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Cloud computing is among the most important services extensively utilized in IT ecosystems. It is anticipated that in the coming years, upwards of 90% of enterprises within the IT sector will lean towards adopting cloud-based solutions. With a diverse and continuously expanding service portfolio offered by cloud service providers and the flexible pay-as-you-go pricing model, cloud computing becomes increasingly appealing. However, irresponsible resource usage can lead to adverse outcomes. According to cloud financial operations principles, it is crucial to focus on appropriate strategies enabling cost-conscious management, which maximizes the value derived from cloud investments and supports informed decision-making. However, due to the diverse utilization of cloud environments, selecting tailored optimization methods poses a significant challenge. This article presents a comprehensive overview of solutions aimed at optimizing the consumption of cloud resources. A novel categorization of resource usage optimization methods has been proposed, discussing properties, limitations, capabilities, and deployment potential of each semantic category and enhancing the selection of tailored optimization categories through the property-based taxonomy concept. This survey encompasses more than 70 articles related to cloud resource usage optimization, as well as concerning related areas where research advancements have the potential for application in the cloud domain. Additionally, it suggests avenues for further exploration and identifies unaddressed research areas, taking into account the growing significance of Green Cloud Computing.

In this paper, we present a framework for exploring the spare capacity of IoT devices for clustered execution of multimedia applications. Applications of this type are usually framed with specific quality parameters that enable a desirable level of service. This means that the IoT cluster must guarantee strict quality ranges of service to work as expected. The framework is totally customizable, and QoS dimensions can be easily added or removed given their relevance in the application scenario. The achieved results clearly demonstrate the utility of using the spare capacity of IoT devices, otherwise unused, to cooperatively execute servies within the desired quality of service levels.

This exploratory multiple-case study aims to analyse the ecological design of wooden houses in Dong villages (Guangxi province, China). Residential and public Dong houses were investigated from ecological and cultural perspectives. The results show that topography (i.e., building near the river in a mountainous area) is the main factor influencing the Dong building construction process. It also affects the building layout decisions. The life cycle assessment was applied to collect general information about the exterior and interior design of the Dong settlements. For this, a range of pictures and historical facts (e.g., demand for fire ponds and balconies, ornament usage, etc.) was analysed. The study suggests fir timber as a basic ecological resource for wooden house buildings. Yet, it is highly flammable and increases the fire risk. A brief discussion on the cultural heritage of the Dong people and its influence on their building system was presented. The present findings can be used in future eco-design projects as a brief guideline for creating a traditional-style ethnic wooden house.

The diversity and scarcity of resources across devices in heterogeneous computing environments can impact their ability to meet users’ quality-of-service (QoS) requirements, especially in open real-time environments where computational loads are unpredictable. Despite this uncertainty, timely responses to events remain essential to ensure desired performance levels. To address this challenge, this paper introduces collaborative service execution, enabling resource-constrained IoT devices to collaboratively execute services with more powerful neighbors at the edge, thus meeting non-functional requirements that might be unattainable through individual execution. Nodes dynamically form clusters, allocating resources to each service and establishing initial configurations that maximize QoS satisfaction while minimizing global QoS impact. However, the complexity of open real-time environments may hinder the computation of optimal local and global resource allocations within reasonable timeframes. Thus, we reformulate the QoS optimization problem as a heuristic-based anytime optimization problem, capable of interrupting and quickly adapting to environmental changes. Extensive simulations demonstrate that our anytime algorithms rapidly yield satisfactory initial service solutions and effectively optimize the solution quality over iterations, with negligible overhead compared to the benefits gained.

This exploratory multiple-case study aims to analyse the ecological design of wooden houses in Dong villages (Guangxi province, China). Residential and public Dong houses were investigated from ecological and cultural perspectives. The results show that topography (i.e., building near the river in a mountainous area) is the main factor influencing the Dong building construction process. It also affects the building layout decisions. The life cycle assessment was applied to collect general information about the exterior and interior design of the Dong settlements. For this, a range of pictures and historical facts (e.g., demand for fire ponds and balconies, ornament usage, etc.) was analysed. The study suggests fir timber as a basic ecological resource for wooden house buildings. Yet, it is highly flammable and increases the fire risk. A brief discussion on the cultural heritage of the Dong people and its influence on their building system is presented. The present findings can be used in future eco-design projects as a brief guideline for creating a traditional-style ethnic wooden house.

