Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems

Prediction-based techniques for the optimization of mobile networks

Mención Internacional en el título de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition of several random phenomena, most of which, related to human activities, such as mobility, communications and network usage. However, when observed in their totality, the many individual components merge into more deterministic patterns and trends start to be identifiable and predictable. In this thesis we analyze a recent branch of network optimization that is commonly referred to as anticipatory networking and that entails the combination of prediction solutions and network optimization schemes. The main intuition behind anticipatory networking is that knowing in advance what is going on in the network can help understanding potentially severe problems and mitigate their impact by applying solution when they are still in their initial states. Conversely, network forecast might also indicate a future improvement in the overall network condition (i.e. load reduction or better signal quality reported from users). In such a case, resources can be assigned more sparingly requiring users to rely on buffered information while waiting for the better condition when it will be more convenient to grant more resources. In the beginning of this thesis we will survey the current anticipatory networking panorama and the many prediction and optimization solutions proposed so far. In the main body of the work, we will propose our novel solutions to the problem, the tools and methodologies we designed to evaluate them and to perform a real world evaluation of our schemes. By the end of this work it will be clear that not only is anticipatory networking a very promising theoretical framework, but also that it is feasible and it can deliver substantial benefit to current and next generation mobile networks. In fact, with both our theoretical and practical results we show evidences that more than one third of the resources can be saved and even larger gain can be achieved for data rate enhancements.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge Ortín Gracia.- Vocal: Guevara Noubi

Multi-criteria decision support for energy-efficient IoT edge computing offloading

Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices’ resource restrictions (e.g. process- ing and memory). In the past, offloading would always utilise remote cloud infrastruc- tures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network, closer to the IoT devices. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage, based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time conditions of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network. This thesis presents a dynamic computation offloading mechanism, which improves the performance (i.e. in terms of response time) and energy consumption of IoT de- vices in a decentralised and autonomous manner. We initially propose the Multi-critEria DecIsion support meChanism for IoT offloading(MEDICI), which runs independently on an IoT device, enabling it to make offloading decisions dynamically, based on multiple criteria, such as the state of the IoT, edge or cloud devices and the conditions of the net- work connecting them. It provides mathematical models of the expected time and energy costs for the different options of offloading a task (i.e. to the edge or the cloud or the IoT device itself). To evaluate its effectiveness, we provide simulation results, by extending the EdgeCloudSim simulator, comparing it against previous families of approaches used in the literature. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, such as IoT cyber intrusion detection, and the energy consumption not only of the individual IoT devices but also of the system as a whole. Furthermore, we present an enhancement of our MEDICI mechanism, the ProbeLess Multi-critEria DecIsion support meChanism for IoT offloading (PL-MEDICI), which en- ables MEDICI to operate in real IoT environments without the need for probing or having pre-defined parameters in order to estimate or model the network conditions or the com- putation capabilities of the different devices involved. This is the first probeless dynamic and decentralised offloading decision support mechanism for IoT environments. The probeless property is achieved by combining lightweight statistical techniques with the concept of age of knowledge (AoK) to allow us to have accurate enough information to use for our estimations. We provide experimental results performed in a real IoT testbed with three real IoT applications, showcasing that PL-MEDICI outperforms existing techniques in terms of both response time and energy consumption. Finally, in order to further evaluate our PL-MEDICI mechanism, we formulate a mixed- integer linear program optimisation problem that provides the theoretical optimal cen- tralised solution to our problem. This is used to compare our PL-MEDICI against the theoretical optimum, given the same estimated input. Our results showed that our of- floading mechanism is close to the obtained optimal solution in terms of both the re- sponse time and energy consumptio

AI meets CRNs : a prospective review on the application of deep architectures in spectrum management

