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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 19, Iss. 26 — Dec. 12, 2011
  • pp: B503–B508
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Energy aware planning of multiple virtual infrastructures over converged optical network and IT physical resources

Markos P. Anastasopoulos, Anna Tzanakaki, Konstantinos Georgakilas, and Dimitra Simeonidou  »View Author Affiliations


Optics Express, Vol. 19, Issue 26, pp. B503-B508 (2011)
http://dx.doi.org/10.1364/OE.19.00B503


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Abstract

This paper studies energy efficient planning of multiple concurrent virtual infrastructures over a converged physical infrastructure incorporating integrated optical network and IT resources. An MILP model for virtualization of the underlying physical resources is proposed and validated achieving significant energy savings.

© 2011 OSA

1. Introduction

As the scale of information processing is increasing, from Petabyes of Internet data to the projected Exabytes in networked storage at the end of this decade [1

1. M. Handley, “Why the Internet only just works,” BT Technol. J. 24(3), 119–129 (2006). [CrossRef]

], novel network solutions are required to support the Future Internet and its new emerging applications such as UHD IPTV, 3D gaming, virtual worlds etc. These high-performance applications, need to be supported by specific IT resources (e.g. computing and data repositories) that maybe remote and geographically distributed, requiring connectivity with the end users, through a very high capacity and increased flexibility and dynamicity network. Considering that optical networking satisfies these requirements, through recent technology advancements including dynamic control planes, elasticity etc., it provides a strong candidate to support this need. In this context, an infrastructure comprising converged optical network and IT resources that are jointly optimized in terms of infrastructure design and operation can be envisioned as the suitable solution to support the Future Internet.

On the other hand, in order to maximize the utilization and efficiency of infrastructures, supporting converged network and IT resources, the concept of virtualization of physical resources [2

2. A. Tzanakaki, M. Anastasopoulos, K. Georgakilas, J. Buysse, M. De Leenheer, C. Develder, S. Peng, R. Nejabati, E. Escalona, D. Simeonidou, N. Ciulli, G. Landi, M. Brogle, A. Manfredi, E. Lopez, J. F. Riera, J. A. Garcia-Espin, P. Donadio, G. Parladori, and J. Jimenez, “Energy efficiency in integrated IT and optical network infrastructures: the GEYSERS approach,” in IEEE INFOCOM2011, Workshop Green Commun. and Netw., 343–348.

] can be additionally applied. The concept of virtual infrastructures (VIs) facilitates sharing of physical resources among various virtual operators, introducing a new business model that suits well the nature and characteristics of the Future Internet and enables new exploitation opportunities for the underlying physical infrastructures. Through the adoption of VI solutions, optical network and IT resources can be deployed and managed as logical services, rather than physical resources. This results into enhanced enterprise agility, remote access to geographically distributed infrastructures and maximization of network utilization leading to reduced capital and operational costs.

An additional consideration that needs to be taken into account, in the context of Future Internet sustainability, is the energy efficient design and operation of the associated infrastructure, as ICT is responsible for about 4% of all primary energy today worldwide, and this percentage is expected to double by 2020 [3

3. M. Pickavet, W. Vereecken, S. Demeyer, P. Audenaert, B. Vermeulen, C. Develder, D. Colle, B. Dhoedt, and P. Demeester, “Worldwide energy needs for ICT: The rise of power-aware networking,” in 2nd International Symposium on Advanced Networks and Telecommunication Systems (ANTS '08) (IEEE, 2008), pp. 1–3.

]. Specifically in VIs, energy efficiency can be effectively addressed at the VI planning phase [2

2. A. Tzanakaki, M. Anastasopoulos, K. Georgakilas, J. Buysse, M. De Leenheer, C. Develder, S. Peng, R. Nejabati, E. Escalona, D. Simeonidou, N. Ciulli, G. Landi, M. Brogle, A. Manfredi, E. Lopez, J. F. Riera, J. A. Garcia-Espin, P. Donadio, G. Parladori, and J. Jimenez, “Energy efficiency in integrated IT and optical network infrastructures: the GEYSERS approach,” in IEEE INFOCOM2011, Workshop Green Commun. and Netw., 343–348.

]. VI planning is responsible to generate dynamically reconfigurable virtual networks satisfying the VI provider’s-driven requirements and meeting any specific needs such as e.g. energy efficiency. Through this process the least energy consuming VIs that can support the required services are identified, in terms of both topology and resources. In the optimization process involved, joined consideration of the energy consumption of the converged network and IT resources is performed. As IT resources require very high levels of power for their operation and their conventional operating window is commonly not optimized for energy efficiency, allocating IT resources in an energy-aware manner interconnected through a relatively low energy-consuming optical network can potentially offer significant energy savings.