Single-ISA heterogeneous multi-core processors trade-off power with performance; however, threads that co-run on shared resources suffer from resource contention, which induces performance degradation and energy inefficiency. The authors introduce a novel approach to optimise the co-scheduling of multi-threaded applications on heterogeneous processors. The approach is based on the concept of stakes function, which represents the trade-off between isolation and sharing of resources. The authors also develop a co-scheduling algorithm that use stakes functions to optimise resource usage while mitigating resource contention, thus improving performance and energy efficiency. They validated the approach using applications from the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suite, obtaining up to 12.88% performance speed-up, 13.65% energy speed-up and 28.29% energy delay speed-up with respect to the standard Linux heterogeneous multi-processing scheduler.

The paper concerns research on electronics-embedded safety systems. The authors focus on the optimization of the energy consumed by multitasking real-time systems. A new flexible and reconfigurable multi-core architecture based on pipeline processing is proposed. The presented solution uses thread-interleaving mechanisms that allow avoiding hazards and minimizing unpredictability. The proposed architecture is compared with the classical solutions consisting of many processors and based on the scheme using one processor per single task. Energy-efficient task mapping is analyzed and a design methodology, based on minimizing the number of active and utilized resources, is proposed. New techniques for energy optimization are proposed, mainly, clock gating and switching-resources blocking. The authors investigate two main factors of the system: setting the processing frequency, and gating techniques; the latter are used under the assumption that the system meets the requirements of time predictability. The energy consumed by the system is reduced. Theoretical considerations are verified by many experiments of the system’s implementation in an FPGA structure. The set of tasks tested consists of programs that implement Mälardalen WCET benchmark algorithms. The tested scenarios are divided into periodic and non-periodic execution schemes. The obtained results show that it is possible to reduce the dynamic energy consumed by real-time applications’ meeting their other requirements.

This chapter contains sections titled: Introduction to UMA Working on UMA Network Architecture of UMA U p Interface in UMA Protocols in UMA Security Mechanism of UMA Identifiers and Cell Identifiers in UMA Mode and PLMN Selection UMAN Discovery and Registration Procedures UNC Blocks Comparison between Femtocells and UMA Conclusion

Effective load balancing and resource allocation are essential in dynamic cloud computing environments, where the demand for rapidity and continuous service is perpetually increasing. This paper introduces an innovative hybrid optimisation method that combines water wave optimization (WWO) and ant colony optimization (ACO) to tackle these challenges effectively. ACO is acknowledged for its proficiency in conducting local searches effectively, facilitating the swift discovery of high-quality solutions. In contrast, WWO specialises in global exploration, guaranteeing extensive coverage of the solution space. Collectively, these methods harness their distinct advantages to enhance various objectives: decreasing response times, maximising resource efficiency, and lowering operational expenses. We assessed the efficacy of our hybrid methodology by conducting extensive simulations using a cloud-sim simulator and a variety of workload trace files. We assessed our methods in comparison to well-established algorithms, such as WWO, genetic algorithm (GA), spider monkey optimization (SMO), and ACO. Key performance indicators, such as task scheduling duration, execution costs, energy consumption, and resource utilisation, were meticulously assessed. The findings demonstrate that the hybrid WWO-ACO approach enhances task scheduling efficiency by 11%, decreases operational expenses by 8%, and lowers energy usage by 12% relative to conventional methods. In addition, the algorithm consistently achieved an impressive equilibrium in resource allocation, with balance values ranging from 0.87 to 0.95. The results emphasise the hybrid WWO-ACO algorithm’s substantial impact on improving system performance and customer satisfaction, thereby demonstrating a significant improvement in cloud computing optimisation techniques.

The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids.