Coordinated node and link mapping VNE using a new paths algebra strategy

AbstractThe main resource allocation research challenge in network virtualization is the Virtual Network Embedding (VNE) Problem. It consists of two stages, usually performed separately: node mapping and link mapping. A new mathematical multi-constraint routing framework for linear and non-linear metrics called “paths algebra” has already been proposed to solve the second stage, providing good results thanks to its flexibility. Unlike existing approaches, paths algebra is able to include any kind of network parameters (linear and non-linear) to solve VNE in reasonable runtime. While paths algebra had only been used to solve one stage (link mapping) of the VNE, this paper suggests an improvement to solve VNE using the paths algebra-based strategy by coordinating, in a single stage, the mapping of nodes and links based on a ranking made of the bi-directional pair of nodes of the substrate network, ordered by their available resources. Simulation results show that the New Paths Algebra-based strategy shows significant performance improvements when compared against the uncoordinated paths Algebra-based link mapping approach

Study, evaluation and contributions to new algorithms for the embedding problem in a network virtualization environment

Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change and to enable a new business model decoupling the network services from the underlying infrastructure. The problem of embedding virtual networks in a substrate network is the main resource allocation challenge in network virtualization and is usually referred to as the Virtual Network Embedding (VNE) problem. VNE deals with the allocation of virtual resources both in nodes and links. Therefore, it can be divided into two sub-problems: Virtual Node Mapping where virtual nodes have to be allocated in physical nodes and Virtual Link Mapping where virtual links connecting these virtual nodes have to be mapped to paths connecting the corresponding nodes in the substrate network. Application of network virtualization relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as VNE algorithms. This thesis proposes a set of contributions to solve the research challenges of the VNE that have not been tackled by the research community. To do that, it performs a deep and comprehensive survey of virtual network embedding. The first research challenge identified is the lack of proposals to solve the virtual link mapping stage of VNE using single path in the physical network. As this problem is NP-hard, existing proposals solve it using well known shortest path algorithms that limit the mapping considering just one constraint. This thesis proposes the use of a mathematical multi-constraint routing framework called paths algebra to solve the virtual link mapping stage. Besides, the thesis introduces a new demand caused by virtual link demands into physical nodes acting as intermediate (hidden) hops in a path of the physical network. Most of the current VNE approaches are centralized. They suffer of scalability issues and provide a single point of failure. In addition, they are not able to embed virtual network requests arriving at the same time in parallel. To solve this challenge, this thesis proposes a distributed, parallel and universal virtual network embedding framework. The proposed framework can be used to run any existing embedding algorithm in a distributed way. Thereby, computational load for embedding multiple virtual networks is spread across the substrate network Energy efficiency is one of the main challenges in future networking environments. Network virtualization can be used to tackle this problem by sharing hardware, instead of requiring dedicated hardware for each instance. Until now, VNE algorithms do not consider energy as a factor for the mapping. This thesis introduces the energy aware VNE where the main objective is to switch off as many network nodes and interfaces as possible by allocating the virtual demands to a consolidated subset of active physical networking equipment. To evaluate and validate the aforementioned VNE proposals, this thesis helped in the development of a software framework called ALgorithms for Embedding VIrtual Networks (ALEVIN). ALEVIN allows to easily implement, evaluate and compare different VNE algorithms according to a set of metrics, which evaluate the algorithms and compute their results on a given scenario for arbitrary parameters

Online power aware coordinated virtual network embedding with 5G delay constraint

Solving virtual network embedding problem with delay constraint is a key challenge to realize network virtualization for current and future 5G core networks. It is an NP-Hard problem, composed of two sub-problems, one for virtual node embedding, and another one for virtual edges embedding, usually solved separately or with a certain level of coordination, which in general could result on rejecting some virtualization requests. Therefore, the main contributions of this paper focused on introducing an online power aware algorithm to solve the virtual network embedding problem using less resources and less power consumption, while considering end-to-end delay as a main embedding constraint. The new algorithm minimizes the overall power of the physical network through efficiently maximizing the utilization of the active infrastructure resources and putting into sleeping mode all non-active ones. Evaluations of the proposed algorithm conducted against the state of art algorithms, and simulation results showed that, when end-to-end delay was not included the proposed online algorithm managed to reduce the total power consumption of the physical network by 23% lower than the online energy aware with dynamic demands VNE algorithm, EAD-VNE. However, when end-to-end delay was included, it significantly influenced the whole embedding process and resulted on reducing the average acceptance ratios by 16% compared to the cases without delay.Peer ReviewedPostprint (published version

