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Network function virtualization foresees the virtualization of service functions and their execution on virtual machines. Any service is represented by a service function chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF Instances (VNFIs) that in general are software modules executed on virtual machines. The virtualization challenges include: 1) where to instantiate VNFIs; ii) how many resources to allocate to each VNFI; iii) how to route SFC requests to the appropriate VNFIs in the right sequence; and iv) when and how to migrate VNFIs in response to changes to SFC request intensity and location. We develop an approach that uses three algorithms that are used back-to-back resulting in VNFI placement, SFC routing, and VNFI migration in response to changing workload. The objective is to first minimize the rejection of SFC bandwidth and second to consolidate VNFIs in as few servers as possible so as to reduce the energy consumed. The proposed consolidation algorithm is based on a migration policy of VNFIs that considers the revenue loss due to QoS degradation that a user suffers due to information loss occurring during the migrations. The objective is to minimize the total cost given by the energy consumption and the revenue loss due to QoS degradation. We evaluate our suite of algorithms on a test network and show performance gains that can be achieved over using other alternative naive algorithms.

The power consumption of the network equipment has increased significantly and some strategies to contain the power used in the IP network are needed. Among the green networking strategies, the virtualization class and in particular the deployment of migrating virtual routers can lead to a high energy saving. It consists in migrating virtual routers in fewer physical nodes when the traffic decreases allowing for a power consumption saving. In this paper we formulate the problem of minimizing the power consumption as a Mixed Integer Linear Programming (MILP) problem. Due to the hard complexity of the introduced MILP problem, we propose a heuristic for the migration of virtual routers among physical devices in order to turn off as many nodes as possible and save power according to the compliance with network node and link capacity constraints. We show that 50% of nodes may be turned off in the case of a real provider network when traffic percentage reduction of 80% occurs. Finally we also perform a feasibility study by means of an experimental test-bed to evaluate migration time of a routing plane based on QUAGGA routing software.

Network virtualization techniques allow for the coexistence of many virtual networks hosted in the same substrate network. Virtual router migration allows for resource consolidation with the consequence to reduce the power consumption in low traffic periods. Unfortunately, virtual router migration has the effect of degrading the quality of service during the downtime in which the router is not able to carry on its forwarding function. For this reason, despite the advantages in power consumption saving, the migration technique should be applied with parsimony to avoid an excessive QoS degradation. The objective of this paper is to study virtual network embedding (VNE) problems aware of both operation and reconfiguration costs that are characterized by the energy consumption and the revenue loss due to QoS degradation. Both embedding optimization problem formulation and feasible solutions to the problem in cycle-stationary traffic scenario and when the possible embeddings are determined a priori will be given. Two migration policies will be introduced and compared. The first one, referred to as “global,” is based on the knowledge of the entire daily traffic profile and it is determined by solving a Markov decision process. The second one, referred to as “local,” is only based on the knowledge of the current traffic. The results achieved show how the application of the global policy allows better performance than the local one and its application can lead to a cost reduction in the order of 35% with respect to traditional migration policies.

The Network Function Virtualization (NFV) technology aims at virtualizing the network service with the execution of the single service components in Virtual Machines activated on Commercial-off-the-shelf (COTS) servers. Any service is represented by the Service Function Chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF instances (VNFI) that in general are software components executed on Virtual Machines. In this paper we cope with the routing and resource dimensioning problem in NFV architectures. We formulate the optimization problem and due to its NP-hard complexity, heuristics are proposed for both cases of offline and online traffic demand. We show how the heuristics works correctly by guaranteeing a uniform occupancy of the server processing capacity and the network link bandwidth. A consolidation algorithm for the power consumption minimization is also proposed. The application of the consolidation algorithm allows for a high power consumption saving that however is to be paid with an increase in SFC blocking probability.

