Design and Service Provisioning Methods for Optical Networks in 5G and Beyond Scenarios

Network operators are deploying 5G while also considering the evolution towards 6G. They consider different enablers and address various challenges. One trend in the 5G deployment is network densification, i.e., deploying many small cell sites close to the users, which need a well-designed transport network (TN). The choice of the TN technology and the location for processing the 5G protocol stack functions are critical to contain capital and operational expenditures. Furthermore, it is crucial to ensure the resiliency of the TN infrastructure in case of a failure in nodes and/or links while the resource efficiency is maximized.Operators are also interested in 5G networks with flexibility and scalability features. In this context, one main question is where to deploy network functions so that the connectivity and compute resources are utilized efficiently while meeting strict service latency and availability requirements. Off-loading compute resources to large and central data centers (DCs) has some advantages, i.e., better utilization of compute resources at a lower cost. A backup path can be added to address service availability requirements when using compute off-loading strategies. This might impact the service blocking ratio and limit operators’ profit. The importance of this trade-off becomes more critical with the emergence of new 6G verticals.This thesis proposes novel methods to address the issues outlined above. To address the challenge of cost-efficient TN deployment, the thesis introduces a framework to study the total cost of ownership (TCO), latency, and reliability performance of a set of TN architectures for high-layer and low-layer functional split options. The architectural options are fiber- or microwave-based. To address the strict availability requirement, the thesis proposes a resource-efficient protection strategy against single node/link failure of the midhaul segment. The method selects primary and backup DCs for each aggregation node (i.e., nodes to which cell sites are connected) while maximizing the sharing of backup resources. Finally, to address the challenge of resource efficiency while provisioning services, the thesis proposes a backup-enhanced compute off-loading strategy (i.e., resource-efficient provisioning (REP)). REP selects a DC, a connectivity path, and (optionally) a backup path for each service request with the aim of minimizing resource usage while the service latency and availability requirements are met.Our results of the techno-economic assessment of the TN options reveal that, in some cases, microwave can be a good substitute for fiber technology. Several factors, including the geo-type, functional split option, and the cost of fiber trenching and microwave equipment, influence the effectiveness of the microwave. The considered architectures show similar latency and reliability performance and meet the 5G service requirements. The thesis also shows that a protection strategy based on shared connectivity and compute resources can lead to significant cost savings compared to benchmarks based on dedicated backup resources. Finally, the thesis shows that the proposed backup-enhanced compute off-loading strategy offers advantages in service blocking ratio and profit gain compared to a conventional off-loading approach that does not add a backup path. Benefits are even more evident considering next-generation services, e.g., expected on the market in 3 to 5 years, as the demand for services with stringent latency and availability will increase

Location, Location, Location: Maximizing mmWave LAN Performance through Intelligent Wireless Networking Strategies

The main objective of this dissertation is to design and evaluate intelligent techniques to maximize mmWave wireless local-area network (WLAN) performance. To meet the ever-increasing data demand of various bandwidth-hungry applications, we propose techniques to enable consistently ultra-high-rate mmWave communication in the wireless environment. However, the weak diffraction of mmWave signals makes them extremely sensitive to blockage effects caused by real-world obstacles, and this is a primary challenge to overcome for the feasibility of mmWave communications. To this end, we exploit location sensitivity to explore robust mmWave WLAN designs that expedite the full realization of ubiquitous mmWave wireless connectivity. The techniques investigated to exploit location sensitivity are the use of multiple access points (APs), controlled mobility, AP-user association mechanisms, and environment-aware prediction We first develop optimal multi-AP planning approaches to maximize line-of-sight connectivity and aggregate throughput in mmWave WLANs, and then study multi-AP association mechanisms to achieve low-overhead and blockage-robust mmWave wireless communications among multiple users and multiple APs. Furthermore, we explore the potential benefits achievable from AP mobility technology, which yields insights on the best configurations of mobile APs. We also develop an environment-aware link-quality predictor to accurately derive dynamic mmWave link quality due to static blockages and small changes in device locations, which provides a basis for the development of anticipatory networking with proactive resource-allocation schemes. In a complementary direction for evaluating the performance of mmWave networks, we develop and implement advanced features for dense wireless networks that increasingly characterize many mmWave scenarios of interest in the widely-used network simulator ns-3, including a sparse cluster-based wireless channel model that statistically models multi-path components in mmWave WLANs.Ph.D

