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外文翻译
译文题目：在WDM代理网络中基于蚁群的动态路由和波长分配
原稿题目：Dynamic Routing and Wavelength Assignment in WDM Net- works with Ant-Based Agents
Embedded and Ubiquitous Compter science V olume
原稿出处：
829-838
在WDM代理网络中基于蚁群的动态路由和波长分派
【摘要】在这篇论文中，咱们提出一种在波长持续性约束波分复用（WDM）光网络中基于蚁群算法的动态路由与波长分派。

通过采纳一个新的路由表结构和维持大量的蚂蚁在网络中合作探讨网络状态和不断更新路由表的方式，咱们新的蚁群算法能够有效地支持蚁群觅食任务的路由选择波分复用（WDM）网络中波长分派，并许诺一个连接设置迅速抵达小的设置时刻。

大量基于ns-2网络仿真结果模拟说明，该算法能够专门好得适应流量转变和达到一个比起固定路由算法较低的阻塞概率。

【关键词】路由，波长分派，算法，WDM（波分复用），蚁群算法
1.介绍
所有采纳波分复用（WDM）光网络都有一个庞大的带宽容量，他们显示成为下一代互联网骨干。

在所有光网络中，数据路由在光学通道被叫做光路。

路由和波长分派（RWA）问题是如何为一个连接请求确信路由和波长。

没有了波长转换功能，一个光路必需在所有链接中利用相同的波长，这被称为波长持续性限制。

路由和波长分派(RWA)问题通常被归类为静态和动态两种。

在静态路由和波长分派问题中，连接请问是预先给出的，问题就变成如何为所有请求成立光路，使得总数量的波长被最小化。

静态路由和波长分派问题已经被证明是一个NP完全问题。

在动态路由和波长分派问题中，流量是动态的和连接请求抵达的随机性使得它变得更为困难。

启发式算法通常被用来解决那个问题。

一样来讲，一个动态的路由和波长分派算法的目的是使在整个网络中总阻塞概率最小化。

在咱们的工作中，咱们关注波长持续性约束的动态RWA问题。

在高作中，动态RWA问题通常被分为两种子问题，别离能够解决：路由和波长分派。

路由方案能够分为固定路由，固定备用路由和自适应路由。

在固定的路由方案，有一个专门为源和目的的线路。

每当一个请求出此刻这一对源和目的中，这条线路就会试图对波长进行分派。

固定路由简单，可是通常致使高阻塞概率。

固定备用路由方式具有更好的性能在多途径节点对方面。

在自适应路由方案中，当一个连接请求到来时，线路的计算是基于当前网络状况，从而取得最好的性能。

但是，自适应路由需要很高的复杂计算。

一个更详细的路由调查和波长分派能够在[2]中找到。

自适应RWA方案在论文中老是需要来自于操纵协议的特殊支持以取得全世界网络状态。

另外，启发式算法在一个请求抵达以后执行路由和波长搜索任务必需衡量复杂度和性能。

这也造成高设置延迟和操纵开销。

一个可能的方式来克服这些问题是利用基于蚁群的移动代理[3]。

基于蚁群的代理路由方式继承了移动代理行为和蚁群系
统的优势。

最近的结果说明，这种方式能够在电路操纵的操纵和分组互换网络中产生高效的性能。

在本文中，咱们要紧研究一个新的基于蚁群代理算法为波分复用网络的动态RWA问题在波长持续性约束之下。

咱们的研究旨在通过利用适当数量的蚂蚁来减少阻塞概率和途径设置时刻，蚁群在连接请求带来之前持续执行途径搜索任务，如此的路由选择和波长分派请求的执行是简单地查找路由表。

为了实现这一目标，为咱们新的算法开发一个新的路由表结构，一个蚁群操纵方案和一个信息素更新机制。

本文余下部份组织如下：在第二节，咱们讨论相关工作。

第三节，在波长持续性约束之下的波分复用网络的中咱们为动态RWA问题提出新的方式。

第四节，描述咱们初步仿真和分析结果。

最后，咱们的结论和以后的工作在第五节进行了讨论。

2.相关工作
最近的研究结果说明，通信网络中的路由能够通过蚁群优化（ACO）[3]方式有效地解决。

路由解决方案成立在基于蚁群在代理网络状态中的觅食行为。

这些集体代理通过环境中信息素拖拽（stigmergy）间接沟通。

通过下面的的另一个信息素轨迹，一个代理能够找到一个“好”的线路，这条线路最短，从源到目的地路由数据最不拥堵。

两种大体算法是由Schoonderwoerd et al.[4]提出的对网络的基于蚁群操纵（ABC）和由Di Caro et al.[5]提出的基于蚁群的分组互换网络。