Network virtualization techniques allow for the coexistence of many virtual networks hosted in a same substrate network. Virtual router migrations allow for resource consolidation with the consequence to reduce the power consumption in low traffic periods. Unfortunately virtual router migrations have the effect of degrading the Quality of Service during the downtime in which the router is not able to carry on its forwarding function. For this reason, despite the advantages in power consumption saving, the migration technique should be applied with parsimony to avoid an excessive QoS degradation. The objective of this paper is to study Virtual Network Embedding problems aware of both operation and reconfiguration costs that are characterized by the energy consumption and the revenue loss due to QoS degradation. An Embedding Optimization Problem formulation will be given in which the objective is to minimize an objective function taking into account both the operation and reconfiguration costs. Because the problem is NP-hard, we propose a feasible solution to the problem in cycle-stationary traffic scenario and when the possible embeddings are a-priory determined. The solution consists in determining the optimal policy of a Markov Decision Process. The results obtained show how the application of the introduced solution allows for cost reduction in the order of 30% as well as to limit the reconfiguration costs with respect to traditional migration policies.

The definition of a virtual router layer allows the application of energy saving techniques based on the migration of virtual routers within the physical infrastructure as traffic conditions change. As traffic decreases, physical resources can be vacated through virtual router migration, thus, saving energy. Such resources can be reclaimed if needed because of traffic increase. Unfortunately, the migration of virtual routers involves reconfiguration costs depending on implementation details and characterized, for instance, by the revenue loss due to quality-of-service (QoS) degradation during the migration process. In this paper, we propose a methodology based on the Markov decision processes for the evaluation of optimal migration policies taking into account both the operation (in terms of energy consumption) and reconfiguration costs.

Virtualization and migration techniques have been largely investigated in Data Center applications to reduce the power consumption and to better utilize the physical resources. In this paper we studied the adoption of such techniques in an IP network and validate our approach in a real testbed based on the use of Software Routers. The contribution is organized in two parts. The first one is devoted to study and to evaluate a power saving solution for IP networks based on the virtualization and migration techniques. We also consider the integration of such techniques with a power saving solution available on network devices, i.e. adaptive link rate, able to adapt line cards power consumption to incoming traffic. The main results show that the effectiveness of the virtualization and migration techniques depends on the fixed power consumption of the routers and increases for high values of this component. The second part of the paper is devoted to evaluate the impact of router migration on the network performance; we consider two different virtualization environments, Linux Container (LXC) and Kernel-based Virtual Machine (KVM). The experimental results highlights that the impact of router migration, expressed by means of the downtime index, on network performance is negligible.

As a consequence of the rapid growth of Internet, the power consumption of the network equipment has increased significantly. This trend will continue in the next years and some strategies to contain the power used in the IP network are needed. Among the green networking strategies the virtualization class and in particular the deployment of virtual routers can lead to a high energy saving. In this paper we propose an heuristic for the migration of virtual routers among physical devices in order to turn off as many nodes as possible and save power according to the compliance with network node and link capacity constraints. We show that the 28% of nodes may be turn off when traffic percentage reduction of 80% occurs.

We illustrate an experimental test-bed to evaluate the routing plane migration time based on QUAGGA routing software. The operation mode correctness of the OSPF routing protocol is also verified. The realized test-bed allows for an evaluation of the migration time as a function of the number of nodes of an emulated network. We show that even when the network links are loaded with data traffic, the migration time is low and the migration does not degrade the performance of the OSPF routing protocol. For instance the migration time is less than 12 ms in the case of an emulated network of 500 nodes and when the links are 70% loaded.

The service evolution and the rapid increase in traffic levels fuel the interest toward switching paradigms enabling the fast allocation of Wavelength Division Multiplexing WDM channels in an on demand fashion with fine granularities (microsecond scales). For this reason, in the last years, different optical switching paradigms have been proposed (Sabella et al., 2000): optical-packet switching (OPS), optical-burst switching (OBS), wavelength-routed OBS, etc. Among the various all-optical switching paradigms, OPS attracts increasing attention. Owing to the high switching rate, Semiconductor Optical Amplifier (SOA) is a key technology to realize Optical Packet Switches. We propose some Optical Packet Switch (OPS) architectures and illustrate their realization in SOA technology. The effectiveness of the technology in reducing the power consumption is also analyzed. The chapter is organized in three sections. The main blocks (Switching Fabric, Wavelength Conversion stage, Synchronization stage) of an OPS are illustrated in Section 2 where we also show some examples of realizing wavelength converters and synchronizers in SOA technology. Section 3 introduces SOA-based single-stage and multi-stage switching fabrics. Finally the SOA-based OPS power consumption is investigated in Section 4.