Cost Effective Provisioning of 5G Transport Networks: Architectures and Modelling

The next generation of mobile network (5G) has to face a completely new set of requirements coming from novel services. Massive machine type communications, enhanced mobile broadband, ultra reliable low latency communication will be supported by single infrastructure. Mobile network operators are in need of a flexible network capable of supporting services with a wide set of different requirements over the same physical resources, possibly at the same or at a lower cost than today. Centralized radio access network (C-RAN) architecture is a promising solution to improve both network flexibility and scalability. In C-RAN, baseband processing units (BBUs) are decoupled from remote radio units (RRUs) at the antenna sites and are placed in one of few selected locations, called BBU hotels. Thanks to the centralization, more efficient hardware can be employed, advanced radio interference management techniques can be implemented, cooling and power supply units can be shared, and network maintenance is simplified. However, the centralization of BBUs requires high capacity and low latency dedicated links to transport data, known as fronthaul links. This may be expensive and calls for novel deployment strategies to contain the costs. This Ph.D. thesis investigates the cost-efficient and resilient design of C-RAN. Minimization of network equipment as well as reuse of already deployed infrastructure, either based on fiber or copper cables, is investigated and shown to be effective to reduce the overall cost. Moreover, the introduction of wireless devices (e.g., based on free space optic) in fronthaul links is included in the proposed deployment strategies and shown to significantly lower capital expenditure. The adoption of Ethernet-based fronthaul and the introduction of hybrid switches is pursued to further decrease network cost by increasing optical resources usage. Finally, the problem of single BBU hotel failure is addressed and included in the optimal deployment of BBU resources

Unmanned aerial vehicle communications for civil applications: a review

The use of drones, formally known as unmanned aerial vehicles (UAVs), has significantly increased across a variety of applications over the past few years. This is due to the rapid advancement towards the design and production of inexpensive and dependable UAVs and the growing request for the utilization of such platforms particularly in civil applications. With their intrinsic attributes such as high mobility, rapid deployment and flexible altitude, UAVs have the potential to be utilized in many wireless system applications. On the one hand, UAVs are able to operate as flying mobile terminals within wireless/cellular networks to support a variety of missions such as goods delivery, search and rescue, precision agriculture monitoring, and remote sensing. On the other hand, UAVs can be utilized as aerial base stations to increase wireless communication coverage, reliability, and the capacity of wireless systems without additional investment in wireless systems infrastructure. The aim of this article is to review the current applications of UAVs for civil and commercial purposes. The focus of this paper is on the challenges and communication requirements associated with UAV-based communication systems. This article initially classifies UAVs in terms of various parameters, some of which can impact UAVs’ communication performance. It then provides an overview of aerial networking and investigates UAVs routing protocols specifically, which are considered as one of the challenges in UAV communication. This article later investigates the use of UAV networks in a variety of civil applications and considers many challenges and communication demands of these applications. Subsequently, different types of simulation platforms are investigated from a communication and networking viewpoint. Finally, it identifies areas of future research

