Energy-Efficient Software

The energy consumption of ICT is growing at an unprecedented pace. The main drivers for this growth are the widespread diffusion of mobile devices and the proliferation of datacenters, the most power-hungry IT facilities. In addition, it is predicted that the demand for ICT technologies and services will increase in the coming years. Finding solutions to decrease ICT energy footprint is and will be a top priority for researchers and professionals in the field. As a matter of fact, hardware technology has substantially improved throughout the years: modern ICT devices are definitely more energy efficient than their predecessors, in terms of performance per watt. However, as recent studies show, these improvements are not effectively reducing the growth rate of ICT energy consumption. This suggests that these devices are not used in an energy-efficient way. Hence, we have to look at software. Modern software applications are not designed and implemented with energy efficiency in mind. As hardware became more and more powerful (and cheaper), software developers were not concerned anymore with optimizing resource usage. Rather, they focused on providing additional features, adding layers of abstraction and complexity to their products. This ultimately resulted in bloated, slow software applications that waste hardware resources -- and consequently, energy. In this dissertation, the relationship between software behavior and hardware energy consumption is explored in detail. For this purpose, the abstraction levels of software are traversed upwards, from source code to architectural components. Empirical research methods and evidence-based software engineering approaches serve as a basis. First of all, this dissertation shows the relevance of software over energy consumption. Secondly, it gives examples of best practices and tactics that can be adopted to improve software energy efficiency, or design energy-efficient software from scratch. Finally, this knowledge is synthesized in a conceptual framework that gives the reader an overview of possible strategies for software energy efficiency, along with examples and suggestions for future research

Nature-inspired survivability: Prey-inspired survivability countermeasures for cloud computing security challenges

As cloud computing environments become complex, adversaries have become highly sophisticated and unpredictable. Moreover, they can easily increase attack power and persist longer before detection. Uncertain malicious actions, latent risks, Unobserved or Unobservable risks (UUURs) characterise this new threat domain. This thesis proposes prey-inspired survivability to address unpredictable security challenges borne out of UUURs. While survivability is a well-addressed phenomenon in non-extinct prey animals, applying prey survivability to cloud computing directly is challenging due to contradicting end goals. How to manage evolving survivability goals and requirements under contradicting environmental conditions adds to the challenges. To address these challenges, this thesis proposes a holistic taxonomy which integrate multiple and disparate perspectives of cloud security challenges. In addition, it proposes the TRIZ (Teorija Rezbenija Izobretatelskib Zadach) to derive prey-inspired solutions through resolving contradiction. First, it develops a 3-step process to facilitate interdomain transfer of concepts from nature to cloud. Moreover, TRIZ’s generic approach suggests specific solutions for cloud computing survivability. Then, the thesis presents the conceptual prey-inspired cloud computing survivability framework (Pi-CCSF), built upon TRIZ derived solutions. The framework run-time is pushed to the user-space to support evolving survivability design goals. Furthermore, a target-based decision-making technique (TBDM) is proposed to manage survivability decisions. To evaluate the prey-inspired survivability concept, Pi-CCSF simulator is developed and implemented. Evaluation results shows that escalating survivability actions improve the vitality of vulnerable and compromised virtual machines (VMs) by 5% and dramatically improve their overall survivability. Hypothesis testing conclusively supports the hypothesis that the escalation mechanisms can be applied to enhance the survivability of cloud computing systems. Numeric analysis of TBDM shows that by considering survivability preferences and attitudes (these directly impacts survivability actions), the TBDM method brings unpredictable survivability information closer to decision processes. This enables efficient execution of variable escalating survivability actions, which enables the Pi-CCSF’s decision system (DS) to focus upon decisions that achieve survivability outcomes under unpredictability imposed by UUUR