Network Virtualization strategy based on Paths Algebra to implement the concept of Infrastructure as a Service

One of the main challenges of network virtualization is the virtual network embedding problem(VNE). The objective of the VNE is to map a set of Virtual Network Request (VNR) to a physical node and link. VNR is composed by a set of virtual nodes and links with several demands (Processing power, Bandwidth.), which they need to be assigned into a set of paths in the substrate network with sufficient resources to accomplish their demands. Furthermore, these embedding can be optimized with regard to several parameters, such as: embedding cost, link bandwidth, energy- efficiency, packet loss rate, throughput, etc. To solve the VNE program a mathematical tools was proposed, called “Paths algebra”. This framework can helped solve the multiple multi- constraint routing problems of VNE using linear metrics as bandwidth, number of hops and delay, or non-linear metrics as availability and package loss rate Most of the existing VNE proposals treat the single-path virtual link-mapping problem as a mono-constraint, that is, their objective is to map the virtual links in substrate paths that minimize/maximize the usage of one resource (typically bandwidth). This paper introduces a virtual link mapping approach supporting multiple constraints using the “paths algebra” routing framewor

Power aware resource allocation and virtualization algorithms for 5G core networks

Most of the algorithms that solved the resource allocation problem, used to apply greedy algorithms to select the physical nodes and shortest paths to select the physical edges, without sufficient coordination between selecting the physical nodes and edges. This lack of coordination may degrade the overall acceptance ratios and network performance as whole, in addition, that may include non-necessary physical resources, which will consume more power and computational processing capacities, as well as cause more delays. Therefore, the main objective of this PhD thesis is to develop power aware resource allocation and virtualization algorithms for 5G core networks, which will be achieved through developing a virtualization resource allocation technique to perform virtual nodes and edges allocations in full coordination, and on the least physical resources. The algorithms will be general and solve the resource allocation problem for virtual network embedding and network function virtualization frameworks, while minimizing the total consumed power in the physical network, and consider end-to-end delay and migration as new optional features. This thesis suggested to solve the power aware resource allocation problem through brand new algorithms adopting a new technique called segmentation, which fully coordinates allocating the virtual nodes and edges together, and guarantees to use the very least physical resources to minimize the total power consumption, through consolidating the virtual machines into least number of nodes as much as possible. The proposed algorithms, solves virtual network embedding problem for off-line and on-line scenarios, and solves resource allocations for network function virtualization environment for off-line, on-line, and migration scenarios. The evaluations of the proposed off-line virtual network embedding algorithm, PaCoVNE, showed that it managed to save physical network power consumption by 57% in average, and the on-line algorithm, oPaCoVNE, managed to minimize the average power consumption in the physical network by 24% in average. Regarding allocation times of PaCoVNE and oPaCoVNE, they were in the ranges of 20-40 ms. For network function virtualization environment, the evaluations of the proposed offline NFV power aware algorithm, PaNFV, showed that on average it had lower total costs and lower migration cost by 32% and 65:5% respectively, compared to the state-of-art algorithms, while the on-line algorithm, oPaNFV, managed to allocate the Network Services in average times of 60 ms, and it had very negligible migrations. Nevertheless, this thesis suggests that future enhancements for the proposed algorithms need to be focused around modifying the proposed segmentation technique to solve the resource allocation problem for multiple paths, in addition to consider power