Traffic Scheduling in Software-defined Backhaul Network

In the past few years, severe challenges have arisen for network operators, as explosive growth and service differentiation in data demands require an increasing number of network capacity as well as dynamic traffic management. To adapt to the network densification, wireless backhaul solution is attracting more and more attentions due to its flexible deployment. Meanwhile, the software-defined network (SDN) proposes an promising architecture that can achieve dynamic control and management for various functionalities. In this case, by applying the SDN architecture to wireless backhaul networks, the traffic scheduling functionality may satisfy the ever-increasing and differentiated traffic demands. To tackle the traffic demand challenges, traffic scheduling for software-defined backhaul networks (SDBN) is investigated from three aspects in this thesis. In the first aspect, various virtual networks based on service types are embedded to the same wireless backhaul infrastructure. An algorithm, named VNE-SDBN, is proposed to solve the virtual network embedding (VNE) problem to improve the performance of the revenue of infrastructure providers and virtual network request acceptance ratio by exploiting the unique characteristics of SDBNs. In the second aspect, incoming traffic is scheduled online by joint routing and resource allocation approach in backhaul networks operated in low-frequency microwave (LFM) and those operated in millimetre wave (mmW). A digraph-based greedy algorithm (DBGA) is proposed considering the relationship between the degrees of vertices in the constructed interference digraph and system throughput with low complexity. In the third aspect, quality-of-service is provided in terms of delay and throughput with two proposed algorithms for backhaul networks with insufficient spectral resources. At last, as a trial research on E-band, a conceptual adaptive modulation system with channel estimation based on rain rate for E-band SDBN is proposed to exploit the rain attenuation feature of E-band. The results of the research works are mainly achieved through heuristic algorithms. Genetic algorithm, which is a meta-heuristic algorithm, is employed to obtain near-optimal solutions to the proposed NP-hard problems. Low complexity greedy algorithms are developed based on the specific problem analysis. Finally, the evaluation of proposed systems and algorithms are performed through numerical simulations. Simulations for backhaul networks with respect to VNE, routing and resource allocation are developed

Integrated IT and SDN Orchestration of multi-domain multi-layer transport networks

