Multi-link failure restoration with dynamic load balancing in spectrum-elastic
optical path networks
Bowen Chen
a,⇑, Jie Zhang
a, Yongli Zhao
a, Chunhui Lv
a, Wei Zhang
a, Shanguo Huang
a, Xian Zhang
b,
Wanyi Gu
aa
State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
bNetworks Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London, London, United Kingdom
a r t i c l e
i n f o
Article history: Received 11 July 2011 Revised 22 October 2011 Available online 29 November 2011 Keywords:
SLICE Survivability
Multi-link failure restoration
Dynamical load balancing shared protection
a b s t r a c t
In this paper, we investigate network performance of multi-link failure restoration in spectrum-elastic optical path networks (SLICE). To efficiently restore traffic under multi-link failures, a novel survivable algorithm, named dynamic load balancing shared-path protection (DLBSPP), is proposed to compute pri-mary and link-disjoint shared backup paths. The DLBSPP algorithm employs first fit (FF) and random fit (RF) schemes to search and assign the available spectrum resource. Traffic-aware restoration (TAR) mech-anism is adopted in the DLBSPP algorithm to compute new routes for carrying the traffic affected by the multi-link failures and then the multi-link failures can be efficiently restored. Simulation results show that, compared with the conventional shared-path protection (SPP) algorithm, the DLBSPP algorithm achieves lower blocking probability (BP), better spectrum utilization ratio (SUR), more reasonable aver-age hop (AH) and higher failure restoration ratio (FRR). Thus, the proposed DLBSPP algorithm has much higher spectrum efficiency and much better survivability than SPP algorithm.
Ó 2011 Elsevier Inc. All rights reserved.
1. Introduction
With the continuing growth in the amount of bandwidth ser-vices, improving the cost-effectiveness and ensuring survivability of the underlying optical networks become more and more impor-tant. To overcome the single-link failure, shared-path protection (SPP) algorithm[1]has been proposed and the resources along a backup path are shared with other backup paths. The primary traf-fic can be switched to the backup path by protection switching when the primary path is interrupted by any link failure. However, multi-link failures cannot be recovered efficiently by SPP algo-rithm. To overcome this drawback, two different strategies, named enhanced shared-path protection (ESPP) algorithm [2] and self-organizing shared-path protection (SSPP) algorithm[3], have been proposed to tolerate multi-link failures and they have considered different load balancing schemes for primary path and shared backup path computations. For routing with load balancing, the key idea is that light-load links are preferred when computing paths. Using load balancing schemes[4], low blocking probability can be achieved and thus network performance is much better. Due to shared risk link group (SRLG), the topology of the physical layer and optical layer is heterogeneous, i.e., logically separated links could physically go through the same duct. However, in our
paper, we assume the topology of the physical layer and optical layer is homogeneous. The failure of each link is independent, which is different from SRLG in which many links interrupt simul-taneously because of a SRLG failed. Therefore, we do not consider the SRLG as the reference[5–7]and the failures of different links are independent in this paper.
Although traditional WDM networks offer well-known advanta-ges, they still exhibit some major drawbacks due to their fixed-grid and coarse granularity, and mismatch of granularities between the client layer and the wavelength layer. To provide flexible bandwidth demands and introduce finer granularity into the optical networks, the architecture of the spectrum-sliced elastic optical path network, named SLICE[8,9], has been proposed and experimentally demon-strated by Jinno et al. The key idea of SLICE networks is to allocate the necessary spectral resources with a finer granularity according to client traffic demands, which can be achieved by taking advan-tage of bandwidth-variable modulation technologies and the use of bandwidth-variable wavelength cross-connects (BV-OXCs). The SLICE architecture also enables sub-wavelength, super-wavelength, multiple-rate data traffic accommodation, and elastic variation of allocated spectrum resources, and offers cost-effective, highly-available and energy-effective connectivity service in a highly spec-trum-efficient manner[10].
For spectrum and bandwidth allocation, Jinno et al. have pro-posed the distance-based adaptive spectrum allocation scheme
[11–14]. Christodoulopoulos et al. have also proposed a dynamic
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doi:10.1016/j.yofte.2011.10.002
⇑ Corresponding author.
