5,923 research outputs found
Trade-off among timeliness, messages and accuracy for large-Ssale information management
The increasing amount of data and the number of nodes in large-scale environments
require new techniques for information management. Examples of such environments
are the decentralized infrastructures of Computational Grid and Computational
Cloud applications. These large-scale applications need different kinds
of aggregated information such as resource monitoring, resource discovery or economic
information. The challenge of providing timely and accurate information
in large scale environments arise from the distribution of the information. Reasons
for delays in distributed information system are a long information transmission
time due to the distribution, churn and failures.
A problem of large applications such as peer-to-peer (P2P) systems is the increasing
retrieval time of the information due to the decentralization of the data
and the failure proneness. However, many applications need a timely information
provision. Another problem is an increasing network consumption when the application
scales to millions of users and data. Using approximation techniques allows
reducing the retrieval time and the network consumption. However, the usage of
approximation techniques decreases the accuracy of the results. Thus, the remaining
problem is to offer a trade-off in order to solve the conflicting requirements of
fast information retrieval, accurate results and low messaging cost.
Our goal is to reach a self-adaptive decision mechanism to offer a trade-off
among the retrieval time, the network consumption and the accuracy of the result.
Self-adaption enables distributed software to modify its behavior based on
changes in the operating environment. In large-scale information systems that use
hierarchical data aggregation, we apply self-adaptation to control the approximation
used for the information retrieval and reduces the network consumption and
the retrieval time. The hypothesis of the thesis is that approximation techniquescan reduce the retrieval time and the network consumption while guaranteeing an
accuracy of the results, while considering user’s defined priorities.
First, this presented research addresses the problem of a trade-off among a
timely information retrieval, accurate results and low messaging cost by proposing
a summarization algorithm for resource discovery in P2P-content networks.
After identifying how summarization can improve the discovery process, we propose
an algorithm which uses a precision-recall metric to compare the accuracy
and to offer a user-driven trade-off. Second, we propose an algorithm that applies
a self-adaptive decision making on each node. The decision is about the pruning
of the query and returning the result instead of continuing the query. The pruning
reduces the retrieval time and the network consumption at the cost of a lower accuracy
in contrast to continuing the query. The algorithm uses an analytic hierarchy
process to assess the user’s priorities and to propose a trade-off in order to satisfy
the accuracy requirements with a low message cost and a short delay.
A quantitative analysis evaluates our presented algorithms with a simulator,
which is fed with real data of a network topology and the nodes’ attributes. The
usage of a simulator instead of the prototype allows the evaluation in a large scale
of several thousands of nodes. The algorithm for content summarization is evaluated
with half a million of resources and with different query types. The selfadaptive
algorithm is evaluated with a simulator of several thousands of nodes
that are created from real data. A qualitative analysis addresses the integration
of the simulator’s components in existing market frameworks for Computational
Grid and Cloud applications.
The proposed content summarization algorithm reduces the information retrieval
time from a logarithmic increase to a constant factor. Furthermore, the
message size is reduced significantly by applying the summarization technique.
For the user, a precision-recall metric allows defining the relation between the retrieval
time and the accuracy. The self-adaptive algorithm reduces the number of
messages needed from an exponential increase to a constant factor. At the same
time, the retrieval time is reduced to a constant factor under an increasing number
of nodes. Finally, the algorithm delivers the data with the required accuracy
adjusting the depth of the query according to the network conditions.La gestió de la informació exigeix noves tècniques que tractin amb la creixent
quantitat de dades i nodes en entorns a gran escala. Alguns exemples d’aquests
entorns són les infraestructures descentralitzades de Computacional Grid i Cloud.
Les aplicacions a gran escala necessiten diferents classes d’informació agregada
com monitorització de recursos i informació econòmica. El desafiament de proporcionar
una provisió ràpida i acurada d’informació en ambients de grans escala
sorgeix de la distribució de la informació. Una raó és que el sistema d’informació
ha de tractar amb l’adaptabilitat i fracassos d’aquests ambients.
