147 research outputs found
Soft Cache Hits and the Impact of Alternative Content Recommendations on Mobile Edge Caching
Caching popular content at the edge of future mobile networks has been widely
considered in order to alleviate the impact of the data tsunami on both the
access and backhaul networks. A number of interesting techniques have been
proposed, including femto-caching and "delayed" or opportunistic cache access.
Nevertheless, the majority of these approaches suffer from the rather limited
storage capacity of the edge caches, compared to the tremendous and rapidly
increasing size of the Internet content catalog. We propose to depart from the
assumption of hard cache misses, common in most existing works, and consider
"soft" cache misses, where if the original content is not available, an
alternative content that is locally cached can be recommended. Given that
Internet content consumption is increasingly entertainment-oriented, we believe
that a related content could often lead to complete or at least partial user
satisfaction, without the need to retrieve the original content over expensive
links. In this paper, we formulate the problem of optimal edge caching with
soft cache hits, in the context of delayed access, and analyze the expected
gains. We then show using synthetic and real datasets of related video contents
that promising caching gains could be achieved in practice
A\c{C}AI: Ascent Similarity Caching with Approximate Indexes
Similarity search is a key operation in multimedia retrieval systems and
recommender systems, and it will play an important role also for future machine
learning and augmented reality applications. When these systems need to serve
large objects with tight delay constraints, edge servers close to the end-user
can operate as similarity caches to speed up the retrieval. In this paper we
present A\c{C}AI, a new similarity caching policy which improves on the state
of the art by using (i) an (approximate) index for the whole catalog to decide
which objects to serve locally and which to retrieve from the remote server,
and (ii) a mirror ascent algorithm to update the set of local objects with
strong guarantees even when the request process does not exhibit any
statistical regularity
Provision of adaptive and context-aware service discovery for the Internet of Things
The IoT concept has revolutionised the vision of the future Internet with the advent of standards such as 6LoWPAN making it feasible to extend the Internet into previously isolated environments, e.g., WSNs. The abstraction of resources as services, has opened these environments to a new plethora of potential applications. Moreover, the web service paradigm can be used to provide interoperability by offering a standard interface to interact with these services to enable WoT paradigm. However, these networks pose many challenges, in terms of limited resources, that make the adaptability of existing IP-based solutions infeasible. As traditional service discovery and selection solutions demand heavy communication and use bulky formats, which are unsuitable for these resource-constrained devices incorporating sleep cycles to save energy. Even a registry based approach exhibits burdensome traffic in maintaining the availability status of the devices. The feasible solution for service discovery and selection is instrumental to enable the wide application coverage of these networks in the future.
This research project proposes, TRENDY, a new compact and adaptive registry-based SDP with context awareness for the IoT, with more emphasis given to constrained networks, e.g., 6LoWPAN It uses CoAP-based light-weight and RESTful web services to provide standard interoperable interfaces, which can be easily translated from HTTP. TRENDY's service selection mechanism collects and intelligently uses the context information to select appropriate services for user applications based on the available context information of users and services. In addition, TRENDY introduces an adaptive timer algorithm to minimise control overhead for status maintenance, which also reduces energy consumption. Its context-aware grouping technique divides the network at the application layer, by creating location-based groups. This grouping of nodes localises the control overhead and provides the base for service composition, localised aggregation and processing of data. Different grouping roles enable the resource-awareness by offering profiles with varied responsibilities, where high capability devices can implement powerful profiles to share the load of other low capability devices. Thus, it allows the productive usage of network resources. Furthermore, this research project proposes APPUB, an adaptive caching technique, that has the following benefits: it allows service hosts to share their load with the resource directory and also decreases the service invocation delay.
