2,425 research outputs found
A Survey of Energy Efficiency in SDN Software Based Methods and Optimization Models
Software Defined Networking (SDN) paradigm has the benefits of programmable
network elements by separating the control and the forwarding planes,
efficiency through optimized routing and flexibility in network management. As
the energy costs contribute largely to the overall costs in networks, energy
efficiency has become a significant design requirement for modern networking
mechanisms. However, designing energy efficient solutions is non-trivial since
they need to tackle the trade-off between energy efficiency and network
performance. In this article, we address the energy efficiency capabilities
that can be utilized in the emerging SDN. We provide a comprehensive and novel
classification of software-based energy efficient solutions into subcategories
of traffic aware, end system aware and rule placement. We propose general
optimization models for each subcategory, and present the objective function,
the parameters and constraints to be considered in each model. Detailed
information on the characteristics of state-of-the-art methods, their
advantages, drawbacks are provided. Hardware-based solutions used to enhance
the efficiency of switches are also described. Furthermore, we discuss the open
issues and future research directions in the area of energy efficiency in SDN.Comment: 17 double column pages, 3 figures, 6 table
Multi-resource Energy-efficient Routing in Cloud Data Centers with Networks-as-a-Service
With the rapid development of software defined networking and network
function virtualization, researchers have proposed a new cloud networking model
called Network-as-a-Service (NaaS) which enables both in-network packet
processing and application-specific network control. In this paper, we revisit
the problem of achieving network energy efficiency in data centers and identify
some new optimization challenges under the NaaS model. Particularly, we extend
the energy-efficient routing optimization from single-resource to
multi-resource settings. We characterize the problem through a detailed model
and provide a formal problem definition. Due to the high complexity of direct
solutions, we propose a greedy routing scheme to approximate the optimum, where
flows are selected progressively to exhaust residual capacities of active
nodes, and routing paths are assigned based on the distributions of both node
residual capacities and flow demands. By leveraging the structural regularity
of data center networks, we also provide a fast topology-aware heuristic method
based on hierarchically solving a series of vector bin packing instances. Our
simulations show that the proposed routing scheme can achieve significant gain
on energy savings and the topology-aware heuristic can produce comparably good
results while reducing the computation time to a large extent.Comment: 9 page
HyMER: A Hybrid Machine Learning Framework for Energy Efficient Routing in SDN
Software-defined networks (SDN) with programmable data plane and machine
learning for discovering patterns are utilized in security, traffic
classification, quality of services prediction, and network performance, that
has increasing research attention. Addressing the significance of energy
efficiency in networks, we propose a novel hybrid machine learning-based
framework named HyMER that combines the capabilities of SDN and machine
learning for traffic-aware energy efficient routing. To the best of our
knowledge, HyMER is the first that utilizes a hybrid machine learning solution
with supervised and reinforcement learning components for energy efficiency and
network performance in SDN. The supervised learning component consists of
feature extraction, training, and testing. The reinforcement learning component
learns from existing data or from scratch by iteratively interacting with the
network environment. The HyMER framework is developed on POX controller and is
evaluated on Mininet using real-world topologies and dynamic traffic traces.
Experimental results show that the supervised component achieves up to 70%
feature size reduction and more than 80\% accuracy in parameter prediction. We
demonstrate that combining the supervised and reinforcement methods not only
does capture the dynamic change more efficiently but also increases the
convergence speed. As compared to state-of-the-art utility based energy saving
approaches, HyMER heuristics has shown up to 50% link saving, and also exhibits
up to 14.7 watts less power consumption for realistic network topology and
traffic traces.Comment: Double column 12 pages, 13 figures, 6 table
Energy-aware Traffic Engineering in Hybrid SDN/IP Backbone Networks
Software Defined Networking (SDN) can effectively improve the performance of
traffic engineering and has promising application foreground in backbone
networks. Therefore, new energy saving schemes must take SDN into account,
which is extremely important considering the rapidly increasing energy
consumption from Telecom and ISP networks. At the same time, the introduction
of SDN in a current network must be incremental in most cases, for both
technical and economic reasons. During this period, operators have to manage
hybrid networks, where SDN and traditional protocols coexist. In this paper, we
study the energy efficient traffic engineering problem in hybrid SDN/IP
networks. We first formulate the mathematic optimization model considering
SDN/IP hybrid routing mode. As the problem is NP-hard, we propose the fast
heuristic algorithm named HEATE (Hybrid Energy-Aware Traffic Engineering). In
our proposed HEATE algorithm, the IP routers perform the shortest path routing
using the distribute OSPF link weight optimization. The SDNs perform the
multi-path routing with traffic flow splitting by the global SDN controller.
The HEATE algorithm finds the optimal setting of OSPF link weight and splitting
ratio of SDNs. Thus traffic flow is aggregated onto partial links and the
underutilized links can be turned off to save energy. By computer simulation
results, we show that our algorithm has a significant improvement in energy
efficiency in hybrid SDN/IP networks.Comment: 8 pages, 7 figures. Accepted by Journal of Communications and
Network
Linear Programming Approaches for Power Savings in Software-defined Networks (The Extended Version)
Software-defined networks have been proposed as a viable solution to decrease
the power consumption of the networking component in data center networks.
Still the question remains on which scheduling algorithms are most suited to
achieve this goal. We propose 4 different linear programming approaches that
schedule requested traffic flows on SDN switches according to different
objectives. Depending on pre-defined software quality requirements such as
delay and performance, a single variation or a combination of variations can be
selected to optimize the power saving and the performance metrics. Our
simulation results demonstrate that all our algorithm variations outperform the
shortest path scheduling algorithm, our baseline on power savings, less or more
strongly depending on the power model chosen. We show that in FatTree networks,
where switches can save up to 60% of power in sleeping mode, we can achieve 15%
minimum improvement assuming a one-to-one traffic scenario. Two of our
algorithm variations privilege performance over power saving and still provide
around 45% of the maximum achievable savings
Software-Defined Networking: State of the Art and Research Challenges
Plug-and-play information technology (IT) infrastructure has been expanding
very rapidly in recent years. With the advent of cloud computing, many
ecosystem and business paradigms are encountering potential changes and may be
able to eliminate their IT infrastructure maintenance processes. Real-time
performance and high availability requirements have induced telecom networks to
adopt the new concepts of the cloud model: software-defined networking (SDN)
and network function virtualization (NFV). NFV introduces and deploys new
network functions in an open and standardized IT environment, while SDN aims to
transform the way networks function. SDN and NFV are complementary
technologies; they do not depend on each other. However, both concepts can be
merged and have the potential to mitigate the challenges of legacy networks. In
this paper, our aim is to describe the benefits of using SDN in a multitude of
environments such as in data centers, data center networks, and Network as
Service offerings. We also present the various challenges facing SDN, from
scalability to reliability and security concerns, and discuss existing
solutions to these challenges
Management and Orchestration of Network Slices in 5G, Fog, Edge and Clouds
Network slicing allows network operators to build multiple isolated virtual
networks on a shared physical network to accommodate a wide variety of services
and applications. With network slicing, service providers can provide a
cost-efficient solution towards meeting diverse performance requirements of
deployed applications and services. Despite slicing benefits, End-to-End
orchestration and management of network slices is a challenging and complicated
task. In this chapter, we intend to survey all the relevant aspects of network
slicing, with the focus on networking technologies such as Software-defined
networking (SDN) and Network Function Virtualization (NFV) in 5G, Fog/Edge and
Cloud Computing platforms. To build the required background, this chapter
begins with a brief overview of 5G, Fog/Edge and Cloud computing, and their
interplay. Then we cover the 5G vision for network slicing and extend it to the
Fog and Cloud computing through surveying the state-of-the-art slicing
approaches in these platforms. We conclude the chapter by discussing future
directions, analyzing gaps and trends towards the network slicing realization.Comment: 31 pages, 4 figures, Fog and Edge Computing: Principles and
Paradigms, Wiley Press, New York, USA, 201
Resource Allocation in a Network-Based Cloud Computing Environment: Design Challenges
Cloud computing is an increasingly popular computing paradigm, now proving a
necessity for utility computing services. Each provider offers a unique service
portfolio with a range of resource configurations. Resource provisioning for
cloud services in a comprehensive way is crucial to any resource allocation
model. Any model should consider both computational resources and network
resources to accurately represent and serve practical needs. Another aspect
that should be considered while provisioning resources is energy consumption.
This aspect is getting more attention from industry and governments parties.
Calls of support for the green clouds are gaining momentum. With that in mind,
resource allocation algorithms aim to accomplish the task of scheduling virtual
machines on data center servers and then scheduling connection requests on the
network paths available while complying with the problem constraints. Several
external and internal factors that affect the performance of resource
allocation models are introduced in this paper. These factors are discussed in
detail and research gaps are pointed out. Design challenges are discussed with
the aim of providing a reference to be used when designing a comprehensive
energy aware resource allocation model for cloud computing data centers.Comment: To appear in IEEE Communications Magazine, November 201
PON-based connectivity for fog computing
Fog computing plays a crucial role in satisfying the requirements of
delay-sensitive applications such as connected vehicles, smart grids, and
actuator networks by moving data processing close to end users. Passive optical
networks (PONs) are widely used in access networks to reduce the power
consumption while providing high bandwidth to end users under flexible designs.
Typically, distributed fog computing units in access networks have limited
processing and storage capacities that can be under or over utilized depending
on instantaneous demands. To extend the available capacity in access network,
this paper proposes a fog computing architecture based on SDN-enabled PONs to
achieve full connectivity among distributed fog computing servers. The power
consumption results show that this architecture can achieve up to about 80%
power savings in comparison to legacy fog computing based on spine and leaf
data centers with the same number of servers
Software Defined Optical Networks (SDONs): A Comprehensive Survey
The emerging Software Defined Networking (SDN) paradigm separates the data
plane from the control plane and centralizes network control in an SDN
controller. Applications interact with controllers to implement network
services, such as network transport with Quality of Service (QoS). SDN
facilitates the virtualization of network functions so that multiple virtual
networks can operate over a given installed physical network infrastructure.
Due to the specific characteristics of optical (photonic) communication
components and the high optical transmission capacities, SDN based optical
networking poses particular challenges, but holds also great potential. In this
article, we comprehensively survey studies that examine the SDN paradigm in
optical networks; in brief, we survey the area of Software Defined Optical
Networks (SDONs). We mainly organize the SDON studies into studies focused on
the infrastructure layer, the control layer, and the application layer.
Moreover, we cover SDON studies focused on network virtualization, as well as
SDON studies focused on the orchestration of multilayer and multidomain
networking. Based on the survey, we identify open challenges for SDONs and
outline future directions
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