Route Management Strategies for Grade of Service Differentiation in ...

Route Management Strategies for Grade of Service Differentiation in Optical Networks Andrzej Szymański, Artur Lasoń, Jacek Rząsa, and Andrzej Jajszczyk AGH University of Science and Technology Kraków, Poland {szymanski, lason, rzasa, jajszczyk}@kt.agh.edu.pl Abstract – In the paper we propose three route management strategies that allow an optical network operator to achieve differentiated service for two classes of lightpath requests. Simulation results show that all three strategies achieve the stated goal, which is to maintain blocking probability of high priority requests below an assumed value in a broad range of network loads. Moreover, the presented strategies require only little support from the control plane. Detailed algorithms are presented in two versions, one suitable for networks with centralized control, and the other for networks with distributed control. Quality of Service; Grade of Service; Optical Network Route Management; RWA; Wavelength Assignment; Resource Reservation

I. INTRODUCTION A search for the optimal use of network resources and growing user demands are the two most important drivers for network evolution. Network operators look for profits mainly through cost reduction or more flexible use of an existing network infrastructure. However, it seems that low prices for telecommunication services cannot be the only way to attract clients in a highly competitive environment. We believe that diversified services, tailored to customers’ needs are the key for both retention of old and attraction of new customers. On one hand this requires a high bandwidth, on–demand provisioned connections, on the other, providing connections with the quality of service (QoS) and grade of service (GoS) adequate to customers’ needs. The first problem, i.e., the need for on demand connection provisioning, has been noticed by the main standardization bodies and some first standards are already available. For example, the ITU-T published a series of recommendations, which are aimed at characterizing optical networks with on– demand provisioned connections (see ITU-T G.8080 [1]). Similarly, there are some proposals provided by the IETF (see RFC 3945 [2]). The second problem receives only partial attention. Although there are some works on QoS in optical networks [3], we found little information regarding GoS, and particularly blocking probability of lightpath requests, which is one of the most important GoS parameters in wavelength routed optical networks. This work has been carried out within the IST Nobel Project and was partially funded by the Polish Ministry of Science and Information Society Technologies under Grant No. 4 T11D 012 25.

This paper aims at filling this gap by providing three route management strategies that differentiate blocking probability of high and low priority lightpath requests. The goal of the presented strategies is to achieve blocking probability of high priority requests below an assumed value in a wide range of network loads. Simultaneously, the strategies should require as little additional information as possible, to keep the control plane complexity at a reasonable level. The rest of the paper is organized as follows. In Section II we provide a description of the proposed strategies together with a short discussion on their strengths and weaknesses. Section III describes the simulation environment and is followed by Section IV which provides simulation results. II. STRATEGIES We have proposed three strategies that provide differentiation of blocking probabilities between two classes of lightpath requests. All of them use the shortest path (least hop) routing and the first–fit wavelength assignment. However, the strategies are not restricted from using any other wavelength assignment algorithm. Although for simulations in Section III we use a network where a single, centralized controller performs all computations, the strategies have been created with distributed environment in mind. All of them require some information about the network state, but this information is either locally available, or can be easily gathered during lightpath setup. There is no need to include this information in a TE-extended routing protocol updates. However, routing protocol may be used to disseminate configurable parameters used in the strategies. For each strategy we provide two algorithms. The first one is suitable for networks with centralized control, and this version is used in our simulations. The second one, based on backward reservation with conservative wavelength choice, is suitable for networks with distributed control. (For details on backward reservation see [4].) The following notation is used: The algorithms operate on a lightpath request R from node S to node D. Request R is either a low or high priority request. Each link carries W wavelengths in each direction and Ω={λ1, ..., λW } is the set of wavelengths in a network.

1-4244-0355-3/06/$20.00 (c) 2006 IEEE This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2006 proceedings.

P denotes the path (a sequence of links), which will be used for setup of request R. A is the set of wavelengths considered for a lightpath setup. |A| is the number of elements in A. A0 is the set of unused (free) wavelengths on the first link of path P.

For networks with distributed control we prepared the second version of the algorithm. The algorithm is based on backward reservation [4]. However, in order to be able to distinguish between high and low priority requests in intermediate nodes, the algorithm uses two probe messages, PROBE_H and PROBE_L, respectively. For a lightpath request R from node S to node D:

T is a configurable parameter that determines how much resources are reserved for high priority requests.

1.

In node S, compute path P from node S to D according to the shortest path (least hop) rule.

The algorithms terminate when either SET UP REQUEST or BLOCK REQUEST decision is reached. For clarity of presentation, in the algorithms for networks with distributed control we do not describe the second phase of backward reservation, in which the actual wavelength reservation is performed. Instead, we use:

2.

BLOCK REQUEST decision to indicate that the destination node should appropriately inform the source node about setup failure.

In node S, if R is a high priority request then send PROBE_H message to node D along path P that contains an initial set of available wavelengths A=A0, else send PROBE_L message to node D along path P that contains an initial set of available wavelengths A=A0.

3.

SET UP REQUEST decision to indicate that the reservation of resources and lightpath setup should be performed using a given path and wavelength.

While traversing path P intermediate nodes remove wavelengths that are busy from set A carried by PROBE_H message.

4.

In the second case it is still possible that the lightpath request will be blocked due to resource allocation collisions. We are aware that this phenomenon will have a negative impact on an achieved grade of service. However, investigation of this impact is outside the scope of this paper.

While traversing path P intermediate nodes remove wavelengths that are busy from set A carried by PROBE_L message, if there are T or less free wavelengths on a link on path P then all wavelengths are removed from set A (A=∅).

5.

In node D, receive message PROBE_L or PROBE_H, if A=∅ then BLOCK REQUEST R.

6.

In node D, choose λj according to the first-fit algorithm so that λj∈A.

7.

In node D, SET UP REQUEST R on path P and wavelength λj.

•

•

A.

The strategy with link capacity threshold The idea behind this strategy is to preserve some capacity on each link for use by high priority lightpath requests. The amount of the reserved capacity is controlled by using threshold T. If the number of free wavelengths on a given link is greater than T, all lightpath requests are accepted on the link. If the number of free wavelengths on the link is equal to or less than T, then high priority lightpath requests are accepted only. The strategy may be implemented in networks with centralized control with the following algorithm: For a lightpath request R from node S to node D: 1.

Compute path P from node S to D according to the shortest path (least hop) rule.

2.

If R is a high priority request then go to step 4.

3.

For each link pi on path P if the number of free wavelengths on link pi is less than or equal to T then BLOCK REQUEST R.

4.

Compute set of available wavelengths A⊂Ω so that each λi∈A is free on all links on path P.

5.

If A=∅ then BLOCK REQUEST R.

6.

Choose the minimum j so that λj∈A.

7.

SET UP REQUEST R on path P and wavelength λj.

As we assume no wavelength conversion in the network, this strategy, regardless of the algorithm version chosen, is expected to have some problems with wavelength continuity. In a busy network, an incoming high priority lightpath request might find that there are some wavelengths free on each link on path P, but none of them is continuously free on the whole path. To overcome this problem, another strategy was proposed. B. The strategy with wavelength pools In this strategy, instead of reserving wavelengths individually on each link we divide all wavelengths into two pools. Wavelengths λ1 … λW-T are common wavelengths which may be used by all lightpath requests. Wavelengths {λW-T+1 … λW} are reserved wavelengths, that can be used by high priority requests only. A high priority request should first try common wavelengths, and use the reserved wavelengths {λW-T+1, ..., λW } as the last resort. With the first–fit wavelength assignment this is achieved by using wavelengths with highest indexes as the reserved wavelengths.

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2006 proceedings.

The following algorithm implements the presented strategy in networks with centralized control. For a lightpath request R from node S to node D: 1.


2.

Compute the set of available wavelengths A⊂Ω so that each λi∈A is free on all links on path P.

3.


4.

If R is a high priority request then choose the minimum j so that λj∈A else choose the minimum j so that λj∈A and j≤ W-T, if such j does not exist BLOCK REQUEST R.

5.

high priority requests only

all classes of requests

A


The following version of the algorithm was prepared for networks with distributed control. For a lightpath request R from node S to node D: 1.


2.

In node S, if R is a high priority request then send PROBE message to node D along path P that contains an initial set of available wavelengths A=A0 else send PROBE message to node D along path P that contains an initial set of available wavelengths A=A0\{λW-T+1 … λW}.

3.

While traversing path P intermediate nodes remove wavelengths that are busy from set A carried by PROBE message.

4.

In node D, receive PROBE message, if A=∅ then BLOCK REQUEST R.

5.


6.


