Energy aware planning of multiple virtual infrastructures over converged optical network and IT physical resources

Markos P. Anastasopoulos; Anna Tzanakaki; Konstantinos Georgakilas; Dimitra Simeonidou

doi:10.1364/OE.19.00B503

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

In this paper we propose for the first time a Mixed Integer Linear Programming (MILP) model, suitable for the planning of multiple VIs formed over an integrated IT and optical network infrastructure, extending our previous work on energy efficient single VI planning [2]. To identify the least energy consuming VIs, the detailed power consumption models and figures of the underlying physical infrastructure, including joint consideration of optical network and IT resources are taken into consideration [‎4]. Mapping the virtual to physical resources and defining the energy consumption parameters of the VIs themselves is also part of the VI planning phase.

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]. The objective is to produce as an output a layer that contains a set of VIs. For each $V I_{i}$ , $i = 1, 2, ..., I$ , there is a set of demands $d_{i}$ ( $d_{i} = 1_{i}, 2_{i}, ..., D_{i}$ ) 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 $V I_{i}$ will be identified through the optimization performed by the proposed model. In order to formulate this problem, the binary variable $a_{d_{i} s}$ is introduced to indicate whether demand $d_{i}$ that is handled by $V I_{i}$ is assigned to server s or not and it equals 1, if and only if demand $d_{i}$ is processed on server s. Moreover, it is assumed that each demand can be assigned only to one server:

\sum_{s} a_{d_{i} s} = 1, d_{i} = 1_{i}, 2_{i}, \dots, D_{i}, i = 1, 2, ..., I,

Furthermore, for each demand $d_{i}$ , its volume $h_{d_{i}}$ is realized by means of a number of lightpaths assigned to paths of the $V I_{i}$ . Let $p_{d_{i} s} = 1_{d_{i} s}, 2_{d_{i} s}, ..., P_{d_{i} s}$ be the candidate path list in the $V I_{i}$ for the lightpaths required to support demand $d_{i}$ at server s and $x_{p_{d_{i} S}}$ the non-negative number of lightpaths allocated to path $p_{d_{i} s}$ . The following demand constraints should be satisfied in the VI:

\sum_{s} \sum_{p_{d_{i} s}} a_{d_{i} s} x_{p_{d_{i} S}} = h_{d_{i}}, d_{i} = 1, 2, ..., D_{i}, i = 1, 2, ..., I

Summing up the lightpaths through each link $e_{i}$ ( $e_{i} = 1_{i}, 2_{i}, ...., E_{i}$ ) of the $V I_{i}$ the necessary link capacity $y_{e_{i}}$ for link $e_{i}$ should satisfy the following constraint

\sum_{s} \sum_{d_{i}} \sum_{p_{d_{i} S}} δ_{e_{i} d_{i} s} x_{p_{d_{i} S}} \leq y_{e_{i}}, e_{i} = 1_{i}, 2_{i}, ..., E_{i}, i = 1, 2, ... I

_{$δ_{e_{i} d_{i} s}$} is a binary variable taking value equal to 1 if link

e_{i}

of

V I_{i}

belongs to path

p_{d_{i} s}

realizing demand

d_{i}

at server s, 0 otherwise. Using the same rationale, the capacity of each link

e_{i}

in

V I_{i}

is allocated by identifying the required lightpaths in the PI. The resulting PI lightpaths z determine the load of each link g (g = 1, 2,…, G) of the PI, and hence it capacity ug. Assuming that

q_{i} = 1_{i}, 2_{i}, ..., Q_{i}

is used for denoting the PI’s candidate path list realizing link

e_{i}

, then, the following demand constraint for link

e_{i}

should be satisfied:

\sum_{q_{i}} z_{e_{i} q_{i}} = y_{e_{i}}, e_{i} = 1_{i}, 2_{i}, ..., E_{i}, i = 1, 2, ... I

Note that the summation is taken over all paths $q_{i}$ on the routing list Qi of link $e_{i}$ . Finally, in the PI the following capacity constraint should be satisfied

\sum_{i} \sum_{e_{i}} \sum_{q_{i}} γ_{g e_{i} q_{i}} z_{e_{i} q_{i}} \leq u_{g}, g = 1, 2, \dots, G,

where G is the total number of links in the PI and the summation for each link g is taken over all lightpaths in the PI and

γ_{g e_{i} q_{i}}

the link-path incidence coefficients for the PI taking value equal to 1 link g belongs to path

q_{i}

realizing

e_{i}

.

