4,735 research outputs found
Predicting quality of experience for online video service provisioning
The expansion of the online video content continues
in every area of the modern connected world and the need for
measuring and predicting the Quality of Experience (QoE) for
online video systems has never been this important. This paper
has designed and developed a machine learning based
methodology to derive QoE for online video systems. For this
purpose, a platform has been developed where video content is
unicasted to users so that objective video metrics are collected
into a database. At the end of each video session, users are
queried with a subjective survey about their experience. Both
quantitative statistics and qualitative user survey information are
used as training data to a variety of machine learning techniques
including Artificial Neural Network (ANN), K-nearest
Neighbours Algorithm (KNN) and Support Vector Machine
(SVM) with a collection of cross-validation strategies. This
methodology can efficiently answer the problem of predicting
user experience for any online video service provider, while
overcoming the problematic interpretation of subjective
consumer experience in terms of quantitative system capacity
metrics
Towards critical event monitoring, detection and prediction for self-adaptive future Internet applications
The Future Internet (FI) will be composed of a multitude of diverse types of services that offer flexible, remote access to software features, content, computing resources, and middleware solutions through different cloud delivery models, such as IaaS, PaaS and SaaS. Ultimately, this means that loosely coupled Internet services will form a comprehensive base for developing value added applications in an agile way. Unlike traditional application development, which uses computing resources and software components under local administrative control, FI applications will thus strongly depend on third-party services. To maintain their quality of service, those applications therefore need to dynamically and autonomously adapt to an unprecedented level of changes that may occur during runtime. In this paper, we present our recent experiences on monitoring, detection, and prediction of critical events for both software services and multimedia applications. Based on these findings we introduce potential directions for future research on self-adaptive FI applications, bringing together those research directions
Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services
The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.
The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms
QoE-Aware Resource Allocation For Crowdsourced Live Streaming: A Machine Learning Approach
In the last decade, empowered by the technological advancements of mobile devices
and the revolution of wireless mobile network access, the world has witnessed an
explosion in crowdsourced live streaming. Ensuring a stable high-quality playback
experience is compulsory to maximize the viewers’ Quality of Experience and the
content providers’ profits. This can be achieved by advocating a geo-distributed cloud
infrastructure to allocate the multimedia resources as close as possible to viewers, in
order to minimize the access delay and video stalls.
Additionally, because of the instability of network condition and the heterogeneity of
the end-users capabilities, transcoding the original video into multiple bitrates is
required. Video transcoding is a computationally expensive process, where generally a
single cloud instance needs to be reserved to produce one single video bitrate
representation. On demand renting of resources or inadequate resources reservation
may cause delay of the video playback or serving the viewers with a lower quality. On
the other hand, if resources provisioning is much higher than the required, the
extra resources will be wasted.
In this thesis, we introduce a prediction-driven resource allocation framework, to
maximize the QoE of viewers and minimize the resources allocation cost. First, by
exploiting the viewers’ locations available in our unique dataset, we implement a machine learning model to predict the viewers’ number near each geo-distributed cloud
site. Second, based on the predicted results that showed to be close to the actual values,
we formulate an optimization problem to proactively allocate resources at the viewers’
proximity. Additionally, we will present a trade-off between the video access delay and
the cost of resource allocation.
Considering the complexity and infeasibility of our offline optimization to respond to
the volume of viewing requests in real-time, we further extend our work, by introducing
a resources forecasting and reservation framework for geo-distributed cloud sites. First,
we formulate an offline optimization problem to allocate transcoding resources at the
viewers’ proximity, while creating a tradeoff between the network cost and viewers
QoE. Second, based on the optimizer resource allocation decisions on historical live
videos, we create our time series datasets containing historical records of the optimal
resources needed at each geo-distributed cloud site. Finally, we adopt machine learning
to build our distributed time series forecasting models to proactively forecast the exact
needed transcoding resources ahead of time at each geo-distributed cloud site.
The results showed that the predicted number of transcoding resources needed in each
cloud site is close to the optimal number of transcoding resources
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