The differentiation of high and low priority requests is achieved only by appropriate modification of the initial set of available wavelengths in Step 2, so there is no need to inform other nodes about the priority of request. Therefore, a single PROBE message is sufficient for this algorithm. The amount of information required from the control plane has thus been reduced, compared to the previous strategy. Essentially, the algorithm for networks with distributed control requires no additional information, except parameter T, compared to a reference case, which is shortest path routing with first fit wavelength assignment and no GoS differentiation. The strategy, regardless of the algorithm used, has one significant drawback. If the reserved wavelengths become busy with high priority requests, the differentiation of further

λ8

λ8

λ7

λ7

λ6

λ6

λ5

λ5

Busy

λ4

λ4

Free

λ3

λ3

λ2

λ2

λ1

λ1

Link 1

B

Link 2

C

Figure 1. The illustration of the strategy with wavelength pools with phenomenon of inability to differentiate requests

requests is suppressed. This phenomenon is illustrated in Fig. 1. Assume that the network had been fully loaded with lightpaths, then all lightpaths on λ1 ... λ6 were disconnected. The new lightpath requests will be treated equally, regardless of their class. This situation will last as long as wavelengths λ7 and λ8 are busy. C. The strategy with path capacity threshold This strategy joins strengths and avoids weaknesses of the two strategies presented earlier. A decision whether to accept or reject a low priority lightpath request is based on wavelength availability on its path. If there are more than T continuous wavelengths, the low priority request is accepted, in the other case the request is blocked. Essentially, with this strategy each low priority request leaves room for at least T high priority lightpaths along its route. The algorithm suitable for networks with centralized control is presented below. For a lightpath request R from node S to node D: 1.


2.

Compute set of available wavelengths A⊂Ω so that each λi∈A is free on all links on path P.

3.


4.

Choose the minimum j so that λj∈A.

5.

If R is a low priority request and |A|≤T then BLOCK REQUEST R.

6.


The following version of the algorithm may be used for networks with distributed control. Similarly to the link capacity threshold strategy, two probe messages (PROBE_H and PROBE_L) are used to distinguish between high and low priority requests.


For a lightpath request R from node S to node D: 1.


2.

In node S, if R is a high priority request then send PROBE_H message to node D along path P that contains an initial set of available wavelengths A=A0 else send PROBE_L message to node D along path P that contains an initial set of available wavelengths A=A0.

3.

While traversing path P intermediate nodes remove wavelengths that are busy from set A carried by PROBE_H or PROBE_L message.

4.

In node D, receive message PROBE_L or PROBE_H, if A=∅ then BLOCK REQUEST R.

5.

In node D, if received message is PROBE_L and |A|≤T then BLOCK REQUEST R.

6.


7.


The offered traffic is generated based on a uniform traffic matrix. In the base case each node pair generates bidirectional lightpath requests, with mean intensity of 0.033 lightpath per unit time and exponentially distributed interarrival time. A given fraction (10 ÷ 50%) of the offered traffic belongs to the high priority class, while the remaining traffic belongs to the low priority class. The lightpath holding time is also exponentially distributed with mean of 10 units time. As a result, the total network load in the base case is 28×27×0.033×10 = 249.48 Erl. To observe network behavior under different loads, this base case traffic is scaled by the factor ranging from 1.0 to 1.9. We simulated the network with centralized control. The main motivation for this choice was to avoid the negative impact of the inconsistent network state, since it depends on a broad range of network parameters. Assessment of this impact, although important for networks with distributed control, is outside the scope of this paper. The simulation environment was built using OMNeT++ simulator [6]. Pseudo-random numbers were generated using Mersenne-Twister generators [7] with period length of 219937-1. The results were obtained using the batch means method and evaluated at 0.95 confidence level.

This strategy (regardless of the algorithm version used) avoids problems encountered by the previous two strategies. The remaining free capacity is estimated on the whole path, taking wavelength continuity into account. Simultaneously, this strategy is not vulnerable to destructive capacity allocation and subsequent suppression of GoS differentiation.

IV. RESULTS All presented strategies achieve the stated goal, which is to keep the blocking probability of high priority requests below an assumed value (here 0.5%) in a broad range of offered loads and with different fractions of high priority traffic. In fact, we have tested those strategies with up to 50% of high priority traffic with good results.

In this section we presented three route management strategies along with algorithms that may be used to implement them. In the next section, the simulation environment that was used to assess performance of the presented strategies is described.

Fig. 3 – 5 show blocking probabilities of high and low priority requests (marked HI and LO, respectively). The overall traffic intensity was scaled from 1.0 to 1.9 of the base traffic (249.48 Erl) and we have chosen the case with 20% of high priority traffic as an illustrative example.

All simulations have been performed on the 28 nodes PanEuropean reference network [5] shown in Fig. 2. Each link consists of two fibers, one in each direction. Each fiber carries 80 wavelengths and there is no wavelength conversion in the network.

Each figure also contains the reference curve representing the blocking probability in the case of a non–GoS scenario, i.e., shortest path routing with first–fit wavelength assignment. 0.1 LO LO no HI HI

Blocking probability

III. SIMULATION ENVIRONMENT

0.08 0.06

23 18 GoS 18 23

0.04 0.02

1

Figure 2. The Pan-European network topology

1.2 1.4 1.6 Offered traffic (relative)

1.8

Figure 3. The strategy with link capacity threshold: blocking probability of high (HI) and low (LO) priority class vs. offered load multiplication factor, for two values of parameter T (18 and 23) and 20% of the high priority traffic in the network. A case with no GoS differentiation is shown as well.


TABLE I. COMPARISON OF BLOCKING PROBABILITIES BETWEEN NON–GOS SCENARIO AND PROPOSED STRATEGIES (20% OF HIGH PRIORITY TRAFFIC)

0.1


LO LO no HI HI

0.08 0.06

12 5 GoS 5 12

Blocking probability Load No GoS

LCAP 23

POOL 12

PCAP 3

HI

LO

HI

LO

HI

LO

0.04 0.02

1


1.8

Figure 4. The strategy with wavelength pools: blocking probability of high (HI) and low (LO) priority class vs. offered load multiplication factor, for two values of parameter T (5 and 12) and 20% of the high priority traffic in the network. A case with no GoS differentiation is shown as well.

1.0

0.000

0.000

0.008

0.000

0.004

0.000

0.000

1.1

0.001

0.000

0.021

0.000

0.014

0.000

0.002

1.2

0.004

0.000

0.041

0.000

0.029

0.000

0.009

1.3

0.011

0.000

0.067

0.000

0.048

0.000

0.023

1.4

0.023

0.000

0.095

0.000

0.068

0.000

0.043

1.5

0.038

0.001

0.124

0.000

0.088

0.001

0.065

1.6

0.055

0.001

0.152

0.001

0.108

0.002

0.089

1.7

0.072

0.002

0.178

0.002

0.127

0.003

0.112

1.8

0.089

0.004

0.203

0.005

0.145

0.005

0.135

1.9

0.106

0.005

0.226

0.009

0.162

0.007

0.156

0.1


LO LO no HI HI

0.08 0.06

3 2 GoS 2 3

TABLE II. COMPARISON OF NETWORK UTILIZATION BETWEEN NON–GOS SCENARIO AND PROPOSED STRATEGIES (20% OF HIGH PRIORITY TRAFFIC) Network utilization [%] Load

0.04 0.02

1


1.8

Figure 5. The strategy with path capacity threshold: blocking probability of high (HI) and low (LO) priority class vs. offered load multiplication factor, for two values of parameter T (2 and 3) and 20% of the high priority traffic in the network. A case with no GoS differentiation is shown as well.

For each strategy two values of parameter T were chosen. The lower one reflects the case when the network operator wants to maintain the blocking probability of high priority requests below 0.5% for traffic scaled up to 1.4. The higher one allows to keep the blocking probability of high priority requests below 0.5% for traffic scaled up to 1.8. Table I contains comparison of the presented strategies in terms of blocking probability. Table II contains a comparison according to network utilization values. The abbreviations HI and LO denote the high and low priority traffic, respectively. The abbreviations LCAP 23, POOL 12, PCAP 3, denote the link capacity threshold strategy with T = 23, the wavelength pools strategy with T = 12 and the path capacity threshold strategy with T = 3, respectively. Values of parameter T are chosen to maintain blocking probability of high priority requests below 0.5% for traffic scaled up to 1.8. Since values of parameter T are chosen to provide similar performance in high priority class with different strategies, the strategies can be compared against each other in terms of introduced blocking in the low priority class, which is an important part of the price paid for GoS differentiation.