We evaluate the average energy consumption per bit in Optical Packet Switches equipped with BENES switching fabric realized in Semiconductor Optical Amplifier (SOA) technology. We also study the impact that the Amplifier Spontaneous Emission (ASE) noise generated by a transmission system has on the power consumption of the BENES switches due to the gain saturation of the SOAs used to realize the switching fabric. As a matter of example for 32×32 switches supporting 64 wavelengths and offered traffic equal to 0,8, the average energy consumption per bit is 2, 34 · 10-1 nJ/bit and increases if ASE noise introduced by the transmission systems is increased., {"references":["R.S. Tucker, Green Optical Communications-Part II: Energy Limitations\nin Networks, IEEE Journal of Selected Topics in Quantum Electronics,\nMarch/April 2011.","R.S. Tucker, Optical Packet Switching: A Reality Check, Optical Switching\nand Networking, vol. 5, pp 2-9, March/April 2007.","S. Aleksic, Analysis of Power in Future High.Capacity Network Nodes,\nIEEE/OSA Journal of Communications and Networking, vol. 1, pp. 245-\n258, August 2009.","S. Aleksic, Power Consumption Issues in Future High-Performance\nSwitches and Routers, ICTON 2008, Athens, Greece, June 2008.","S. Aleksic, N. Fehatovic, Requirements and Limitations of Optical Interconnects\nfor High-Capacity Network Elements, ICTON 2010, Munich,\nGermany, June 2010.","J. Gripp, M. Duelk, J. Simsarian, S. Chandrasekhar, P. Bernasconi, A.\nBhardway, Y. Su, K. Sherman, L. Buhl, E. Laskowski, M. Capuzzo,\nL. Stulz, M. Zirngibl, O. Laznicka, T. Link, O. Mayer, M. Berger,\nDemonstration of a 1.2 Tb/s Optical Packet Switch Fabric (32┬À40) based\non 40 Gb/s burst-mode clock-data-recovery, fast tunable lasers and a\nhigh-performance N ┬À N AWG, 27th European Conference on Optical\nCommunication, Amsterdam, Holland, September 2001.","S.J.B. Yoo, Optical Packet and Burst Switching Technologies for the\nFuture Photonic Internet, IEEE/OSA Journal Lightwave of Technology,\nvol. 24, pp. 4468-4492, December 2006.","O. Liboiron-Ladouceur, A. Shacham, B.A. Small, B.G. lee, H. Wang, C.P.\nLai, A. Biberman, K. Bergman, The Data Vortex Optical Packet Switched\nInterconnection Network, IEEE/OSA Journal Lightwave of Technology,\nvol. 26, pp. 1777-1789, July 2008.","V. Eramo, M. Listanti, Power Consumption in Bufferless Optical Packet\nSwitches in SOA Technology, IEEE/OSA Journal of Communications and\nNetworking, vol. 1, pp B15-B29 August 2009.\n[10] V.E. Benes, Mathematical Theory of Connecting Networks, New York:\nAcademic, 1965.\n[11] K. Hinton, G. Rakutti, P. Farrel, R.S. Tucker, Switching Energy and\nDevice Size Limits on Digital Photonic Signal Processing Technologies,\nIEEE Journal of Selected Topics in Quantum Electronics, vol. 14, pp.\n938-945, May/June 2008.\n[12] G. Jeong, J.W. Goodman, Gain Optimization in Switches Based on\nSemiconductor Optical Amplifiers, IEEE/OSA Journal Lightwave of Technology,\nvol. 13, pp. 598-605, April 1995.\n[13] R.F. Kalman, L.G. Kazovsky, J.W: Goodman, Space Division Switches\nBased on Semiconductor Optical Amplifiers, IEEE Photonic Technology\nLetters, vol. 4, pp. 1048-1051, September 1992\n[14] A. Ehrhardt, M. Eiselt, G. Groβkopf, L. Kuller, R. ludwig, W. Pieper,\nR. Schnabel and H. Weber, Semiconductor Laser Amplifier as Optical\nSwitching Gate, IEEE/OSA Journal Lightwave of Technology, vol. 11,\npp. 1287-1295, August 1993.\n[15] V. Eramo, Effect of ASE noise on the Power Consumption of MVMC and\nBENES Optical Packet Switches, IEEE/OSA Journal of Communications\nand Networking, submitted to publication.\n[16] C.H. Henry, Theory of Spontaneous Emission Noise in Open Resonators\nand its Applications to Laser and Optical Amplifier, IEEE/OSA Journal\nLightwave of Technology, vol. 4, pp. 288-297, April 1986.\n[17] V. Eramo, Comparison in Power Consumption of Synchronous and\nAsynchronous Optical Packet Switches, IEEE/OSA Journal Lightwave of\nTechnology, vol. 28, pp. 847-857, March 2010.\n[18] Y. Ueno, J. Sakaguchi, R. Nakamoto, T. Nishida, Ultrafast, low-energyconsumption,\nsemiconductor-based, all-optical devices, 4-th Asia- Pacific\nPhotonics Conference (APMP 2009), Bejing, China, April 2009.\n[19] R.S. Tucker, Green Optical Communications-Part I: Energy Limitations\nin Transport, IEEE Journal of Selected Topics in Quantum Electronics,\nMarch/April 2011.\n[20] V. Eramo, M. Listanti, C. Nuzman, P. Whiting, Optical Switch Dimensioning\nand the Classical Occupancy Problem, International Journal\nCommunications Systems, vol. 15, pp. 127-141, March/April 2002."]}