E-mail address:[email protected](B. Chen).
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and elastic bandwidth allocation scheme in flexible OFDM-based optical networks, introduced the routing, modulation level and spectrum allocation (RMLSA) problem, and presented various algo-rithms to solve it[15,16]. For routing and frequency slot assign-ment scheme [17–19], Takagi et al. have proposed a dynamic routing and frequency slot assignment algorithm for SLICE that employs distance adaptive modulation; Patel et al. have presented the flexible optical WDM network architecture, and introduced the routing, wavelength assignment, and spectrum allocation in trans-parent flexible optical WDM (FWDM) networks; Wan et al. have also proposed dynamic routing and spectrum assignment algo-rithms for bitrate-flexible lightpaths in OFDM-based optical net-works. For maximizing spectrum efficiency in SLICE, authors have proposed optical path routing and frequency slot assignment algo-rithms that suit elastic optical paths and the distance adaptive modulation scheme in different networks[20].
For network survivability in traditional WDM networks, different schemes of protection and restoration have been maturely re-searched by dedicated protection, shared protection, path restora-tion and link restorarestora-tion, and so on. Tradirestora-tional ideas of protecrestora-tion and restoration in WDM networks can be also applied to the SLICE networks. For example, Ref. [20] used dedicated protection and shared protection to establish working path and backup path. How-ever, to optimize spectrum efficiency in SLICE networks, it adopted the distance adaptive modulation scheme to achieve high spectrum efficiency for routing and frequency slot assignment. For network survivability in SLICE networks, to ensure a highly survivable and spectrally efficient bandwidth, a novel restoration scheme named bandwidth squeezed restoration (BSR) scheme[21,22], has been proposed by the manner of best-effort recovery when the available bandwidth resources are insufficiency in the process of a failure recovery. However, there are some drawbacks when we adopt BSR scheme to restore the failure traffic. On one hand, in order to satisfy acceptable QoT, the transmission rate and modulation format that use OFDM-based modulation technologies need to be changed and it does not guarantee high quality of transmission (QoT), especially in the optical networks of big capacity, high transmission rate and long transmission span. On the other hand, these works do not con-sider the effects of network load on the performance of SLICE. In this paper, we aim at available bandwidth sufficiency in the case of mul-ti-link failures and we do not focus on the insufficiency of available bandwidth that can be as backup path of the affected traffic. There-fore, a novel survivable algorithm, named dynamic load balancing shared-path protection (DLBSPP), is proposed by introducing con-ventional resource assignment schemes first fit (FF) and random fit (RF) to assign the available spectrum resources. Different from previous work which exploits distance-adaptive spectrum assign-ment and BSR techniques, the proposed algorithm jointly exploits the key ideas of dynamical load balancing, spectrum resources shar-ing of backup path and traffic-aware restoration (TAR) mechanism. On one hand, dynamical loading balancing is taken into consider-ation when computing primary and backup paths, which is to make traffic evenly distributed. On the other hand, we adopt the shared spectrum resources to be spectrum resources of backup path, by which the more backup spectrum resources can be saved, the more failure traffic can be recovery. Therefore, high spectrum efficiency is achieved by the shared spectrum resources of backup path. More-over, the TAR mechanism is adopted to compute new routes for car-rying the traffic affected in the case of the multi-link failures and restoring the failure traffic efficiently, i.e., better survivability is achieved by the TAR mechanism.
The rest of this paper is organized as follows: in Section2, we have described: (i) the network model; (ii) routing and spectrum assignment algorithm; (iii) the key idea of dynamic load balancing for primary and backup path computations; and (iv) the TAR mech-anism to compute new routes for carrying the traffic affected in the
case of the multi-link failures. The proposed DLBSPP algorithm is described in Section3, the simulation and analysis are presented in Section4, and Section5gives the conclusions.
2. Problem statement 2.1. Network model
The SLICE architecture is described as G(V, L, F), where V = {
v
1,v
2,v
3, . . . ,v
n} denotes the set of nodes [8,23] which are equipped with bandwidth-variable optical cross-connects (BV-OXCs) function, L = {l1, l2, l3, . . . , ln} is the set of links, F = {x
1,x
2,x
3, . . . ,x
n} is the set of available frequency slots. |N|, |L|, and |F| represent the numbers of nodes, the links, and the fre-quency slots, respectively. For each connection request (CR) from source s to destination d, it requires the bandwidthx
and assigns the same number of frequency slot to primary path and link-dis-joint backup path. Dijkstra algorithm is applied to compute the routes, and spectrum resource reservation considers spectral con-tinuity constraint and spectral consecutiveness constraint when assigning frequency slot resource. In addition, some requisite nota-tions are defined and described asTable 1.2.2. Routing and spectrum assignment
In traditional WDM networks, for accepting an optical CR, rout-ing and wavelength assignment is an important issue and usually wavelength continuity constraint without wavelength conversion should be considered. For wavelength assignment, there are two different schemes, FF and RF schemes. In FF scheme, wavelengths are searched sequentially and the first available one will be se-lected, whereas in RF scheme, wavelengths will be randomly picked from the available wavelengths on each path.
Similar to traditional WDM networks, routing and spectrum assignment (RSA) algorithm also becomes a key technology to en-able OFDM-based spectrum-elastic optical path networks. Instead of traditional wavelength continuity, spectral continuity constraint and spectral consecutiveness constraint are considered in the SLICE architecture. RSA problem is divided into two sub-problems, rout-ing and spectrum assignment. For routrout-ing, Dijkstra algorithm is adopted to compute the shortest path for ith CRi(s, d,
x
) from source s to destination d; for spectrum assignment, the traditional wavelength assignment schemes FF and RF are introduced to allo-cate the frequency slot resource.Spectral continuity constraint is defined as allocation of the same spectrum on each link along a path, and spectral consecutive-ness constraint is defined as the allocated spectrum must be chosen from contiguous frequency slots in the frequency domain on each link. For example, considering an 8-node network in
Table 1
Notations and definitions. Symbol Meaning
N The number of network nodes
x The bandwidth requirement Li,j The link between node i and j
Wi,j The basic cost of link Li,j
NSi,j The number of frequency slot on link Li,j
PSi,j The number of primary frequency slot consumed on link Li,j
BSi,j The number of backup frequency slot consumed on link Li,j
FSi,j The number of free frequency slot on link Li,j
DWi,j The dynamic cost of primary path on link Li,j
BWi,j The dynamic cost of backup path on link Li,j
CRi(s, d,x) The ith CR from source node s to destination node d requires
bandwidthx
Pi The primary path for CRi(s, d,x)
Fig. 1, there are 6 frequency slots on each link, where the numbers on the links represent the available frequency slots and each CR re-quires three frequency slots. The first CR1(A, D, 3) chooses the shortest route A–B–C–D as working path, a set of common fre-quency slots (3, 4, 5) are used for the reserved spectrum resources on each link along this path. Since three frequency slots (3, 4, 5) on each link A–B, B–C and C–D are not only all the same but also contiguous in the frequency domain. Therefore, the first CR1(A, D, 3) can be successfully established since it satisfies with spectral continuity constraint and spectral consecutiveness con-straint. However, on one hand, the second CR2(G, D, 3) chooses the shortest route G–H–D as working path, and frequency slots (2, 3, 4) and (1, 2, 3) that correspond to links G–H and H–D respec-tively are used for the reserved spectrum resources. The second CR2(G, D, 3) cannot be routed successfully on route G–H–D since they are not the same spectrum along this path (spectral continuity constraint). On the other hand, the third CR3(A, H, 3) chooses the shortest route A–G–H as working path, and frequency slots (3, 4, 6) on links A–G and G–H are used for the reserved spectrum resources. The third CR3(A, H, 3) is also not established successfully since the allocated frequency slots (3, 4, 6) are discontinuous in the frequency domain (spectral consecutiveness constraint).
2.3. Dynamical load balancing
One of the key ideas of the DLBSPP algorithm is to consider dynamical load balancing in path computation, i.e., light-load links are preferred when computing both the primary and backup paths for a CR. Free spectrum resources are only used for the reserved spectrum resources of primary path. However, based on the princi-ple of spectrum resources sharing for the backup path, the reserva-tion of spectrum resources depends on the reserved backup spectrum resources and free spectrum resources. Therefore, con-sidering the difference of reserved spectrum resources between primary path and backup path, we adopt two different link-cost functions to update the link cost, by which the chosen primary path and backup path can be optimized. The link-cost function for primary path computation should choose the shortest path as long as every link along the path has plenty of free frequency slots. During the process of computing the least-cost primary path, light-load links are preferred. Thus, the unbalanced state of network load, in which available spectrum resources on some links are excessively consumed, can be avoided. For a link Li,j, the link-cost function for primary path computation is defined as follows:
DWi;j¼
1 if FSi;j¼ 0
a
FSi;jWi;j if FSi;j>0 (
ð1Þ
where
a
is a tunable parameter anda
> 1. As clearly shown in Eq. (1), if the link has no free frequency slots, the link cost is set to infin-ity. Otherwise, the more free frequency slots one link has, the less link cost will be set. This link-cost function is adopted during the process of computing the least-cost path as the primary path Pi using Dijkstra algorithm.Similarly, the link-cost function for shared backup path compu-tation is defined as follows:
BWi;j¼
1 if FSi;j¼ 0 and if BSi;j¼ 0
a
FSi;jþbBSi;jWi;j otherwise (
ð2Þ
where
a
and b are tunable parameters anda
> 1 and b > 1. This link-cost function is adopted during shared backup path Bicomputation. The benefit of dynamical load balancing in path computation can be seen from the example shown inFig. 2. There are three fre-quency slots on each link and each CR requires one frefre-quency slot. Suppose four CRs sequentially arrive from client A to client C, in conventional SPP algorithm without considering dynamical load balancing, the shortest route A–B–C is used for primary paths, and the route A–D–E–C can be used as the shared backup path, which will be blocked since there are no sufficient frequency slot resources for the last CR since there are only three frequency slots on each link. However, if dynamical load balancing is considered, the primary paths of the third and fourth CRs will be routed along path A–F–G–C, and the path A–D–E–C can be also used for their shared backup path since we employ the principle of spectrum re-sources sharing for the backup path. Therefore, dynamical load bal-ancing can help to reduce the blocking probability.2.4. Traffic-aware restoration
If multi-link failures occur and affect both primary path and backup path for a CR, the failed traffic will be dropped since it can-not be recovered by the conventional SPP algorithm. The more fre-quency slots are, the more data can be carried, i.e., the coarse granularity traffic is more important than the small granularity traffic. Therefore, for the coarse granularity traffic, we give them higher priority than small granularity traffic in the process of the failure restoration. Traffic-aware restoration (TAR) mechanism is adopted in our proposed DLBSPP algorithm to compute new routes for carrying the traffic affected by the multi-link failures. The key idea of TAR mechanism is that, (i) the affected traffic will be clas-sified by priority, i.e., the coarse granularity traffic has high prior-ity, the small granularity traffic will be recovered with a low priority; (ii) new routes for carrying the affected traffic can be com-puted again. According to the classification of the affected traffic based on different granularity traffic and employ the TAR mecha-nism, the affected traffic can be effectively recovered one by one in the case of the multi-link failures.
Fig. 1. Illustration of spectral continuity constraint and spectral consecutiveness constraint.
An example is illustrated inFig. 3, in which we assume there are two CRs (service I and service II) from client A to client C and the bandwidth of service I and service II requires one and three fre-quency slots respectively. Service I chooses the route A–B–C as the primary path and the first frequency slot is used for the re-served spectrum resource, and the link-disjoint shared backup path is the route A–D–E–C and the first frequency slot is used for the reserved backup spectrum resource along this backup path. Service II also chooses the shortest route A–B–C as the primary path and uses the second, third and fourth frequency slots as re-served spectrum resource, and the first, second and third frequency slots are used for the reserved backup spectrum resources along backup path A–D–E–C based on the principle of spectrum re-sources sharing. The red and green represent first service I and sec-ond service II that have used spectrum resource, respectively; and the pink and dark blue denote shared backup and free spectrum re-source, respectively. For conventional SPP algorithm, the estab-lished CRs will be dropped when the links LB,C and LE,C are interrupted simultaneously. However, if the DLBSPP algorithm is adopted, a new recovery route A–F–G–C for effected service II can be computed by the TAR mechanism and the first, second and third frequency slots(green frequency slots) are used for the reserved spectrum resources, i.e., service II is firstly recovered. And then service I also chooses the recovery path A–F–G–C by the TAR mechanism and the fourth frequency slot (red frequency slot) is used for its spectrum resource. Therefore, the multi-link failures can be efficiently restored.
3. Heuristic algorithm
In the proposed DLBSPP algorithm, two constraints are consid-ered, i.e., spectral continuity constraint and spectral consecutive-ness constraint. Service CRs are divided into two kinds of events, i.e., normal event pair and failure event pair. For normal event pair, it is divided into two subevents, i.e., arrival event (AE) that a CR launches setup path and reserves spectrum resource and departure event (DE) that a CR launches tearing down path and releases spec-trum resource; and for failure event pair, it is also classified into two subevents, i.e., failure arrival event (FAE) that some links fail in a running time and failure departure event (FDE) that failure links can be automatically recovered after their failures. The gen-eral steps of the proposed DLBSPP algorithm are described as follows:
Step1: Initialize the network information (physical topology, initialize parameters) and generate X CRs (X > 0).
Step2: Waiting for an event for CR (X > 0). If an event is AE, turn to Step 3, while for an event DE, turn to Step 4; an event FAE
happens, turn to Step 5, while for an event FDE, turn to Step 6. If waiting for an event for CR (X = 0), turn to Step7.
Step3: For an event AE, update the link state using the link-cost function Eq.(1), and (i) compute the primary path from source node s to destination node d, and then (ii) reserve spectrum resource along primary path; update the link state by the link-cost function Eq.(2), and (iii) compute the shared backup path from source node s to destination node d, and then (iv) spectrum resource will be searched sequentially and the avail-able max-shared spectrum will be selected as reservation resource of this event AE along backup path. If the (i), (ii), (iii) and (iv) are all executed successfully, return to Step2; other-wise, this CR drops and blocks, and let X = X 1, return to Step2. Step4: For an event DE, both primary path and backup path are torn down and release their spectrum resource. Let X = X 1, and return to Step2.
Step5: For an event FAE, link failures happen, and it randomly generates Y failure links, which satisfy uniform distribution. If Y = 0, return to Step2; otherwise, for Y > 0, (i) if only primary path fails, release its spectrum resource, and then switch the failure traffic to backup path; (ii) if both primary path and backup path fail, firstly release their spectrum resource, and then adopt TAR mechanism to recover the effected CRs one by one; otherwise, the CRs drop; update the link state, return to Step 2.
Step6: For an event FDE, Y failure links are automatically restored at the same time and the link state is updated and return to Step2.
Step7: If all events have been dealt with, simulation ends. For each CR, the DLBSPP algorithm computes primary and shared backup paths exploiting the Dijkstra algorithm with dis-tinctive link-cost functions. Thus the time complexity of DLBSPP algorithm is O(2|N|2). In the worst case, when the multi-link fail-ures occur and both primary and backup paths are affected, the DLBSPP algorithm adopts the TAR mechanism to recover the af-fected CR, which will run the Dijkstra algorithm another time. Therefore, the time complexity of the DLBSPP algorithm is approx-imately O(3|N|2
) in the worst case.
4. Simulation and analysis
We adopt NSFNET (14 nodes, 21 links) and USNET (24 nodes, 43 links) as shown inFig. 4to evaluate the performance of the pro-posed DLBSPP algorithm as compared with the traditional SPP algorithm with spectrum allocation schemes FF and RF in surviv-able SLICE network, where each link is bidirectional. CRs are uni-formly distributed between all source–destination pairs whilst the arrival of traffic follows Poisson distribution with arrival rate k(s, d) and the traffic holding time follows a negative exponential distribution. It is assumed that the available spectrum width of each link is 2500 GHz, each frequency slot is 25 GHz, the link cost denotes the link length, and according to a large number of simu-lations the parameters
a
and b to Eqs.(1) and (2)are set to 1 and 20 respectively.Four different schemes are simulated: the DLBSPP algorithm with FF (DLBSPP with FF), DLBSPP algorithm with RF (DLBSPP with RF), SPP algorithm with FF (SPP with FF) and SPP algorithm with RF (SPP with RF). For the bandwidth requirement of each CR, the num-bers of frequency slot are uniformly distributed between 4 and 8, and the reserved spectrum resource for each CR must satisfy spec-tral continuity constraint and specspec-tral consecutiveness constraint, when assigning frequency slot based on FF and RF schemes in the pair of primary and link-disjoint shared backup path. The met-rics adopted here for performance evaluation include blocking
probability (BP), spectrum utilization ratio (SUR), average hop (AH) and failure restoration ratio (FRR). These statistical average values are obtained when the traffic flow becomes steady. The computer in our simulation is configured with Intel Celeron 2.53 GHz CPU and 1.5G RAM under the software of Visual Studio 2008.
BP is the ratio of the number of CRs rejected by the network over the number of all CRs arriving at the network. SUR is the ratio of the total consumed backup frequency slots over the total con-sumed primary frequency slots. The smaller SUR is, the better spec-trum efficiency is. FRR is the ratio of the number of CRs undropped over total number of CRs affected by the failures. A bigger FRR means a better survivability.
As shown inFig. 5, it can be seen clearly, in both test networks NSFNET (a) and USNET (b), that DLBSPP with FF achieves the best performance of blocking probability than other schemes and DLBSPP algorithm have much lower BP than that of SPP algorithm, and spectrum allocation FF scheme has also lower BP than that of RF scheme. There are several reasons for these. Firstly, dynamical load balancing is considered in path computation, i.e., light-load links are preferred when computing the primary and shared back-up paths, this can help to avoid the unbalanced state in which available frequency slot resources on some links are more over-loaded than others. Secondly, the DLBSPP algorithm has more shared frequency slot resources than SSP algorithm since the max-shared spectrum resource can be used as backup spectrum re-source of new arrival CR along shared backup path, and there has
more free frequency slot resources saved that can accommodate new CRs. Finally, RF scheme has more discontinuous frequency slot fragmentation than FF scheme under the spectral continuity con-straint and spectral consecutiveness concon-straint since the random frequency slot selection increases the discontinuous spectrum re-source fragmentation, so the BP performance of the RF scheme is worse.
The SUR of different schemes for NSFNET (a) and USNET (b) can be seen inFig. 6. Firstly, the SUR of SPP with FF has lower value than that of SPP with RF, i.e., the former achieves better spectrum efficiency than the later, since random frequency slot allocation in the resource reservation processing can generate more discontinu-ous spectrum fragmentation than that of sequentially selected spectrum resource. However, between DLBSPP with FF and DLBSPP with FF, the SURs of DLBSPP with FF and DLBSPP with RF have al-most the same performance but FF scheme has relatively larger fluctuation than RF scheme with the increase of traffic load. The reason is that using dynamical load balancing in path computation can reduce the discontinuous spectrum fragmentation of random frequency slot selection. Moreover, it can also be deduced that the SURs of DLBSPP algorithm have smaller value than those of SPP algorithm in both NSFNET and USNET networks, i.e., DLBSPP algorithm has much better spectrum efficiency than SPP algorithm. To analyze the better performance of the DLBSPP algorithm, two main reasons are identified: (i) the distribution of frequency slot resources is more uniform in the network and avoid appearance
7 0 1 13 4 10 12 9 11 2 8 6 3 5 0 1 2 3 4 6 5 7 8 9 10 11 12 13 14 17 22 18 15 16 20 23 19 21
(b)
(a)
Fig. 4. Test networks NSFNET (a) and USNET (b).
