Future wireless networks are expected to be highly heterogeneous and ultra dense with different types of small cells underlaid with traditional macro cells. In the presence of hundreds of different types of small cells, centralized control and manual intervention in network management will be inefficient and expensive. In this case, self-organization has been proposed as a key feature in future wireless networks. In a self-organizing network, the nodes are expected to take individual decisions on their behavior. Therefore, individual decision making in resource allocation (i.e., Distributed Resource Allocation) is of vital important. The objective of this thesis is to develop a distributed resource allocation framework for self-organizing small cell networks. Game theory is a powerful mathematical tool which can model and analyze interactive decision making problems of the agents with conflicting interests. Therefore, it is a well-appropriate tool for modeling the distributed resource allocation problem of small cell networks. In this thesis, I consider three different scenarios of distributed resource allocation in self-organizing small cell networks i.e., i). Distributed downlink power and spectrum allocation to ensure fairness for a small cell network of base stations with bounded rationality, ii). Distributed downlink power control for an ultra dense small cell network of base stations with energy constraints, iii). Distributed joint uplink-downlink power control for a small cell network of possibly deceitful nodes with full-duplexing capabilities. Specifically, I utilize evolutionary games, mean field games, and repeated games to model and analyze the three aforementioned scenarios. I also use stochastic geometry, which is a very powerful mathematical tool that can model the characteristics of the networks with random topologies, to design the payoff functions for the formulated evolutionary game and the mean field game. / October 2016

This work presents a software framework for power system analysis, PowerFrame. It is composed of four layers. This four-layer architecture is designed for extensibility and reusability so that more complex power system problems can be tackled within the architecture. In the context of PowerFrame, this work explores complex power system problems. Included in these problems are parallel-placed cables with multiple conductors, and distributed resources operating in unbalanced power distribution systems. Mathematical models are derived. Errors between more exact models and conventional approaches are presented. PowerFrame is also designed to handle distributed computation for intensive power system calculations on multiple, networked computers. Distributed power flow algorithms are presented. Tests on Ethernet LANs show the feasibility of distributed computation under current computer network bandwidth. / Ph. D.

We are witnessing exponential growth in the number of powerful, multiply-connected, energy-rich stationary and mobile nodes, which will make available a massive pool of computing and communication resources. We claim that cloud computing can provide resilient on-demand computing, and more effective and efficient utilization of potentially infinite array of resources. Current cloud computing systems are primarily built using stationary resources. Recently, principles of cloud computing have been extended to the mobile computing domain aiming to form local clouds using mobile devices sharing their computing resources to run cloud-based services. However, current cloud computing systems by and large fail to provide true on-demand computing due to their lack of the following capabilities: 1) providing resilience and autonomous adaptation to the real-time variation of the underlying dynamic and scattered resources as they join or leave the formed cloud; 2) decoupling cloud management from resource management, and hiding the heterogeneous resource capabilities of participant nodes; and 3) ensuring reputable resource providers and preserving the privacy and security constraints of these providers while allowing multiple users to share their resources. Consequently, systems and consumers are hindered from effectively and efficiently utilizing the virtually infinite pool of computing resources. We propose a platform for mobile cloud computing that integrates: 1) a dynamic real-time resource scheduling, tracking, and forecasting mechanism; 2) an autonomous resource management system; and 3) a cloud management capability for cloud services that hides the heterogeneity, dynamicity, and geographical diversity concerns from the cloud operation. We hypothesize that this would enable 'Collaborative Computing Cloud (C3)' for on-demand computing, which is a dynamically formed cloud of stationary and/or mobile resources to provide ubiquitous computing on-demand. The C3 would support a new resource-infinite computing paradigm to expand problem solving beyond the confines of walled-in resources and services by utilizing the massive pool of computing resources, in both stationary and mobile nodes. In this dissertation, we present a C3 management platform, named PlanetCloud, for enabling both a new resource-infinite computing paradigm using cloud computing over stationary and mobile nodes, and a true ubiquitous on-demand cloud computing. This has the potential to liberate cloud users from being concerned about resource constraints and provides access to cloud anytime and anywhere. PlanetCloud synergistically manages 1) resources to include resource harvesting, forecasting and selection, and 2) cloud services concerned with resilient cloud services to include resource provider collaboration, application execution isolation from resource layer concerns, seamless load migration, fault-tolerance, the task deployment, migration, revocation, etc. Specifically, our main contributions in the context of PlanetCloud are as follows. 