KINE[SIS]TEM'17 From Nature to Architectural Matter

Kine[SiS]tem – From Kinesis + System. Kinesis is a non-linear movement or activity of an organism in response to a stimulus. A system is a set of interacting and interdependent agents forming a complex whole, delineated by its spatial and temporal boundaries, influenced by its environment. How can architectural systems moderate the external environment to enhance comfort conditions in a simple, sustainable and smart way? This is the starting question for the Kine[SiS]tem’17 – From Nature to Architectural Matter International Conference. For decades, architectural design was developed despite (and not with) the climate, based on mechanical heating and cooling. Today, the argument for net zero energy buildings needs very effective strategies to reduce energy requirements. The challenge ahead requires design processes that are built upon consolidated knowledge, make use of advanced technologies and are inspired by nature. These design processes should lead to responsive smart systems that deliver the best performance in each specific design scenario. To control solar radiation is one key factor in low-energy thermal comfort. Computational-controlled sensor-based kinetic surfaces are one of the possible answers to control solar energy in an effective way, within the scope of contradictory objectives throughout the year.FC

Attack Prevention in IoT through Hybrid Optimization Mechanism and Deep Learning Framework

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware specimens have been collected from the Mailing database for testing purposes. The experimental findings show that the suggested technique for measuring cyber security hazards in IoT has superior classification results to existing methods. Hence to detect the attack prevention in IoT process Whale with Grey wolf optimization (WGWO) and deep convolution network is used