No GoS

LCAP 23

POOL 12

PCAP 3

1.0

27,1%

26,8%

26,9%

27,1%

1.1

29,7%

29,1%

29,3%

29,7%

1.2

32,3%

31,1%

31,3%

32,1%

1.3

34,6%

32,7%

33,2%

34,2%

1.4

36,6%

34,2%

34,8%

35,9%

1.5

38,3%

35,5%

36,3%

37,4%

1.6

39,7%

36,6%

37,7%

38,7%

1.7

41,1%

37,7%

39,1%

39,8%

1.8

42,2%

38,6%

40,3%

40,9%

1.9

43,4%

39,5%

41,5%

41,8%

The presented results support our observations, regarding strengths and drawbacks of the strategies, expressed in Section II. The least promising strategy is the strategy with a link capacity threshold since it has the highest blocking probabilities in the low priority class and the lowest network utilization values. The unnecessary blocking of low priority requests observed in a low load region is especially harmful. The strategy with wavelength pools behaves better, but still introduces an unnecessary increase of blocking probability for low priority requests in the low load region. The most promising strategy is the strategy with path capacity threshold, since it introduces least overhead to low class blocking probability and has highest network utilization. It is important to note that this strategy does not negatively impact network utilization and blocking probabilities in the low–load region (relative loads from 1.0 to 1.2).


With the last strategy, the blocking probability of low priority requests in the high load region does not even increase excessively, compared to the non–GoS case. At the maximum tested load, introduction of the GoS strategy resulted in an increase of the blocking probability of low priority requests from 10% to approximately 16%. Finally, we checked, how the presented strategies react to change in the traffic mix, i.e., the amount of high priority traffic offered to the network. The results are shown in Fig. 6 where we fixed the overall network load at 1.4 and changed the fraction of high priority traffic in the range of 10 – 50%. The strategy with the link capacity threshold very weakly reacts to changes in the traffic mix, but requires a high value of T for low load. On the other hand, this value of T is almost sufficient for high loads. This further supports our observations on drawbacks of this strategy, since with large values of T it is easier to find a wavelength that is continuously free on a given path. In contrast to the previously mentioned strategy, the strategy with wavelength pools has a very steep slope and is very sensitive to the amount of high priority traffic entering the network. This sensitivity might be an indication that the situation of the suppressed differentiation, described in Section II, affects the performance of this strategy. The last strategy, based on the path capacity threshold, is moderately sensitive to changes in the traffic mix and do not require large values of T. It seems, that this strategy succeeded in avoiding drawbacks of the other two. The presented results should be helpful in choosing the right strategy and the value of parameter T for a given network. However, the choice is not straightforward, and depends on the goals, the operator wants to achieve and costs it is willing to accept.

It is important to note that all tests have been performed on a single network topology, with a given number of wavelengths and a uniform traffic matrix. It is also important, that this network was equipped with centralized control. Testing those strategies in a more diverse set of topologies, traffic matrices, and in a network with distributed control is for further study. V. CONCLUSIONS All strategies managed to achieve the stated goals in assumed network conditions. The goals are realized at the cost of an increased blocking probability for low priority requests, decreased network utilization and increased complexity of control procedures. However, in our opinion, this cost is reasonably small, compared to the obtained benefits. There are two situations, in which the presented strategies may be especially useful. In the first one, an operator deals with a network which experiences high fluctuations in the offered traffic intensity. Without GoS differentiation, this network must be dimensioned so that offered GoS satisfies the most demanding services in the peak traffic conditions. Thus, such a network is usually considerably overprovisioned and underutilized most of the time. Introducing GoS differentiation allows to limit overprovisioning of the network. The most demanding services, will then receive the required GoS by using high priority lightpaths, while the remaining services will be able to tolerate periodically degraded network performance. In the second situation, a network that experiences long term traffic growth faces a slowly degrading network performance. This forces the operator to consider network upgrade. The presented strategies can delay this upgrade by providing the controlled blocking probability for key customers. REFERENCES [1]


0.014

LCAP 18

0.012

POOL 5

0.01

PCAP 2

[2] [3]

0.008

[4]

0.006 0.004 0.002

[5] 10

20 30 40 % of the HI class traffic

50

Figure 6. High priority class blocking probability vs. fraction of high priority traffic. The curves represent three presented strategies: link capacity threshold (LCAP), wavelength pools (POOL) and path capacity threshold (PCAP). Offered traffic is fixed at 1.4 of base traffic, parameter T have been chosen to achieve approx. 0.5% blocking probability for load 1.4 and 20% of high priority traffic.

[6] [7]

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