aware network slicing, especially for mobile edge computing, and modify the algorithms for application aware resource allocations for very large scale networks. Moreover, future work can modify the segmentation technique and the proposed algorithms, by integrating machine learning techniques for smart traffic and optimal paths prediction, as well as applying machine learning for better energy efficiency, faster load balancing, much accurate resource allocations based on verity of quality of service metrics.La mayoría de los algoritmos que resolvieron el problema de asignación de recursos, se utilizaron para aplicar algoritmos codiciosos para seleccionar los nodos físicos y las rutas más cortas para seleccionar los bordes físicos, sin una coordinación suficiente entre la selección de los nodos físicos y los bordes. Esta falta de coordinación puede degradar los índices de aceptación generales y el rendimiento de la red en su totalidad, además, que puede incluir recursos físicos no necesarios, que consumirán más potencia y capacidades de procesamiento computacional, además de causar más retrasos. Por lo tanto, el objetivo principal de esta tesis doctoral es desarrollar algoritmos de virtualización y asignación de recursos para las redes centrales 5G, que se lograrán mediante el desarrollo de una técnica de asignación de recursos de virtualización para realizar nodos virtuales y asignaciones de bordes en total coordinación, y al menos recursos físicos. Los algoritmos serán generales y resolverán el problema de asignación de recursos para la integración de redes virtuales y los marcos de virtualización de funciones de red, al tiempo que minimizan la potencia total consumida en la red física y consideran el retraso y la migración de extremo a extremo como nuevas características opcionales. Esta tesis sugirió resolver el problema de la asignación de recursos conscientes de la potencia a través de nuevos algoritmos que adoptan una nueva técnica llamada segmentación, que coordina completamente la asignación de los nodos virtuales y los bordes, y garantiza el uso de los recursos físicos mínimos para minimizar el consumo total de energía, a través de consolidar las máquinas virtuales en el menor número de nodos tanto como sea posible. Los algoritmos propuestos solucionan el problema de integración de la red virtual para los escenarios sin conexión y en línea, y resuelve las asignaciones de recursos para el entorno de virtualización de la función de red para los escenarios sin conexión, en línea y de migración. Las evaluaciones del algoritmo de integración de red virtual sin conexión propuesto, PaCoVNE, mostraron que logró ahorrar el consumo de energía de la red física en un 57% en promedio, y el algoritmo en línea, oPaCoVNE, logró minimizar el consumo de energía promedio en la red física en un 24% en promedio. Con respecto a los tiempos de asignación de PaCoVNE y oPaCoVNE, estuvieron en los rangos de 20-40 ms. Para el entorno de virtualización de la función de red, las evaluaciones del algoritmo consciente de la potencia NFV sin conexión propuesto, PaNFV, mostraron que, en promedio, tenía menores costos totales y menores costos de migración en un 32% y 65: 5% respectivamente, en comparación con el estado de la técnica. Los algoritmos, mientras que el algoritmo en línea, oPaNFV, logró asignar los Servicios de Red en tiempos promedio de 60 ms, y tuvo migraciones muy insignificantes. Sin embargo, esta tesis sugiere que las futuras mejoras para los algoritmos propuestos deben centrarse en modificar la técnica de segmentación propuesta para resolver el problema de asignación de recursos para múltiples rutas, además de considerar el corte de la red que requiere energía, especialmente para la computación de borde móvil, y modificar el Algoritmos para asignaciones de recursos conscientes de la aplicación para redes de gran escala. Además, el trabajo futuro puede modificar la técnica de segmentación y los algoritmos propuestos, mediante la integración de técnicas de aprendizaje automático para el tráfico inteligente y la predicción de rutas óptimas, así como la aplicación del aprendizaje automático para una mejor eficiencia energética, un equilibrio de carga más rápido, asignaciones de recursos mucho más precisas basadas en la veracidad de Métricas de calidad de servicio

