1,694 research outputs found
Algorithms for advance bandwidth reservation in media production networks
Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results
Automated Network Service Scaling in NFV: Concepts, Mechanisms and Scaling Workflow
Next-generation systems are anticipated to be digital platforms supporting
innovative services with rapidly changing traffic patterns. To cope with this
dynamicity in a cost-efficient manner, operators need advanced service
management capabilities such as those provided by NFV. NFV enables operators to
scale network services with higher granularity and agility than today. For this
end, automation is key. In search of this automation, the European
Telecommunications Standards Institute (ETSI) has defined a reference NFV
framework that make use of model-driven templates called Network Service
Descriptors (NSDs) to operate network services through their lifecycle. For the
scaling operation, an NSD defines a discrete set of instantiation levels among
which a network service instance can be resized throughout its lifecycle. Thus,
the design of these levels is key for ensuring an effective scaling. In this
article, we provide an overview of the automation of the network service
scaling operation in NFV, addressing the options and boundaries introduced by
ETSI normative specifications. We start by providing a description of the NSD
structure, focusing on how instantiation levels are constructed. For
illustrative purposes, we propose an NSD for a representative NS. This NSD
includes different instantiation levels that enable different ways to
automatically scale this NS. Then, we show the different scaling procedures the
NFV framework has available, and how it may automate their triggering. Finally,
we propose an ETSI-compliant workflow to describe in detail a representative
scaling procedure. This workflow clarifies the interactions and information
exchanges between the functional blocks in the NFV framework when performing
the scaling operation.Comment: This work has been accepted for publication in the IEEE
Communications Magazin
APMEC: An Automated Provisioning Framework for Multi-access Edge Computing
Novel use cases and verticals such as connected cars and human-robot
cooperation in the areas of 5G and Tactile Internet can significantly benefit
from the flexibility and reduced latency provided by Network Function
Virtualization (NFV) and Multi-Access Edge Computing (MEC). Existing frameworks
managing and orchestrating MEC and NFV are either tightly coupled or completely
separated. The former design is inflexible and increases the complexity of one
framework. Whereas, the latter leads to inefficient use of computation
resources because information are not shared. We introduce APMEC, a dedicated
framework for MEC while enabling the collaboration with the management and
orchestration (MANO) frameworks for NFV. The new design allows to reuse
allocated network services, thus maximizing resource utilization. Measurement
results have shown that APMEC can allocate up to 60% more number of network
services. Being developed on top of OpenStack, APMEC is an open source project,
available for collaboration and facilitating further research activities
Effectiveness of segment routing technology in reducing the bandwidth and cloud resources provisioning times in network function virtualization architectures
Network Function Virtualization is a new technology allowing for a elastic cloud and bandwidth resource allocation. The technology requires an orchestrator whose role is the service and resource orchestration. It receives service requests, each one characterized by a Service Function Chain, which is a set of service functions to be executed according to a given order. It implements an algorithm for deciding where both to allocate the cloud and bandwidth resources and to route the SFCs. In a traditional orchestration algorithm, the orchestrator has a detailed knowledge of the cloud and network infrastructures and that can lead to high computational complexity of the SFC Routing and Cloud and Bandwidth resource Allocation (SRCBA) algorithm. In this paper, we propose and evaluate the effectiveness of a scalable orchestration architecture inherited by the one proposed within the European Telecommunications Standards Institute (ETSI) and based on the functional separation of an NFV orchestrator in Resource Orchestrator (RO) and Network Service Orchestrator (NSO). Each cloud domain is equipped with an RO whose task is to provide a simple and abstract representation of the cloud infrastructure. These representations are notified of the NSO that can apply a simplified and less complex SRCBA algorithm. In addition, we show how the segment routing technology can help to simplify the SFC routing by means of an effective addressing of the service functions. The scalable orchestration solution has been investigated and compared to the one of a traditional orchestrator in some network scenarios and varying the number of cloud domains. We have verified that the execution time of the SRCBA algorithm can be drastically reduced without degrading the performance in terms of cloud and bandwidth resource costs
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