ABSTRACT Data deduplication plays an important role in modern data management as it reduces storage costs and ensures consistency by eliminating redundant records. The traditional data deduplication methods are effective for exact matches but struggle with adaptability and detecting near‐exact duplicate records in unstructured or complex data. Machine learning (ML) addresses these limitations by using pattern recognition, feature learning, and statistical modeling to identify subtle similarities between records. This review classifies ML‐based deduplication techniques into supervised, unsupervised, semi‐supervised, and deep learning methodologies. It also discusses key challenges, including class imbalance, model interpretability, and computational overhead. The paper also explores recent developments in federated learning, real‐time deduplication, and multimodal techniques to highlight current trends in these areas. Finally, the paper identifies key open issues and proposes a unified perspective for scalable, real‐time deduplication systems that can accommodate diverse data types, structures, and system requirements.

Summary In today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

Summary Food is one of the most underrated entities with respect to diseases. Food or Diet plays a vital role in healing or recovery of a patient which brings forth the need of analyzing disease and diets associations. The study of such associations is crucial for recommending appropriate diets to patients but is an arduous task due to the complex interdependencies as is evident in literature. Thus, it becomes necessary to automate the analysis and make it available as a service. The main aim of this work is to efficiently collate and analyze disease‐diet associations and provide it as an accessible and adaptable service using a combination of advanced techniques. Complex disease‐diet interdependencies are curated from the literature and transformed into a network offering various parameters for further analysis. The analysis is done using machine learning algorithms to predict accurate and robust recommendations. Cloud computing aids this analysis by providing sufficient resources and making it more accessible. Thus, the recommendations are constructed using amalgamation of techniques including network analysis, machine learning, and cloud computing. The deduced associations from the analysis of medical data using these technologies would aid doctors and healthcare institutions in decision making thereby improving the prognosis of a disease.

Multi-access Edge Computing (MEC) enables IoT applications to place their services in the edge servers of mobile networks, balancing Quality-of-Service (QoS) and energy-efficiency. Previous works consider compute requirements, while the IoT and latency/bandwidth per-flow communicate requirements are largely ignored. Moreover, the Smart City domain presents unique challenges – modeling the Urban Smart Things (USTs – urban IoT clients), their connectivity with MEC network, diverse resource requirements (compute, communicate, and IoT) of application services, modeling the federation of multiple MEC providers in a city, which we consider in this article. To address these research gaps, we propose: i)UrbanEnQoSMDP– formulation for energy and QoS (latency) optimized service placement for a set of applications in the ‘Urban IoT-Federated MEC-Cloud’ architecture to satisfy applications’ compute, per-flow communicate, and IoT requirements; ii)‘’$\epsilon$ε-greedy with mask”policy for apriori satisfaction of IoT requirements by shortlisting suitable USTs; iii)UrbanEnQoSPlace– proposed multi-action Deep Reinforcement Learning (DRL) model, designed from Dueling Deep-Q Network, that uses the proposed policy to solve the UrbanEnQoSMDP for simultaneously placing all services of an application. Extensive simulation results illustrate efficacy and scalability of proposed model against state-of-the-art DRL algorithms (better convergence, higher rewards, lesser runtime; proposed policy w.r.t fewer violations).

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.

Summary This paper presents the framework of cloud‐based software test data generation service (CSTS) that caters to cost‐effective test data generation service in a cloud environment. In contrast to existing conventional or cloud‐based testing frameworks, CSTS has a number of unique benefits. First, CSTS is designed to facilitate test data generation in minimum time and cost. Second, unlike existing frameworks which mandates clients to opt for resources to test their jobs, CSTS guides customer for selecting best cluster configuration in order to minimize the cost. While the existing models do not provide any solution for trust establishment in cloud computing services, CSTS delivers it by implementing security mechanism with the provision of role based access control. The security mechanism proposed in this paper ensures the protection of data and code of different users. Third, CSTS provides a mathematical pricing model to fulfill the expectations of customers and also to maximize the net profit of service providers. Cloud service request model has also been designed that postulates service level agreements between customers and service providers. We have evaluated, compared, and analyzed our framework and have found that it outperforms other existing cloud‐based frameworks.

Summary Cloud computing is a form of distributed computing, which promises to deliver reliable services through next‐generation data centers that are built on virtualized compute and storage technologies. It is becoming truly ubiquitous and with cloud infrastructures becoming essential components for providing Internet services, there is an increase in energy‐hungry data centers deployed by cloud providers. As cloud providers often rely on large data centers to offer the resources required by the users, the energy consumed by cloud infrastructures has become a key environmental and economical concern. Much energy is wasted in these data centers because of under‐utilized resources hence contributing to global warming. To conserve energy, these under‐utilized resources need to be efficiently utilized and to achieve this, jobs need to be allocated to the cloud resources in such a way so that the resources are used efficiently and there is a gain in performance and energy efficiency. In this paper, a model for energy‐aware resource utilization technique has been proposed to efficiently manage cloud resources and enhance their utilization. It further helps in reducing the energy consumption of clouds by using server consolidation through virtualization without degrading the performance of users’ applications. An artificial bee colony based energy‐aware resource utilization technique corresponding to the model has been designed to allocate jobs to the resources in a cloud environment. The performance of the proposed algorithm has been evaluated with the existing algorithms through the CloudSim toolkit. The experimental results demonstrate that the proposed technique outperforms the existing techniques by minimizing energy consumption and execution time of applications submitted to the cloud. Copyright © 2014 John Wiley & Sons, Ltd.

An efficient management of the resources in Grid computing crucially depends on the efficient mapping of the jobs to resources according to the user's requirements. Grid resources scheduling has become a challenge in the computational Grid. The mapping of the jobs to appropriate resources for execution of the application in Grid computing is an NP-Complete problem. In this paper, hyper-heuristic based resource scheduling algorithm is designed to effectively schedule the jobs on available resources in a Grid environment. The performance of the proposed algorithm is evaluated using the GridSim toolkit. Empirical results illustrate that our algorithm outperformed the existing algorithm by minimizing cost and make span of user's submitted applications.