Un problema amb aplicacions molt grans com en sistemes peer-to-peer (P2P)
és el creixent temps de recuperació de l’informació a causa de la descentralització
de les dades i la facilitat al fracàs. No obstant això, moltes aplicacions necessiten
una provisió d’informació puntual. A més, alguns usuaris i aplicacions accepten
inexactituds dels resultats si la informació es reparteix a temps. A més i més, el
consum de xarxa creixent fa que sorgeixi un altre problema per l’escalabilitat del
sistema. La utilització de tècniques d’aproximació permet reduir el temps de recuperació
i el consum de xarxa. No obstant això, l’ús de tècniques d’aproximació
disminueix la precisió dels resultats. Així, el problema restant és oferir un compromís
per resoldre els requisits en conflicte d’extracció de la informació ràpida,
resultats acurats i cost d’enviament baix.
El nostre objectiu és obtenir un mecanisme de decisió completament autoadaptatiu
per tal d’oferir el compromís entre temps de recuperació, consum de
xarxa i precisió del resultat. Autoadaptacío permet al programari distribuït modificar
el seu comportament en funció dels canvis a l’entorn d’operació. En sistemes
d’informació de gran escala que utilitzen agregació de dades jeràrquica,
l’auto-adaptació permet controlar l’aproximació utilitzada per a l’extracció de la informació i redueixen el consum de xarxa i el temps de recuperació. La hipòtesi
principal d’aquesta tesi és que els tècniques d’aproximació permeten reduir el
temps de recuperació i el consum de xarxa mentre es garanteix una precisió adequada
definida per l’usari.
La recerca que es presenta, introdueix un algoritme de sumarització de continguts
per a la descoberta de recursos a xarxes de contingut P2P. Després d’identificar
com sumarització pot millorar el procés de descoberta, proposem una mètrica que
s’utilitza per comparar la precisió i oferir un compromís definit per l’usuari. Després,
introduïm un algoritme nou que aplica l’auto-adaptació a un ordre per satisfer
els requisits de precisió amb un cost de missatge baix i un retard curt. Basat
en les prioritats d’usuari, l’algoritme troba automàticament un compromís.
L’anàlisi quantitativa avalua els algoritmes presentats amb un simulador per
permetre l’evacuació d’uns quants milers de nodes. El simulador s’alimenta amb
dades d’una topologia de xarxa i uns atributs dels nodes reals. L’algoritme de
sumarització de contingut s’avalua amb mig milió de recursos i amb diferents
tipus de sol·licituds. L’anàlisi qualitativa avalua la integració del components del
simulador en estructures de mercat existents per a aplicacions de Computacional
Grid i Cloud. Així, la funcionalitat implementada del simulador (com el procés
d’agregació i la query language) és comprovada per la integració de prototips.
L’algoritme de sumarització de contingut proposat redueix el temps d’extracció
de l’informació d’un augment logarítmic a un factor constant. A més, també permet
que la mida del missatge es redueix significativament. Per a l’usuari, una
precision-recall mètric permet definir la relació entre el nivell de precisió i el
temps d’extracció de la informació. Alhora, el temps de recuperació es redueix
a un factor constant sota un nombre creixent de nodes. Finalment, l’algoritme
reparteix les dades amb la precisió exigida i ajusta la profunditat de la sol·licitud
segons les condicions de xarxa. Els algoritmes introduïts són prometedors per ser
utilitzats per l’agregació d’informació en nous sistemes de gestió de la informació
de gran escala en el futur.Postprint (published version
Trade-off among timeliness, messages and accuracy for large-Ssale information management
The increasing amount of data and the number of nodes in large-scale environments
require new techniques for information management. Examples of such environments
are the decentralized infrastructures of Computational Grid and Computational
Cloud applications. These large-scale applications need different kinds
of aggregated information such as resource monitoring, resource discovery or economic
information. The challenge of providing timely and accurate information
in large scale environments arise from the distribution of the information. Reasons
for delays in distributed information system are a long information transmission
time due to the distribution, churn and failures.