In this paper we propose an analytical model able to analytically evaluate the performance of an asynchronous Optical Packet Switch (OPS) realized in Semiconductor Optical Amplifier (SOA). The considered switch has a bufferless architecture that shares the wavelength converters for input wavelength. The obtained results show how the proposed model is more accurate than others proposed in literature in the case of small size switches.

The power consumption of an Optical Packet Switch equipped with SOA technology based Spanke switching fabric is evaluated. Sophisticated analytical models are introduced to evaluate the power consumption versus the offered traffic, the main switch parameters, and the used device characteristics. The impact of Amplifier Spontaneous Emission (ASE) noise generated by a transmission system on the power consumption is investigated. As a matter of example for 32 × 32 switches supporting 64 wavelengths and offered traffic equal to 0,8, the average energy consumption per bit is 5,07 · 10−2 nJ/bit and increases if ASE noise introduced by the transmission systems is increased.

We evaluate the average energy consumption per bit in Optical Packet Switches equipped with BENES switching fabric realized in Semiconductor Optical Amplifier (SOA) technology. We also study the impact that the Amplifier Spontaneous Emission (ASE) noise generated by a transmission system has on the power consumption of the BENES switches due to the gain saturation of the SOAs used to realize the switching fabric. As a matter of example for 32×32 switches supporting 64 wavelengths and offered traffic equal to 0,8, the average energy consumption per bit is 2, 34·10−1 nJ/bit and increases if ASE noise introduced by the transmission systems is increased.

