2,453 research outputs found
Enabling virtual radio functions on software defined radio for future wireless networks
Today's wired networks have become highly flexible, thanks to the fact that an increasing number of functionalities are realized by software rather than dedicated hardware. This trend is still in its early stages for wireless networks, but it has the potential to improve the network's flexibility and resource utilization regarding both the abundant computational resources and the scarce radio spectrum resources. In this work we provide an overview of the enabling technologies for network reconfiguration, such as Network Function Virtualization, Software Defined Networking, and Software Defined Radio. We review frequently used terminology such as softwarization, virtualization, and orchestration, and how these concepts apply to wireless networks. We introduce the concept of Virtual Radio Function, and illustrate how softwarized/virtualized radio functions can be placed and initialized at runtime, allowing radio access technologies and spectrum allocation schemes to be formed dynamically. Finally we focus on embedded Software-Defined Radio as an end device, and illustrate how to realize the placement, initialization and configuration of virtual radio functions on such kind of devices
On the Benefit of Virtualization: Strategies for Flexible Server Allocation
Virtualization technology facilitates a dynamic, demand-driven allocation and
migration of servers. This paper studies how the flexibility offered by network
virtualization can be used to improve Quality-of-Service parameters such as
latency, while taking into account allocation costs. A generic use case is
considered where both the overall demand issued for a certain service (for
example, an SAP application in the cloud, or a gaming application) as well as
the origins of the requests change over time (e.g., due to time zone effects or
due to user mobility), and we present online and optimal offline strategies to
compute the number and location of the servers implementing this service. These
algorithms also allow us to study the fundamental benefits of dynamic resource
allocation compared to static systems. Our simulation results confirm our
expectations that the gain of flexible server allocation is particularly high
in scenarios with moderate dynamics
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
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