A problem of large applications such as peer-to-peer (P2P) systems is the increasing
retrieval time of the information due to the decentralization of the data
and the failure proneness. However, many applications need a timely information
provision. Another problem is an increasing network consumption when the application
scales to millions of users and data. Using approximation techniques allows
reducing the retrieval time and the network consumption. However, the usage of
approximation techniques decreases the accuracy of the results. Thus, the remaining
problem is to offer a trade-off in order to solve the conflicting requirements of
fast information retrieval, accurate results and low messaging cost.
Our goal is to reach a self-adaptive decision mechanism to offer a trade-off
among the retrieval time, the network consumption and the accuracy of the result.
Self-adaption enables distributed software to modify its behavior based on
changes in the operating environment. In large-scale information systems that use
hierarchical data aggregation, we apply self-adaptation to control the approximation
used for the information retrieval and reduces the network consumption and
the retrieval time. The hypothesis of the thesis is that approximation techniquescan reduce the retrieval time and the network consumption while guaranteeing an
accuracy of the results, while considering user’s defined priorities.
First, this presented research addresses the problem of a trade-off among a
timely information retrieval, accurate results and low messaging cost by proposing
a summarization algorithm for resource discovery in P2P-content networks.
After identifying how summarization can improve the discovery process, we propose
an algorithm which uses a precision-recall metric to compare the accuracy
and to offer a user-driven trade-off. Second, we propose an algorithm that applies
a self-adaptive decision making on each node. The decision is about the pruning
of the query and returning the result instead of continuing the query. The pruning
reduces the retrieval time and the network consumption at the cost of a lower accuracy
in contrast to continuing the query. The algorithm uses an analytic hierarchy
process to assess the user’s priorities and to propose a trade-off in order to satisfy
the accuracy requirements with a low message cost and a short delay.
A quantitative analysis evaluates our presented algorithms with a simulator,
which is fed with real data of a network topology and the nodes’ attributes. The
usage of a simulator instead of the prototype allows the evaluation in a large scale
of several thousands of nodes. The algorithm for content summarization is evaluated
with half a million of resources and with different query types. The selfadaptive
algorithm is evaluated with a simulator of several thousands of nodes
that are created from real data. A qualitative analysis addresses the integration
of the simulator’s components in existing market frameworks for Computational
Grid and Cloud applications.
The proposed content summarization algorithm reduces the information retrieval
time from a logarithmic increase to a constant factor. Furthermore, the
message size is reduced significantly by applying the summarization technique.
For the user, a precision-recall metric allows defining the relation between the retrieval
time and the accuracy. The self-adaptive algorithm reduces the number of
messages needed from an exponential increase to a constant factor. At the same
time, the retrieval time is reduced to a constant factor under an increasing number
of nodes. Finally, the algorithm delivers the data with the required accuracy
adjusting the depth of the query according to the network conditions.La gestió de la informació exigeix noves tècniques que tractin amb la creixent
quantitat de dades i nodes en entorns a gran escala. Alguns exemples d’aquests
entorns són les infraestructures descentralitzades de Computacional Grid i Cloud.
Les aplicacions a gran escala necessiten diferents classes d’informació agregada
com monitorització de recursos i informació econòmica. El desafiament de proporcionar
una provisió ràpida i acurada d’informació en ambients de grans escala
sorgeix de la distribució de la informació. Una raó és que el sistema d’informació
ha de tractar amb l’adaptabilitat i fracassos d’aquests ambients.
Un problema amb aplicacions molt grans com en sistemes peer-to-peer (P2P)
és el creixent temps de recuperació de l’informació a causa de la descentralització
de les dades i la facilitat al fracàs. No obstant això, moltes aplicacions necessiten
una provisió d’informació puntual. A més, alguns usuaris i aplicacions accepten
inexactituds dels resultats si la informació es reparteix a temps. A més i més, el
consum de xarxa creixent fa que sorgeixi un altre problema per l’escalabilitat del
sistema. La utilització de tècniques d’aproximació permet reduir el temps de recuperació
i el consum de xarxa. No obstant això, l’ús de tècniques d’aproximació
disminueix la precisió dels resultats. Així, el problema restant és oferir un compromís
per resoldre els requisits en conflicte d’extracció de la informació ràpida,
resultats acurats i cost d’enviament baix.