Telecom operators networks' management and control remains partitioned by technology, equipment supplier and networking layer. In some segments, the network operations are highly costly due to the need of the individual, and even manual, configuration of the network equipment by highly specialized personnel. In multi-vendor networks, expensive and never ending integration processes between Network Management Systems (NMSs) and the rest of systems (OSSs, BSSs) is a common situation, due to lack of adoption of standard interfaces in the management systems of the different equipment suppliers. Moreover, the increasing impact of the new traffic flows introduced by the deployment of massive Data Centers (DCs) is also imposing new challenges that traditional networking is not ready to overcome. The Fifth Generation of Mobile Technology (5G) is also introducing stringent network requirements such as the need of connecting to the network billions of new devices in IoT paradigm, new ultra-low latency applications (i.e., remote surgery) and vehicular communications. All these new services, together with enhanced broadband network access, are supposed to be delivered over the same network infrastructure. In this PhD Thesis, an holistic view of Network and Cloud Computing resources, based on the recent innovations introduced by Software Defined Networking (SDN), is proposed as the solution for designing an end-to-end multi-layer, multi-technology and multi-domain cloud and transport network management architecture, capable to offer end-to-end services from the DC networks to customers access networks and the virtualization of network resources, allowing new ways of slicing the network resources for the forthcoming 5G deployments. The first contribution of this PhD Thesis deals with the design and validation of SDN based network orchestration architectures capable to improve the current solutions for the management and control of multi-layer, multi-domain backbone transport networks. These problems have been assessed and progressively solved by different control and management architectures which has been designed and evaluated in real evaluation environments. One of the major findings of this work has been the need of developed a common information model for transport network's management, capable to describe the resources and services of multilayer networks. In this line, the Control Orchestration Protocol (COP) has been proposed as a first contriution towards an standard management interface based on the main principles driven by SDN. Furthermore, this PhD Thesis introduces a novel architecture capable to coordinate the management of IT computing resources together with inter- and intra-DC networks. The provisioning and migration of virtual machines together with the dynamic reconfiguration of the network has been successfully demonstrated in a feasible timescale. Moreover, a resource optimization engine is introduced in the architecture to introduce optimization algorithms capable to solve allocation problems such the optimal deployment of Virtual Machine Graphs over different DCs locations minimizing the inter-DC network resources allocation. A baseline blocking probability results over different network loads are also presented. The third major contribution is the result of the previous two. With a converged cloud and network infrastructure controlled and operated jointly, the holistic view of the network allows the on-demand provisioning of network slices consisting of dedicated network and cloud resources over a distributed DC infrastructure interconnected by an optical transport network. The last chapters of this thesis discuss the management and orchestration of 5G slices based over the control and management components designed in the previous chapters. The design of one of the first network slicing architectures and the deployment of a 5G network slice in a real Testbed, is one of the major contributions of this PhD Thesis.La gestión y el control de las redes de los operadores de red (Telcos), todavía hoy, está segmentado por tecnología, por proveedor de equipamiento y por capa de red. En algunos segmentos (por ejemplo en IP) la operación de la red es tremendamente costosa, ya que en muchos casos aún se requiere con guración individual, e incluso manual, de los equipos por parte de personal altamente especializado. En redes con múltiples proveedores, los procesos de integración entre los sistemas de gestión de red (NMS) y el resto de sistemas (p. ej., OSS/BSS) son habitualmente largos y extremadamente costosos debido a la falta de adopción de interfaces estándar por parte de los diferentes proveedores de red. Además, el impacto creciente en las redes de transporte de los nuevos flujos de tráfico introducidos por el despliegue masivo de Data Centers (DC), introduce nuevos desafíos que las arquitecturas de gestión y control de las redes tradicionales no están preparadas para afrontar. La quinta generación de tecnología móvil (5G) introduce nuevos requisitos de red, como la necesidad de conectar a la red billones de dispositivos nuevos (Internet de las cosas - IoT), aplicaciones de ultra baja latencia (p. ej., cirugía a distancia) y las comunicaciones vehiculares. Todos estos servicios, junto con un acceso mejorado a la red de banda ancha, deberán ser proporcionados a través de la misma infraestructura de red. Esta tesis doctoral propone una visión holística de los recursos de red y cloud, basada en los principios introducidos por Software Defined Networking (SDN), como la solución para el diseño de una arquitectura de gestión extremo a extremo (E2E) para escenarios de red multi-capa y multi-dominio, capaz de ofrecer servicios de E2E, desde las redes intra-DC hasta las redes de acceso, y ofrecer ademas virtualización de los recursos de la red, permitiendo nuevas formas de segmentación en las redes de transporte y la infrastructura de cloud, para los próximos despliegues de 5G. La primera contribución de esta tesis consiste en la validación de arquitecturas de orquestración de red, basadas en SDN, para la gestión y control de redes de transporte troncales multi-dominio y multi-capa. Estos problemas (gestion de redes multi-capa y multi-dominio), han sido evaluados de manera incremental, mediante el diseño y la evaluación experimental, en entornos de pruebas reales, de diferentes arquitecturas de control y gestión. Uno de los principales hallazgos de este