Resource discovery for distributed computing systems: A comprehensive survey

Large-scale distributed computing environments provide a vast amount of heterogeneous computing resources from different sources for resource sharing and distributed computing. Discovering appropriate resources in such environments is a challenge which involves several different subjects. In this paper, we provide an investigation on the current state of resource discovery protocols, mechanisms, and platforms for large-scale distributed environments, focusing on the design aspects. We classify all related aspects, general steps, and requirements to construct a novel resource discovery solution in three categories consisting of structures, methods, and issues. Accordingly, we review the literature, analyzing various aspects for each category

Dragon: Multidimensional Range Queries on Distributed Aggregation Trees,

Distributed query processing is of paramount importance in next-generation distribution services, such as Internet of Things (IoT) and cyber-physical systems. Even if several multi-attribute range queries supports have been proposed for peer-to-peer systems, these solutions must be rethought to fully meet the requirements of new computational paradigms for IoT, like fog computing. This paper proposes dragon, an ecient support for distributed multi-dimensional range query processing targeting ecient query resolution on highly dynamic data. In dragon nodes at the edges of the network collect and publish multi-dimensional data. The nodes collectively manage an aggregation tree storing data digests which are then exploited, when resolving queries, to prune the sub-trees containing few or no relevant matches. Multi-attribute queries are managed by linearising the attribute space through space lling curves. We extensively analysed dierent aggregation and query resolution strategies in a wide spectrum of experimental set-ups. We show that dragon manages eciently fast changing data values. Further, we show that dragon resolves queries by contacting a lower number of nodes when compared to a similar approach in the state of the art

Descoberta de recursos para sistemas de escala arbitrarias

Doutoramento em InformáticaTecnologias de Computação Distribuída em larga escala tais como Cloud, Grid, Cluster e Supercomputadores HPC estão a evoluir juntamente com a emergência revolucionária de modelos de múltiplos núcleos (por exemplo: GPU, CPUs num único die, Supercomputadores em single die, Supercomputadores em chip, etc) e avanços significativos em redes e soluções de interligação. No futuro, nós de computação com milhares de núcleos podem ser ligados entre si para formar uma única unidade de computação transparente que esconde das aplicações a complexidade e a natureza distribuída desses sistemas com múltiplos núcleos. A fim de beneficiar de forma eficiente de todos os potenciais recursos nesses ambientes de computação em grande escala com múltiplos núcleos ativos, a descoberta de recursos é um elemento crucial para explorar ao máximo as capacidade de todos os recursos heterogéneos distribuídos, através do reconhecimento preciso e localização desses recursos no sistema. A descoberta eficiente e escalável de recursos ´e um desafio para tais sistemas futuros, onde os recursos e as infira-estruturas de computação e comunicação subjacentes são altamente dinâmicas, hierarquizadas e heterogéneas. Nesta tese, investigamos o problema da descoberta de recursos no que diz respeito aos requisitos gerais da escalabilidade arbitrária de ambientes de computação futuros com múltiplos núcleos ativos. A principal contribuição desta tese ´e a proposta de uma entidade de descoberta de recursos adaptativa híbrida (Hybrid Adaptive Resource Discovery - HARD), uma abordagem de descoberta de recursos eficiente e altamente escalável, construída sobre uma sobreposição hierárquica virtual baseada na auto-organizaçãoo e auto-adaptação de recursos de processamento no sistema, onde os recursos computacionais são organizados em hierarquias distribuídas de acordo com uma proposta de modelo de descriçãoo de recursos multi-camadas hierárquicas. Operacionalmente, em cada camada, que consiste numa arquitetura ponto-a-ponto de módulos que, interagindo uns com os outros, fornecem uma visão global da disponibilidade de recursos num ambiente distribuído grande, dinâmico e heterogéneo. O modelo de descoberta de recursos proposto fornece a adaptabilidade e flexibilidade para executar consultas complexas através do apoio a um conjunto de características significativas (tais como multi-dimensional, variedade e consulta agregada) apoiadas por uma correspondência exata e parcial, tanto para o conteúdo de objetos estéticos e dinâmicos. Simulações mostram que o HARD pode ser aplicado a escalas arbitrárias de dinamismo, tanto em termos de complexidade como de escala, posicionando esta proposta como uma arquitetura adequada para sistemas futuros de múltiplos núcleos. Também contribuímos com a proposta de um regime de gestão eficiente dos recursos para sistemas futuros que podem utilizar recursos distribuíos de forma eficiente e de uma forma totalmente descentralizada. Além disso, aproveitando componentes de descoberta (RR-RPs) permite que a nossa plataforma de gestão de recursos encontre e aloque dinamicamente recursos disponíeis que garantam os parâmetros de QoS pedidos.Large scale distributed computing technologies such as Cloud, Grid, Cluster and HPC supercomputers are progressing along with the revolutionary emergence of many-core designs (e.g. GPU, CPUs on single die, supercomputers on chip, etc.) and significant advances in networking and interconnect solutions. In future, computing nodes with thousands of cores may be connected together to form a single transparent computing unit which hides from applications the complexity and distributed nature of these many core systems. In order to efficiently benefit from all the potential resources in such large scale many-core-enabled computing environments, resource discovery is the vital building block to maximally exploit the capabilities of all distributed heterogeneous resources through precisely recognizing and locating those resources in the system. The efficient and scalable resource discovery is challenging for such future systems where the resources and the underlying computation and communication infrastructures are highly-dynamic, highly-hierarchical and highly-heterogeneous. In this thesis, we investigate the problem of resource discovery with respect to the general requirements of arbitrary scale future many-core-enabled computing environments. The main contribution of this thesis is to propose Hybrid Adaptive Resource Discovery (HARD), a novel efficient and highly scalable resource-discovery approach which is built upon a virtual hierarchical overlay based on self-organization and self-adaptation of processing resources in the system, where the computing resources are organized into distributed hierarchies according to a proposed hierarchical multi-layered resource description model. Operationally, at each layer, it consists of a peer-to-peer architecture of modules that, by interacting with each other, provide a global view of the resource availability in a large, dynamic and heterogeneous distributed environment. The proposed resource discovery model provides the adaptability and flexibility to perform complex querying by supporting a set of significant querying features (such as multi-dimensional, range and aggregate querying) while supporting exact and partial matching, both for static and dynamic object contents. The simulation shows that HARD can be applied to arbitrary scales of dynamicity, both in terms of complexity and of scale, positioning this proposal as a proper architecture for future many-core systems. We also contributed to propose a novel resource management scheme for future systems which efficiently can utilize distributed resources in a fully decentralized fashion. Moreover, leveraging discovery components (RR-RPs) enables our resource management platform to dynamically find and allocate available resources that guarantee the QoS parameters on demand