The spectrum low utilization and high demand conundrum created a bottleneck towards ful lling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became the rst step towards the intelligentization of mobile and wireless networks, and in order to strengthen its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of arti cial intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at lling the gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies of spectrum management. This is a prospective review whose primary objective is to provide an in-depth exploration of the recent trends in AI strategies employed in mobile and wireless communication networks. The existing reviews in this area have not considered the relevance of incorporating the mathematical fundamentals of each AI strategy and how to tailor them to speci c mobile and wireless networking problems. Therefore, this reviewaddresses that problem by detailing howdeep architectures can be integrated into spectrum management problems. Beyond reviewing different ways in which deep architectures can be integrated into spectrum management, model selection strategies and how different deep architectures can be tailored into the CR space to achieve better performance in complex environments are then reported in the context of future research directions.The Sentech Chair in Broadband Wireless Multimedia Communications (BWMC) at the University of Pretoria.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2022Electrical, Electronic and Computer Engineerin

Network virtualization in next generation cellular networks

The complexity of operation and management of emerging cellular networks significantly increases, as they evolve to correspond to increasing QoS needs, data rates and diversity of offered services. Thus critical challenges appear regarding their performance. At the same time, network sustainability pushes toward the utilization of haring Radio Access Network (RAN) infrastructure between Mobile Network Operators (MNOs). This requires advanced network management techniques which have to be developed based on characteristics of these networks and traffic demands. Therefore it is necessary to provide solutions enabling the creation of logically isolated network partitions over shared physical network infrastructure. Multiple heterogeneous virtual networks should simultaneously coexist and support resource aggregation so as to appear as a single resource to serve different traffic types on demand. Hence in this thesis, we study RAN virtualization and slicing solutions destined to tackle these challenges. In the first part, we present our approach to map virtual network elements onto radio resources of the substrate physical network, in a dense multi-tier LTE-A scenario owned by a MNO. We propose a virtualization solution at BS level, where baseband modules of distributed BSs, interconnected via logical point-to-point X2 interface, cooperate to reallocate radio resources on a traffic need basis. Our proposal enhances system performance by achieving 53% throughput gain compared with benchmark schemes without substantial signaling overhead. In the second part of the thesis, we concentrate on facilitating resource provisioning between multiple Virtual MNOs (MVNOs), by integrating the capacity broker in the 3GPP network management architecture with minimum set of enhancements. A MNO owns the network and provides RAN access on demand to several MVNOs. Furthermore we propose an algorithm for on-demand resource allocation considering two types of traffic. Our proposal achieves 50% more admitted requests without Service Level Agreement (SLA) violation compared with benchmark schemes. In the third part, we devise and study a solution for BS agnostic network slicing leveraging BS virtualization in a multi-tenant scenario. This scenario is composed of different traffic types (e.g., tight latency requirements and high data rate demands) along with BSs characterized by different access and transport capabilities (i.e., Remote Radio Heads, RRHs, Small Cells, SCs and future 5G NodeBs, gNBs with various functional splits having ideal and non-ideal transport network). Our solution achieves 67% average spectrum usage gain and 16.6% Baseband Unit processing load reduction compared with baseline scenarios. Finally, we conclude the thesis by providing insightful research challenges for future works.La complejidad de la operación y la gestión de las emergentes redes celulares aumenta a medida que evolucionan para hacer frente a las crecientes necesidades de calidad de servicio (QoS), las tasas de datos y la diversidad de los servicios ofrecidos. De esta forma aparecen desafíos críticos con respecto a su rendimiento. Al mismo tiempo, la sostenibilidad de la red empuja hacia la utilización de la infraestructura de red de acceso radio (RAN) compartida entre operadores de redes móviles (MNO). Esto requiere técnicas avanzadas de gestión de redes que deben desarrollarse en función de las características especiales de estas redes y las demandas de tráfico. Por lo tanto, es necesario proporcionar soluciones que permitan la creación de particiones de red aisladas lógicamente sobre la infraestructura de red física compartida. Para ello, en esta tesis, estudiamos las soluciones de virtualización de la RAN destinadas a abordar estos desafíos. En la primera parte de la tesis, nos centramos en mapear elementos de red virtual en recursos de radio de la red física, en un escenario LTE-A de múltiples niveles que es propiedad de un solo MNO. Proponemos una solución de virtualización a nivel de estación base (BS), donde los módulos de banda base de BSs distribuidas, interconectadas a través de la interfaz lógica X2, cooperan para reasignar los recursos radio en función de las necesidades de tráfico. Nuestra propuesta mejora el rendimiento del sistema al obtener un rendimiento 53% en comparación con esquemas de referencia. En la segunda parte de la tesis, nos concentramos en facilitar el aprovisionamiento de recursos entre muchos operadores de redes virtuales móviles (MVNO), al integrar el capacity broker en la arquitectura de administración de red 3GPP con un conjunto míinimo de mejoras. En este escenario, un MNO es el propietario de la red y proporciona acceso bajo demanda (en inglés on-demand) a varios MVNOs. Además, para aprovechar al máximo las capacidades del capacity broker, proponemos un algoritmo para la asignación de recursos bajo demanda, considerando dos tipos de tráfico con distintas características. Nuestra propuesta alcanza 50% más de solicitudes admitidas sin violación del Acuerdo de Nivel de Servicio (SLA) en comparación con otros esquemas. En la tercera parte de la tesis, estudiamos una solución para el slicing de red independiente del tipo de BS, considerando la virtualización de BS en un escenario de múltiples MVNOs (multi-tenants). Este escenario se compone de diferentes tipos de tráfico (por ejemplo, usuarios con requisitos de latencia estrictos y usuarios con altas demandas de velocidad de datos) junto con BSs caracterizadas por diferentes capacidades de acceso y transporte (por ejemplo, Remote Radio Heads, RRHs, Small cells, SC y 5G NodeBs, gNBs con varias divisiones funcionales que tienen una red de transporte ideal y no ideal). Nuestra solución logra una ganancia promedio de uso de espectro de 67% y una reducción de la carga de procesamiento de la banda base de 16.6% en comparación con escenarios de referencia. Finalmente, concluimos la tesis al proporcionando los desafíos y retos de investigación para trabajos futuros.Postprint (published version