有一些后续的提高路由性能的改良方案，包括利用动态编程[9]的智能代理，增强蚁群对环境适应能力[10]的强化学习和适应蚁群搜索进程操纵参数[11]的遗传算法。

而以上的研究要紧集中在电子通信网络中的路由问题，咱们在本文中的爱好是波长持续性约束下的波分复用光网络的动态RWA问题。

Valera et al.[12]提出了一种蚁群算法来解决静态RWA问题。

目标在于使一个给定网络拓扑和流量矩阵的波长要求数量尽可能减少。

波长分派仅仅利用一个贪婪的方式，它为每一个链接指定最低可用波长。

一个蚂蚁的路由选择是基于每一个连接的吸引力。

每只蚂蚁都有自己的能够被其他蚂蚁拒绝的信息素。

每只蚂蚁都留有一个用于线路回溯和循环回避的之前访问节点的“禁忌”列表。

信息素的更新能够利用不同的方式。

该方式最好的结果是吸引蚂蚁的途径数量随着穿越的蚂蚁数量愈来愈多而取得全世界更新。

那个结果能够相较于Nagasu 启发式[13]，可是他需要更长的计算时刻。

然后，那个方式不能直接应用于动态RWA问题。

Garlick et al.[14]提出了一种基于蚁群的算法来解决动态RWA问题。

当一个新的连接请求到来时，大量的蚂蚁从源动身到目的地。

蚂蚁评估一条途径是基于其长度和这条途径的平都可用波长。

当一只蚂蚁抵达目的地，全世界信息素更新被执行。

信息素更新的需求依据：一旦一个连接被成立，网络信息素矩阵重置。

为一个连接请求的最后最好途径的产生是当所有的蚂蚁完成他们的探讨任务。

作者说明，该算法在所有
可用波长中探求最短途径[15]比一个详尽的探讨具有更高的性能。

作为一个新组蚂蚁必需为新的连接请求启动，设置延时会由于大型网络等待蚂蚁而变得超级高。

事实上，这种方式可不能显示来自于不同请求的蚂蚁的集体行为，这是基于蚁群系统的一个重要方面。

3.基于蚁群的RWA算法
一个光学波分复用(WDM)网络能够表示为由N个节点和E 链接的图。

咱们假设每一个链接是双向容量的W波段和节点没有波长转换能力(波长持续性限制)。

为了支持蚂蚁路由选择,每一个网络节点有一个路由表和N-1条款。

在一个i和ki相邻的节点，路由表有一个ki序列。

每一个条款对应到目的节点,每一列对应一个相邻节
点。

当一只蚂蚁向目的节点d运动时，那个值
r d n1.用作邻居节点n的选择概率。

为了支持波长分派,咱们引入了选择概率的每一个波长到路由表。

关于每一个相邻的节
点,让p
j概率是一只蚂蚁选择波长j，当它移动到目的地d。

图1所示的是当W=1的一个新的路由表的新的例子。

当一个连接请求发生
在源节点1和目的节点6，节点3将被选择作为下一跳,因为r16.2< r16.3。

在这种情形下，因为P1 < P2，波长2是优于波长1。

Fig. 1. A network and its routing table from node 1
在一个节点上,蚂蚁是由一个给定的概率随机选择ρ到目的地，每T个时刻单位。

那个地址ρ和T是设计参数。

一只蚂蚁被以为是一个移动代理:它负责在其旅行线路上搜集信息,执行路由表更新访问节点,并继续前进见图2。

Ant launched Update pheromone Ant killed
Fig. 2. Ant’s moving and updating tasks
一只蚂蚁从源移动到目的地,在一Fig. 2. Ant’s moving and updating tasks个选定的波长上一
个节点到一个节点运动。