Optimisation de l'intégration des requêtes de réseaux virtuels dans un environnement multiCloud

De nos jours, l’Infrastructure-service ou Infrastructure as a Service (IaaS) est devenue le modèle de service du Cloud Computing le plus largement adopté. Dans ce modèle d’affaires, un fournisseur de service ou Service Provider (SP) peut louer, à partir d’un ou de plusieurs fournisseurs d’infrastructure ou Cloud Providers (CPs), des ressources physiques proposées en tant que services (calcul, stockage, accès réseau, routage, etc.). Ces derniers sont encapsulés dans des machines virtuelles ou Virtual Machines (VMs), interconnectées et assemblées sous forme de requête de réseau virtuel ou Virual Network Request (VNR), dans le but de créer des réseaux virtuels hétérogènes offrant des applications et des services personnalisés à des utilisateurs finaux. Malgré son adoption largement réussie, le modèle IaaS reste toujours confronté à un défi fondamental en matière de gestion de ressources, qui consiste en l’optimisation de l’intégration efficace et dynamique des VNRs dans les infrastructures sous-jacentes distribuées et partagées. En effet, des ressources hétérogènes doivent être efficacement allouées afin de pouvoir héberger les VMs dans des centres de données ou data centers (DCs) spécifiques, et de faire router les liaisons virtuelles ou Virtual Links (VLs), représentant le trafic échangé entre les VMs interconnectées, sur des chemins appropriés entre les DCs. Cette allocation de ressources et de services vise généralement à satisfaire des contraintes de performance, de Qualité de Service (QdS), de sécurité et de localisation géographique, imposées par le SP. Dans le contexte de la virtualisation de réseau, ce problème est connu NP-difficile, sous le nom d’intégration de réseau virtuel ou Virtual Network Embedding (VNE), qui n’a été abordé que récemment dans la littérature dans le cadre d’un réseau multiCloud, où les infrastructures Cloud sous-jacents appartiennent à différents CPs indépendants. Le VNE dans un environnement multiCloud ajoute plus de complexité et des défis d’évolutivitité au problème, car l’ensemble du processus nécessite une approche de résolution hiérarchique, dans laquelle deux phases principales d’opération sont réalisées, chacune ayant des objectifs différents selon les acteurs : la phase de partitionnement des VNRs à travers le réseau multiCloud, suivie de la phase d’intégration des segments de VNRs dans les infrastructures intraCloud sélectionnées. Dans la première phase réalisée indirectement par le SP, ce dernier mandate généralement un fournisseur de réseau virtuel ou Virtual Network Provider (VNP). Le VNP agit en tant que service de courtage virtuel pour le compte du SP, afin de sélectionner adéquatement des CPs capables de répondre efficacement aux objectifs et exigences du SP, puis partitionne les VNRs en plusieurs segments. Dans la deuxième phase, qui correspond notamment au problème bien connu du VNE dans le cadre d’un seul CP et qui a été largement abordé dans des travaux de recherche antérieurs, chaque CP sélectionné utilise une approche d’hébergement adéquate pour intégrer les segments de VNRs qui lui sont attribués dans son réseau intraCloud.----------ABSTRACT: Nowadays, the Infrastructure as a Service (IaaS) has become the most widely adopted cloud service model. In this business paradigm, a Service Provider (SP) can lease, from one or more Cloud Providers (CPs), infrastructure layer resources (processing, storage, network access, routing services, etc.) packaged into interconnected virtual machines (VMs) and assembled as a virtual network request (VNR), in order to build heterogeneous virtual networks that will offer customized services and applications to its end users. Despite its successful adoption, the IaaS model faces a fundamental resource management challenge lying in the efficient and dynamic embedding of VNRs onto distributed and shared substrate infrastructures. Heterogenous resources need to be efficiently allocated to host VMs in specific substrate data centers (DCs) and to route virtual links (VLs), representing the exchanged traffic between interconnected VMs, onto suitable substrate paths between the hosting DCs, in order to satisfy performance, Quality of Service (QoS), security and geographical location constraints imposed by the SP. In the context of network virtualization, this issue is usually referred to as the NP-hard Virtual Network Embedding (VNE) problem, which has been only recently addressed in the literature within a multicloud network, where the substrate infrastructures are owned by different and independent CPs. Such a context adds more complexity and scalability issues, since the whole VNE process requires a hierarchical resolution approach, where two major phases of operation are performed, each of them having different purposes according to the acting player: the multicloud VNRs splitting phase, followed by the intra-cloud VNR segments mapping phase. In the first phase played indirectly by the SP, the latter generally mandates a Virtual Network Provider (VNP), which acts as a virtual brokerage service on behalf of the SP, in order to select eligible CPs based on the SP’s goals and requirements, and split the VNRs into segments. In the second phase, which corresponds to the well known VNE within a single CP largely addressed in past research works, each selected CP uses a mapping approach to embed the assigned VNR segments into its intra-cloud network

Contribution to multi-domain network slicing : resource orchestration framework and algorithms