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Energy efficient enabling technologies for semantic video processing on mobile devices

Semantic object-based processing will play an increasingly important role in future multimedia systems due to the ubiquity of digital multimedia capture/playback technologies and increasing storage capacity. Although the object based paradigm has many undeniable benefits, numerous technical challenges remain before the applications becomes pervasive, particularly on computational constrained mobile devices. A fundamental issue is the ill-posed problem of semantic object segmentation. Furthermore, on battery powered mobile computing devices, the additional algorithmic complexity of semantic object based processing compared to conventional video processing is highly undesirable both from a real-time operation and battery life perspective. This thesis attempts to tackle these issues by firstly constraining the solution space and focusing on the human face as a primary semantic concept of use to users of mobile devices. A novel face detection algorithm is proposed, which from the outset was designed to be amenable to be offloaded from the host microprocessor to dedicated hardware, thereby providing real-time performance and reducing power consumption. The algorithm uses an Artificial Neural Network (ANN), whose topology and weights are evolved via a genetic algorithm (GA). The computational burden of the ANN evaluation is offloaded to a dedicated hardware accelerator, which is capable of processing any evolved network topology. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design. To tackle the increased computational costs associated with object tracking or object based shape encoding, a novel energy efficient binary motion estimation architecture is proposed. Energy is reduced in the proposed motion estimation architecture by minimising the redundant operations inherent in the binary data. Both architectures are shown to compare favourable with the relevant prior art

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields

SIMULATING SEISMIC WAVE PROPAGATION IN TWO-DIMENSIONAL MEDIA USING DISCONTINUOUS SPECTRAL ELEMENT METHODS

We introduce a discontinuous spectral element method for simulating seismic wave in 2- dimensional elastic media. The methods combine the flexibility of a discontinuous finite element method with the accuracy of a spectral method. The elastodynamic equations are discretized using high-degree of Lagrange interpolants and integration over an element is accomplished based upon the Gauss-Lobatto-Legendre integration rule. This combination of discretization and integration results in a diagonal mass matrix and the use of discontinuous finite element method makes the calculation can be done locally in each element. Thus, the algorithm is simplified drastically. We validated the results of one-dimensional problem by comparing them with finite-difference time-domain method and exact solution. The comparisons show excellent agreement