它的下一站是随机决定的：一个相邻点的选择概率是基于路
由表的。

当一只蚂蚁抵达目的地节点或当它不能选择一个空闲的波长选择的途径为其
下一步行动时将被剔除。

为了幸免“冻结”状态，所有蚂蚁专注于一个线路(停滞)，
随机方案介绍：每一个蚂蚁选择下一跳的随机与利用概率。

当一个连接请求抵达时，途径将决定基于最高的选择概率相邻节点的条款。

波长分派是基于路由表的波长选择
概率,或其他一些传统能够利用的启发式方式。

当一只蚂蚁访问一个节点,它以其旅行进程中搜集的信息来更新路由表的元素。

信息素更新的原理描述如下：假设一只蚂蚁从源移动到目标d 后的s 途径
(s,…,i-1,i,…,d)。

当蚂蚁抵达节点i ，它将对应节点s 更新条款。

当其它相邻节点概率
减少时，相邻i-1节点概率也减少。

关于最近一次访问的相邻i-1节点，相应的空闲
波长概率增加了，但是波长对应的概率忙碌程度降低了。

更为正式的是，假设在时刻t ，蚂蚁访问节点i ，因此在下次t + 1路由条款是由
下面的公式决定的(记住,所有的相邻总概率总和是1):
()r r i
s i i
s i r r t δδ++=
+--11,1.1 （1） ()()1,11,,-≠+=+i n t t r i
s n i s n r r
δ （2）
Dynamic Routing and Wavelength Assignment in WDM Networks with Ant-Based Agents
Son-Hong Ngo, Xiaohong Jiang, Susumu Horiguchi, and
Minyi Guo
Graduate School of Information Science, Japan AdvancedInstitute of ScienceTechnology,
Japan
2 School of Computer Science and Engineering, The University of Aizu, Abstract
In this paper, we propose an ant-based algorithm for dynamic routing and wavelength assignment (RWA) in WDM optical networks under the wavelength continuity constraint. By adopting a new routing table structure and keeping a number of ants in the network to cooperatively explore the network states and continuously update the routing tables, our new ant algorithm can efficiently support the ants’ foraging tasks of route selection and wavelength assignment in WDM networks, and allow a connection to be setup promptly on arrival with a small setup time. Extensive simulation results based on the ns-2 network simulator indicate that the proposed algorithm can adapt well to traffic variations and achieves a lower blocking probability than the fixed routing algorithm.
1 Introduction
All optical networks that adopt wavelength-division-multiplexing (WDM) technology have a huge bandwidth capacity, and they show promise as the backbone of the next generation Internet. In all optical networks, data are routed in optical channels called lightpaths. The Routing and Wavelength Assignment (RWA) problem is how to determine both a route and wavelengths for a connection request. Without wavelength conversion capability, a lightpath must use the same wavelength on all the links along its route, which is referred to as the wavelength continuity constraint.
The RWA problem is usually classified as the static RWA problem and the dynamic RWA problem. In the static RWA problem, the connection requests are given in advance, and the problem becomes how to establish lightpaths for all these requests so that the total number of wavelengths is minimized. Static RWA has been proved to be an NP-complete
problem [1]. In the dynamic RWA problem, the traffic is dynamic with connection requests arriving randomly, making it more difficult. Heuristic algorithms are usually employed to resolve this problem. Generally, a dynamic RWA algorithm aims to minimize the total blocking probability in the entire network。

In our work, we focus on the dynamic RWA problem with wavelength continuity constraint. In the literature, the dynamic RWA problem is usually divided into two sub-problems that can be solved separately: routing and wavelength assignment. Routing schemes can be classified into fixed routing, fixed-alternate routing and adaptive routing.
In the fixed routing scheme, one route is dedicated for a sourcedestinationpair. Whenever a request occurs between this source-destination pair, this route is attempted for wavelength assignment. The fixed routing method is simple but usually causes a high blocking probability. The fixed-alternate routing method has better performance with multiple paths dedicated for a node pair. In the adaptive routing scheme, the route is computed at the time the connection request arrives, based on the current network state, thus it yields the best performance. However, adaptive routing requires high computational complexity. A more detailed survey of routing and wavelength assignment can be found in [2]。

The adaptive RWA solutions in the literature always need special support from control protocol to obtain the global state of the network. Moreover, heuristic algorithms that perform route and wavelength searching tasks after a request arrives must take into account the tradeoff between complexity and performance. This also contributes to high setup delay and control overhead. A possible approach to overcome these problems is the use of ant-based mobile agents [3]. The ant-based agent routing approach inherits advantages from both mobile agents behaviors and an ant colony system. Recent results show that this approach could yield efficient performance in the control of both circuit switching [4] and packet switching networks [5]。

In this paper, we investigate a new ant-based agent algorithm for the dynamic RWA problem in WDM networks under the constraint of wavelength continuity. Our study aims to reduce both blocking probability and path setup time by using a suitable amount of ants, which continuously perform path searching tasks before the connection request’s arrival so that the route selection and wavelength assignment of a request are performed by simply looking up the routing tables. To achieve that goal, we develop a new routing table structure, a scheme for ant population control and a mechanism for pheromone updating, for our new algorithm。