The power consumption of an Optical Packet Switch equipped with SOA technology based Spanke switching fabric is evaluated. Sophisticated analytical models are introduced to evaluate the power consumption versus the offered traffic, the main switch parameters, and the used device characteristics. The impact of Amplifier Spontaneous Emission (ASE) noise generated by a transmission system on the power consumption is investigated. As a matter of example for 32×32 switches supporting 64 wavelengths and offered traffic equal to 0,8, the average energy consumption per bit is 5, 07 · 10-2 nJ/bit and increases if ASE noise introduced by the transmission systems is increased., {"references":["C. Bianco, F. Cucchietti, G. Griffa, Energy consumption trends in the\nnext generation access network a telco perspective, Proc. 29th Internat.\nTelecommunications Energy Conf. (INTELEC 2007), Rome, Italy,\nSeptember 2007, pp.737-742.","Telecom Italia, The Environment, available at\nhttp://www.telecomitalia.it/sostenibilita2006/English/B05.html.","BT Press, BT announces major wind power plans, available\nat http://www.btplc.com/News/Articles/Showarticle.cfm?ArticleID\n=dd615e9c-71ad-4daa-951a-55651baae5bb, October 2007.","J. Chabarek, J. Sommers, P. Barford, C. Estan, D. Tsiang, S. Wright,\nPower Awareness in Network Design and Routing, IEEE INFOCOM\n2008, Phoeniz, Arizona, April 2008.","D. Blumenthal, P. Prucnal, J. Sauer, Photonic Packet Switches: Architectures\nand Experimental Implementations, Proceeding IEEE, vol. 82, pp.\n84-94, February 2000.","D. Chiaroni, Packet Switching Matrix: A Key Element for the Backbone\nand the Metro, IEEE Journal Selected Area Communications, vol. 21, pp.\n1018-1025, September 2003.","S. Aleksic, Analysis of Power in Future High Capacity Network Nodes,\nIEEE/OSA Journal of Communications and Networking, vol. 1, pp. 245-\n258, August 2009.","V. Eramo, M. Listanti, Power Consumption in Bufferless Optical Packet\nSwitches in SOA Technology, IEEE/OSA Journal of Communications and\nNetworking, vol. 1, August 2009.","K. Hinton, G. Rakutti, P. Farrel, R.S. Tucker, Switching Energy and\nDevice Size Limits on Digital Photonic Signal Processing Technologies,\nIEEE Journal of Selected Topics in Quantum Electronics, vol. 14, pp.\n938-945, May/June 2008.\n[10] G. Jeong, J.W. Goodman, Gain Optimization in Switches Based on\nSemiconductor Optical Amplifiers, IEEE/OSA Journal Lightwave of Technology,\nvol. 13, pp. 598-605, April 1995.\n[11] A. Ehrhardt, M. Eiselt, G. Groβkopf, L. Kuller, R. ludwig, W. Pieper,\nR. Schnabel and H. Weber, Semiconductor Laser Amplifier as Optical\nSwitching Gate, IEEE/OSA Journal Lightwave of Technology, vol. 11,\npp. 1287-1295, August 1993.\n[12] V. Eramo, Comparison in Power Consumption of Synchronous and\nAsynchronous Optical Packet Switches, IEEE/OSA Journal Lightwave of\nTechnology, vol. 28, pp. 847-857, March 2010.\n[13] R.F. Kalman, L.G. Kazovsky, J.W: Goodman, Space Division Switches\nBased on Semiconductor Optical Amplifiers, IEEE Photonic Technology\nLetters, vol. 4, pp. 1048-1051, September 1992\n[14] V. Eramo, Effect of ASE noise on the Power Consumption of MVMC and\nBENES Optical Packet Switches, IEEE/OSA Journal of Communications\nand Networking, submitted to publication.\n[15] C.H. Henry, Theory of Spontaneous Emission Noise in Open Resonators\nand its Applications to Laser and Optical Amplifier, IEEE/OSA Journal\nLightwave of Technology, vol. 4, pp. 288-297, April 1986.\n[16] Y. Ueno, J. Sakaguchi, R. Nakamoto, T. Nishida, Ultrafast, low-energyconsumption,\nsemiconductor-based, all-optical devices, 4-th Asia- Pacific\nPhotonics Conference (APMP 2009), Bejing, China, April 2009.\n[17] R.S. Tucker, Green Optical Communications-Part I: Energy Limitations\nin Transport, IEEE Journal of Selected Topics in Quantum Electronics,\nMarch/April 2011.\n[18] V. Eramo, M. Listanti, C. Nuzman, P. Whiting, Optical Switch Dimensioning\nand the Classical Occupancy Problem, International Journal\nCommunications Systems, vol. 15, pp. 127-141, March/April 2002."]}

The power consumption of an Optical Packet Switch equipped with SOA technology based Spanke switching fabric is evaluated. Sophisticated analytical models are introduced to evaluate the power consumption versus the offered traffic, the main switch parameters, and the used device characteristics. The impact of Amplifier Spontaneous Emission (ASE) noise generated by a transmission system on the power consumption is investigated. As a matter of example for 32×32 switches supporting 64 wavelengths and offered traffic equal to 0,8, the average energy consumption per bit is 5, 07 · 10-2 nJ/bit and increases if ASE noise introduced by the transmission systems is increased.