trabajo ha sido la necesidad de un modelo de información común para las interfaces de gestión entre entidades de control SDN. En esta línea, el Protocolo de Control Orchestration (COP) ha sido propuesto como interfaz de gestión de red estándar para redes SDN de transporte multi-capa. Además, en esta tesis presentamos una arquitectura capaz de coordinar la gestión de los recursos IT y red. La provisión y la migración de máquinas virtuales junto con la reconfiguración dinámica de la red, han sido demostradas con éxito en una escala de tiempo factible. Además, la arquitectura incorpora una plataforma para la ejecución de algoritmos de optimización de recursos capaces de resolver diferentes problemas de asignación, como el despliegue óptimo de Grafos de Máquinas Virtuales (VMG) en diferentes DCs que minimizan la asignación de recursos de red. Esta tesis propone una solución para este problema, que ha sido evaluada en terminos de probabilidad de bloqueo para diferentes cargas de red. La tercera contribución es el resultado de las dos anteriores. La arquitectura integrada de red y cloud presentada permite la creación bajo demanda de "network slices", que consisten en sub-conjuntos de recursos de red y cloud dedicados para diferentes clientes sobre una infraestructura común. El diseño de una de las primeras arquitecturas de "network slicing" y el despliegue de un "slice" de red 5G totalmente operativo en un Testbed real, es una de las principales contribuciones de esta tesis.La gestió i el control de les xarxes dels operadors de telecomunicacions (Telcos), encara avui, està segmentat per tecnologia, per proveïdors d’equipament i per capes de xarxa. En alguns segments (Per exemple en IP) l’operació de la xarxa és tremendament costosa, ja que en molts casos encara es requereix de configuració individual, i fins i tot manual, dels equips per part de personal altament especialitzat. En xarxes amb múltiples proveïdors, els processos d’integració entre els Sistemes de gestió de xarxa (NMS) i la resta de sistemes (per exemple, Sistemes de suport d’operacions - OSS i Sistemes de suport de negocis - BSS) són habitualment interminables i extremadament costosos a causa de la falta d’adopció d’interfícies estàndard per part dels diferents proveïdors de xarxa. A més, l’impacte creixent en les xarxes de transport dels nous fluxos de trànsit introduïts pel desplegament massius de Data Centers (DC), introdueix nous desafiaments que les arquitectures de gestió i control de les xarxes tradicionals que no estan llestes per afrontar. Per acabar de descriure el context, la cinquena generació de tecnologia mòbil (5G) també presenta nous requisits de xarxa altament exigents, com la necessitat de connectar a la xarxa milers de milions de dispositius nous, dins el context de l’Internet de les coses (IOT), o les noves aplicacions d’ultra baixa latència (com ara la cirurgia a distància) i les comunicacions vehiculars. Se suposa que tots aquests nous serveis, juntament amb l’accés millorat a la xarxa de banda ampla, es lliuraran a través de la mateixa infraestructura de xarxa. Aquesta tesi doctoral proposa una visió holística dels recursos de xarxa i cloud, basada en els principis introduïts per Software Defined Networking (SDN), com la solució per al disseny de una arquitectura de gestió extrem a extrem per a escenaris de xarxa multi-capa, multi-domini i consistents en múltiples tecnologies de transport. Aquesta arquitectura de gestió i control de xarxes transport i recursos IT, ha de ser capaç d’oferir serveis d’extrem a extrem, des de les xarxes intra-DC fins a les xarxes d’accés dels clients i oferir a més virtualització dels recursos de la xarxa, obrint la porta a noves formes de segmentació a les xarxes de transport i la infrastructura de cloud, pels propers desplegaments de 5G. La primera contribució d’aquesta tesi doctoral consisteix en la validació de diferents arquitectures d’orquestració de xarxa basades en SDN capaces de millorar les solucions existents per a la gestió i control de xarxes de transport troncals multi-domini i multicapa. Aquests problemes (gestió de xarxes multicapa i multi-domini), han estat avaluats de manera incremental, mitjançant el disseny i l’avaluació experimental, en entorns de proves reals, de diferents arquitectures de control i gestió. Un dels principals troballes d’aquest treball ha estat la necessitat de dissenyar un model d’informació comú per a les interfícies de gestió de xarxes, capaç de descriure els recursos i serveis de la xarxes transport multicapa. En aquesta línia, el Protocol de Control Orchestration (COP, en les seves sigles en anglès) ha estat proposat en aquesta Tesi, com una primera contribució cap a una interfície de gestió de xarxa estàndard basada en els principis bàsics de SDN. A més, en aquesta tesi presentem una arquitectura innovadora capaç de coordinar la gestió de els recursos IT juntament amb les xarxes inter i intra-DC. L’aprovisionament i la migració de màquines virtuals juntament amb la reconfiguració dinàmica de la xarxa, ha estat demostrat amb èxit en una escala de temps factible. A més, l’arquitectura incorpora una plataforma per a l’execució d’algorismes d’optimització de recursos, capaços de resoldre diferents problemes d’assignació, com el desplegament òptim de Grafs de Màquines Virtuals (VMG) en diferents ubicacions de DC que minimitzen la assignació de recursos de xarxa entre DC. També es presenta una solució bàsica per a aquest problema, així com els resultats de probabilitat de bloqueig per a diferents càrregues de xarxa. La tercera contribució principal és el resultat dels dos anteriors. Amb una infraestructura de xarxa i cloud convergent, controlada i operada de manera conjunta, la visió holística de la xarxa permet l’aprovisionament sota demanda de "network slices" que consisteixen en subconjunts de recursos d’xarxa i cloud, dedicats per a diferents clients, sobre una infraestructura de Data Centers distribuïda i interconnectada per una xarxa de transport òptica. Els últims capítols d’aquesta tesi tracten sobre la gestió i organització de "network slices" per a xarxes 5G en funció dels components de control i administració dissenyats i desenvolupats en els capítols anteriors. El disseny d’una de les primeres arquitectures de "network slicing" i el desplegament d’un "slice" de xarxa 5G totalment operatiu en un Testbed real, és una de les principals contribucions d’aquesta tesi.Postprint (published version

