Green provisioning of the traffic partition grooming in robust, reconfigurable and heterogeneous optical networks

Optical Fiber Technology 19 (2013) 16–25

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Green provisioning of the traffic partition grooming in robust, reconfigurable and heterogeneous optical networks Weigang Hou, Yao Yu ⇑, Qingyang Song, Xiaoxue Gong College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

a r t i c l e

i n f o

Article history: Received 5 July 2012 Revised 25 August 2012 Available online 22 October 2012 Keywords: Heterogeneous grooming Robustness Self-reconfiguration Green provisioning

a b s t r a c t In recent years, various high-speed network architectures have been widespread deployed. Dense Wavelength Division Multiplexing (DWDM) has gained favor as a terabit solution. The optical circuit switching has also been provided for ‘‘sub-rate’’ aggregation. Such that, the granular types of demands tend to be diverse and must be evaluated. However, current dedicated optical networks do not offer sufficient flexibility to satisfy the requirements of demands with such wide range of granularities. The traffic grooming becomes a power-efficient one only when it does not utilize the aggregation of Coarse-Granularity (CG) demands. The waveband switching merely provides port-cost-effective connections for CG demands regardless of fine-granularity ones. Consequently, in this paper, we devise a heterogeneous grooming method called traffic partition grooming. It combines the power efficiency advantage of the traffic grooming under fine-granularity environment and the port savings advantage of the waveband switching under coarse-granularity environment to provide green provisioning. In addition, the optical virtual topology self-reconfigures along with various optimization objectives variation and has the robustness to determine the pre-unknown information. This paper is also the first work on investigating the issue of Robust, Reconfigurable and Heterogeneous Optical Networking (R2HON). The effective green provisioning and OPEX savings of our R2HON have been demonstrated by numerical simulations. Ó 2012 Elsevier Inc. All rights reserved.

1. Introduction In recent years, various high-speed network architectures have seen widespread deployment. Dense Wavelength Division Multiplexing (DWDM) has gained favor as a terabit solution. The optical circuit switching has also been provided for ‘‘sub-rate’’ aggregation. Such that, the granular types of demands tend to be diverse and must be evaluated [1–4]. Among which, the Coarse-Granularity (CG) demand holds a similar or identical bandwidth of one wavelength channel (e.g., 90-Gb/s). On the contrary, the bandwidth tailored for a Fine-Granularity (FG) demand is far smaller (e.g., 30-Gb/s). However, current dedicated optical networks do not offer sufficient flexibility to satisfy the requirements of demands with such wide range of granularities. In detail, the traffic grooming becomes a power-efficient one only when it does not utilize the aggregation of CG demands; the waveband switching merely provides port-cost-effective connections for CG demands regardless of FG ones. Then, the heterogeneous grooming, which combines the power efficiency advantage of the traffic grooming under finegranularity environment and the port savings advantage of the ⇑ Corresponding author. Address: College of Information Science and Engineering, Northeastern University, P.O. Box 365, Shenyang 110819, China. Fax: +86 24 83684219. E-mail address: [email protected] (Y. Yu). 1068-5200/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.yofte.2012.09.004

waveband switching under coarse-granularity environment, should be devised for green provisioning. Although some works on the heterogeneous optical networking have been done, the traffic grooming and the waveband switching are not effectively integrated or related components lack reconfiguration capabilities so that their function switching is still located at the comprehensively manual operation level. In view of aforementioned defects, the future optical networks should provide heterogeneous transportations for a wide range of granularities and self-reconfigure according to various optimization objectives. Moreover, since the current optimization networking scheme does not consider the inherent unpredictability metric under real world scenarios, the future optimization networking scheme needs the robustness to determine pre-unknown information. Therefore, the robust, reconfigurable and heterogeneous optical network is a potential candidate.

1.1. Previous works In Wavelength Routing Networks (WRNs), the traffic grooming multiplexes various granular demands into lightpaths. Multiple electronic router ports and Optical–Electrical–Optical (OEO) conversions at intermediate Optical Cross-Connects (OXCs) are saved by this technique along with an optical bypass. Therefore, most

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of researchers recently found an important role of the traffic grooming in greening optical networks. Table 1 gives a survey of recent works on green grooming classified by time information is considered or not. However, there are still several problems of the traffic (green) grooming when we consider the granular diversity in dedicate WRNs: (1) whether fine- or coarse-granularity, two grooming matrix ports are always consumed for aggregating and (de)-aggregating each accepted demand. Fig. 1a only gives the example of aggregating demands (from the top to the bottom) and port 1 is used to aggregate the FG demand and port 2 is used for the aggregation of the CG demand. The (de)-aggregation is the inverse procedure operated in another destination-end node; (2) the lightpath could be overloaded or have resource fragment once it holds a CG demand. As shown in Fig. 1b, after the 30-Gb/s FG demand and the 60-Gb/s CG demand are aggregated, the 90-Gb/s lightpath has no space to accommodate other demands. A new lightpath must be established and two more transceivers (i.e., two grooming matrix ports used for transmitting and receiving the optical signal) are

consumed. We can see that, these cases bring with the power thirsty of grooming matrix ports and it is the granular diversity problem in dedicated WRNs. Fortunately, this problem can be solved if there does not exist any operation of CG demands. But an important question is how to handle these CG demands regardless of the traffic grooming? In WaveBand Switching (WBS) Networks, it binds various granular demands into waveband tunnels. Marginal port cost is saved because each waveband only consumes two switching ports at an intermediate node [17]. Similarly, there are still several problems of the waveband switching when we consider the granular diversity in dedicate WBS networks: (1) whether fine- or coarsegranularity, each accepted demand always consumes two transponders and four WXC ports of the Multi-Granularity OXC (MGOXC). Among which, one transponder and two WXC ports are used for locally adding and others are exploited for the local dropping. Fig. 1c only shows the example of adding demands (from the top to the bottom) and ports 1  3 are consumed for adding the FG demand and ports 4  6 are used for adding the CG demand; (2) if an entire waveband spectrum is occupied by all of FG demands, the

Table 1 Works on green grooming. Category

Reference

Traffic model

Approach

Objective function

Optical bypass

Power

Xia et al. [5,6] Farahmand et al. [7] Shen et al. [8] Kim et al. [9] Youssef et al. [10] Yetginer et al. [11] Huang et al. [12]

Static/dynamic Static Static Static Static/dynamic Static Static/dynamic

Heuristics Heuristics Heuristics & MILP Heuristics and ILP ILP ILP Heuristics and ILP

Power OPEX Power Power Power Power Power

equipment elements elements lightpaths provisions

Enabled Enabled Enabled Enabled Enabled Enabled Enabled

Zhang et al. [13] Chen et al. [14] Farahmand et al. [15] Hasan et al. [16]

Static/dynamic Static Dynamic Dynamic

Heuristics and ILP ILP Heuristics Heuristics

Energy of lightpaths Energy of lightpaths Energy of equipment OPEX

Enabled Enabled Enabled Enabled

Energy

Fig. 1. The granular diversity problems in dedicated optical networks.

of provisions of of of of of

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number of consumed ports above will be very huge. As shown in Fig. 1d, we assume that only 30-Gb/s FG demands and the 180Gb/s waveband spectrum emerge. Once this waveband tunnel holds six 30-Gb/s FG demands, 12 transponders and 24 WXC ports are consumed. We can see that, these cases bring with the port thirsty of transponders and cross-connects and it is the granular diversity problem in dedicated WBS networks. Fortunately, this problem can be solved if there does not exist any operation of FG demands. But an important question is how to handle these FG demands regardless of the waveband switching? 1.2. Our contributions 1.2.1. Heterogeneous grooming design In order to solve granular diversity problems above, we devise a hybrid node structure mixed with the slow (lightpath) and the fast (waveband tunnel) switching, to support the heterogeneous grooming. The main idea of the heterogeneous grooming is described as follows: (1) only FG demands are processed by the traffic grooming. Such that, the advantage of the power efficiency is guaranteed under the fine-granular environment meanwhile the granular diversity problem of the power thirsty is also solved; (2) only CG demands are processed by the waveband switching. Such that, the effectiveness of port savings is guaranteed under the coarsegranular environment meanwhile the granular diversity problem of the port thirsty is also solved. Fig. 2a shows our aforementioned hybrid node structure. If the required bandwidth is smaller than the bandwidth threshold, the left part of Fig. 2a (traffic grooming) is utilized for this demand; otherwise, the right part (waveband switching) is considered. Our heterogeneous grooming design has two notable advantages in terms of power reduction and port savings. In comparison to Fig. 1a, the grooming matrix ports consumed by (de)-aggregating CG demands (e.g., port 2 in Fig. 1a) are saved. Instead of holding CG demands in Fig. 1b, the 90-Gb/s lightpath still has residual bandwidth even if two 30-Gb/s FG demands are aggregated in Fig. 2b. It does not establish the new lightpath and the power of transceivers is thus saved; compared with Fig. 1c, transponders and WXC ports consumed by adding/dropping FG demands (e.g., ports 1  3

in Fig. 1c) are saved. Instead of holding FG demands in Fig. 1d, the entire 180-Gb/s waveband spectrum is shared by three 60-Gb/s CG demands in Fig. 2c and only six transponders and 12 WXC ports are consumed. Half of ports in Fig. 1d are saved. Therefore, our heterogeneous grooming design based on the hybrid node structure solves granular diversity problems very well meanwhile the green provisioning is also realized. Because we make the bandwidth threshold as our traffic partition criterion, we call this hybrid node structure as TPG-OXC (Traffic Partition Grooming OXC). 1.2.2. Reconfiguration design However, in the current heterogeneous optical network [18], some of nodes are equipped with OXCs and other nodes have MG-OXCs; each demand is processed by the waveband switching unless it is blocked meanwhile a dedicate lightpath is established to carry this blocked demand without grooming others. Therefore, the current heterogeneous optical networking still supports a single grooming technique (i.e., waveband switching). In other words, the granular diversity problems still exist. Furthermore, the current heterogeneous optical networking obtains the lower component costs (i.e., capital expenditures, CAPEX in short) by switching the expensive MG-OXC into the ordinary OXC at some of nodes. However, the authors in [18] do not consider another important cost (i.e., the operational expenditure, OPEX in short). Once a higher certain value of the CAPEX is given, a more efficient operation can still ensure the green provisioning by reducing the long-term OPEX. Inversely, if the long-term OPEX is very high, we cannot obtain the green provisioning even we have the lower CAPEX initially. Obviously, the OPEX will become huge if the function switching locates at the comprehensively manual operation level. Therefore, the idea in [18] is not applicable to the dynamic bandwidth requirements under granular-diversity environment since the frequent manual-operation-level function switching brings with the huge long-term OPEX. In our heterogeneous grooming design, we also need the function switching such as the one from the slow switching (i.e., the left part of Fig. 2a) to the fast switching (i.e., the right part of Fig. 2a). Fortunately, by means of Digital/Analog Conversion (DAC), the Software-Defined Optical Transmission (SDOT) technique [19] helps to facilitate the

(b)

(c) (a)

Fig. 2. TPG-OXC structure.

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self-reconfiguration of our TPG-OXC and the OPEX is decreased. In this case, the judgment criterion becomes requisite and plays the crucial role of determining when and how to ensure the DAC implementation. In our TPG-OXC, we treat the bandwidth threshold u (i.e., half of one wavelength channel) as our judgment criterion. Correspondingly, given a traffic matrix, we can partite this traffic matrix into the FG traffic matrix and the CG traffic matrix. The values of elements in the FG traffic matrix are all lower than this bandwidth threshold while the CG traffic matrix has the opposite characteristic. Then, we need to construct an optical virtual topology twice, i.e., establishing lightpaths for the FG traffic matrix and waveband tunnels for the CG traffic matrix. On the optical virtual topology, the lightpath and the waveband tunnel could share the same link (i.e., the overlapped segment). At the overlapped segment: (1) if the lightpath is established in priority, this overlapped link runs the SDOT-based bandwidth extension from the lightpath to the waveband for CG traffic. In other words, the end node of this overlapped link runs the SDOT-based function switching from the left to the right part of our TPG-OXC in Fig. 2a, regardless of any artificial operations; (2) otherwise, this overlapped link runs the SDOT-based bandwidth compression from the waveband to the lightpath for FG traffic. Recently, the SDOT-based bandwidth extension/compression is also utilized in the Orthogonal Frequency-Division Multiplexing (OFDM) core optical networks [19]. The variability of bandwidth is performed by assigning the different number of contiguous frequency slots in Bandwidth-Variable (BV) transponders and just enough spectrums can be sliced off to improve the spectrum efficiency. While considering the green provisioning, more expensive components, such as the BV-OXC along with Wavelength-Selected Switches (WSSs), are required. Inversely, our design still use ordinary cross-connects with the lower CAPEX to perform the bandwidth variability. Furthermore, it is fairer to compare the green provision of three different WDM

techniques because they use the identical or similar components. Finally, the spectrum efficiency is not within the scope of this work. 1.2.3. Robustness design During the optical virtual topology construction, we establish lightpaths for FG traffic under the optimization objective of maximizing the power efficiency while establish waveband tunnels for CG traffic under the optimization objective of minimizing the switching port cost. Each optimization networking scheme has the high robustness to determine the pre-unknown information under real world scenario, where only node capacity limit is given. Therefore, it is very important to determine pre-unknown information for the follow-up topology construction. The notation definitions are listed in Table 2. To reflect network/node capacity limits, we use the symmetrical hose model [20] denoted as h(e(|V|), l(|V|)), where only values of e(ni) and l(ni) are given and e(|V|) = {e(n1), e(n2), . . ., e(n|V|)} = l(|V|) = {l(n1), l(n2), . . ., l(n|V|)}. Utilizing the hose model to reflect node capacity limits has two advantages: (1) the simpler description and (2) the higher flexibility (i.e., the traffic entering or leaving the network can be assigned to any other nodes). Based on this modeling, we operate two-phase routing (as an example of Fig. 3) for the robustness design. ‘‘Two-phase’’ means: (1) the load balancing is easier to be guaranteed; (2) we have the higher probability of merging several CG demands into a waveband tunnel because the number of physical routing hops owned by each waveband tunnel should be at least two [17]; (3) we have the higher probability of obtaining the better power efficiency because the power saved by the traffic grooming is proportional to the number of physical routing hops owned by each lightpath [21]. During two-phase routing in Fig. 3, each node not only transmits/receives its own traffic, but also functions as an intermediate

Table 2 Notation definitions. V e(ni) ecg(ni) efg(ni) l(ni) lcg(ni) lfg(ni) h(ni) hcg(ni) hfg(ni) h(|V|) C(ni) Cfg(ni) Ccg(ni) g(ni) w(ni, nj) b(ni, nj) Wc G dxe t(ni, nj) tfg(ni, nj, nk) tcg(ni, nj, nk) din dout Pt Pp h(ni, nj) s_h(ni) d_h(ni)

The node set The upper bound on the total amount of traffic entering the network at the node ni The upper bound on the total amount of coarse-granularity traffic entering the network at the node ni and we have ecg(ni) = e(ni)/u The upper bound on the total amount of fine-granularity traffic entering the network at the node ni and we have efg ðni Þ ¼ modðeðni Þ; /Þ The upper bound on the total amount of traffic leaving the network at the node ni The upper bound on the total amount of coarse-granularity traffic leaving the network at the node ni and we have lcg(ni) = l(ni)/u The The The The The

fg

upper bound on the total amount of fine-granularity traffic leaving the network at the node ni and we have l ðni Þ ¼ modðlðni Þ; /Þ traffic distribution fraction from the sending node to the node ni and we have h(ni) e (0, 1) coarse-granularity traffic distribution fraction from the sending node to the node ni and we have hcg(ni) e (0, 1) fine-granularity traffic distribution fraction from the sending node to the node ni and we have hsg(ni) e (0, 1) P traffic distribution vector and we have h(|V|) = {h(n1), h(n2), . . ., h(n|V|)}, where ni 2V hðni Þ ¼ 1

The exchange capacity at node ni (i.e., the sum of maximum traffic routed from the node ni to any other node) a The exchange fine-granularity capacity at the node ni The exchange coarse-granularity capacity at the node ni The fan-out of the node ni The maximum number of wavelengths needed to route traffic from the node ni to the node nj The maximum number of wavebands needed to route traffic from the node ni to the node nj The wavelength capacity, i.e., the maximum bandwidth provided by each wavelength channel The waveband granularity, i.e., the maximum number of wavelength channels that can be merged into each waveband tunnel The smallest value that is not smaller than x The amount of traffic from the node ni to the node nj and we have tðni ; nj Þ P 0 and t(ni, ni) = 0 The amount of fine-granularity traffic from the sending node ni along the intermediate node nj to the destination node nk The amount of coarse-granularity traffic from the sending node ni along the intermediate node nj to the destination node nk The aggregating coefficient (i.e., the ratio of the actual number of aggregated demands to the maximum number of demands entering the network at each node) and we have 0 < din 6 1 The de-aggregating coefficient (i.e., the ratio of the actual number of de-aggregated demands to the maximum number of demands leaving the network at each node) and we have 0 < dout 6 1 The consumed power per transponder The consumed power per router port/grooming matrix port The number of routing hops of the shortest path between the node-pair (ni, nj) PjVj The average number of routing hops in phase 1 where the node ni is the sending node and we have s hðni Þ ¼ ð j¼1 hðni ; nj ÞÞ=jVj PjVj The average number of routing hops in phase 2 where the node ni is the receiving node and we have d hðni Þ ¼ ð j¼1 hðnj ; ni ÞÞ=jVj

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Phase 1

Phase 2 Sending node

Destination node

Intermediate node Fig. 3. Two-phase routing.

node to forward some traffic to other destination nodes. The preunknown information includes the number of available wavelengths/wavebands used for establishing the optical virtual topology, etc. Therefore, determining the required bandwidth of each link is very important. In terms of the link (i, j), as a sending node in phase 1, node ni sends its traffic to other (|V|  1) intermediate nodes in the network. Among which, ni sends h(nj)  e(ni) traffic to the intermediate node nj. At the end of phase 1, as one of intermediate nodes, node ni receives h(ni)  l(nj) traffic destine for nj. Thus, the required bandwidth of the link (i, j) is [h(nj)  e(ni) + h(ni)  l(nj)]. Once the Traffic Distribution Vector (TDV) h(|V|) is obtained, the previously unknown information can be determined as follows:

2

3

X

Cðni Þ ¼ 6 ðhðnj Þ  eðni Þ þ hðni Þ  lðnj ÞÞ7 7; 8ni 2 V 6 7 6nj 2V;j–i      1  ð hðnj Þ  eðni Þ þ hðni Þ  lðnj Þ Þ Wc

2.2.1. Optimization networking scheme for FG demands In view of the FG traffic matrix M jVjjVj , Eq. (5) demonstrates our fg optimization objective during the OVT construction: Minimize the network-level power ratio:

ð2Þ

-0 ¼ min

ð3Þ

 )   X e  eðni Þ 1þ  2  hðni Þeðni Þ ¼ lðni Þ; e ¼  eðni Þ n 2V

(

X

-0 ðni Þ

ð5Þ

ni 2V

bðni ; nj Þ ¼ wðni ; nj Þ=G Cðni Þ gðni Þ ¼ ¼ eðni Þ

As mentioned in Subsection 1.2.1, in our heterogeneous grooming design, only FG demands are processed by the traffic grooming. In other words, we construct the Optical Virtual Topology (OVT) for the FG traffic matrix through establishing lightpaths and our optimization objective is maximizing the power efficiency. Only CG demands are processed by the waveband switching, i.e., we construct the OVT for the CG traffic matrix through establishing waveband tunnels and our optimization objective is changed into the switching port savings.

ð1Þ

 wðni ; nj Þ ¼

2.2. Heterogeneous optimization networking schemes

where The node-level power ratio:



i

ð4Þ Therefore, the TDV computation is a very important step of the robustness design. The value of TDV is determined according to the optimization objective, which will be discussed in Subsection 2.2. To the best of our knowledge, there are no prior studies simultaneously focusing on robustness, reconfiguration and heterogeneous grooming designs. In other words, it is the first time to present the traffic partition grooming based on the Robust, Reconfigurable and Heterogeneous Optical Networking (R2HON) issue. The rest of this paper is organized as follows. In Section 2, we describe the traffic partition grooming based on our R2HON. The performance evaluation is given in Section 3. Section 4 concludes this paper. 2. Traffic partition grooming based on R2HON 2.1. Traffic partition As mentioned in Subsection 1.2.3, we utilize the hose model to reflect node capacity limits. Given the hose model h(e(|V|), l(|V|)), any allowable traffic matrix M|V||V| = (t(ni, nj)) is randomly generP ated as long as two  variants f nj 2V tðni ; nj Þ 6 eðni Þj8ni 2 V g and P f ni 2V tðni ; nj Þ 6 lðnj Þ; 8nj 2 V g are both satisfied. Various granular levels determine the different types of allowable traffic matrices. Once the traffic matrix M|V||V| is generated, we partite it into two parts, which is called traffic partition. In order to support the traffic partition, we need to separate the hose model h(e(|V|), l(|V|)) into hcg(e(|V|)/u, l(|V|)/u) corresponding to the CG traffic matrix fg jjV j MjV ¼ ðtðni ; nj Þ=/Þ and h ðmodðeðjVjÞ; /Þ; modðlðjVjÞ; /ÞÞ correcg sponding to the FG traffic matrix M jVjjVj ¼ ðmodðtðni ; nj Þ=/ÞÞ. Here, fg the function modða; bÞ returns the remainder after a is divided by b and u is the bandwidth threshold mentioned in Subsection 1.2.2.

fg

-0 ðni Þ ¼

P t g C ðni Þ CPðni Þ 1 þ W c þ Pp  d  efg ðni Þ ¼ SPðni Þ W c  hðni Þ  2

ð6Þ

The node-level capacity exchanging coefficient:

0
ð7Þ

The node-level (de)-aggregating coefficient:

d ¼ 2  din ¼ 2  dout

ð8Þ

The node-level average number of routing hops:

hðni Þ ¼ s hðni Þ þ d hðni Þ

ð9Þ

In Eq. (6), the power ratio of node ni is the ratio of the consumed power from establishing lightpaths CP(ni) over the saved power SP(ni) from the traffic grooming when we treat the node ni as the end node (i.e., slow switching at the left part of Fig. 2a). We make the two-phase analysis of -0 (ni) value because we consider the two-phase routing mentioned in Subsection 1.2.3. According to the status of the slow switching in Fig. 4 (the node ni is the pre-end node in phase 1 and becomes the post-end node in phase (2), then we have:

CPðni Þ ¼ CP phase 1 ðni Þ þ CP phase 2 ðni Þ þ Pt 

CPphase 1 ðni Þ ¼ Pp  din  efg ðni Þ þ Pp 

g  C fg ðni Þ Wc

din  efg ðni Þ Wc

ð10Þ

ð11Þ

fg

fg

CPphase 2 ðni Þ ¼ Pp  dout  l ðni Þ þ Pp 

dout  l ðni Þ Wc

ð12Þ

In Eq. (10), the third part Pt  (g  Cfg(ni)/Wc) is the consumed power of transponders because each used wavelength consumes one transponder; in Eq. (11), din  efg(ni) denotes the actual number of aggregated demands at the pre-end node ni. Because each accepted

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demand consumes one aggregating port as described in Fig. 2a, the first part of Eq. (11) is the consumed power of aggregating FG demands. Similarly, the second part records the consumed power of transmitting the optical signal; in Eq. (12), the first part is the consumed power of de-aggregating FG demands and the second part records the consumed power of receiving the optical signal because the node ni becomes the post-end node in phase 2. All of information above can be seen in Fig. 4. As shown in Fig. 4, compared with the IP-level routing, the saved power from the traffic grooming is divided into two main parts: the saved power of IP router ports and the saved power of grooming matrix (core router) ports. Among which, the former is proportional to both the number of routing hops and the number of FG demands carried by lightpaths, and the latter is the difference of the power consumed by (de)-aggregating FG demands and the power consumed by receiving/transmitting optical signals. Then, we have:

SPðni Þ ¼ SPphase 1 ðni Þ þ SPphase 2 ðni Þ SPphase 1 ðni Þ ¼ SP phase 1 ðni ÞGrooming

ð13Þ

matrix

þ SPphase 1 ðni ÞIP

router

¼ 2  P p  ðdin  efg ðni Þ  ðdin  efg ðni ÞÞ=W c Þ þ 2  Pp

where

s ¼ ðPt =Pp Þ  ðg=dÞ ðefg =efg ðni ÞÞ  2 W c  hðni Þ  2

K fg ðni Þ ¼

SP

phase 2

ðni Þ ¼ SP

phase 2

hfg ðni Þ ¼ ð1=K fg ðni ÞÞ

ðni Þ

þ SP

fg

phase 2

.X ni 2V

ð1=K fg ðni ÞÞ

ð19Þ

Continuingly, we can determine the pre-unknown information w(ni, nj) according to Eq. (2). 2.2.2. Optimization networking scheme for CG demands In view of the CG traffic matrix M jVjjVj , Eq. (20) demonstrates cg our optimization objective during the OVT construction: Minimize the node-level fan-out

wðni Þ0 ¼ min

C cg ðni Þ ¼ min 1 þ K cg ðni Þ  hcg ðni Þ ecg ðni Þ

ð20Þ

where

1 Þ  din  efg ðni Þ Wc

Grooming matrix

ð18Þ

In Eq. (16), if we want to obtain the lowest -0 , the hfg(ni) value in the TDV should be inversely proportional to Kfg(ni). Because P fg fg ni 2V h ðni Þ ¼ 1, we can determine the value of h (ni) in the following:

 ðs hðni Þ  1Þ  din  efg ðni Þ ¼ 2  P p  ðs hðni Þ 

ð17Þ

ð14Þ K cg ðni Þ ¼ ðni Þ

IP router

ecg ¼

fg

¼ 2  P p  ðdout  l ðni Þ  ðdout  l ðni ÞÞ=W c Þ þ 2  Pp

X

ecg ecg ðn

iÞ

2

ð21Þ

ecg ðni Þ

ð22Þ

ni 2V

fg

 ðd hðni Þ  1Þ  dout  l ðni Þ ¼ 2  P p  ðd hðni Þ 

1 fg Þ  dout  l ðni Þ Wc

ð15Þ

So far, we have demonstrated the value of -0 (ni) in Eq. (6). The network-level power ratio is just the sum of node-level power ratios within the whole network. Obviously, a lower network-level power ratio means the higher power efficiency. According to Eq. (4), the current fan-out of node ni is fg 1 þ ðefgeðn Þ  2Þ  hfg ðni Þ and we put it into Eq. (6). Then, we have ani other mathematical description of -0 in the following:

-0 ¼ min

X

, X h ðni Þ ¼ ð1=K ðni ÞÞ ð1=K cg ðni ÞÞ cg

cg

ð23Þ

ni2V

Continuingly, we can determine the pre-unknown information b(ni, nj) according to Eq. (3).

-0 ðni Þ 2.3. Reconfigurable optical virtual topology construction

ni 2V

! X fg 1 þ Wc þ s fg K ðni Þ  h ðni Þ þs ¼ min ðW c  hðni ÞÞ  2 n 2V n 2V X i

By guaranteeing the minimum node-level fan-out, we can reduce the number of waveband tunnels established between the node ni and other nodes as a consequence of minimizing the switching port cost. As efforts to obtain the lowest fan-out, the value of the traffic distribution factor hcg(ni) should be inversely proP portional to Kcg(ni). Because ni 2V hcg ðni Þ ¼ 1, then we have:

i

ð16Þ

Various granular levels also determine the different types of three-dimensional grooming matrices, and they are:

Fig. 4. Status of the slow switching.

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M jVjjVjjVj ¼ ðt cg ðni ; nj ; nk ÞÞ ¼ cg jVjjVjjVj

M fg



ðtðni ; nk Þ=/Þ  hcg ðnj Þ



 l m ¼ t fg ðni ; nj ; nk Þ ¼ modðtðni ; nk Þ; /Þ  hfg ðnj Þ

ð24Þ ð25Þ

Therefore, the OVT is automatic reconfigured along with the different three-dimensional grooming matrices under the corresponding optimization networking schemes. As mentioned in jjVjjVj Subsection 1.2.2, we must construct an OVT twice: if M jV fg first, lightpaths are established as described in the sub-algorithm 1, in priority. In detail, we first put FG traffic into the list L in descending order according to the number of two-phase routing hops. We first establish lightpaths for FG traffic along with the more routing hops because the saved power is proportional to the number of routing hops. Then, we try to find two lightpaths lpðk; ni ; nj Þ and lpðk0 ; nj ; nk Þ for tfg(ni, nj, nk) in the list L. Here, k ðk 2 ½1; wðni ; nj ÞÞ denotes the used wavelength of lpðk; ni ; nj Þ and k0 ðk0 2 ½1; wðnj ; nk ÞÞ denotes the used wavelength of lpðk0 ; nj ; nk Þ. jjVjjVj Second, the waveband tunnels are found for MjV as described cg in the sub-algorithm 2. In detail, we first put CG traffic into the list R in ascending order according to the number of two-phase routing hops. We first establish waveband tunnels for CG traffic along with the less routing hops because the switching port cost is inversely proportional to the number of two-phase routing hops and twophase routing has guaranteed CG traffic satisfies the fundamental qualification of being merged (i.e., at least two routing hops). Then, we try to find two waveband tunnels wb(b, ni, nj) and wb(b0 , nj, nk) for tcg(ni, nj, nk) in the list R. Here, b ðb 2 ½1; bðni ; nj ÞÞ denotes the used waveband of wb(b, ni, nj) and b0 (b0 2 [1, b(nj, nk)]) denotes the used waveband of wb(b0 , nj, nk).

At each overlapped segment shared by the lightpath and the waveband tunnel (e.g., s(i ? j1) in Fig. 5a), the link s(i ? j1) exclusively runs the SDOT-based bandwidth extension from the lightpath to the waveband tunnel and its end nodes ni and nj1 run the SDOTbased function switching from the slow switching to the fast switching of our TPG-OXC in Fig. 2a, regardless of any artificial operations. jjV j If MjVjjV first, the waveband tunnels will be established in cg priority for the OVT construction and next for lightpaths. Similarly, as a consequence of artificial operation cost (OPEX in short) savings at each overlapped segment (e.g., s(i ? j1) in Fig. 5b), the link s(i ? j1) exclusively runs the SDOT-based bandwidth compression from the waveband tunnel to the lightpath and its end nodes ni and nj1 run the SDOT-based function switching from the fast switching to the slow switching of our TPG-OXC in Fig. 2a, regardless of any artificial operations. Time complexity of each sub-algorithm is mainly dependent on the capacity of the list, i.e., the total amount of traffic in the netj work. In our traffic partition method, we set M jVjjV ¼ ðtðni ; nj Þ=/Þ cg jV jjVj and Mfg ¼ ðmodðtðni ; nj Þ=/ÞÞ, respectively. Therefore, if the same traffic matrix is given, there are more FG demands in the network. Actually, this kind of traffic distribution is satisfied with the current range of bandwidth requirements. Because we have |L| > |R|, the time complexity of constructing OVT for CG demands is lower than that of OVT construction for FG demands. 3. Simulation and analysis We adopt the following parameters for the numerical simulations: (1) two physical network topologies [21], known as NSFNET (14 nodes and 21 links) and another more meshed USANET (24 nodes and 42 links), are used in Fig. 6; (2) asymmetry (i.e., t(ni, nj) – t(nj, ni)) and symmetry (i.e., t(ni, nj) = t(nj, ni)) traffic are distributed for allowable traffic matrices; (3) wavelength capacity Wc = OC  12 and waveband granularity G = 4 are used; (4) each switching port has one unit of cost; (5) the power consumption of each grooming matrix port is Pp = 103 W, referenced by a Cisco CRS-1 8-Slot [8]; (6) the transponder used is Alcatel–Lucent WaveStar OLS 1.6T, whose average power consumption is Pt = 73 W [8]. According to [18], we have known the waveband switching still decreases the network cost although the MG-OXC is more expensive than an ordinary OXC. In other words, it makes the long-term reduction of switching ports in each ordinary OXC even if it has the higher CAPEX initially. In the following simulation test, we have demonstrated that our R2HON design also can make the long-term reduction of switching ports in each MG-OXC even if the TPG-OXC has the larger scale of cross-connects. It is not difficult for us to infer that, the network cost is finally decreased in our design. Meanwhile, we consider another important factor of OPEX, the function switching. Different from the previous heterogeneous optical networking, the function switching of our R2HON design is performed by self-configuration without any artificial operations. The following simulation test also demonstrates our lower OPEX of the function switching even if the slight overhead of A/D conversions is involved. Undoubtedly, the artificial function switching means the replacement of components, and the corresponding expenditure will be much higher than that of A/D conversions implemented by the software. 3.1. Power efficiency Fig. 7 compares power ratios under the different hose models between our R2HON design and the Homogeneous Traffic Grooming (HTG) scheme (e.g., approaches in Ref. [22]). In HTG scheme, whether FG- or CG-demand, it must be groomed into the lightpath.

23

W. Hou et al. / Optical Fiber Technology 19 (2013) 16–25

(a)

(b)

Fine granularity first

3.2. Switching port cost

Coarse granularity first

i

Fig. 8 displays the numbers of consumed switching ports experienced by the different hose models for our R2HON design and the Homogeneous Waveband Switching (HWS) design (e.g., approaches in Ref. [17]). In HWS scheme, whether FG- or CG-demand, it must be merged into the waveband tunnel. Then, the granular diversity problem of port thirsty generates in HWS approach as described in Subsection 1.1. The simulation results validate the port saving benefit of our R2HON because FG demands are merely processed by the traffic grooming without consuming the marginal switching ports equipped in WXCs. (Two lines along with ‘‘Asymmetry’’ arrows show the compared results under identical asymmetrical traffic matrix, and the improvement ratio is over 14%; the other two lines show the results under identical symmetrical traffic matrix, and the improvement ratio is over 20%). Therefore, the traffic partition grooming under R2HON is effective on solving the granular diversity problem of port thirsty in dedicated WBS networks, which also promotes the green provisioning of our traffic partition grooming to be realized.

i

Optical virtual topology j1 j2 construction k1

j1

j2

k1 K2

K2 Lightpath

Waveband

Bandwidth extension

Bandwidth compression

Waveband Overlapped segment

Lightpath Overlapped segment

Fig. 5. SDOT-based function switching and bandwidth variations.

Then, the granular diversity problem of power thirsty generates in HTG approach as described in Subsection 1.1. According to simulation results, R2HON has the higher power efficiency. R2HON runs the optimization networking in terms of minimizing the power ratio when lightpaths are established on the OVT. Furthermore, CG demands are only processed by the waveband switching in our R2HON, the consumed power of grooming matrix ports used to (de)-aggregate CG demands is saved. Meanwhile, the consumed power of transceivers is also decreased because each lightpath has enough space to carry more FG demands without establishing new lightpaths. Therefore, the traffic partition grooming under R2HON is effective on solving the granular diversity problem of power thirsty in dedicated WRNs, which promotes the green provisioning of our traffic partition grooming to be realized.

3.3. Saved OPEX Because we consider the heterogeneous grooming along with the self-reconfiguration, the marginal OPEX, especially for the artificial expenditure consumed by the function switching, can be saved. Based on the SDOT technique in R2HON, the OVT can be automatic reconfigured along with the variation of the different optimization networking schemes without any artificial operations. Therefore, the various OPEX saving effects are determined under the different hose models in Table 3, where one unit of saved OPEX indicates that one artificial operation is negligible.

10

(a) 1

11

8 7

3

0

4

6

13 2

12

5 9

(b)

18

0

1

10

5

2

6

11

8

15

20 21

3 12 4

19

14

7

22

16

9 13 17 Fig. 6. Test topologies.

23

24

W. Hou et al. / Optical Fiber Technology 19 (2013) 16–25 0.7

0.7 Asymmetry

0.6

0.5

0.5

Symmetry

power ratio (PR)

power ratio (PR)

Symmetry

0.6

0.4 2

R HON in NSFNET 2

R HON in NSFNET HTG in NSFNET HTG in NSFNET

0.3

Symmetry

0.2

Asymmetry

0.4

2

R HON in USANET 2

R HON in USANET HTG in USANET HTG in USANET

0.3

Asymmetry

0.2

0.1

0.1 Symmetry

Asymmetry

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

0

15

1

2

3

4

5

6

hose model #

7

8

9

10

11

12

13

14

15

hose model #

Fig. 7. Comparison of power ratios between R2HON and HTG in (a) NSFNET and (b) USANET.

10000

11000 R HON in USANET

Symmetry

R HON in USANET HWS in USANET HWS in USANET

10000

8000

Asymmetry

consumed switching ports (CSP)

consumed switching ports (CSP)

2

Asymmetry

2

9000

Symmetry

7000

6000

5000 Asymmetry

4000

2

R HON in NSFNET

9000

8000

7000

Asymmetry

6000

2

R HON in NSFNET HWS in NSFNET HWS in NSFNET Symmetry

3000

5000 Symmetry

2000

4000 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

hose model #

hose model #

Fig. 8. Comparison of consumed switching ports between R2HON and HWS in (a) NSFNET and (b) USANET.

Table 3 The Saved OPEX (SO) by using SDOT technique in R2HON. NSFNET

USANET

Symmetry

Asymmetry

Symmetry

Asymmetry

Hose model

SO

Hose model

SO

Hose model

SO

Hose model

SO

1 2 3 4 5

52 64 70 72 50

1 2 3 4 5

64 60 70 76 44

1 2 3 4 5

126 132 130 112 122

1 2 3 4 5

122 126 128 116 124

4. Conclusion This paper has presented a novel traffic partition grooming under the R2HON design. The traffic partition grooming has made the robustness analysis in a more realistic environment and provided green provisioning of demands along with various granular levels. Also, the granular diversity problems of dedicated optical networks

have been solved. Furthermore, along with the different grooming matrices and optimization networking schemes variation, the optical virtual topology has been self-reconfigured in a standalone manner without any artificial operations. Finally, the effective green provisioning and OPEX savings of our traffic partition grooming under R2HON have been demonstrated by numerical simulations. Acknowledgments The preliminary work of this paper was presented at the Asia Communications and Photonics Conference (ACP) 2012. This work was supported in part by the National Natural Science Foundation of China (61172051, 61071124, 60903211), the Fok Ying Tung Education Foundation (121065), the Program for New Century Excellent Talents in University (11-0075), the Fundamental Research Funds for the Central Universities (N110204001, N110604008), and the Specialized Research Fund for the Doctoral Program of Higher Education (20110042110023, 20110042120035).

W. Hou et al. / Optical Fiber Technology 19 (2013) 16–25

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