En los últimos años y con el auge las tecnologías de virtualización y de las infraestructuras cloud, se abre un nuevo abanico de posibilidades para acceso de recursos de cómputo para el ámbito científico. Estas tecnologías permiten "acceso ubicuo, adaptado y bajo demanda en red a un conjunto compartido de recursos de computación". Estas tecnologías permiten que el acceso a grandes cantidades de recursos virtualizados sea mucho más sencillo para el científico. Si bien la adaptación de aplicaciones a un entorno distribuido sigue requiriendo de una experiencia importante, es posible utilizar de forma eficiente software adaptado a sistemas de colas e incluso computación paralela de memoria distribuida. A pesar de todo, en la actualidad existen diferentes proveedores cloud, diferente software para el despliegue de plataformas cloud, diferentes gestores de máquinas virtuales, y otros componentes que complican el acceso de forma sencilla y homogénea. Por tanto el objetivo principal de esta tesis es la de proporcionar a la comunidad científica el acceso a las tecnologías de virtualización y cloud de manera sencilla. De tal manera que sea muy sencillo el despliegue y gestión de sus infraestructuras virtuales, para que los investigadores solo tengan que centrarse en las tareas propias de su aplicación. Una plataforma Cloud para investigación debe contemplar todos los aspectos necesarios para creación y gestión de las infraestructuras, partiendo de que el investigador debe poder expresar sus requerimientos, tanto hardware como software, sobre los recursos que va a necesitar para la ejecución de su aplicación. En base a los requerimientos definidos por el usuario el sistema debe crear la infraestructura del usuario, teniendo en cuenta aspectos como la selección de despliegues cloud, de imágenes de máquinas virtuales, procesos de contextualización, etc. El sistema también debe permitir que el usuario modifique la cantidad de recursos (elasticidad horizontal) así como las características de los mismos (elasticidad vertical). Por último la plataforma debe proporcionar interfaces tanto a nivel de usuario, mediante aplicaciones de comandos o interfaces gráficas, como a nivel programático para que capas de mayor nivel puedan hacer uso de la funcionalidad mediante un API. La tesis pretende tanto avanzar en las especificaciones y arquitecturas software como desarrollar y testear un prototipo. / Caballer Fernández, M. (2014). Gestión de infraestructuras virtuales configuradas dinámicamente [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37376 / Premios Extraordinarios de tesis doctorales

Yes / The governments, nowadays, throughout the world are increasingly becoming dependent on public opinion regarding the framing and implementation of certain policies for the welfare of the general public. The role of social media is vital to this emerging trend. Traditionally, lack of public participation in various policy making decision used to be a major cause of concern particularly when formulating and evaluating such policies. However, the exponential rise in usage of social media platforms by general public has given the government a wider insight to overcome this long pending dilemma. Cloud-based e-governance is currently being realized due to IT infrastructure availability along with mindset changes of government advisors towards realizing the various policies in a best possible manner. This paper presents a pragmatic approach that combines the capabilities of both cloud computing and social media analytics towards efficient monitoring and controlling of governmental policies through public involvement. The proposed system has provided us some encouraging results, when tested for Goods and Services Tax (GST) implementation by Indian government and established that it can be successfully implemented for efficient policy making and implementation.

No / Augmented reality (AR) within the context of higher education is an approach to engage students with experiential learning by utilising AR technology. This paper discusses the process undertaken by a teacher in higher education in designing and implementing cloud-based AR lesson for the students. The methodology engaged was case study at one institution of higher learning in Malaysia. The AR teaching process involves six stages, beginning with the selection of the course, followed by selection of the topic, designing of the AR teaching plan and the implementation of the AR lesson. Upon completion of the implementation of the AR lesson, the teacher and students would provide reflection of their experiences. The process concludes by the improvement of the AR teaching plan by the teacher. The study found that cloud based has indeed disrupted higher education in terms of providing richer learning experiences to the students, as well as enhanced teaching practices for the teachers. Hopefully, this paper would provide insights into the practices of AR teaching and learning approach for teachers in general, and within the context of higher education in particular. It is also intended that the six-steps process outlined in this paper becomes a reference and be duplicated by teachers at large who might be interested to design and implement AR lessons for their own courses.

Yes / Next-generation cellular systems like fth generation (5G) is are expected to experience tremendous tra c growth. To accommodate such tra c demand, there is a need to increase the network capacity that eventually requires the deployment of more base stations (BSs). Nevertheless, BSs are very expensive and consume a lot of energy. With growing complexity of signal processing, baseband units are now consuming a signi cant amount of energy. As a result, cloud radio access networks (C-RAN) have been proposed as anenergy e cient (EE) architecture that leverages cloud computing technology where baseband processing is performed in the cloud. This paper proposes an energy reduction technique based on baseband workload consolidation using virtualized general purpose processors (GPPs) in the cloud. The rationale for the cloud based workload consolidation technique model is to switch o idle baseband units (BBUs) to reduce the overall network energy consumption. The power consumption model for C-RAN is also formulated with considering radio side, fronthaul and BS cloud power consumption. Simulation results demonstrate that the proposed scheme achieves an enhanced energy performance compared to the existing distributed long term evolution (LTE) RAN system. The proposed scheme saves up to 80% of energy during low tra c periods and 12% during peak tra c periods compared to baseline LTE system. Moreover, the proposed scheme saves 38% of energy compared to the baseline system on a daily average.

No / Conventional Call Admission Control (CAC) schemes are based on stand-alone Radio Access Networks (RAN) Base Station (BS) architectures which have their independent and fixed spectral and computing resources, which are not shared with other BSs to address their varied traffic needs, causing poor resource utilization, and high call blocking and dropping probabilities. It is envisaged that in future communication systems like 5G, Cloud RAN (C-RAN) will be adopted in order to share this spectrum and computing resources between BSs in order to further improve the Quality of Service (QoS) and network utilization. In this paper, an intelligent Elastic CAC scheme using Fuzzy Logic in C-RAN is proposed. In the proposed scheme, the BS resources are consolidated to the cloud using virtualization technology and dynamically provisioned using the elasticity concept of cloud computing in accordance to traffic demands. Simulations shows that the proposed CAC algorithm has high call acceptance rate compared to conventional CAC.

Yes / By 2020, next generation (5G) cellular networks are expected to support a 1000 fold traffic increase. To meet such traffic demands, Base Station (BS) densification through small cells are deployed. However, BSs are costly and consume over half of the cellular network energy. Meanwhile, Cloud Radio Access Networks (C-RAN) has been proposed as an energy efficient architecture that leverage cloud computing technology where baseband processing is performed in the cloud. With such an arrangement, more energy gains can be acquired through statistical multiplexing by reducing the number of BBUs used. This paper proposes a green Intelligent Traffic and Resource Elastic Energy (iTREE) scheme for C-RAN. In iTREE, BBUs are reduced by matching the right amount of baseband processing with traffic load. This is a bin packing problem where items (BS aggregate traffic) are to be packed into bins (BBUs) such that the number of bins used are minimized. Idle BBUs can then be switched off to save energy. Simulation results show that iTREE can reduce BBUs by up to 97% during off peak and 66% at peak times with RAN power reductions of up to 27% and 18% respectively compared with conventional deployments.

The increased usage of cloud computing has transformed modern information technology by providing organisations with a scalable, flexible, and cost-effective alternative to the traditional on-premise service model. Both service models have their own set of advantages and disadvantages. One key aspect both service models have in common is the importance of keeping private data secure. There is an ongoing debate on whether cloud computing is safe enough to store private data. This thesis will help organisations understand the security considerations of the different service models. This will be accomplished through a case study researching the different authentication models of both service models and an experiment to gain further insights. The case study and experiment will conclude with a heuristic that organisations can use when picking an authentication model. The main conclusion of this thesis is that we consider the cloud computing service model less secure than the on-premise Windows service model. We also concluded that we consider an LDAP on-premise Windows authentication model and the Azure authentication model to have a higher chance of being less secure than the other authentication models researched in this thesis.