1. PlanetCloud Resource Management • Global Resource Positioning System (GRPS): • Global mobile and stationary resource discovery and monitoring. A novel distributed spatiotemporal resource calendaring mechanism with real-time synchronization is proposed to mitigate the effect of failures occurring due to unstable connectivity and availability in the dynamic mobile environment, as well as the poor utilization of resources. This mechanism provides a dynamic real-time scheduling and tracking of idle mobile and stationary resources. This would enhance resource discovery and status tracking to provide access to the right-sized cloud resources anytime and anywhere. • Collaborative Autonomic Resource Management System (CARMS): Efficient use of idle mobile resources. Our platform allows sharing of resources, among stationary and mobile devices, which enables cloud computing systems to offer much higher utilization, resulting in higher efficiency. CARMS provides system-managed cloud services such as configuration, adaptation and resilience through collaborative autonomic management of dynamic cloud resources and membership. This helps in eliminating the limited self and situation awareness and collaboration of the idle mobile resources. 2. PlanetCloud Cloud Management Architecture for resilient cloud operation on dynamic mobile resources to provide stable cloud in a continuously changing operational environment. This is achieved by using trustworthy fine-grained virtualization and task management layer, which isolates the running application from the underlying physical resource enabling seamless execution over heterogeneous stationary and mobile resources. This prevents the service disruption due to variable resource availability. The virtualization and task management layer comprises a set of distributed powerful nodes that collaborate autonomously with resource providers to manage the virtualized application partitions. / Ph. D.

Ao considerar um sistema distribuído composto por um conjunto de servidores, clientes e recursos, que caracterizam ambientes como grades ou nuvens computacionais, que oferecem um grande número de recursos distribuídos como CPUs ou máquinas virtuais, os quais são utilizados conjuntamente por diferentes tipos de aplicações, tem-se a necessidade de se ter uma solução para alocação destes recursos. O apoio à alocação dos recursos fornecidos por tais ambientes deve satisfazer todas as solicitações de recursos das aplicações, e fornecer respostas afirmativas para alocação eficiente de recursos, fazer justiça na alocação no caso de pedidos simultâneos entre vários clientes de recursos e responder em um tempo finito a requisições. Considerando tal contexto de grande escala em sistemas distribuídos, este trabalho propõe um algoritmo distribuído para alocação de recursos. Este algoritmo explora a Lógica Fuzzy sempre que um servidor está impossibilitado de atender a uma solicitação feita por um cliente, encaminhando esta solicitação a um servidor remoto. O algoritmo utiliza o conceito de relógio lógico para garantir justiça no atendimento das solicitações feitas em todos os servidores que compartilham recursos. Este algoritmo segue o modelo distribuído, onde uma cópia do algoritmo é executada em cada servidor que compartilha recursos para seus clientes, e todos os servidores tomam parte das decisões com relação a alocação destes recursos. A estratégia desenvolvida tem como objetivo minimizar o tempo de resposta na alocação de recursos, funcionando como um balanceamento de carga em um ambiente cliente-servidor com alto índice de solicitações de recursos pelos clientes. A eficiência do algoritmo desenvolvido neste trabalho foi comprovada através da implementação e comparação com outros algoritmos tradicionais, mostrando a possibilidade de utilização de recursos que pertencem a distintos servidores por uma mesma solicitação de recursos, com a garantia de que esta requisição será atendida, e em um tempo finito. / When considering a distributed system composed of a set of servers, clients, and resources that characterize environments like computational grids or clouds that offer a large number of distributed resources such as CPUs or virtual machines, which are used jointly by different types of applications, there is the need to have a solution for allocating these resources. Support the allocation of resources provided by such environments must satisfy all Requests for resources such applications, and provide affirmative answers to the efficient allocation of resources, to do justice in this allocation in the case of simultaneous Requests from multiple clients and answer these resources in a finite time these Requests. Considering such a context of large- scale distributed systems, this paper proposes a distributed algorithm for resource allocation This algorithm exploits fuzzy logic whenever a server is unable to meet a request made by a client, forwarding this request to a remote server. The algorithm uses the concept of logical clock to ensure fairness in meeting the demands made on all servers that share resources. This algorithm follows a distributed model, where a copy of the algorithm runs on each server that shares resources for its clients and all servers take part in decisions regarding allocation of resources. The strategy developed aims to minimize the response time in allocating resources, functioning as a load-balancing in a client-server environment with high resource Requests by customers.