Apart from link capacity constraints Eqs. (3) and (5), the total demands that are assigned to each server should not exceed its capacity, $p_{s}$ . This capacity corresponds to the underlying physical resources, such as CPU, memory, disk storage etc. The inequality specifying servers’ capacity constraints is given by

(1 + c_{0} \sum_{i} \sum_{d_{i}} a_{d_{i} s}) \sum_{i} \sum_{d_{i}} \sum_{p} a_{d_{i} s} c_{d_{i} s} (x_{p_{d_{i} S}}) \leq p_{s}, s = 1, 2, \dots, S

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]. Parameter $c_{d_{i} s} (x_{p_{d_{i} S}})$ specifies the computational requirements for demand $d_{i}$ 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].

The objective of the current problem formulation is to minimize the total cost of the resulting network configuration as this cost consists of the following components: (a) $k_{g}$ that is the cost of the capacity of link g of the PI. It consists of the energy consumed by each lightpath due to transmission and reception of the optical signal, optical amplification at each fiber span and switching and, (b) $E_{s}$ that the energy consumption for processing $u_{s}$ wavelengths in the IT server s. The power consumption model adopted in this paper mainly concentrates on the power consumption associated with the CPU load of IT resources and is described via the following linear equation [‎8]:

E_{s} (u_{s}) = P_{s}^{I d l e} + P_{s}^{b u s y} u_{s}

where

P_{s}^{i d l e}

,

P_{s}^{b u s y}

are parameters describing the energy consumption of the IT server s at idle state and per wavelength, respectively. In addition to the power consumption due to data processing, a 100% power overhead due to cooling has been incorporated to the energy consumption model described above. In this context, minimum energy consuming VIs are obtained by minimizing the following cost function

M i n i m i z e F = \sum_{g} k_{g} u_{g} + \sum_{s} E_{s} (u_{s})

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

3. Numerical results

To investigate the energy efficiency of the proposed VI design scheme, the architecture illustrated in Fig. 1 is considered: the lower layer depicts the PI and the layer above depicts the VIs. For the PI, the COST239 [‎10] 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.

Fig. 1 Multiple VI architecture over a converged optical network and IT servers.

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An example of the optimal VI topology design with two VIs is depicted in Fig. 1. In this scenario, VI #1 includes four source nodes that are located in London, Vienna, Copenhagen and Paris generating demands equal to 15 wavelengths each, while VI #2 incorporates three source nodes that are located in London, Vienna and Copenhagen generating demands equal to 25 wavelengths. The generated VI #1 topology consists of 5 virtual links and 6 virtual nodes, while all demands are routed to the IT server in Luxemburg. The capacity of each virtual link along with its mapping to the PI is given in Table 1 where e.g. it is observed that virtual link Y3 connecting Copenhagen and Luxemburg is realized via the physical layer path u5-u10, with capacity 25 wavelengths.

Table 1. Virtual to Physical Mapping

View Table

Figure 2a , 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.

Fig. 2 (a) Comparison of the Energy Aware with Closest IT scheme (3 VIs) (b) Impact of VIs on power consumption.

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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 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.

Fig. 3 Utilization of optical network and IT Resources.

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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

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Virtual Links	VI #1 S1-S4 (demands 15 wv/source)		VI #2 S1-S3 (demands 25 wv/source)
Virtual Links	*Capacity of Virtual links (WV)*	*Physical Layer Paths Realizing Virtual Links*	*Capacity of Virtual links (WV)*	*Physical Layer Paths Realizing Virtual Links*
Y1	0	-	0
Y2	15	u3-u11	25	u3-u11
Y3	15	u5-u10	55	u5-u10 (25wv) u1-u4-u12 (5wv) u8-u17-u13 (25wv)
Y4	0	-	15	-
Y5	0	-	0	-
Y6	15	u22	25	u22
Y7	15	u16	25	u16
Y8	30	u12	40	u12 (15wv) u14-u15 (25wv)
Y9	0	-	15	u4

Energy aware planning of multiple virtual infrastructures over converged optical network and IT physical resources

Abstract

1. Introduction

2. Energy aware multiple Virtual Infrastructure planning

3. Numerical results

4. Conclusions

Acknowledgments

References and links

Cited By

Figures (3)

Tables (1)

Equations (8)

Optics Express