5G/6G services and applications, in the context of the eMBB, mMTC and uRLLC network slicing framework, whose network infrastructure requirements may span beyond the coverage area of a single Infrastructure Provider (InP), are envisaged to be supported by leasing resources from multiple InPs. A challenging aspect for a Service Provider (SP) is how to obtain an optimal set of InPs on which to provision the requests and the particular substrate nodes and links within each InP on which to map the different VNFs and virtual links of the service requests, respectively, for a seamless, reliable and cost-effective orchestration of service requests. Existing works in this area either perform service mapping in uncoordinated manner, do not incorporate service reliability or do so from the perspective of stateless VNFs. Also they assume full information disclosure, or are based on exact approaches, which considerations are not well suited for future network scenarios characterized by delay sensitive mission critical applications and resource constrained networks. This thesis contributes to the above challenge by breaking the multi-domain service orchestration problem into two interlinked sub-problems that are solved in a coordinated manner: (1) Request splitting/partitioning (sub-problem 1), involving obtaining a subset of InPs and the corresponding inter-domain links on which to provision the different VNFs and virtual links of the service request; (2) Intra-domain VNF orchestration (sub-problem 2), involving obtaining the intra-domain nodes and links to provision the VNFs and virtual links of the sub-SFC associated with each InP. In this way, the thesis sets out four key targets that are necessary to align with the mission critical and delay sensitive use-cases envisaged in 5G and future networks in terms of service deployment cost and QoS: (1) coordinated mapping of service requests, with a view of realizing better utilization of the substrate resources; (2) survivability and fault-tolerant orchestration of service requests, to tame both QoS violations and the penalties from such violations; (3) limited disclosure of InP internal information, in order adhere to the privacy requirements InPs, and (4) achieving all the above targets in polynomial time. In order to realize the above targets, the thesis sought for solution techniques that are: (1) able to incorporate information learned in the previous solutions search space and historical mapping decisions, hence, resulting in acceptable performance even in scenarios of limited information exposure and fuzzy environments; (2) robust and less problem specific, hence, can be tailored to different optimization objectives, network topologies and service request constraints, thus enabling to deal with requests with either chained topologies or with bifurcated paths; (3) capable of dealing with an optimization problem that is jointly affected by multiple attributes, since in practice, the service deployment cost is jointly affected by multiple conflicting costs; (4) able to realize near-optimal solutions in practical run-times, thus rendering well suited approaches for delay sensitive and resource constrained scenarios. Three different algorithms namely, an RL, Genetic Algorithm (GA) and a fully distributed multi-stage graph-based algorithms are proposed for sub-problem 1. In addition, five different algorithms based on GA, Harmony search, RL, and multi-stage graph approach are proposed for sub-problem 2. Finally, in order to guide the implementation and adherence of the thesis proposals to the four main targets of the thesis, an architectural framework is proposed, aligned with the ETSI NFV-MANO architectural framework. Overall, the simulations results proved that the thesis proposals are optimized in terms of request acceptance ratios, mapping cost and execution time, hence, rendering such proposals well suited for 5G and future scenarios.Els serveis que es poden presentar en el marc de la tecnologia de “slicing” de xarxa de 5G/6G, com ara eMBB, mMTC o uRLLC, es possible que no els pugui oferir un sol proveïdor d’infraestructura (InP) degut a les limitacions que pot tenir la seva xarxa, i per tant que faci necessària la cooperació de múltiples InPs. En aquest cas, el primer repte que afronta el Proveïdor de Servei (SP) que rep la sol·licitud de desplegament es determinar el conjunt òptim de InPs que hi han d’intervenir i en concret els nodes i enllaços de cada un d’ells que s’han d’utilitzar per al mapatge de les diferents VNFs i enllaços virtuals de la sol·licitud. Els treballs que existeixen en aquesta àrea duen a terme el mapatge del servei be sigui de manera no coordinada, o no incorporen la fiabilitat, o ho fan des de la perspectiva de VNFs sense estat. També, pressuposen la divulgació total de la informació, o estan basats en metodologies exactes que fa que no siguin idonis per a escenaris de xarxes del futur, caracteritzats per aplicacions de missió critica, sensibles al retard i sobre xarxes amb recursos limitats. Aquesta tesi contribueix a afrontar aquests reptes dividint el problema d’orquestració de serveis multi domini en dos subproblemes relacionats, que es resolen de manera coordinada. (1) Divisió / partició de la sol·licitud de servei (sub-problema 1), que implica l'obtenció d'un subconjunt d'InPs i els enllaços interdomini corresponents sobre els quals proporcionar les diferents VNF i enllaços virtuals de la sol·licitud de servei; (2) Orquestració VNF intradomini (sub-problema 2), que implica l'obtenció dels nodes i enllaços intradomini per aprovisionar les VNF i enllaços virtuals dels sub-SFC associats a cada InP. D'aquesta manera, la tesi estableix quatre objectius clau que són necessaris per alinear-se amb els casos d'ús de missió crítica i sensibles al retard previstos en 5G i xarxes futures en termes de cost de desplegament del servei i QoS: (1) mapatge coordinat de les sol·licituds de servei, amb l'objectiu de realitzar una millor utilització dels recursos del substrat; (2) orquestració de les sol·licituds de servei contemplant la supervivència del servei en situacions de fallides, minimitzant les violacions de la QoS i les sancions derivades d'aquestes violacions; (3) divulgació limitada de la informació interna de l’InP, per tal d'adherir-se als requisits de privadesa dels InPs, i (4) aconseguir tots els objectius anteriors en temps polinòmic. Per tal de realitzar els objectius anteriors, la tesi busca solucions que siguin: (1) capaces d'incorporar informació apresa en les solucions anteriors de l'espai de cerca i decisions de mapatge històric, donant lloc a un rendiment acceptable fins i tot en escenaris d'exposició limitada a la informació i entorns difusos; (2) robustes i menys dependents dels problemes específics, i per tant, que es poden adaptar a diferents objectius d'optimització, topologies de xarxa i restriccions de sol·licitud de servei, permetent així fer front a sol·licituds amb cadenes de funcions de topologies molt diverses; (3) capaces de fer front a un problema d'optimització de múltiples atributs, ja que a la pràctica, el cost de desplegament del servei depèn de múltiples costos; (4) capaces de trobar solucions gairebé òptimes en temps suficientment breus, resultant així adequades a escenaris sensibles al retard i amb limitació de recursos. La tesi proposa tres algorismes diferents per al sub-problema 1: un algorisme de RL, un algorisme genètic (GA) i un algorisme multi etapa basat en grafs i completament distribuït. A més, es proposen cinc algorismes diferents basats en l'enfocament de grafs, un algorisme GA, un algorisme de cerca d’harmonia, un algorisme de RL i un algorisme multi-etapa per al sub-problema 2. Finalment, per tal de guiar la implementació i l'adhesió de les propostes als quatre objectius principals de la tesi, es proposa...Postprint (published version