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

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

Smartcells : a Bio-Cloud theory towards intelligent cloud computing system

Cloud computing is the future of web technologies and the goal for all web companies as well. It reinforces some old concepts of building highly scalable Internet architectures and introduces some new concepts that entirely change the way applications are built and deployed. In the recent years, some technology companies adopted the cloud computing strategy. This adoption took place when these companies have predicted that cloud computing will be the solutions of Web problems such as availability. However, organizations find it almost impossible to launch the cloud idea without adopting previous approaches like that of Service-Oriented approach. As a result of this dependency, web service problems are transferred into the cloud. Indeed, the current cloud’s availability is too expensive due to service replication, some cloud services face performance problem, a majority of these services is weak regarding security, and cloud services are randomly discovered while it is difficult to precisely select the best ones in addition to being spontaneously fabricated in an ocean of services. Moreover, it is impossible to validate cloud services especially before runtime. Finally, according to the W3C standards, cloud services are not yet internationalized. Indeed, the predicted web is a smart service model while it lacks intelligence and autonomy. This is why the adoption of service-oriented model was not an ideal decision. In order to minimize the consequences of cloud problems and achieve more benefits, each cloud company builds its own cloud platform. Currently, cloud vendors are facing a big problem that can be summarized by the “Cloud Platform Battle”. The budget of this battle will cost about billions of dollars due to the absence of an agreement to reach a standard cloud platform. Why intelligent collaboration is not applied between distributed clouds to achieve better Cloud Computing results? The appropriate approach is to restructure the cloud model basis to recover its issues. Multiple intelligent techniques may be used to develop advanced intelligent Cloud systems. Classical examples of distributed intelligent systems include: human body, social insect colonies, flocks of vertebrates, multi-agent systems, transportation systems, multi-robot systems, and wireless sensor networks. However, the intelligent system that could be imitated is the human body system, in which billions of body cells work together to achieve accurate results. Inspired by Bio-Informatics strategy that benefits from technologies to solve biological facts (like our genes), this thesis research proposes a novel Bio-Cloud strategy which imitates biological facts (like brain and genes) in solving the Cloud Computing issues. Based on Bio-Cloud strategy, I have developed through this thesis project the “SmartCells” framework as a smart solution for Cloud problems. SmartCells framework covers: 1) Cloud problems which are inherited from the service paradigm (like issues of service reusability, security, etc.); 2) The intelligence insufficiency problem in Cloud Computing systems. SmartCells depends on collaborations between smart components (Cells) that take advantage of the variety of already built web service components to produce an intelligent Cloud system. Le « Cloud Computing » est certes le futur des technologies du web. Il renforce certains vieux concepts de construction d’architectures internet hautement évolutifs, et introduit de nouveaux concepts qui changent complètement la façon dont les applications sont développées et déployées. Au cours des dernières années, certaines entreprises technologiques ont adopté la stratégie du Cloud Computing. Cette adoption a eu lieu lorsque ces entreprises ont prédit que le Cloud Computing sera les solutions des plusieurs problèmes Web tels que la disponibilité. Toutefois, les organisations pensent qu'il est presque impossible de lancer l'idée du « Cloud » sans adopter les concepts et les normes antérieures comme celle du paradigme orienté service (Service-Oriented Paradigm). En raison de cette dépendance, les problèmes de l'approche orientée service et services web sont transférés au Cloud. En effet, la disponibilité du Cloud actuel s’avère trop chère à cause de la reproduction de services, certains services Cloud sont confrontés à des problèmes de performances, une majorité des services Cloud est faible en matière de sécurité, et ces services sont découverts d’une façon aléatoire, il est difficile de choisir le meilleur d’entre eux ainsi qu’ils sont composés d’un groupe de services web dans un monde de services. Egalement, il est impossible de valider les services Cloud en particulier, avant le temps d’exécution. Finalement, selon les normes du W3C, les services Cloud ne sont pas encore internationalisés. En effet, le web comme prévu, est un modèle de service intelligent bien qu’il manque d’intelligence et d’autonomie. Ainsi, l'adoption d'un modèle axé sur le service n’était pas une décision idéale. Afin de minimiser les conséquences des problèmes du Cloud et réaliser plus de profits, certaines entreprises de Cloud développent leurs propres plateformes de Cloud Computing. Actuellement, les fournisseurs du Cloud font face à un grand problème qui peut se résumer par la « Bataille de la plateforme Cloud ». Le budget de cette bataille coûte des milliards de dollars en l’absence d’un accord pour accéder à une plateforme Cloud standard. Pourquoi une collaboration intelligente n’est pas mise en place entre les nuages distribués pour obtenir de meilleurs résultats ? L’approche appropriée est de restructurer le modèle de cloud afin de couvrir ses problèmes. Des techniques intelligentes multiples peuvent être utilisées pour développer des systèmes Cloud intelligents avancés. Parmi les exemples classiques de systèmes intelligents distribués se trouvent : le corps humain, les colonies d’insectes sociaux, les troupeaux de vertébrés, les systèmes multi-agents, les systèmes de transport, les systèmes multi-robots, et les réseaux de capteurs sans fils. Toutefois, le système intelligent qui pourrait être imité est le système du corps humain dans lequel vivent des milliards de cellules du corps et travaillent ensemble pour atteindre des résultats précis. En s’inspirant de la stratégie Bio-Informatique qui bénéficie de technologies pour résoudre des faits biologiques (comme les gènes). Cette thèse propose une nouvelle stratégie Bio-Cloud qui imite des faits biologiques (comme le cerveau et les gènes) pour résoudre les problèmes du Cloud Computing mentionnés ci-haut. Ainsi, en me basant sur la stratégie Bio-Cloud, j’ai développé au cours de cette thèse la théorie « SmartCells » conçue comme une proposition (approche) cherchant à résoudre les problèmes du Cloud Computing. Cette approche couvre : 1) les problèmes hérités du paradigme services (comme les questions de réutilisation de services, les questions de sécurité, etc.); 2) le problème d’insuffisance d’intelligence dans les systèmes du Cloud Computing. SmartCells se base sur la collaboration entre les composants intelligents (les Cellules) qui profitent de la variété des composants des services web déjà construits afin de produire un système de Cloud intelligent

Advances on Mechanics, Design Engineering and Manufacturing III

This open access book gathers contributions presented at the International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing (JCM 2020), held as a web conference on June 2–4, 2020. It reports on cutting-edge topics in product design and manufacturing, such as industrial methods for integrated product and process design; innovative design; and computer-aided design. Further topics covered include virtual simulation and reverse engineering; additive manufacturing; product manufacturing; engineering methods in medicine and education; representation techniques; and nautical, aeronautics and aerospace design and modeling. The book is organized into four main parts, reflecting the focus and primary themes of the conference. The contributions presented here not only provide researchers, engineers and experts in a range of industrial engineering subfields with extensive information to support their daily work; they are also intended to stimulate new research directions, advanced applications of the methods discussed and future interdisciplinary collaborations