Ao considerar um sistema distribuído composto por um conjunto de servidores, clientes e recursos, que caracterizam ambientes como grades ou nuvens computacionais, que oferecem um grande número de recursos distribuídos como CPUs ou máquinas virtuais, os quais são utilizados conjuntamente por diferentes tipos de aplicações, tem-se a necessidade de se ter uma solução para alocação destes recursos. O apoio à alocação dos recursos fornecidos por tais ambientes deve satisfazer todas as solicitações de recursos das aplicações, e fornecer respostas afirmativas para alocação eficiente de recursos, fazer justiça na alocação no caso de pedidos simultâneos entre vários clientes de recursos e responder em um tempo finito a requisições. Considerando tal contexto de grande escala em sistemas distribuídos, este trabalho propõe um algoritmo distribuído para alocação de recursos. Este algoritmo explora a Lógica Fuzzy sempre que um servidor está impossibilitado de atender a uma solicitação feita por um cliente, encaminhando esta solicitação a um servidor remoto. O algoritmo utiliza o conceito de relógio lógico para garantir justiça no atendimento das solicitações feitas em todos os servidores que compartilham recursos. Este algoritmo segue o modelo distribuído, onde uma cópia do algoritmo é executada em cada servidor que compartilha recursos para seus clientes, e todos os servidores tomam parte das decisões com relação a alocação destes recursos. A estratégia desenvolvida tem como objetivo minimizar o tempo de resposta na alocação de recursos, funcionando como um balanceamento de carga em um ambiente cliente-servidor com alto índice de solicitações de recursos pelos clientes. A eficiência do algoritmo desenvolvido neste trabalho foi comprovada através da implementação e comparação com outros algoritmos tradicionais, mostrando a possibilidade de utilização de recursos que pertencem a distintos servidores por uma mesma solicitação de recursos, com a garantia de que esta requisição será atendida, e em um tempo finito. / When considering a distributed system composed of a set of servers, clients, and resources that characterize environments like computational grids or clouds that offer a large number of distributed resources such as CPUs or virtual machines, which are used jointly by different types of applications, there is the need to have a solution for allocating these resources. Support the allocation of resources provided by such environments must satisfy all Requests for resources such applications, and provide affirmative answers to the efficient allocation of resources, to do justice in this allocation in the case of simultaneous Requests from multiple clients and answer these resources in a finite time these Requests. Considering such a context of large- scale distributed systems, this paper proposes a distributed algorithm for resource allocation This algorithm exploits fuzzy logic whenever a server is unable to meet a request made by a client, forwarding this request to a remote server. The algorithm uses the concept of logical clock to ensure fairness in meeting the demands made on all servers that share resources. This algorithm follows a distributed model, where a copy of the algorithm runs on each server that shares resources for its clients and all servers take part in decisions regarding allocation of resources. The strategy developed aims to minimize the response time in allocating resources, functioning as a load-balancing in a client-server environment with high resource Requests by customers.