El nostre objectiu és obtenir un mecanisme de decisió completament autoadaptatiu
per tal d’oferir el compromís entre temps de recuperació, consum de
xarxa i precisió del resultat. Autoadaptacío permet al programari distribuït modificar
el seu comportament en funció dels canvis a l’entorn d’operació. En sistemes
d’informació de gran escala que utilitzen agregació de dades jeràrquica,
l’auto-adaptació permet controlar l’aproximació utilitzada per a l’extracció de la informació i redueixen el consum de xarxa i el temps de recuperació. La hipòtesi
principal d’aquesta tesi és que els tècniques d’aproximació permeten reduir el
temps de recuperació i el consum de xarxa mentre es garanteix una precisió adequada
definida per l’usari.
La recerca que es presenta, introdueix un algoritme de sumarització de continguts
per a la descoberta de recursos a xarxes de contingut P2P. Després d’identificar
com sumarització pot millorar el procés de descoberta, proposem una mètrica que
s’utilitza per comparar la precisió i oferir un compromís definit per l’usuari. Després,
introduïm un algoritme nou que aplica l’auto-adaptació a un ordre per satisfer
els requisits de precisió amb un cost de missatge baix i un retard curt. Basat
en les prioritats d’usuari, l’algoritme troba automàticament un compromís.
L’anàlisi quantitativa avalua els algoritmes presentats amb un simulador per
permetre l’evacuació d’uns quants milers de nodes. El simulador s’alimenta amb
dades d’una topologia de xarxa i uns atributs dels nodes reals. L’algoritme de
sumarització de contingut s’avalua amb mig milió de recursos i amb diferents
tipus de sol·licituds. L’anàlisi qualitativa avalua la integració del components del
simulador en estructures de mercat existents per a aplicacions de Computacional
Grid i Cloud. Així, la funcionalitat implementada del simulador (com el procés
d’agregació i la query language) és comprovada per la integració de prototips.
L’algoritme de sumarització de contingut proposat redueix el temps d’extracció
de l’informació d’un augment logarítmic a un factor constant. A més, també permet
que la mida del missatge es redueix significativament. Per a l’usuari, una
precision-recall mètric permet definir la relació entre el nivell de precisió i el
temps d’extracció de la informació. Alhora, el temps de recuperació es redueix
a un factor constant sota un nombre creixent de nodes. Finalment, l’algoritme
reparteix les dades amb la precisió exigida i ajusta la profunditat de la sol·licitud
segons les condicions de xarxa. Els algoritmes introduïts són prometedors per ser
utilitzats per l’agregació d’informació en nous sistemes de gestió de la informació
de gran escala en el futur
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
A HOLISTIC REDUNDANCY- AND INCENTIVE-BASED FRAMEWORK TO IMPROVE CONTENT AVAILABILITY IN PEER-TO-PEER NETWORKS
Peer-to-Peer (P2P) technology has emerged as an important alternative to the traditional client-server communication paradigm to build large-scale distributed systems. P2P enables the creation, dissemination and access to information at low cost and without the need of dedicated coordinating entities. However, existing P2P systems fail to provide high-levels of content availability, which limit their applicability and adoption. This dissertation takes a holistic approach to device mechanisms to improve content availability in large-scale P2P systems.
Content availability in P2P can be impacted by hardware failures and churn. Hardware failures, in the form of disk or node failures, render information inaccessible. Churn, an inherent property of P2P, is the collective effect of the users’ uncoordinated behavior, which occurs when a large percentage of nodes join and leave frequently. Such a behavior reduces content availability significantly. Mitigating the combined effect of hardware failures and churn on content availability in P2P requires new and innovative solutions that go beyond those applied in existing distributed systems. To addresses this challenge, the thesis proposes two complementary, low cost mechanisms, whereby nodes self-organize to overcome failures and improve content availability. The first mechanism is a low complexity and highly flexible hybrid redundancy scheme, referred to as Proactive Repair (PR). The second mechanism is an incentive-based scheme that promotes cooperation and enforces fair exchange of resources among peers. These mechanisms provide the basis for the development of distributed self-organizing algorithms to automate PR and, through incentives, maximize their effectiveness in realistic P2P environments.