A lightweight distributed super peer election algorithm for unstructured dynamic P2P systems

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de ComputadoresNowadays with the current growth of information exchange, and the increasing mobility of devices, it becomes essential to use technology to monitor this development. For that P2P networks are used, the exchange of information between agencies is facilitated, these now being applied in mobile networks, including MANETs, where they have special features such as the fact that they are semi-centralized, where it takes peers more ability to make a greater role in the network. But those peer with more capacity, which are used in the optimization of various parameters of these systems, such as optimization\to research, are difficult to identify due to the fact that the network does not have a fixed topology, be constantly changing, (we like to go online and offline, to change position, etc.) and not to allow the exchange of large messages. To this end, this thesis proposes a distributed election algorithm of us greater capacity among several possible goals, enhance research in the network. This includes distinguishing characteristics, such as election without global knowledge network, minimal exchange of messages, distributed decision made without dependence on us and the possibility of influencing the election outcome as the special needs of the network

Peer-to-Peer Networks and Computation: Current Trends and Future Perspectives

This research papers examines the state-of-the-art in the area of P2P networks/computation. It attempts to identify the challenges that confront the community of P2P researchers and developers, which need to be addressed before the potential of P2P-based systems, can be effectively realized beyond content distribution and file-sharing applications to build real-world, intelligent and commercial software systems. Future perspectives and some thoughts on the evolution of P2P-based systems are also provided