La connectivité totale offerte par la communication sans fil pose un grand nombre d'avantages et de défis pour les concepteurs de la future génération des réseaux sans fil. Un des principaux défis qui se posent est lié à l'interference au niveau des récepteurs. Il est bien reconnu que ce défi réside dans la conception des systèmes d'allocation des ressources qui offrent le meilleur compromis entre l'efficacité et la complexité. L'exploration de ce compromis nécessite des choix judicieux d'indicateurs de performance et des modèles mathématiques. À cet égard, cette thèse est consacrée à certains aspects techniques et mathématiques d'allocation des ressources dans les réseaux sans fil. En particulier, nous demontrons que l'allocation de ressources efficace dans les réseaux sans fil doit prendre en compte les paramètres suivants: (i) le taux de changement de l'environnement, (ii) le modèle de trafic, et (iii) la quantité d'informations disponibles aux émetteurs. Comme modeles mathématiques dans cet étude, nous utilisons la théorie d'optimisation et la théorie des jeux. Nous sommes particulièrement intéressés à l'allocation distribuée des ressources dans les réseaux avec des canaux à évanouissement lent et avec des informations partielles du canal aux émetteurs. Les émetteurs avec information partielle disposent d'informations exactes de leur propre canal ainsi que la connaissance statistique des autres canaux. Dans un tel contexte, le système est fondamentalement détérioré par une probabilité outage non nul. Nous proposons des algorithmes distribués à faible complexité d'allocation conjointe du débit et de la puissance visant à maximiser le "throughput" individuel.

In recent years, the demand for data center computing has increased significantly due to the growing popularity of cloud applications and Internet-based services. Today's large data centers host hundreds of thousands of servers and the peak power rating of a single data center may even exceed 100MW. The combined electricity consumption of global data centers accounts for about 3% of worldwide production, raising serious concerns about their carbon footprint. The utility providers and governments are consistently pressuring data center operators to reduce their carbon footprint and energy consumption. While these operators (e.g., Apple, Facebook, and Google) have taken steps to reduce their carbon footprints (e.g., by installing on-site/off-site renewable energy facility), they are aggressively looking for new approaches that do not require expensive hardware installation or modification. This dissertation focuses on developing algorithms and systems to improve the sustainability in data centers without incurring significant additional operational or setup costs. In the first part, we propose a provably-efficient resource management solution for a self-managed data center to cap and reduce the carbon emission while maintaining satisfactory service performance. Our solution reduces the carbon emission of a self-managed data center to net-zero level and achieves carbon neutrality. In the second part, we consider minimizing the carbon emission in a hybrid data center infrastructure that includes geographically distributed self-managed and colocation data centers. This segment identifies and addresses the challenges of resource management in a hybrid data center infrastructure and proposes an efficient distributed solution to optimize the workload and resource allocation jointly in both self-managed and colocation data centers. In the final part, we explore sustainable resource management from cloud service users' point of view. A cloud service user purchases computing resources (e.g., virtual machines) from the service provider and does not have direct control over the carbon emission of the service provider's data center. Our proposed solution encourages a user to take part in sustainable (both economical and environmental) computing by limiting its spending on cloud resource purchase while satisfying its application performance requirements.