The performance of TRENDY and its mechanisms is evaluated using an extensive number of experiments performed using emulated Tmote sky nodes in the COOJA environment. The analysis of the results validates the benefit of performance gain for all techniques. The service selection and APPUB mechanisms improve the service invocation delay considerably that, consequently, reduces the traffic in the network. The timer technique consistently achieved the lowest control overhead, which eventually decreased the energy consumption of the nodes to prolong the network lifetime. Moreover, the low traffic in dense networks decreases the service invocations delay, and makes the solution more scalable. The grouping mechanism localises the traffic, which increases the energy efficiency while improving the scalability. In summary, the experiments demonstrate the benefit of using TRENDY and its techniques in terms of increased energy efficiency and network lifetime, reduced control overhead, better scalability and optimised service invocation time
Entrega de conteúdos multimédia em over-the-top: caso de estudo das gravações automáticas
Doutoramento em Engenharia EletrotécnicaOver-The-Top (OTT) multimedia delivery is a very appealing approach for providing
ubiquitous,
exible, and globally accessible services capable of low-cost
and unrestrained device targeting. In spite of its appeal, the underlying delivery
architecture must be carefully planned and optimized to maintain a high Qualityof-
Experience (QoE) and rational resource usage, especially when migrating from
services running on managed networks with established quality guarantees. To address
the lack of holistic research works on OTT multimedia delivery systems, this
Thesis focuses on an end-to-end optimization challenge, considering a migration
use-case of a popular Catch-up TV service from managed IP Television (IPTV)
networks to OTT. A global study is conducted on the importance of Catch-up
TV and its impact in today's society, demonstrating the growing popularity of
this time-shift service, its relevance in the multimedia landscape, and tness as
an OTT migration use-case. Catch-up TV consumption logs are obtained from
a Pay-TV operator's live production IPTV service containing over 1 million subscribers
to characterize demand and extract insights from service utilization at a
scale and scope not yet addressed in the literature. This characterization is used
to build demand forecasting models relying on machine learning techniques to enable
static and dynamic optimization of OTT multimedia delivery solutions, which
are able to produce accurate bandwidth and storage requirements' forecasts, and
may be used to achieve considerable power and cost savings whilst maintaining a
high QoE. A novel caching algorithm, Most Popularly Used (MPU), is proposed,
implemented, and shown to outperform established caching algorithms in both
simulation and experimental scenarios. The need for accurate QoE measurements
in OTT scenarios supporting HTTP Adaptive Streaming (HAS) motivates the creation
of a new QoE model capable of taking into account the impact of key HAS
aspects. By addressing the complete content delivery pipeline in the envisioned
content-aware OTT Content Delivery Network (CDN), this Thesis demonstrates
that signi cant improvements are possible in next-generation multimedia delivery
solutions.A entrega de conteúdos multimédia em Over-The-Top (OTT) e uma proposta
atractiva para fornecer um serviço flexível e globalmente acessível, capaz de alcançar qualquer dispositivo, com uma promessa de baixos custos. Apesar das suas vantagens, e necessario um planeamento arquitectural detalhado e optimizado para manter níveis elevados de Qualidade de Experiência (QoE), em particular aquando da migração dos serviços suportados em redes geridas com garantias de qualidade pré-estabelecidas. Para colmatar a falta de trabalhos de investigação na área de sistemas de entrega de conteúdos multimédia em OTT, esta Tese foca-se na optimização destas soluções como um todo, partindo do caso de uso de migração de um serviço popular de Gravações Automáticas suportado em redes de Televisão sobre IP (IPTV) geridas, para um cenário de entrega em OTT. Um estudo global para aferir a importância das Gravações Automáticas revela a sua relevância no panorama de serviços multimédia e a sua adequação enquanto caso de uso de
migração para cenários OTT. São obtidos registos de consumos de um serviço
de produção de Gravações Automáticas, representando mais de 1 milhão de assinantes,
para caracterizar e extrair informação de consumos numa escala e âmbito
não contemplados ate a data na literatura. Esta caracterização e utilizada para
construir modelos de previsão de carga, tirando partido de sistemas de machine
learning, que permitem optimizações estáticas e dinâmicas dos sistemas de entrega
de conteúdos em OTT através de previsões das necessidades de largura de banda e
armazenamento, potenciando ganhos significativos em consumo energético e custos.
Um novo mecanismo de caching, Most Popularly Used (MPU), demonstra um
desempenho superior as soluções de referencia, quer em cenários de simulação quer
experimentais. A necessidade de medição exacta da QoE em streaming adaptativo
HTTP motiva a criaçao de um modelo capaz de endereçar aspectos específicos
destas tecnologias adaptativas. Ao endereçar a cadeia completa de entrega através
de uma arquitectura consciente dos seus conteúdos, esta Tese demonstra que são
possíveis melhorias de desempenho muito significativas nas redes de entregas de
conteúdos em OTT de próxima geração
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