2. Energy aware multiple Virtual Infrastructure planning

The problem is formulated using a network that is composed of one resource layer that contains the physical infrastructure (PI) is described through an eleven-node Pan-European topology. A similar multi-layer network optimization approach may be found in [‎5

5. E. Kubilinskas, P. Nilsson, and M. Pioro, “Design Models for robust multi-layer next generation internet core networks carrying elastic traffic,” in Proceedings of DRCN 2003, 61–68 (2003).

]. The objective is to produce as an output a layer that contains a set of VIs. For eachVIi, i=1,2,...,I, there is a set of demands di (di=1i,2i,...,Di) to be served by a set of IT servers s (s = 1,2,…,S). VIs traffic demands are carried through the PI. For simplicity, the granularity of demands is the wavelength and the IT locations (demand destinations) at which the services will be handled, are not specified and are of no importance to the services themselves. Therefore, the demand destinations for each VIi will be identified through the optimization performed by the proposed model. In order to formulate this problem, the binary variable adis is introduced to indicate whether demand di that is handled by VIi is assigned to server s or not and it equals 1, if and only if demand di is processed on server s. Moreover, it is assumed that each demand can be assigned only to one server:

sadis=1,di=1i,2i,,Di,i=1,2,...,I,
(1)

Furthermore, for each demand di, its volume hdi is realized by means of a number of lightpaths assigned to paths of the VIi. Let pdis=1dis,2dis,...,Pdis be the candidate path list in the VIifor the lightpaths required to support demand di at server s and xpdiS the non-negative number of lightpaths allocated to path pdis. The following demand constraints should be satisfied in the VI:

spdisadisxpdiS=hdi,di=1,2,...,Di,i=1,2,...,I
(2)

qizeiqi=yei,ei=1i,2i,...,Ei,i=1,2,...I
(4)

(1+c0idiadis)idipadiscdis(xpdiS)ps,s=1,2,,S
(6)

The first term of Eq. (6) captures the additional processing requirements due to virtualization, while the second the total demands that arrive at server s. Processing overhead due to virtualization depends on the virtualization technology that is used. In this approach, the User Mode Linux (UML) system has been adopted for the IT servers in which processing overhead increases linearly with the number of VIs [‎6

6. B. Quetier, V. Neri, and F. Cappello, “Selecting a virtualization system for grid/P2P large scale emulation,” in Proc. EXPGRID’06 (2006).

]. Parameter cdis(xpdiS) specifies the computational requirements for demand di on server s and in practice is determined by the set of relevant benchmarks for computer systems provided by the Standard Performance Evaluation Corporation (SPEC) [‎7

7. Standard Performance Evaluation Corporation (SPEC) (www.spec.org).

].

MinimizeF=gkgug+sEs(us)
(8)

The above MILP problem has been solved analytically employing the methods of Lagrangian relaxation and dual decomposition [‎9

9. M. P. Anastasopoulos, A. Panagopoulos, and P. Cottis, “A distributed routing protocol for QoS provisioning in wireless mesh networks operating above 10 GHz,” Wirel. Commun. Mob. Comp. 8(10), 1233–1245 (2008). [CrossRef]

].

3. Numerical results

To investigate the energy efficiency of the proposed VI design scheme, the architecture illustrated in Fig. 1
Fig. 1 Multiple VI architecture over a converged optical network and IT servers.
is considered: the lower layer depicts the PI and the layer above depicts the VIs. For the PI, the COST239 [‎10

10. P. Batchelor, B. Daino, P. Heinzmann, D. R. Hjelme, R. Inkret, H. A. Jäger, M. Joindot, A. Kuchar, E. L. Coquil, P. Leuthold, G. D. Marchis, F. Matera, B. Mikac, H.-P. Nolting, J. Späth, F. Tillerot, B. V. Caenegem, N. Wauters, and C. Weinert, “Study on the implementation of optical transparent transport networks in the European environment-results of the research project COST 239,” Photonic Netw. Commun. 2(1), 15–32 (2000). [CrossRef]

] European topology has been used in which four randomly selected nodes generate demands to be served by three IT servers. Furthermore, we assume a single fiber per link, 40 wavelengths per fiber, and wavelength channels of 10Gb/s each. It is also assumed that each IT server can process up to 2Tb/s and its power consumption ranges from 6.6 to 13.2KW, under idle and full load, respectively. Finally, the virtualization processing overhead is 3% per VI.