Abstract Peer-to-Peer (P2P) networks have been applied in many applications for sharing resources such as storage space, media files or network bandwidth. Their main benefits include decentralization, self-organization, and scalability. Moreover, P2P technologies are evolving towards hybrid systems, where P2P networks are used in those parts of a larger system to leverage the decentralization most efficiently. The examples include cloud computing, where P2P networks are used in sharing computing resources, and Machine-to-Machine communication, where P2P networks are used for resource discovery. In super-peer overlays, the nodes are either regular nodes or super nodes that are located higher in the node hierarchy. This type of overlay explores the heterogeneity of peers in the overlay network to enable applications to run more efficiently. Leveraging the advantage of a super-peer overlay for service provision is an important issue. This thesis contributes to the research and development of super-peer-based coordination service provision from three aspects. Firstly, a super-peer-based coordinated service provision framework is proposed to coordinate different service groups and service peers in resource sharing, aiming to enable service groups to adapt to dynamic service demands. The proposed framework is evaluated using the following performance metrics: service response time, scalability, robustness, and communication traffic, in comparison to related work. Secondly, an efficient algorithm for rapidly constructing a robust super-peer overlay is proposed. The algorithm introduces a super-peer candidate based method for super-peer selection and a two-hop search method for finding client peers. Performance evaluation takes into account the convergence time of building a super-peer overlay, communication overhead, scalability, robustness. A comparison to related work is also conducted. Thirdly, the architecture of distributed resource discovery based on P2P overlay for Machine-to-Machine service provision is proposed. The architecture supports heterogeneous devices using different communication protocols in resource registration and discovery for achieving interoperability. As a part of the thesis, a functional real-world prototype is implemented and verified with a simple demonstration. Preliminary evaluation on the prototype indicates that caching can improve the response time of resource lookup dramatically. / Tiivistelmä Vertaisverkkoja on hyödynnetty resurssien kuten tallennustilan, mediasisältöjen ja tietoliikennekapasiteetin jakamisessa. Niiden etuja perinteisiin keskitettyihin järjestelmiin verrattuna ovat hajautettu arkkitehtuuri, itseorganisoituvuus ja skaalautuvuus. Vertaisverkkoja käytetään yhä useammin järjestelmän osien toteuttamisessa, joissa hajautettujen resurssien hyödyntämisellä saavutetaan suurimmat edut. Esimerkkeinä ovat pilvilaskenta, jossa vertaisverkkoa käytetään laskentaresurssien jakamiseen, sekä laitteidenvälinen kommunikaatio, jossa vertaisverkkoja käytetään resurssien löytämiseen. Hierarkkisissa vertaisverkoissa niihin kytkeytyneet laitteet jaotellaan laitteiden kapasiteetin mukaan tavallisiin noodeihin ja näiden yläpuolella hierarkiassa toimiviin ylinoodeihin. Ylinoodeihin perustuva vertaisverkon kuoriverkko hyödyntää yksittäisten verkon noodien eli laitteiden erilaisuutta, jotta verkko voisi toimia tehokkaammin. Tämän ominaisuuden hyödyntäminen on erityisen tärkeää palvelun tarjonnassa. Tässä työssä on tutkittu ylinoodeihin perustuvan vertaisverkon palvelun tarjontaa kolmesta näkökulmasta. Ensimmäiseksi, työssä ehdotetaan ylinoodien koordinoimaa palveluntarjonnan toimintamallia resurssien jakamisessa. Toimintamallissa palveluryhmät ja palvelunoodit adaptoituvat dynaamisesti palvelupyyntöjen tarpeisiin. Tämän ratkaisun suorituskykyä arvioidaan palvelun vasteajan, skaalautuvuuden, robustisuuden ja tietoliikennemäärän suhteen verrattuna aiempiin ratkaisuihin. Toiseksi, työssä esitellään tehokas algoritmi robustin ylinoodikuoriverkon nopeaan muodostamiseen. Algoritmi käyttää ylinoodiehdokasmenetelmää ja kahden hypyn hakumetodia asiakasnoodien etsimisessä. Suorituskyvyn arvioinnissa otetaan huomioon ylinoodikuoriverkon konvergoitumisaika, tietoliikenneviestinnän aiheuttama ylimääräinen kuormitus, sekä järjestelmän skaalautuvuus ja robustisuus. Esitetyn algoritmin tehokkuutta arvioidaan vertaamalla näitä suorituskykymittareita aiempiin ratkaisuihin. Kolmanneksi, työssä esitellään hajautettu resurssihakemiston arkkitehtuuri, joka perustuu laitteiden välisen kuoriverkon palveluntarjontaan. Arkkitehtuuri tukee erilaisten laitteiden ja niiden käyttämien protokollien resurssien rekisteröintiä ja löytämistä yhteensopivuuden saavuttamiseksi. Väitöskirjatyön osana on toteutettu toimiva prototyyppi, jonka toimivuus on todennettu demonstraation avulla. Prototyypillä tehdyt mittaukset antavat perustellun syyn olettaa, että esitetyn ratkaisun mukainen välimuistin käyttö voi merkittävästi lyhentää resurssien etsimisen vasteaikaa.