The rest of this paper is organized as follows: In section 2, we discuss related works. Section 3 presents our new approach to the dynamic RWA problem in WDM networks under wavelength continuity constraint. Section 4 describes our preliminary simulation and analysis results. Finally, our conclusions and future works are discussed in Section 5
2 Related Work
Recent research results show that the routing in communication networks can be resolved efficiently by means of Ant Colony Optimization (ACO) [3]. The routing solution can be built using ant-based agents behavior in their foraging of network states. These collective agents indirectly communicate through pheromone trailing (stigmergy) in the environment. By following the pheromone trail of another, an agent can find a “good” route in terms of shortest, least congested path from the source to the destination to route the network data. Two basic algorithms are ant-based control (ABC) for telephone networks, which was proposed by Schoonderwoerd et al. [4] and AntNet for packet switching networks, which was proposed by Di Caro et al. [5]. Some subsequent enhancement schemes to improve the ant-based routing performance include smart agents which use dynamic programming [9], reinforcement learning which enhances the ant’s adaptability to its environment [10], and a genetic algorithm which adapts the ant control parameters to the search process [11]. While the Dynamic Routing and Wavelength Assignment in WDM Networks 831 above research focuses on the routing problem in electronic communication networks, our interest in this paper is the dynamic RWA problem in WDM optical networks with the constraint of wavelength continuity.
Valera et al. [12] proposed an ACO approach for solving the static RWA problem. The goal is to minimize the number of wavelength requirement given a network topology and a traffic matrix. The wavelength assignment simply uses a greedy method that assigns the lowest available wavelength to each link. An ant’s route is selected based on the weight of attraction of each link. Each ant has its own pheromone that can be repulsed by others. Each ant keeps a “tabu” list of previously visited node for route backtracking and loop avoidance. The pheromone updating can use different methods; the best result of this approach is obtained through global update when the weight of attraction of ant for a path increases with the number of traversed ants. The result can be compared to the conventional Nagatsu heuristic [13], but it requires a much longer computational time. However, this approach cannot be applied directly to the dynamic RWA problem.
Garlick et al. [14] proposed an ACO-based algorithm to solve the dynamic RWA problem. When a new connection request arrives, a number of ants are launched from the
source to the destination. Ants evaluate a path based on its length and the mean available wavelengths along the path. Global pheromone updating is performed when an ant reaches its destination. The pheromone updating is on a per-demand basis: the network pheromone matrix is reset once a connection is established. The final best path for a connection request is made when all ants complete their exploitation tasks. The authors showed that this algorithm has better performance than an exhaustive search over all available wavelengths for the shortest path [15]. As a new set of ants must be launched for each new connection request, the setup delay will be very high due to the waiting for ants in large networks. In fact, this approach does not show the collective behavior of ants that come from different requests, which is an important aspect of ant-based systems。

3 Ant-Based RWA Algorithm
An optical WDM network is represented by a graph with N nodes and E links. We assume that each link is bi-directional with a capacity of W wavelengths and no nodes have a wavelength conversion capability (wavelength continuity constraint). In order to support the route selection by ants, each network node has a routing table with N–1 entry. In a node i with ki neighbors, the routing table has a ki column. Each entry corresponds to a destination node and each column corresponds to a neighbor node. The value r d n1.is used as the selection probability of neighbor node n when an ant is moving towards its destination node d. In order to support the wavelength assignment, we introduce the selection probability of each wavelength into the routing table. For each neighbor node, let p
be the probability that an ant selects the wavelength j when it moves to destination d.
j
An example of the new routing table when W=2 is shown in . When a connection request occurs between source node 1 and destination node 6, node 3 will be selected as next hop because r16.2< r16.3Wavelength 2 is preferred over wavelength 1 because P1 < P2 in that case.
Fig. 1. A network and its routing table from node 1
On a node, ants are launched with a given probability ρto a randomly selected
destination every T time units. Here ρ and T are design parameters. Each ant is considered to be a mobile agent: it collects information on its trip, performs routing table updating on visited nodes, and continues to move forward as illustrated in .
Ant launched Update pheromone Ant killed
Fig. 2. Ant’s moving and updating tasks
An ant moves from a source to a destination, node by node on a selected wavelength. Its next hop is determined stochastically: a neighbor is selected based on its selecting probabilities in the routing table. An ant is killed when it reaches its destination node or when it cannot select a free wavelength on the selected path for its next move. To avoid a “frozen” status in which all ants concentrate on one route (sta gnation), a random scheme is introduced: each ant selects its next hop randomly with an exploiting probability (p noise ). When a connection request arrives, the path will be determined based on the highest selection probability node among ne ighbor’s entries. The wavelength assignment is based on the wavelength selection probabilities from the routing table, or some others conventional heuristics can be used 。