Figure 2a
Fig. 2 (a) Comparison of the Energy Aware with Closest IT scheme (3 VIs) (b) Impact of VIs on power consumption.
, illustrates the total power consumption of the infrastructure (optical network and IT resources) when applying the proposed MILP approach optimizing for energy or distance between sources and IT servers (closest It scheme). Comparing these two schemes, it is observed that the energy aware VI design consumes significantly lower energy to serve the same amount of demands compared to the closest IT scheme providing an overall saving of the order of 40%. This is due to that, in the former approach fewer IT servers are activated to serve the same amount of demands. Given that the power consumption required for the operation of the IT servers is dominant in this type of infrastructures, switching-off the unused IT resources achieves significant reduction of energy consumption. Furthermore, it is observed that in both schemes the average power consumption increases almost linearly with the number of demands. However, the relative benefit of the energy aware design decreases slightly with the number of demands, as we get closer to full system load. Figure 2b depicts the impact of the number of VIs on power consumption. It is observed that the virtualization cost has a more severe impact in terms of power consumption when applying the closest IT scheme than that observed in the case of energy aware VI planning, as in the case of the closest IT scheme more virtual machines per IT server are activated.

In addition, it is observed that there is a clear trade-off between the utilization of optical network resources and the number of active IT servers. Specifically, as the energy cost for activating an IT server predominately affects the overall energy consumption of the converged infrastructure, the energy aware VI planning scheme forwards traffic to the minimum possible number of IT servers. However, following this approach more optical network resources are employed as data travel larger distances to arrive to their destination (IT server). On the other hand when the VIs are planned using the Closest IT scheme, all demands are routed to their closest IT servers, thus minimizing the utilization of the optical network resources at the expense of the total number of active IT servers required. This trade-off can is illustrated in Fig. 3a
Fig. 3 Utilization of optical network and IT Resources.
where for example, in the closest IT scheme it is observed that less than 30% of the total optical network resources are employed to transfer demands from the source nodes to three active IT servers. In contrast, as depicted in Fig. 3b the energy aware scheme (supporting the same demands) routes all demands to only a single IT server at the expense of increased utilization of the optical network resources.

4. Conclusions

The paper studied the problem of energy efficient service provisioning in converged optical network and IT infrastructures. An MILP model for virtualization of the underlying physical resources was proposed and validated achieving significantly lower energy consumption for serving the same amount of demands (with an overall saving of the order of 40%). Furthermore, switching-off unused IT resources achieves significant reduction of energy consumption. It was also proven that the virtualization cost has a more severe impact in terms of power consumption when applying the closest IT scheme compared to the proposed model since more virtual machines per IT server are activated. Finally, numerical results indicate that there is trade-off between the utilization of optical network resources and the number of active IT servers determining the level of the overall power consumption.

Acknowledgments

This work was carried out with the support of the GEYSERS (FP7-ICT-248657) project funded by the European Commission through the 7th ICT Framework Program.

References and links

1.

M. Handley, “Why the Internet only just works,” BT Technol. J. 24(3), 119–129 (2006). [CrossRef]

2.

A. Tzanakaki, M. Anastasopoulos, K. Georgakilas, J. Buysse, M. De Leenheer, C. Develder, S. Peng, R. Nejabati, E. Escalona, D. Simeonidou, N. Ciulli, G. Landi, M. Brogle, A. Manfredi, E. Lopez, J. F. Riera, J. A. Garcia-Espin, P. Donadio, G. Parladori, and J. Jimenez, “Energy efficiency in integrated IT and optical network infrastructures: the GEYSERS approach,” in IEEE INFOCOM2011, Workshop Green Commun. and Netw., 343–348.

3.

M. Pickavet, W. Vereecken, S. Demeyer, P. Audenaert, B. Vermeulen, C. Develder, D. Colle, B. Dhoedt, and P. Demeester, “Worldwide energy needs for ICT: The rise of power-aware networking,” in 2nd International Symposium on Advanced Networks and Telecommunication Systems (ANTS '08) (IEEE, 2008), pp. 1–3.

4.

A. Tzanakaki, K. Katrinis, T. Politi, A. Stavdas, M. Pickavet, P. Van Daele, D. Simeonidou, M. O'Mahony, S. Aleksić, L. Wosinska, and P. Monti, “Dimensioning the future Pan-European optical network with energy efficiency considerations,” J. Opt. Commun. Netw. 3(4), 272–280 (2011). [CrossRef]

5.

E. Kubilinskas, P. Nilsson, and M. Pioro, “Design Models for robust multi-layer next generation internet core networks carrying elastic traffic,” in Proceedings of DRCN 2003, 61–68 (2003).

6.

B. Quetier, V. Neri, and F. Cappello, “Selecting a virtualization system for grid/P2P large scale emulation,” in Proc. EXPGRID’06 (2006).

7.

Standard Performance Evaluation Corporation (SPEC) (www.spec.org).

8.

