88,561 research outputs found
Analysis domain model for shared virtual environments
The field of shared virtual environments, which also
encompasses online games and social 3D environments, has a
system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model
Resource provisioning in Science Clouds: Requirements and challenges
Cloud computing has permeated into the information technology industry in the
last few years, and it is emerging nowadays in scientific environments. Science
user communities are demanding a broad range of computing power to satisfy the
needs of high-performance applications, such as local clusters,
high-performance computing systems, and computing grids. Different workloads
are needed from different computational models, and the cloud is already
considered as a promising paradigm. The scheduling and allocation of resources
is always a challenging matter in any form of computation and clouds are not an
exception. Science applications have unique features that differentiate their
workloads, hence, their requirements have to be taken into consideration to be
fulfilled when building a Science Cloud. This paper will discuss what are the
main scheduling and resource allocation challenges for any Infrastructure as a
Service provider supporting scientific applications
The evaluation of an active networking approach for supporting the QOS requirements of distributed virtual environments
This paper describes work that is part of a more general investigation into how Active Network ideas
might benefit large scale Distributed-Virtual-Environments (DVEs). Active Network approaches have been
shown to offer improved solutions to the Scalable Reliable Multicast problem, and this is in a sense the lowest
level at which Active Networks might benefit DVEs in supporting the peer-to-peer architectures considered
most promising for large scale DVEs. To go further than this, the key benefit of Active Networking is the ability
to take away from the application the need to understand the network topology and delegate the execution of
certain actions, for example intelligent message pruning, to the network itself. The need to exchange geometrical
information results in a type of traffic that can place occasional, short-lived, but heavy loads on the network.
However, the Level of Detail (LoD) concept provides the potential to reduce this loading in certain circumstances.
This paper introduces the performance modelling approach being used to evaluate the effectiveness of
active network approaches for supporting DVEs and presents an evaluation of messages filtering mechanisms,
which are based on the (LoD) concept. It describes the simulation experiment used to carry out the evaluation,
presents its results and discusses plans for future work
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
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