<p dir="ltr">This thesis addresses critical optimization challenges in large-scale, heterogeneous data centers: logical cluster formation for virtual machine placement and physical rack movement for efficient infrastructure management. As data centers grow in size and complexity, these systems face rising demands to minimize costs related to fault tolerance, reformation, and resource constraints while adapting to diverse hardware and operational requirements. </p><p dir="ltr">The first part focuses on logical cluster formation, where capacity guarantees must be maintained across millions of servers despite ongoing infrastructure events, such as maintenance and failures. Traditional offline methods fall short under these dynamic, large-scale conditions. To address this, a two-tier approach combining deep reinforcement learning (DRL) with mixed-integer linear programming (MILP) enables real-time resource allocation, reducing server relocations and enhancing resilience across complex server environments.</p><p dir="ltr">The second part tackles optimized rack placement in highly heterogeneous settings, where balancing fault tolerance, energy efficiency, and load distribution is essential. Static layouts struggle to accommodate diverse hardware configurations and fluctuating resource needs. This research proposes a scalable, tiered optimization approach using the Leader Reward method and a gradient-based heuristic to handle the computational demands of large-scale rack positioning.</p><p dir="ltr">By integrating DRL and heuristic techniques, this work provides a robust, scalable solution for cost efficiency and operational resilience in managing large, heterogeneous data centers, advancing intelligent data center management for modern cloud infrastructure.</p>

<p dir="ltr">Container orchestration systems, such as Kubernetes, often rely on manual resource allocation to manage resources, which can be inefficient and inflexible due to frequent over-provisioning or underprovisioning. Kubernetes horizontal pod autoscaler (HPA), vertical pod autoscaler (VPA), and Google Kubernetes Engine (GKE) Autopilot are primarily threshold-based, making them reactive rather than proactive since they adjust resources after exceeding utilization thresholds, leading to temporary degradation in quality of service~(QoS). While some solutions utilize calls per minute (CPM) counts for requests to microservices to estimate resource consumption dynamically, they do not fully exploit distributed traces or associated microservices' interdependencies. This thesis hypothesizes that more profound insights into future workload patterns can be gained by exploiting microservices' interaction and the CPM counts for each pair of communicating microservices. This thesis proposes a comprehensive machine learning workflow to assess whether factoring in the interdependencies between microservices results in improved workload prediction. The findings of this study indicate that a long short-term memory (LSTM) model performs well, with average mean absolute error (MAE) and root mean square error (RMSE) values of 7.02 and 10.54, respectively. The highest \(R^2\) score observed was 0.07. This suggests that although incorporating distributed traces and inter-microservice CPM counts provides valuable insights, the models fail to capture the full complexity of workload dynamics. These results highlight the potential for enhancing workload prediction accuracy and underscore the need to refine these methods further to achieve more proactive and efficient resource allocation in container orchestration systems.</p>

Recent years have shown a growing concern over increasing traffic volume worldwide. The insufficient road capacity and the resulting congestions have become major problems in many urban areas. Congestions negatively impact the economy, the environment and the health of the population as well as the drivers satisfaction. Current solutions to this topical and timely problem rely on the exploitation of Intelligent Transportation Systems (ITS) technologies. ITS urban traffic management involves the collection and processing of a large amount of geographically distributed information to control distributed infrastructure and individual vehicles. The distributed nature of the problem prompted the development of a novel, scalable ITS-Cloud platform. The ITS-Cloud organises the processing and manages distributed data sources to provide traffic management methods with more accurate information about the state of the traffic. A new approach to service allocation, derived from the existing cloud and grid computing approaches, was created to address the unique needs of ITS traffic management. The ITS-Cloud hosts the collection of software services that form the Cloud based Traffic Management System (CTMS). CTMS combines intersection control algorithms with intersection approach advices to the vehicles and dynamic routing. The CTMS contains a novel Two-Step traffic management method that relies on the ITS-Cloud to deliver a detailed traffic simulation image and integrates an adaptive intersection control algorithm with a microscopic prediction mechanism. It is the first method able to perform simultaneous adaptive intersection control and intersection approach optimization. The Two-Step method builds on a novel pressure based adaptive intersection control algorithm as well as two new traffic prediction schemes. The developed traffic management system was evaluated using a new microscopic traffic simulation tool tightly integrated with the ITS-Cloud. The novel traffic management approaches were shown to outperform benchmark methods for a realistic range of traffic conditions and road network configurations. Unique to the work was the investigation of interactions between ITS components.