Whenever an ant visits a node, it updates the routing table element with the information gathered during its trip. The principle of pheromone update is described as follows. Suppose an ant moves from source s to destination d following the path (s,…, i -1, i,…,d).When the ant arrives at node i, it will update the entry corresponding to the node s: the probability of neighbor i-1 is increased while the probabilities of others neighbors is decreased. For the last visited neighbor i-1, the probabilities corresponding to free wavelengths are increased, whereas the probabilities corresponding to busy wavelengths are decreased.
More formally, suppose that at time t, an ant visits node i, so the values for routing entry in next time t+1 are determined by the following formula
(remember that the sum of probabilities for all neighbors is always 1):
()r r i s i i s i r r
t δδ++=+--11,1.1 （1）
()()1,11,,-≠+=+i n t t r i s n i s n r r
δ （2）
As described in a previous work [9], smart agents can efficiently in improve the performance of ant-based routing systems. Based on the idea of smart agents, the pheromone updating will affect not only the entry corresponding to the source node, but also will affect all the entries corresponding to previous nodes along the path. In order to facility smart updating, an ant must push the information about visited nodes into its stack: node identification, a binary mask that determines the status of free wavelengths on the links it traversed (this mask has W bits corresponding to the number of wavelengths). This stack also serves for loop detection and backtracking, to ensure that ants will not move forever on the network 。

The reason for using a wavelength mask is that under wavelength continuity constraint, the number of free wavelengths (congested information) can be found exactly along a path; this will enhance the performance of the ACO approach. At each node, the wavelength mask is updated as below ：
link ant ant M M M ⊗= （3） and 7 guarantee that 11=∑=K W
k P (the normalization condition for wavelength
selection probability), and they also ensure that the amount of increased pheromone is proportional to the number of free wavelengths. For the wavelength assignment, we use a simple heuristic: the wavelength with the highest probability among the free wavelengths will be selected 。

Our algorithm is briefly described as follows:
{Ant generation}
Do
For each node in network
Select a random destination;
Launch ants to this destination with a probability
End for
Increase time by a time-step for ants’ generation
Until (end of simulation)
{Ant foraging}
For each ant from source s to destination d do (in parallel)
While current node i <> d
Update routing table elements
Push trip’s state into stack
If (found a next hop)
Move to next hop
Else
Kill ant
End if
End while
End for
{Routing and Wavelength Selection}
For each connection request do (in parallel)
Select a path with highest probability
Search a free wavelength with highest probability
If (found)
Setup a lightpath
Else
Consider a blocking case
End if
End for
4 Simulation Results and Analysis
An extensive experimental study based on Network Simulator ns-2 [16] has been performed to validate our new ant-based algorithm for RWA. As the original ns-2 supports packet switching, this feature was used to simulate the ants’ moves. We suppose that the control plane for optical WDM networks is implemented in an electronic netDynamic Routing and Wavelength Assignment in WDM Networks 835 work that has a same topology as the optical network. An optical routing module was added into ns-2 to simulate our RWA algorithm。

We used the fixed routing scheme with shortest path algorithm for performance comparison. All the tests were conducted based on the NSF network topology
with 14 nodes and 21 links as shown in , and W=8 and W=16 were considered in our experiments。

5 Conclusion and Future Works
In this paper, we have proposed an ant-based mobile agents approach to solving the routing and wavelength assignment problems in dynamic WDM networks. We developed a new routing table structure and also a way to adapt the routing table according to network state, using a suitable number of ants that continuously exploit the network. Our simulation shows that the new ant-based algorithm outperforms the fixedrouting algorithm using shortest path and First Fit wavelength assignment scheme. An advantage of this new algorithm is that the path for a connection request is determined。

Comparisons between new Ant-based algorithm and Fixed routing algorithm.
(a) Comparison results when W = 8. (b) Comparison results when W = 16。

Immediately on arrival, based on the adapting routing table, so the setup delay time is significantly reduced compared to the fixed routing scheme. Our new algorithm is very flexible in the sense that the number of ants in the network can be efficiently controlled by simply adjust the launching probability of ants to achieve the best performance。

In our future work, we will extend this algorithm by using a reinforcement learning approach such that others ACO control parameters could be automatically adjusted for a given network condition. The other heuristics for routing and wavelength assignment with wavelength conversion will also be investigated。

Acknowledgement. This research is partly supported by the Grand-In-Aid of scientific research (B) and , Japan Science Promotion Society。

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