2,565 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
A conceptual architecture for integrating software defined network and network virtualization with internet of things
Software defined network (SDN) and network function virtualization (NFV) are new paradigms and technologies of the network which support the best experience of providing functions and services, managing network traffic, and a new way of control. They support virtualization and separating data from control in network devices, as well as provide services in a software-based environment. Internet of things (IoT) is a heterogeneous network with a massive number of connected devices and objects. IoT should be integrated with such technologies for the purpose of providing the capabilities of dynamic reconfiguration with a high level of integration. This paper proposes a conceptual architecture for integrating software defined network (SDN) and NFV with IoT. The proposed work combines the three technologies together in one architecture. It also presents the previous works in this area and takes a look at the theoretical background of those technologies in order to give a complete view of proposed work
An Energy-driven Network Function Virtualization for Multi-domain Software Defined Networks
Network Functions Virtualization (NFV) in Software Defined Networks (SDN)
emerged as a new technology for creating virtual instances for smooth execution
of multiple applications. Their amalgamation provides flexible and programmable
platforms to utilize the network resources for providing Quality of Service
(QoS) to various applications. In SDN-enabled NFV setups, the underlying
network services can be viewed as a series of virtual network functions (VNFs)
and their optimal deployment on physical/virtual nodes is considered a
challenging task to perform. However, SDNs have evolved from single-domain to
multi-domain setups in the recent era. Thus, the complexity of the underlying
VNF deployment problem in multi-domain setups has increased manifold. Moreover,
the energy utilization aspect is relatively unexplored with respect to an
optimal mapping of VNFs across multiple SDN domains. Hence, in this work, the
VNF deployment problem in multi-domain SDN setup has been addressed with a
primary emphasis on reducing the overall energy consumption for deploying the
maximum number of VNFs with guaranteed QoS. The problem in hand is initially
formulated as a "Multi-objective Optimization Problem" based on Integer Linear
Programming (ILP) to obtain an optimal solution. However, the formulated ILP
becomes complex to solve with an increasing number of decision variables and
constraints with an increase in the size of the network. Thus, we leverage the
benefits of the popular evolutionary optimization algorithms to solve the
problem under consideration. In order to deduce the most appropriate
evolutionary optimization algorithm to solve the considered problem, it is
subjected to different variants of evolutionary algorithms on the widely used
MOEA framework (an open source java framework based on multi-objective
evolutionary algorithms).Comment: Accepted for publication in IEEE INFOCOM 2019 Workshop on Intelligent
Cloud Computing and Networking (ICCN 2019
Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices
Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth
Generation (5G) mobile networks. MEC facilitates distributed cloud computing
capabilities and information technology service environment for applications
and services at the edges of mobile networks. This architectural modification
serves to reduce congestion, latency, and improve the performance of such edge
colocated applications and devices. In this paper, we demonstrate how reactive
service migration can be orchestrated for low-power MEC-enabled Internet of
Things (IoT) devices. Here, we use open-source Kubernetes as container
orchestration system. Our demo is based on traditional client-server system
from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As
the use case scenario, we post-process live video received over web real-time
communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1
handovers, demonstrating MEC-based software defined network (SDN). Now, edge
applications may reactively follow the UE within the radio access network
(RAN), expediting low-latency. The collected data is used to analyze the
benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end
(E2E) latency and power requirements of the UE are improved. We further discuss
the challenges of implementing such schemes and future research directions
therein
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