildings. The comprehensive analysis shows the effectiveness of IoTWC in terms of IoT workflow abstraction and composition.

Summary With the proliferation of Internet of Things (IoT) and edge computing paradigms, billions of IoT devices are being networked to support data‐driven and real‐time decision making across numerous application domains, including smart homes, smart transport, and smart buildings. These ubiquitously distributed IoT devices send the raw data to their respective edge device (eg, IoT gateways) or the cloud directly. The wide spectrum of possible application use cases make the design and networking of IoT and edge computing layers a very tedious process due to the: (i) complexity and heterogeneity of end‐point networks (eg, Wi‐Fi, 4G, and Bluetooth); (ii) heterogeneity of edge and IoT hardware resources and software stack; (iv) mobility of IoT devices; and (iii) the complex interplay between the IoT and edge layers. Unlike cloud computing, where researchers and developers seeking to test capacity planning, resource selection, network configuration, computation placement, and security management strategies had access to public cloud infrastructure (eg, Amazon and Azure), establishing an IoT and edge computing testbed that offers a high degree of verisimilitude is not only complex, costly, and resource‐intensive but also time‐intensive. Moreover, testing in real IoT and edge computing environments is not feasible due to the high cost and diverse domain knowledge required in order to reason about their diversity, scalability, and usability. To support performance testing and validation of IoT and edge computing configurations and algorithms at scale, simulation frameworks should be developed. Hence, this article proposes a novel simulator IoTSim‐Edge, which captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure and framework in an easy and configurable manner. IoTSim‐Edge extends the capability of CloudSim to incorporate the different features of edge and IoT devices. The effectiveness of IoTSim‐Edge is described using three test cases. Results show the varying capability of IoTSim‐Edge in terms of application composition, battery‐oriented modeling, heterogeneous protocols modeling, and mobility modeling along with the resources provisioning for IoT applications.

The growth of Internet of Things (IoT) technology brings many new opportunities for applications in areas including smart healthcare, smart buildings, and smart agriculture. These applications must normally distribute the computations, required for extracting value from sensor data, over the IoT infrastructure platforms (e.g., sensors, phones, field-gateways, and clouds). This can be very challenging for IoT application developers due to the heterogeneity of the aforementioned platforms, potentially conflicting nonfunctional requirements (e.g., battery power, latency, and cost), and related deployment criteria, which is impossible to resolve manually. To address the above challenges, we have developed the PATH2iot framework that decomposes a complex IoT application into self-contained micro-operations. Based on the deployment criteria, PATH2iot automatically distributes the set of micro-operations across IoT infrastructure platforms, while respecting their run-time data and control flow dependencies. In our previous work, we have shown how to use the PATH2iot to optimize the battery life of a healthcare wearable. In this article, we describe a new research that significantly extends PATH2iot, which introduces a heuristic model capable of making optimal deployment decisions based on multiple conflicting nonfunctional requirements and selection criteria (user preferences). It does so by leveraging a well-known multicriteria decision-making method called the analytic hierarchical processes (AHP). The applicability of the deployment model is validated based on a real-world digital healthcare analytics use case. The results show that our model is able to find the optimal deployment solution for different user preferences.

Microservices have emerged as a new approach for developing and deploying cloud applications that require higher levels of agility, scale, and reliability. To this end, a microservice-based cloud application architecture advocates decomposition of monolithic application components into independent software components called "microservices". As the independent microservices can be developed, deployed, and updated independently of each other, it leads to complex run-time performance monitoring and management challenges. To solve this problem, we propose a generic monitoring framework, Multi-microservices Multi-virtualization Multi-cloud (M3) that monitors the performance of microservices deployed across heterogeneous virtualization platforms in a multi-cloud environment. We validated the efficacy and efficiency of M3 using a Book-Shop application executing across AWS and Azure.

Containerized web-applications have gained popularity recently due to the advantages provided by the containers including light-weight, packaged, fast start up and shut down and easy scalability. As there are more than 267 cloud providers, finding a flexible deployment option for containerized web-applications is very difficult as each cloud offers numerous deployment infrastructure. Benchmarking is one of the eminent options to evaluate the provisioned resources before product-level deployment. However, benchmarking the massive infrastructure resources provisioned by various cloud providers is a time consuming, tedious and costly process and is not practical to accomplish manually.