Adaptive Data-driven Optimization using Transfer Learning for Resilient, Energy-efficient, Resource-aware, and Secure Network Slicing in 5G-Advanced and 6G Wireless Systems

Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 134-141)Dissertation (Ph.D)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 20225G–Advanced is the next step in the evolution of the fifth–generation (5G) technology. It will introduce a new level of expanded capabilities beyond connections and enables a broader range of advanced applications and use cases. 5G–Advanced will support modern applications with greater mobility and high dependability. Artificial intelligence and Machine Learning will enhance network performance with spectral efficiency and energy savings enhancements. This research established a framework to optimally control and manage an appropriate selection of network slices for incoming requests from diverse applications and services in Beyond 5G networks. The developed DeepSlice model is used to optimize the network and individual slice load efficiency across isolated slices and manage slice lifecycle in case of failure. The DeepSlice framework can predict the unknown connections by utilizing the learning from a developed deep-learning neural network model. The research also addresses threats to the performance, availability, and robustness of B5G networks by proactively preventing and resolving threats. The study proposed a Secure5G framework for authentication, authorization, trust, and control for a network slicing architecture in 5G systems. The developed model prevents the 5G infrastructure from Distributed Denial of Service by analyzing incoming connections and learning from the developed model. The research demonstrates the preventive measure against volume attacks, flooding attacks, and masking (spoofing) attacks. This research builds the framework towards the zero trust objective (never trust, always verify, and verify continuously) that improves resilience. Another fundamental difficulty for wireless network systems is providing a desirable user experience in various network conditions, such as those with varying network loads and bandwidth fluctuations. Mobile Network Operators have long battled unforeseen network traffic events. This research proposed ADAPTIVE6G to tackle the network load estimation problem using knowledge-inspired Transfer Learning by utilizing radio network Key Performance Indicators from network slices to understand and learn network load estimation problems. These algorithms enable Mobile Network Operators to optimally coordinate their computational tasks in stochastic and time-varying network states. Energy efficiency is another significant KPI in tracking the sustainability of network slicing. Increasing traffic demands in 5G dramatically increase the energy consumption of mobile networks. This increase is unsustainable in terms of dollar cost and environmental impact. This research proposed an innovative ECO6G model to attain sustainability and energy efficiency. Research findings suggested that the developed model can reduce network energy costs without negatively impacting performance or end customer experience against the classical Machine Learning and Statistical driven models. The proposed model is validated against the industry-standardized energy efficiency definition, and operational expenditure savings are derived, showing significant cost savings to MNOs.Introduction -- A deep neural network framework towards a resilient, efficient, and secure network slicing in Beyond 5G Networks -- Adaptive resource management techniques for network slicing in Beyond 5G networks using transfer learning -- Energy and cost analysis for network slicing deployment in Beyond 5G networks -- Conclusion and future scop

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications