5,679 research outputs found
Robust Energy Management for Green and Survivable IP Networks
Despite the growing necessity to make Internet greener, it is worth pointing
out that energy-aware strategies to minimize network energy consumption must
not undermine the normal network operation. In particular, two very important
issues that may limit the application of green networking techniques concern,
respectively, network survivability, i.e. the network capability to react to
device failures, and robustness to traffic variations. We propose novel
modelling techniques to minimize the daily energy consumption of IP networks,
while explicitly guaranteeing, in addition to typical QoS requirements, both
network survivability and robustness to traffic variations. The impact of such
limitations on final network consumption is exhaustively investigated. Daily
traffic variations are modelled by dividing a single day into multiple time
intervals (multi-period problem), and network consumption is reduced by putting
to sleep idle line cards and chassis. To preserve network resiliency we
consider two different protection schemes, i.e. dedicated and shared
protection, according to which a backup path is assigned to each demand and a
certain amount of spare capacity has to be available on each link. Robustness
to traffic variations is provided by means of a specific modelling framework
that allows to tune the conservatism degree of the solutions and to take into
account load variations of different magnitude. Furthermore, we impose some
inter-period constraints necessary to guarantee network stability and preserve
the device lifetime. Both exact and heuristic methods are proposed.
Experimentations carried out with realistic networks operated with flow-based
routing protocols (i.e. MPLS) show that significant savings, up to 30%, can be
achieved also when both survivability and robustness are fully guaranteed
Energy management in communication networks: a journey through modelling and optimization glasses
The widespread proliferation of Internet and wireless applications has
produced a significant increase of ICT energy footprint. As a response, in the
last five years, significant efforts have been undertaken to include
energy-awareness into network management. Several green networking frameworks
have been proposed by carefully managing the network routing and the power
state of network devices.
Even though approaches proposed differ based on network technologies and
sleep modes of nodes and interfaces, they all aim at tailoring the active
network resources to the varying traffic needs in order to minimize energy
consumption. From a modeling point of view, this has several commonalities with
classical network design and routing problems, even if with different
objectives and in a dynamic context.
With most researchers focused on addressing the complex and crucial
technological aspects of green networking schemes, there has been so far little
attention on understanding the modeling similarities and differences of
proposed solutions. This paper fills the gap surveying the literature with
optimization modeling glasses, following a tutorial approach that guides
through the different components of the models with a unified symbolism. A
detailed classification of the previous work based on the modeling issues
included is also proposed
A fast robust optimization-based heuristic for the deployment of green virtual network functions
Network Function Virtualization (NFV) has attracted a lot of attention in the telecommunication field because it allows to virtualize core-business network functions on top of a NFV Infrastructure. Typically, virtual network functions (VNFs) can be represented as chains of Virtual Machines (VMs) or containers that exchange network traffic which are deployed inside datacenters on commodity hardware. In order to achieve cost efficiency, network operators aim at minimizing the power consumption of their NFV infrastructure. This can be achieved by using the minimum set of physical servers and networking equipment that are able to provide the quality of service required by the virtual functions in terms of computing, memory, disk and network related parameters. However, it is very difficult to predict precisely the resource demands required by the VNFs to execute their tasks. In this work, we apply the theory of robust optimization to deal with such parameter uncertainty. We model the problem of robust VNF placement and network embedding under resource demand uncertainty and network latency constraints using robust mixed integer optimization techniques. For online optimization, we develop fast solution heuristics. By using the virtualized Evolved Packet Core as use case, we perform a comprehensive evaluation in terms of performance, solution time and complexity and show that our heuristic can calculate robust solutions for large instances under one second.Peer ReviewedPostprint (author's final draft
Dynamic Network State Learning Model for Mobility Based WMSN Routing Protocol
The rising demand of wireless multimedia sensor networks (WMSNs) has motivated academia-industries to develop energy efficient, Quality of Service (QoS) and delay sensitive communication systems to meet major real-world demands like multimedia broadcast, security and surveillance systems, intelligent transport system, etc. Typically, energy efficiency, QoS and delay sensitive transmission are the inevitable requirements of WMSNs. Majority of the existing approaches either use physical layer or system level schemes that individually can’t assure optimal transmission decision to meet the demand. The cumulative efficiency of physical layer power control, adaptive modulation and coding and system level dynamic power management (DPM) are found significant to achieve these demands. With this motivation, in this paper a unified model is derived using enhanced reinforcement learning and stochastic optimization method. Exploiting physical as well as system level network state information, our proposed dynamic network state learning model (NSLM) applies stochastic optimization to learn network state-activity that derives an optimal DPM policy and PHY switching scheduling. NSLM applies known as well as unknown network state variables to derive transmission and PHY switching policy, where it considers DPM as constrained Markov decision process (MDP) problem. Here,the use of Hidden Markov Model and Lagrangian relaxation has made NSLM convergence swift that assures delay-sensitive, QoS enriched, and bandwidth and energy efficient transmission for WMSN under uncertain network conditions. Our proposed NSLM DPM model has outperformed traditional Q-Learning based DPM in terms of buffer cost, holding cost, overflow, energy consumption and bandwidth utilization
Boltzmann meets Nash: Energy-efficient routing in optical networks under uncertainty
Motivated by the massive deployment of power-hungry data centers for service
provisioning, we examine the problem of routing in optical networks with the
aim of minimizing traffic-driven power consumption. To tackle this issue,
routing must take into account energy efficiency as well as capacity
considerations; moreover, in rapidly-varying network environments, this must be
accomplished in a real-time, distributed manner that remains robust in the
presence of random disturbances and noise. In view of this, we derive a pricing
scheme whose Nash equilibria coincide with the network's socially optimum
states, and we propose a distributed learning method based on the Boltzmann
distribution of statistical mechanics. Using tools from stochastic calculus, we
show that the resulting Boltzmann routing scheme exhibits remarkable
convergence properties under uncertainty: specifically, the long-term average
of the network's power consumption converges within of its
minimum value in time which is at most ,
irrespective of the fluctuations' magnitude; additionally, if the network
admits a strict, non-mixing optimum state, the algorithm converges to it -
again, no matter the noise level. Our analysis is supplemented by extensive
numerical simulations which show that Boltzmann routing can lead to a
significant decrease in power consumption over basic, shortest-path routing
schemes in realistic network conditions.Comment: 24 pages, 4 figure
Green Cellular Networks: A Survey, Some Research Issues and Challenges
Energy efficiency in cellular networks is a growing concern for cellular
operators to not only maintain profitability, but also to reduce the overall
environment effects. This emerging trend of achieving energy efficiency in
cellular networks is motivating the standardization authorities and network
operators to continuously explore future technologies in order to bring
improvements in the entire network infrastructure. In this article, we present
a brief survey of methods to improve the power efficiency of cellular networks,
explore some research issues and challenges and suggest some techniques to
enable an energy efficient or "green" cellular network. Since base stations
consume a maximum portion of the total energy used in a cellular system, we
will first provide a comprehensive survey on techniques to obtain energy
savings in base stations. Next, we discuss how heterogeneous network deployment
based on micro, pico and femto-cells can be used to achieve this goal. Since
cognitive radio and cooperative relaying are undisputed future technologies in
this regard, we propose a research vision to make these technologies more
energy efficient. Lastly, we explore some broader perspectives in realizing a
"green" cellular network technologyComment: 16 pages, 5 figures, 2 table
A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities
In the context of cloud-enabled 5G radio access networks with network function virtualization capabilities, we focus on the virtual network function placement problem for a multitenant cluster of small cells that provide mobile edge computing services. Under an emerging distributed network architecture and hardware infrastructure, we employ cloud-enabled small cells that integrate microservers for virtualization execution, equipped with additional hardware appliances. We develop an energy-aware placement solution using a robust optimization approach based on service demand uncertainty in order to minimize the power consumption in the system constrained by network service latency requirements and infrastructure terms. Then, we discuss the results of the proposed placement mechanism in 5G scenarios that combine several service flavours and robust protection values. Once the impact of the service flavour and robust protection on the global power consumption of the system is analyzed, numerical results indicate that our proposal succeeds in efficiently placing the virtual network functions that compose the network services in the available hardware infrastructure while fulfilling service constraints.The research leading to these results has been supported by the EU funded H2020 5G-PPP Project SESAME (Grant Agreement 671596) and the Spanish MINECO Project 5GRANVIR (TEC2016-80090-C2-2-R)
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