40 60 80 100 120 140 160 180 200 220 240 260 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 Blocking probability
Traffic load (Erlang) DLBSPP_FF SPP_FF DLBSPP_RF SPP_RF 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 Blocking probability
Traffic load (Erlang) DLBSPP_FF SPP_FF DLBSPP_RF SPP_RF 40 60 80 100 120 140 160 180 200 220 240 260
(a)
(b)
of heavy-load links since primary path computation considers dynamical load balancing according to Eq.(1); (ii) many backup re-sources can be saved according to Eq.(2)since DLBSPP algorithm can encourage the backup paths to select these links that have max-shared backup frequency slot resources by adjusting the parameter b, by which backup path computation can be optimized.
InFig. 7, the AHs of the sum of primary and shared backup paths
in different schemes for NSFNET (Fig. 7a) and USNET (Fig. 7b) is presented. As can be seen clearly in this graph that, firstly, the AHs of DLBSPP with FF has the larger value than DLBSPP with RF, as well as the relationship between SPP with FF and SPP with RF, i.e., the former consumes more spectrum resource than the later, since the BP performance of DLBSPP with FF achieves better than that of DLBSPP with RF, i.e., former rejected CR number is less than the later, which may result into more spectrum resource consumed and new CR arriving will choose much long primary path and shared backup path; secondly, the AHs of DLBSPP algorithm have larger value than those of SPP algorithm in both NSFNET and US-NET networks, which reflect that DLBSPP algorithm has better spectrum efficiency than SPP algorithm. The reasons for these are that (i) DLBSPP algorithm has less BP than SPP algorithm, which may result into more spectrum resource consumed and new arrival CR will choose much long primary path and backup path; (ii)
primary path and shared backup path computations incorporates the dynamical load balancing strategy, which are not the shortest paths but the least-cost paths. Thus, the AHs of DLBSPP algorithm have much longer than those of SPP algorithm.
Since the FRR is an important performance for the quality of any survivability algorithm, inFig. 8, we evaluate the performance by simulating three-link failures and four-link failures that randomly happen and are uniformly distributed in the network topologies, and then they are automatically restored after holding a period of time. We can see clearly that, in three-link failures (Fig. 8a and b), the average FRRs of DLBSPP with FF, DLBSPP with RF, SPP with FF and SPP with RF are about 79.2%, 76.4%, 25.5%, 30.1%, and 89.0%, 88.4%, 34.4%, 40.4% in both NSFNET and USNET networks, respec-tively; in four-link failures (Fig. 8c and d), their average FRRs for four different schemes are about 76.4%, 73.5%, 20.0%, 24.2%, and 89.5%, 88.1%, 30.0%, 35.4% in both NSFNET and USNET networks, respectively. The FRRs of different schemes for NSFNET (Fig. 8a and c) and USNET (Fig. 8b and d) are evaluated. Firstly, the FRR of SPP with RF has lower value than SPP with FF, i.e., the former has better restoration ability than the later. The reason is that, although random frequency slot allocation can generate more dis-continuous spectrum fragmentation than that of sequentially selected spectrum resource, and many CRs will be blocked so that
40 60 80 100 120 140 160 180 200 220 240 260 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5
Spectrum utilization ratio
Traffic load (Erlang) DLBSPP_FF SPP_FF DLBSPP_RF SPP_RF 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3
Spectrum utilization ratio
Traffic load (Erlang) DLBSPP_FF SPP_FF DLBSPP_RF SPP_RF 40 60 80 100 120 140 160 180 200 220 240 260
(a)
(b)
Fig. 6. Spectrum utilization ratio of different schemes for NSFNET (a) and USNET (b).
5.4 5.5 5.6 5.7 5.8 5.9 6.0 6.1 6.2 Average hop
Traffic load (Erlang) DLBSPP_FF SPP_FF DLBSPP_RF SPP_RF 5.8 6.0 6.2 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0 Average hop
Traffic load (Erlang) DLBSPP_FF SPP_FF DLBSPP_RF SPP_RF 40 60 80 100 120 140 160 180 200 220 240 260 40 60 80 100 120 140 160 180 200 220 240 260
(a)
(b)
there are less CRs and spectrum resource consumed in the net-work, therefore, in SPP with RF, many CRs can be efficiently re-stored when there are multi-link failures. Whereas, the FRR of DLBSPP with FF has better performance than that of DLBSPP with RF since using dynamical load balancing in path computation can reduce the discontinuous spectrum fragmentation of random fre-quency slot selection. Thirdly, we can also see that the FRRs of DLBSPP algorithm have higher value than those of SPP algorithm in both NSFNET and USNET networks, i.e., DLBSPP algorithm has better restoration ability than SPP algorithm in both networks be-cause DLBSPP algorithm adopts TAR mechanism to restore the failed traffic efficiently while SPP algorithm cannot do. Finally, in four FRR schemes, the heavier the traffic load is, the worse FRRs be-come. The reasons for these are that the more CRs will be shared the same backup path and there are less free spectrum resource with the traffic load increasing, so when multi-link failures happen, more CRs will be dropped because there are no enough spectrum resource to provide efficient restoration for failed CRs. Therefore, our proposed DLBSPP algorithm with TAR mechanism has better restoration ability for multi-link failures than that of traditional SPP algorithm, especially, DLBSPP with FF scheme.
5. Conclusion
In this paper, a new survivable algorithm with dynamic load balancing for primary and backup path computations, named
dynamic load balancing shared-path protection (DLBSPP), is pro-posed. The key idea of DLBSPP algorithm is that light-load links are preferred during the route computation process. For primary path computation, a link is assigned to a big cost if the number of free frequency slots in the link is small. For backup path compu-tation, the link cost is a function of free frequency slots and shared frequency slots, by which backup path computation can be opti-mized and backup spectrum resources are make full use of shared spectrum resources by adjusting the parameter b. For spectrum re-source allocation, traditional first fit (FF) scheme and random fit (RF) scheme are introduced to search and select the available spec-trum resource under spectral continuity constraint and spectral consecutiveness constraint. Moreover, to tolerate multi-link fail-ures, the traffic-aware restoration (TAR) mechanism is adopted to compute new routes for carrying the traffic affected by the failures and restoring failed traffic efficiently. We have evaluated the net-work performance and the survivability for four different schemes, i.e., DLBSPP algorithm with FF (DLBSPP with FF), DLBSPP algorithm with RF (DLBSPP with RF), SPP algorithm with FF (SPP with FF) and SPP algorithm with RF (SPP with RF) in two different networks. Simulation results show that, compared with the conventional SPP algorithm, the proposed DLBSPP algorithm achieves lower blocking probability (BP), better spectrum utilization ratio (SUR), more reasonable average hop (AH) and higher failure restoration ratio (FRR). Thus, the proposed algorithm has much higher spec-trum efficiency and much better survivability than SPP algorithm. Future interesting work will be focused on maximized spectrum
40 60 80 100 120 140 160 180 200 220 240 260 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2
Failure restoration ratio
Traffic load (Erlang) DLBSPP_FF SPP_FF DLBSPP_RF SPP_RF 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2
Failure restoration ratio
Traffic load (Erlang) DLBSPP_FF SPP_FF DLBSPP_RF SPP_RF 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1
Failure restoration ratio
Traffic load (Erlang) DLBSPP_FF SPP_FF DLBSPP_RF SPP_RF 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2
Failure restoration ratio
Traffic load (Erlang) DLBSPP_FF SPP_FF DLBSPP_RF SPP_RF 40 60 80 100 120 140 160 180 200 220 240 260 40 60 80 100 120 140 160 180 200 220 240 260 40 60 80 100 120 140 160 180 200 220 240 260
(a)
(b)
(c)
(d)
resources sharing for backup paths and spectrum defragmentation that multi-links failures lead to spectrum fragmentation in dy-namic SLICE networks, which possibly makes for further improving the spectrum utilization.
Acknowledgments
This work was supported by 973 Program (2010CB328204), 863 Program (2008AA01A328, 2009AA01Z255), NSFC Project (60932004), RFDP Project (20090005110013) of China, and the fundamental research funds for the central universities.
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