A Hybrid Reliable Heuristic Mapping Method Based on Survivable Virtual Networks for Network Virtualization

The reliable mapping of virtual networks is one of the hot issues in network virtualization researches. Unlike the traditional protection mechanisms based on redundancy and recovery mechanisms, we take the solution of the survivable virtual topology routing problem for reference to ensure that the rest of the mapped virtual networks keeps connected under a single node failure condition in the substrate network, which guarantees the completeness of the virtual network and continuity of services. In order to reduce the cost of the substrate network, a hybrid reliable heuristic mapping method based on survivable virtual networks (Hybrid-RHM-SVN) is proposed. In Hybrid-RHM-SVN, we formulate the reliable mapping problem as an integer linear program. Firstly, we calculate the primary-cut set of the virtual network subgraph where the failed node has been removed. Then, we use the ant colony optimization algorithm to achieve the approximate optimal mapping. The links in primary-cut set should select a substrate path that does not pass through the substrate node corresponding to the virtual node that has been removed first. The simulation results show that the acceptance rate of virtual networks, the average revenue of mapping, and the recovery rate of virtual networks are increased compared with the existing reliable mapping algorithms, respectively

Architectures and algorithms for dynamic overlay networks

Most of today’s Internet of Things (IoT) applications assume that data will be moved offdevices into centralized cloud platforms. While existing IoT systems leverage cloud-based analytics for meaningful data reasoning, the assumption that data should always be moved off the devices is problematic. The amount of data to be moved from devices over Internet gateways to cloud platforms is huge which potentially make it cost inefficient. In other scenarios, privacy concerns of customers or organizational rules complicate the process of transferring data to third-party data centers.This dissertation proposes architectures and dynamic overlay network algorithms for in-networkand edge processing of data offered by the globally available IoT devices and provides a global platform for meaningful and responsive data analysis and decision making. The proposed techniques shift IoT analytics from a ”collect data now and analyze it later” scenario to directlyproviding meaningful information from the in-network processing of devices data at or near thedevices. The techniques serve future IoT use cases including distributed context awareness, on-demand data analysis, and in-network decision making. The dissertation comprises three main components.The first component is a device management protocol for cloning devices’ data in proximateEdge Computing platforms. Unlike existing application-layer IoT management protocols theproposed protocol uses the LTE LTE-A radio frame structure, device-to-device communication,and IoT data properties to avoid excessive network access latency in existing technologies.The second component realizes distributed IoT analytics as overlay networks of devices clones. By means of virtual network embedding, it selects and interconnects devices’ clones to efficiently realize applications’ virtual topologies to achieve goals such as minimum latency, minimum infrastructure cost, or maximum infrastructure utilization.Finally, the dissertation presents a communication middleware that allows autonomous discovery, self-deployment, and online migration of devices’ clones across heterogeneous Edge computing platforms. The middleware ensures that communication latency between clones is kept minimum despite the uncontrolled variability of the network and hosting platforms conditions.We evaluate the proposed architectures and algorithms through simulations and prototypeimplementation of various components in controlled testbed environments, which we evaluateusing real user applications. We explore the feasibility of the proposed techniques from boththeoretical and practical perspectives.Keywords: Cloud Computing, Internet of Things, Algorithmic Game Theory, Compressive Sensin