10,203 research outputs found
MIPv6 Experimental Evaluation using Overlay Networks
The commercial deployment of Mobile IPv6 has been hastened by the concepts of Integrated
Wireless Networks and Overlay Networks, which are present in the notion of the
forthcoming generation of wireless communications. Individual wireless access networks
show limitations that can be overcome through the integration of different technologies
into a single unified platform (i.e., 4G systems). This paper summarises practical experiments
performed to evaluate the impact of inter-networking (i.e. vertical handovers) on
the Network and Transport layers. Based on our observations, we propose and evaluate a
number of inter-technology handover optimisation techniques, e.g., Router Advertisements
frequency values, Binding Update simulcasting, Router Advertisement caching, and Soft
Handovers. The paper concludes with the description of a policy-based mobility support
middleware (PROTON) that hides 4G networking complexities from mobile users, provides
informed handover-related decisions, and enables the application of different vertical
handover methods and optimisations according to context.Publicad
Achieving Autonomic Computing through the Use of Variability Models at Run-time
Increasingly, software needs to dynamically adapt its behavior at run-time in response
to changing conditions in the supporting computing infrastructure and in
the surrounding physical environment. Adaptability is emerging as a necessary underlying
capability, particularly for highly dynamic systems such as context-aware
or ubiquitous systems.
By automating tasks such as installation, adaptation, or healing, Autonomic
Computing envisions computing environments that evolve without the need for human
intervention. Even though there is a fair amount of work on architectures
and their theoretical design, Autonomic Computing was criticised as being a \hype
topic" because very little of it has been implemented fully. Furthermore, given that
the autonomic system must change states at runtime and that some of those states
may emerge and are much less deterministic, there is a great challenge to provide
new guidelines, techniques and tools to help autonomic system development.
This thesis shows that building up on the central ideas of Model Driven Development
(Models as rst-order citizens) and Software Product Lines (Variability
Management) can play a signi cant role as we move towards implementing the key
self-management properties associated with autonomic computing. The presented
approach encompass systems that are capable of modifying their own behavior with
respect to changes in their operating environment, by using variability models as if
they were the policies that drive the system's autonomic recon guration at runtime.
Under a set of recon guration commands, the components that make up the architecture
dynamically cooperate to change the con guration of the architecture to a
new con guration.
This work also provides the implementation of a Model-Based Recon guration
Engine (MoRE) to blend the above ideas. Given a context event, MoRE queries the variability models to determine how the system should evolve, and then it provides
the mechanisms for modifying the system.Cetina Englada, C. (2010). Achieving Autonomic Computing through the Use of Variability Models at Run-time [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7484Palanci
Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges
Cloud computing is offering utility-oriented IT services to users worldwide.
Based on a pay-as-you-go model, it enables hosting of pervasive applications
from consumer, scientific, and business domains. However, data centers hosting
Cloud applications consume huge amounts of energy, contributing to high
operational costs and carbon footprints to the environment. Therefore, we need
Green Cloud computing solutions that can not only save energy for the
environment but also reduce operational costs. This paper presents vision,
challenges, and architectural elements for energy-efficient management of Cloud
computing environments. We focus on the development of dynamic resource
provisioning and allocation algorithms that consider the synergy between
various data center infrastructures (i.e., the hardware, power units, cooling
and software), and holistically work to boost data center energy efficiency and
performance. In particular, this paper proposes (a) architectural principles
for energy-efficient management of Clouds; (b) energy-efficient resource
allocation policies and scheduling algorithms considering quality-of-service
expectations, and devices power usage characteristics; and (c) a novel software
technology for energy-efficient management of Clouds. We have validated our
approach by conducting a set of rigorous performance evaluation study using the
CloudSim toolkit. The results demonstrate that Cloud computing model has
immense potential as it offers significant performance gains as regards to
response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference
on Parallel and Distributed Processing Techniques and Applications (PDPTA
2010), Las Vegas, USA, July 12-15, 201
Self-* overload control for distributed web systems
Unexpected increases in demand and most of all flash crowds are considered
the bane of every web application as they may cause intolerable delays or even
service unavailability. Proper quality of service policies must guarantee rapid
reactivity and responsiveness even in such critical situations. Previous
solutions fail to meet common performance requirements when the system has to
face sudden and unpredictable surges of traffic. Indeed they often rely on a
proper setting of key parameters which requires laborious manual tuning,
preventing a fast adaptation of the control policies. We contribute an original
Self-* Overload Control (SOC) policy. This allows the system to self-configure
a dynamic constraint on the rate of admitted sessions in order to respect
service level agreements and maximize the resource utilization at the same
time. Our policy does not require any prior information on the incoming traffic
or manual configuration of key parameters. We ran extensive simulations under a
wide range of operating conditions, showing that SOC rapidly adapts to time
varying traffic and self-optimizes the resource utilization. It admits as many
new sessions as possible in observance of the agreements, even under intense
workload variations. We compared our algorithm to previously proposed
approaches highlighting a more stable behavior and a better performance.Comment: The full version of this paper, titled "Self-* through self-learning:
overload control for distributed web systems", has been published on Computer
Networks, Elsevier. The simulator used for the evaluation of the proposed
algorithm is available for download at the address:
http://www.dsi.uniroma1.it/~novella/qos_web
General-purpose autonomic computing
The success of mainstream computing is largely due to the widespread availability of general-purpose architectures and of generic approaches that can be used to solve real-world problems cost-effectively and across a broad range of application domains. In this chapter, we propose that a similar generic framework is used to make the development of autonomic solutions cost effective, and to establish autonomic computing as a major approach to managing the complexity of today’s large-scale systems and systems of systems. To demonstrate the feasibility of general-purpose autonomic computing, we introduce a generic autonomic computing framework comprising a policy-based autonomic architecture and a novel four-step method for the effective development of self-managing systems. A prototype implementation of the reconfigurable policy engine at the core of our architecture is then used to develop autonomic solutions for case studies from several application domains. Looking into the future, we describe a methodology for the engineering of self-managing systems that extends and generalises our autonomic computing framework further
A Conceptual Framework for Adapation
This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions
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