Latency reduction by dynamic channel estimator selection in C-RAN networks using fuzzy logic

Due to a dramatic increase in the number of mobile users, operators are forced to expand their networks accordingly. Cloud Radio Access Network (C-RAN) was introduced to tackle the problems of the current generation of mobile networks and to support future 5G networks. However, many challenges have arisen through the centralised structure of C-RAN. The accuracy of the channel state information acquisition in the C-RAN for large numbers of remote radio heads and user equipment is one of the main challenges in this architecture. In order to minimize the time required to acquire the channel information in C-RAN and to reduce the end-to-end latency, in this paper a dynamic channel estimator selection algorithm is proposed. The idea is to assign different channel estimation algorithms to the users of mobile networks based on their link status (particularly the SNR threshold). For the purpose of automatic and adaptive selection to channel estimators, a fuzzy logic algorithm is employed as a decision maker to select the best SNR threshold by utilising the bit error rate measurements. The results demonstrate a reduction in the estimation time with low loss in data throughput. It is also observed that the outcome of the proposed algorithm increases at high SNR values

On the feasibility of using current data centre infrastructure for latency-sensitive applications

It has been claimed that the deployment of fog and edge computing infrastructure is a necessity to make high-performance cloud-based applications a possibility. However, there are a large number of middle-ground latency-sensitive applications such as online gaming, interactive photo editing and multimedia conferencing that require servers deployed closer to users than in globally centralised clouds but do not necessarily need the extreme low-latency provided by a new infrastructure of micro data centres located at the network edge, e.g., in base stations and ISP Points of Presence. In this paper we analyse a snapshot of today's data centres and the distribution of users around the globe and conclude that existing infrastructure provides a sufficiently distributed platform for middle-ground applications requiring a response time of $20\hbox{-}200$20-200 ms. However, while placement and selection of edge servers for extreme low-latency applications is a relatively straightforward matter of choosing the closest, providing a high quality of experience for middle-ground latency applications that use the more widespread distribution of today's data centres, as we advocate in this paper, raises new management challenges to develop algorithms for optimising the placement of and the per-request selection between replicated service instances

Cloud computing meets mobile wireless communications in next generation cellular networks

In next generation cellular networks, cloud computing will have profound impacts on mobile wireless communications. On the one hand, the integration of cloud computing into the mobile environment enables MCC systems. On the other hand, the powerful computing platforms in the cloud for radio access networks lead to a novel concept of C-RAN. In this article we study the topology configuration and rate allocation problem in C-RAN with the objective of optimizing the end-to-end performance of MCC users in next generation cellular networks. We use a decision theoretical approach to tackle the delayed channel state information problem in C-RAN. Simulation results show that the design and operation of future mobile wireless networks can be significantly affected by cloud computing, and the proposed scheme is capable of achieving substantial performance gains over existing schemes

Методи підвищення ефективності алгоритмів та методів балансування навантаження у хмарних середовищах інфокомунікаційних систем

Мета роботи: комплексний аналіз алгоритмів та способів балансування навантаження інфокомунікаційних систем, розроблених з використанням "хмарних" технологій, дослідження особливостей розгортання, функціонування та масштабування таких систем, пошук методів підвищення ефективності розподілу навантаження в хмарному середовищіThe рurроse оf the master’s thesis lies in comprehensive analysis of algorithms and methods of load balancing infocommunication systems developed using "cloud" technologies, study of the deployment, operation and scaling of such systems, search for methods to increase the efficiency of load distribution in a cloud environmen

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana