10 research outputs found
Proposal of a Flooding-based Flexible Search Method for Chord Networks
Recently, peer-to-peer (P2P) network models have been attracting considerable attention. P2P models can be classified into structured and unstructured P2P models. Representative search methods for structured and unstructured P2P networks are Chord and Flooding, respectively. It is difficult to realize flexible search in Chord networks. On the other hand, a large number of query transmissions are required for Flooding. In this study, we propose a Flooding-based search method for Chord networks. Our method works separately from the traditional search method for Chord networks. It suppresses the transmission of redundant queries by considering the topological properties of the structured networks and enables flexible search in Chord networks. Through simulation experiments, we evaluate the performance of the proposed search method and show its effectiveness
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
Probabilistic best-fit multi-dimensional range query in Self-Organizing Cloud
With virtual machine (VM) technology being increasingly mature, computing resources in modern Cloud systems can be partitioned in fine granularity and allocated on demand with 'pay-as-you-go' model. In this work, we study the resource query and allocation problems in a Self- Organizing Cloud (SOC), where host machines are connected by a peer-to-peer (P2P) overlay network on the Internet. To run a user task in SOC, the requester needs to perform a multi-dimensional range search over the P2P network for locating host machines that satisfy its minimal demand on each type of resources. The multi-dimensional range search problem is known to be challenging as contentions along multiple dimensions could happen in the presence of the uncoordinated analogous queries. Moreover, low resource matching rate may happen while restricting query delay and network traffic. We design a novel resource discovery protocol, namely Proactive Index Diffusion CAN (PID-CAN), which can proactively diffuse resource indexes over the nodes and randomly route query messages among them. Such a protocol is especially suitable for the range query that needs to maximize its best-fit resource shares under possible competition along multiple resource dimensions. Via simulation, we show that PID-CAN could keep stable and optimized searching performance with low query delay and traffic overhead, for various test cases under different distributions of query ranges and competition degrees. It also performs satisfactorily in dynamic node-churning situation. © 2011 IEEE.published_or_final_versionThe 40th International Conference on Parallel Processing (ICPP-2011), Taipei City, Taiwan, 13-16 September 2011. In Proceedings of the 40th ICPP, 2011, p. 763-77
Optimising Structured P2P Networks for Complex Queries
With network enabled consumer devices becoming increasingly popular, the number of connected devices and available services is growing considerably - with the number of connected devices es- timated to surpass 15 billion devices by 2015. In this increasingly large and dynamic environment it is important that users have a comprehensive, yet efficient, mechanism to discover services.
Many existing wide-area service discovery mechanisms are centralised and do not scale to large numbers of users. Additionally, centralised services suffer from issues such as a single point of failure, high maintenance costs, and difficulty of management. As such, this Thesis seeks a Peer to Peer (P2P) approach.
Distributed Hash Tables (DHTs) are well known for their high scalability, financially low barrier of entry, and ability to self manage. They can be used to provide not just a platform on which peers can offer and consume services, but also as a means for users to discover such services.
Traditionally DHTs provide a distributed key-value store, with no search functionality. In recent years many P2P systems have been proposed providing support for a sub-set of complex query types, such as keyword search, range queries, and semantic search.
This Thesis presents a novel algorithm for performing any type of complex query, from keyword search, to complex regular expressions, to full-text search, over any structured P2P overlay. This is achieved by efficiently broadcasting the search query, allowing each peer to process the query locally, and then efficiently routing responses back to the originating peer. Through experimentation, this technique is shown to be successful when the network is stable, however performance degrades under high levels of network churn.
To address the issue of network churn, this Thesis proposes a number of enhancements which can be made to existing P2P overlays in order to improve the performance of both the existing DHT and the proposed algorithm. Through two case studies these enhancements are shown to improve not only the performance of the proposed algorithm under churn, but also the performance of traditional lookup operations in these networks
Descoberta de recursos para sistemas de escala arbitrarias
Doutoramento em InformáticaTecnologias de Computação DistribuÃda 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 distribuÃda 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 distribuÃdos, 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 hÃbrida (Hybrid Adaptive
Resource Discovery - HARD), uma abordagem de descoberta de recursos eficiente
e altamente escalável, construÃda 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 distribuÃdas 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 distribuÃdo 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 caracterÃsticas 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 contribuÃmos com a proposta de um regime de gestão eficiente
dos recursos para sistemas futuros que podem utilizar recursos distribuÃos
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
disponÃeis 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