Leveraging Resources on Anonymous Mobile Edge Nodes

Smart devices have become an essential component in the life of mankind. The quick rise of smartphones, IoTs, and wearable devices enabled applications that were not possible few years ago, e.g., health monitoring and online banking. Meanwhile, smart sensing laid the infrastructure for smart homes and smart cities. The intrusive nature of smart devices granted access to huge amounts of raw data. Researchers seized the moment with complex algorithms and data models to process the data over the cloud and extract as much information as possible. However, the pace and amount of data generation, in addition to, networking protocols transmitting data to cloud servers failed short in touching more than 20% of what was generated on the edge of the network. On the other hand, smart devices carry a large set of resources, e.g., CPU, memory, and camera, that sit idle most of the time. Studies showed that for plenty of the time resources are either idle, e.g., sleeping and eating, or underutilized, e.g. inertial sensors during phone calls. These findings articulate a problem in processing large data sets, while having idle resources in the close proximity. In this dissertation, we propose harvesting underutilized edge resources then use them in processing the huge data generated, and currently wasted, through applications running at the edge of the network. We propose flipping the concept of cloud computing, instead of sending massive amounts of data for processing over the cloud, we distribute lightweight applications to process data on users\u27 smart devices. We envision this approach to enhance the network\u27s bandwidth, grant access to larger datasets, provide low latency responses, and more importantly involve up-to-date user\u27s contextual information in processing. However, such benefits come with a set of challenges: How to locate suitable resources? How to match resources with data providers? How to inform resources what to do? and When? How to orchestrate applications\u27 execution on multiple devices? and How to communicate between devices on the edge? Communication between devices at the edge has different parameters in terms of device mobility, topology, and data rate. Standard protocols, e.g., Wi-Fi or Bluetooth, were not designed for edge computing, hence, does not offer a perfect match. Edge computing requires a lightweight protocol that provides quick device discovery, decent data rate, and multicasting to devices in the proximity. Bluetooth features wide acceptance within the IoT community, however, the low data rate and unicast communication limits its use on the edge. Despite being the most suitable communication protocol for edge computing and unlike other protocols, Bluetooth has a closed source code that blocks lower layer in front of all forms of research study, enhancement, and customization. Hence, we offer an open source version of Bluetooth and then customize it for edge computing applications. In this dissertation, we propose Leveraging Resources on Anonymous Mobile Edge Nodes (LAMEN), a three-tier framework where edge devices are clustered by proximities. On having an application to execute, LAMEN clusters discover and allocate resources, share application\u27s executable with resources, and estimate incentives for each participating resource. In a cluster, a single head node, i.e., mediator, is responsible for resource discovery and allocation. Mediators orchestrate cluster resources and present them as a virtually large homogeneous resource. For example, two devices each offering either a camera or a speaker are presented outside the cluster as a single device with both camera and speaker, this can be extended to any combination of resources. Then, mediator handles applications\u27 distribution within a cluster as needed. Also, we provide a communication protocol that is customizable to the edge environment and application\u27s need. Pushing lightweight applications that end devices can execute over their locally generated data have the following benefits: First, avoid sharing user data with cloud server, which is a privacy concern for many of them; Second, introduce mediators as a local cloud controller closer to the edge; Third, hide the user\u27s identity behind mediators; and Finally, enhance bandwidth utilization by keeping raw data at the edge and transmitting processed information. Our evaluation shows an optimized resource lookup and application assignment schemes. In addition to, scalability in handling networks with large number of devices. In order to overcome the communication challenges, we provide an open source communication protocol that we customize for edge computing applications, however, it can be used beyond the scope of LAMEN. Finally, we present three applications to show how LAMEN enables various application domains on the edge of the network. In summary, we propose a framework to orchestrate underutilized resources at the edge of the network towards processing data that are generated in their proximity. Using the approaches explained later in the dissertation, we show how LAMEN enhances the performance of applications and enables a new set of applications that were not feasible

Application of overlay techniques to network monitoring

Measurement and monitoring are important for correct and efficient operation of a network, since these activities provide reliable information and accurate analysis for characterizing and troubleshooting a network’s performance. The focus of network measurement is to measure the volume and types of traffic on a particular network and to record the raw measurement results. The focus of network monitoring is to initiate measurement tasks, collect raw measurement results, and report aggregated outcomes. Network systems are continuously evolving: besides incremental change to accommodate new devices, more drastic changes occur to accommodate new applications, such as overlay-based content delivery networks. As a consequence, a network can experience significant increases in size and significant levels of long-range, coordinated, distributed activity; furthermore, heterogeneous network technologies, services and applications coexist and interact. Reliance upon traditional, point-to-point, ad hoc measurements to manage such networks is becoming increasingly tenuous. In particular, correlated, simultaneous 1-way measurements are needed, as is the ability to access measurement information stored throughout the network of interest. To address these new challenges, this dissertation proposes OverMon, a new paradigm for edge-to-edge network monitoring systems through the application of overlay techniques. Of particular interest, the problem of significant network overheads caused by normal overlay network techniques has been addressed by constructing overlay networks with topology awareness - the network topology information is derived from interior gateway protocol (IGP) traffic, i.e. OSPF traffic, thus eliminating all overlay maintenance network overhead. Through a prototype that uses overlays to initiate measurement tasks and to retrieve measurement results, systematic evaluation has been conducted to demonstrate the feasibility and functionality of OverMon. The measurement results show that OverMon achieves good performance in scalability, flexibility and extensibility, which are important in addressing the new challenges arising from network system evolution. This work, therefore, contributes an innovative approach of applying overly techniques to solve realistic network monitoring problems, and provides valuable first hand experience in building and evaluating such a distributed system