X. Fan, W.-D. Weber, and L. A. Barroso, “Power provisioning for a warehouse-sized computer,” SIGARCH Comput. Archit. News 35(2), 13–23 (2007). [CrossRef]

9.

M. P. Anastasopoulos, A. Panagopoulos, and P. Cottis, “A distributed routing protocol for QoS provisioning in wireless mesh networks operating above 10 GHz,” Wirel. Commun. Mob. Comp. 8(10), 1233–1245 (2008). [CrossRef]

10.

P. Batchelor, B. Daino, P. Heinzmann, D. R. Hjelme, R. Inkret, H. A. Jäger, M. Joindot, A. Kuchar, E. L. Coquil, P. Leuthold, G. D. Marchis, F. Matera, B. Mikac, H.-P. Nolting, J. Späth, F. Tillerot, B. V. Caenegem, N. Wauters, and C. Weinert, “Study on the implementation of optical transparent transport networks in the European environment-results of the research project COST 239,” Photonic Netw. Commun. 2(1), 15–32 (2000). [CrossRef]

OCIS Codes
(060.4254) Fiber optics and optical communications : Networks, combinatorial network design
(060.4256) Fiber optics and optical communications : Networks, network optimization

ToC Category:
Backbone and Core Networks

History
Original Manuscript: September 30, 2011
Manuscript Accepted: November 1, 2011
Published: November 28, 2011

Virtual Issues
European Conference on Optical Communication 2011 (2011) Optics Express

Citation
Markos P. Anastasopoulos, Anna Tzanakaki, Konstantinos Georgakilas, and Dimitra Simeonidou, "Energy aware planning of multiple virtual infrastructures over converged optical network and IT physical resources," Opt. Express 19, B503-B508 (2011)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-19-26-B503


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References

  1. M. Handley, “Why the Internet only just works,” BT Technol. J.24(3), 119–129 (2006). [CrossRef]
  2. A. Tzanakaki, M. Anastasopoulos, K. Georgakilas, J. Buysse, M. De Leenheer, C. Develder, S. Peng, R. Nejabati, E. Escalona, D. Simeonidou, N. Ciulli, G. Landi, M. Brogle, A. Manfredi, E. Lopez, J. F. Riera, J. A. Garcia-Espin, P. Donadio, G. Parladori, and J. Jimenez, “Energy efficiency in integrated IT and optical network infrastructures: the GEYSERS approach,” in IEEE INFOCOM2011, Workshop Green Commun. and Netw., 343–348.
  3. M. Pickavet, W. Vereecken, S. Demeyer, P. Audenaert, B. Vermeulen, C. Develder, D. Colle, B. Dhoedt, and P. Demeester, “Worldwide energy needs for ICT: The rise of power-aware networking,” in 2nd International Symposium on Advanced Networks and Telecommunication Systems (ANTS '08) (IEEE, 2008), pp. 1–3.
  4. A. Tzanakaki, K. Katrinis, T. Politi, A. Stavdas, M. Pickavet, P. Van Daele, D. Simeonidou, M. O'Mahony, S. Aleksić, L. Wosinska, and P. Monti, “Dimensioning the future Pan-European optical network with energy efficiency considerations,” J. Opt. Commun. Netw.3(4), 272–280 (2011). [CrossRef]
  5. E. Kubilinskas, P. Nilsson, and M. Pioro, “Design Models for robust multi-layer next generation internet core networks carrying elastic traffic,” in Proceedings of DRCN 2003, 61–68 (2003).
  6. B. Quetier, V. Neri, and F. Cappello, “Selecting a virtualization system for grid/P2P large scale emulation,” in Proc. EXPGRID’06 (2006).
  7. Standard Performance Evaluation Corporation (SPEC) ( www.spec.org ).
  8. X. Fan, W.-D. Weber, and L. A. Barroso, “Power provisioning for a warehouse-sized computer,” SIGARCH Comput. Archit. News35(2), 13–23 (2007). [CrossRef]
  9. M. P. Anastasopoulos, A. Panagopoulos, and P. Cottis, “A distributed routing protocol for QoS provisioning in wireless mesh networks operating above 10 GHz,” Wirel. Commun. Mob. Comp.8(10), 1233–1245 (2008). [CrossRef]
  10. P. Batchelor, B. Daino, P. Heinzmann, D. R. Hjelme, R. Inkret, H. A. Jäger, M. Joindot, A. Kuchar, E. L. Coquil, P. Leuthold, G. D. Marchis, F. Matera, B. Mikac, H.-P. Nolting, J. Späth, F. Tillerot, B. V. Caenegem, N. Wauters, and C. Weinert, “Study on the implementation of optical transparent transport networks in the European environment-results of the research project COST 239,” Photonic Netw. Commun.2(1), 15–32 (2000). [CrossRef]

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