54,416 research outputs found
User-Centric Traffic Engineering in Software Defined Networks
Software defined networking (SDN) is a relatively new paradigm that decouples individual network elements from the control logic, offering real-time network programmability, translating high level policy abstractions into low level device configurations. The framework comprises of the data (forwarding) plane incorporating network devices, while the control logic and network services reside in the control and application planes respectively. Operators can optimize the network fabric to yield performance gains for individual applications and services utilizing flow metering and application-awareness, the default traffic management method in SDN. Existing approaches to traffic optimization, however, do not explicitly consider user application trends. Recent SDN traffic engineering designs either offer improvements for typical time-critical applications or focus on devising monitoring solutions aimed at measuring performance metrics of the respective services. The performance caveats of isolated service differentiation on the end users may be substantial considering the growth in Internet and network applications on offer and the resulting diversity in user activities. Application-level flow metering schemes therefore, fall short of fully exploiting the real-time network provisioning capability offered by SDN instead relying on rather static traffic control primitives frequent in legacy networking.
For individual users, SDN may lead to substantial improvements if the framework allows operators to allocate resources while accounting for a user-centric mix of applications. This thesis explores the user traffic application trends in different network environments and proposes a novel user traffic profiling framework to aid the SDN control plane (controller) in accurately configuring network elements for a broad spectrum of users without impeding specific application requirements.
This thesis starts with a critical review of existing traffic engineering solutions in SDN and highlights recent and ongoing work in network optimization studies. Predominant existing segregated application policy based controls in SDN do not consider the cost of isolated application gains on parallel SDN services and resulting consequence for users having varying application usage. Therefore, attention is given to investigating techniques which may capture the user behaviour for possible integration in SDN traffic controls. To this end, profiling of user application traffic trends is identified as a technique which may offer insight into the inherent diversity in user activities and offer possible incorporation in SDN based traffic engineering.
A series of subsequent user traffic profiling studies are carried out in this regard employing network flow statistics collected from residential and enterprise network environments. Utilizing machine learning techniques including the prominent unsupervised k-means cluster analysis, user generated traffic flows are cluster analysed and the derived profiles in each networking environment are benchmarked for stability before integration in SDN control solutions. In parallel, a novel flow-based traffic classifier is designed to yield high accuracy in identifying user application flows and the traffic profiling mechanism is automated.
The core functions of the novel user-centric traffic engineering solution are validated by the implementation of traffic profiling based SDN network control applications in residential, data center and campus based SDN environments. A series of simulations highlighting varying traffic conditions and profile based policy controls are designed and evaluated in each network setting using the traffic profiles derived from realistic environments to demonstrate the effectiveness of the traffic management solution. The overall network performance metrics per profile show substantive gains, proportional to operator defined user profile prioritization policies despite high traffic load conditions. The proposed user-centric SDN traffic engineering framework therefore, dynamically provisions data plane resources among different user traffic classes (profiles), capturing user behaviour to define and implement network policy controls, going beyond isolated application management
Temporal word embeddings for dynamic user profiling in Twitter
The research described in this paper focused on exploring
the domain of user profiling, a nascent and contentious technology which
has been steadily attracting increased interest from the research community as its potential for providing personalised digital services is realised.
An extensive review of related literature revealed that limited research
has been conducted into how temporal aspects of users can be captured
using user profiling techniques. This, coupled with the notable lack of
research into the use of word embedding techniques to capture temporal
variances in language, revealed an opportunity to extend the Random Indexing word embedding technique such that the interests of users could
be modelled based on their use of language. To achieve this, this work
concerned itself with extending an existing implementation of Temporal
Random Indexing to model Twitter users across multiple granularities of
time based on their use of language. The product of this is a novel technique for temporal user profiling, where a set of vectors is used to describe
the evolution of a Twitter user’s interests over time through their use of
language. The vectors produced were evaluated against a temporal implementation of another state-of-the-art word embedding technique, the
Word2Vec Dynamic Independent Skip-gram model, where it was found
that Temporal Random Indexing outperformed Word2Vec in the generation of temporal user profiles
Semi-autonomous, context-aware, agent using behaviour modelling and reputation systems to authorize data operation in the Internet of Things
In this paper we address the issue of gathering the "informed consent" of an
end user in the Internet of Things. We start by evaluating the legal importance
and some of the problems linked with this notion of informed consent in the
specific context of the Internet of Things. From this assessment we propose an
approach based on a semi-autonomous, rule based agent that centralize all
authorization decisions on the personal data of a user and that is able to take
decision on his behalf. We complete this initial agent by integrating
context-awareness, behavior modeling and community based reputation system in
the algorithm of the agent. The resulting system is a "smart" application, the
"privacy butler" that can handle data operations on behalf of the end-user
while keeping the user in control. We finally discuss some of the potential
problems and improvements of the system.Comment: This work is currently supported by the BUTLER Project co-financed
under the 7th framework program of the European Commission. published in
Internet of Things (WF-IoT), 2014 IEEE World Forum, 6-8 March 2014, Seoul,
P411-416, DOI: 10.1109/WF-IoT.2014.6803201, INSPEC: 1425565
Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.
This report gives an overview of the most relevant organisational and\ud
behavioural aspects regarding user profiling. It discusses not only the\ud
most important aims of user profiling from both an organisation’s as\ud
well as a user’s perspective, it will also discuss organisational motives\ud
and barriers for user profiling and the most important conditions for\ud
the success of user profiling. Finally recommendations are made and\ud
suggestions for further research are given
FlashProfile: A Framework for Synthesizing Data Profiles
We address the problem of learning a syntactic profile for a collection of
strings, i.e. a set of regex-like patterns that succinctly describe the
syntactic variations in the strings. Real-world datasets, typically curated
from multiple sources, often contain data in various syntactic formats. Thus,
any data processing task is preceded by the critical step of data format
identification. However, manual inspection of data to identify the different
formats is infeasible in standard big-data scenarios.
Prior techniques are restricted to a small set of pre-defined patterns (e.g.
digits, letters, words, etc.), and provide no control over granularity of
profiles. We define syntactic profiling as a problem of clustering strings
based on syntactic similarity, followed by identifying patterns that succinctly
describe each cluster. We present a technique for synthesizing such profiles
over a given language of patterns, that also allows for interactive refinement
by requesting a desired number of clusters.
Using a state-of-the-art inductive synthesis framework, PROSE, we have
implemented our technique as FlashProfile. Across tasks over large
real datasets, we observe a median profiling time of only s.
Furthermore, we show that access to syntactic profiles may allow for more
accurate synthesis of programs, i.e. using fewer examples, in
programming-by-example (PBE) workflows such as FlashFill.Comment: 28 pages, SPLASH (OOPSLA) 201
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