Our proposed solution is evaluated using a combination of analytical and experimental methods. The analytical models are developed to determine the availability and repair cost properties of PR. The results indicate that PR’s repair cost outperforms other redundancy schemes. The experimental analysis was carried out using simulation and the development of a testbed. The simulation results confirm that PR improves content availability in P2P. The proposed mechanisms are implemented and tested using a DHT-based P2P application environment. The experimental results indicate that the incentive-based mechanism can promote fair exchange of resources and limits the impact of uncooperative behaviors such as “free-riding”
Smart PIN: performance and cost-oriented context-aware personal information network
The next generation of networks will involve interconnection of heterogeneous individual
networks such as WPAN, WLAN, WMAN and Cellular network, adopting the IP as common infrastructural protocol and providing virtually always-connected network. Furthermore,
there are many devices which enable easy acquisition and storage of information as pictures, movies, emails, etc. Therefore, the information overload and divergent content’s
characteristics make it difficult for users to handle their data in manual way. Consequently, there is a need for personalised automatic services which would enable data exchange across heterogeneous network and devices. To support these personalised services, user centric approaches
for data delivery across the heterogeneous network are also required.
In this context, this thesis proposes Smart PIN - a novel performance and cost-oriented context-aware Personal Information Network. Smart PIN's architecture is detailed including its network, service and management components. Within the service component, two novel schemes for efficient delivery of context and content data are proposed:
Multimedia Data Replication Scheme (MDRS) and Quality-oriented Algorithm for Multiple-source Multimedia Delivery (QAMMD).
MDRS supports efficient data accessibility among distributed devices using data replication which is based on a utility function and a minimum data set. QAMMD employs a buffer underflow avoidance scheme for streaming, which achieves high multimedia quality without content adaptation to network conditions. Simulation models for MDRS and
QAMMD were built which are based on various heterogeneous network scenarios. Additionally a multiple-source streaming based on QAMMS was implemented as a prototype and tested in an emulated network environment. Comparative tests show that MDRS and QAMMD perform significantly better than other approaches
Throughput and Fairness Considerations in Overlay Networks for Content Distribution
The Internet has been designed as a best-effort network, which does not provide any additional services to applications using the network. Overlay networks, which form an application layer network on top of the underlying Internet, have emerged as popular means to provide specific services and greater control to applications. Overlay networks offer a wide range of services, including content distribution, multicast and multimedia streaming. In my thesis, I focus on overlay networks for content distribution, used by applications such as bulk data transfer, file sharing and web retrieval.
I first investigate the construction of such overlay networks by studying the bootstrapping functionality in an example network (the Gnutella peer-to-peer system). This study comprises the analysis and performance measurements of Gnutella servents and measurement of the GWebCache system that helps new peers find existing peers on the Gnutella network.
Next, I look at fairness issues due to the retrieval of data at a client in the form of multipoint-to-point sessions, formed due to the use of content distribution networks. A multipoint-to-point session comprises multiple connections from multiple servers to a single client over multiple paths, initiated to retrieve a single application-level object. I investigate fairness of rate allocation from a session point of view, and propose fairness definitions and algorithms to achieve these definitions.
Finally, I consider the problem of designing an overlay network for content distribution, which is fair to competing overlay networks, while maximizing the total end-to-end throughput of the data it carries. As a first step, I investigate this design problem for a single path in an Overlay-TCP network. I propose two schemes that dynamically provision the number of TCP connections on each hop of an Overlay-TCP path to maximize the end-to-end throughput using few
extraneous connections. Next, I design an Overlay-TCP network, with the secondary goal of intra-overlay network fairness. I propose four schemes for deciding the number of TCP connections to be used on each overlay hop. I show that one can vary the proportion of sharing between competing overlay networks by varying the maximum number of connections allowed on overlay hops in each competing network.Ph.D.Committee Co-Chair: Ammar, Mostafa; Committee Co-Chair: Zegura, Ellen; Committee Member: Dovrolis, Constantinos; Committee Member: Rabinovich, Misha; Committee Member: Riley, Georg
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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