Dynamic Routing of Anycast and Unicast Traffic in Elastic Optical Networks Krzysztof Walkowiak, Andrzej Kasprzak Department of Systems and Computer Networks Wroclaw University of Technology, Poland
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Mirosław Klinkowski National Institute of Telecommunications Warsaw, Poland
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Abstract—In this paper, we focus on issues related to dynamic routing of anycast and unicast traffic demands in Elastic Optical Networks (EONs). The growing interest in cloud computing and content-oriented services triggers the need to examine anycast traffic in the context of the EON concept, which is perceived as a promising approach for future optical transport networks. Anycasting – defined as one-to-one of many transmission – ideally corresponds to concepts of cloud computing and contentoriented services, where the same service/content is available in many, geographically spread data centers. In the paper, we propose two types of algorithms for dynamic routing of anycast and unicast requests. The former algorithm is based on the shortest path first (SPF) method, while the latter algorithm is based on the selection of a routing path from a set of candidate paths (CP). To examine performance of the algorithms, we run simulations using a real pan-European topology and traffic patterns created according to Cisco predictions. The results show a tradeoff between SPF and CP methods in terms of the blocking probability and execution time. Moreover, we observe that the blocking probability depends on the number of data centers placed in the network. Keywords—elastic optical networks, dynamic anycasting, cloud computing, content-oriented networks
I.
routing,
INTRODUCTION
The concept of EONs, proposed initially in [1] as a SLICE architecture, is a very promising solution for efficient and flexible bandwidth provisioning in future optical transport networks. EONs allow to allocate flexibly appropriate-sized optical bandwidth, by means of contiguous concatenation of optical spectrum, to an end-to-end optical path (lightpath) according to traffic demand. The key elements of EON architectures are: 1) bandwidth-variable transponders (BV-T), making use of advanced modulation formats and techniques, both single-carrier (such as k-PSK, k-QAM) and multi-carrier (such as O-OFDM), and able to transmit the client signals with just required frequency resources, 2) bandwidth variable wavelength cross-connects (BV-WXCs), able to switch transmitted signals within their frequency bandwidth, and 3) flexible frequency grids, which allow for elastic allocation of spectrum in fiber links. For more details and experimental results concerning EON we refer to [1], [2], and [3]. Anycasting can be defined as one-to-one of many transmission where one of the end nodes of the request is fixed
and the second one is to be selected among several alternative nodes [4]. The motivation behind using anycast transmission in current networks is a growing popularity of services like cloud computing, content delivery networks (CDNs), distributed computing, grids, distributed storage systems, etc. In all mentioned systems, the service request of an individual client can be provisioned by various data centers or individual servers spread geographically over the network, and thus the client can be served by any of available sites. In a consequence, a client node requesting a connection for carrying the required data can be connected to and served by any of available sites. EON architectures are considered as one of technological pillars for building effective and cost-efficient cloud- and anycast-ready transport networks due to their elastic and ondemand bandwidth connectivity, which are essential for new services [5]. The virtualization technology with computation and storage resources spread across several data centers connected to the network, the use of migration mechanisms for distribution of load among data centers, and use of anycasting will produce more varying and less predictable patterns of traffic, both in time and geographical domains [5], [6]. In this new context, the conventional approach based on provisioning of permanent lightpath connections operating at fixed bandwidth rates and with bandwidth over-provisioning for accommodating temporal peaks of traffic may be costly and not efficient. Recently, bandwidth adaptation of lightpaths has been proposed for time-varying demands [7]. Dynamic lightpath provisioning is another solution for supporting varying traffic demands in an efficient way. The algorithms for lightpaths provisioning should take into account the character of traffic and, in particular, adequate routing decisions should be taken when aggregated anycast traffic demands, related to a given type of content or service, are served. In this case, such demands should be served by a convenient data center, presumably by the nearest one, under the constraint that the connection can be established in the network. The main contribution of the paper is twofold. First, we propose dynamic routing algorithms for provisioning of lightpaths which will carry aggregated anycast demands in EONs. Second, we report detailed results to show the tradeoffs between shortest path first and candidate paths routing approaches in terms of various performance metrics including
bandwidth blocking probability, spectrum utilization, path length, and algorithm running time. To the best of our knowledge, this is the first work that addresses the problem of dynamic routing of anycast demands in EONs.
network a large number of users use given type of service (e.g., watch youtube videos) and the aggregated traffic is carried by lightpath connections which have to be established over the optical transport network.
The remainder of the paper is organized as follows. In Section II, we discuss dynamic routing of anycast traffic in EONs. Section III includes description of proposed algorithms. In Sections IV and V we present simulation setup and results of experiments, respectively. Finally, in last Section we conclude the work.
Anycast routing brings significant savings in terms of spectrum utilization if BV-Ts are capable of the adaptation of modulation levels [15]. Indeed, since the transmission on shorter paths can be performed with higher modulation, thus requiring less spectrum, the lightpath requests for anycast demands may be directed towards nearer data centers thus allocating less spectrum resources to lightpaths. Note that when anycasting is used the destination node for a connection is not determined, in fact it has to be selected from a set of candidate nodes, and consequently a dedicated algorithm have to be applied in order to solve RMLSA. Although there is a number of works on dynamic lightpath provisioning in EON they concern serving unicast demands and, to the best of our knowledge, the problem of dynamic anycast routing has not be addressed so far in EON.
II.
DYNAMIC ANYCAST ROUTING IN EON
Dynamic (online) routing algorithms are run during network operation and routing decisions are taken online, almost immediately (in some milliseconds), based on the current resource availability. In EON, dynamic routing concerns establishment of lightpaths for individual connection requests. The process is accompanied by solving the problem of Routing and Spectrum Allocation (RSA), which concerns finding a routing path and a contiguous segment of spectrum subject to the constraint of no frequency overlapping (with optical paths of other connections) in network links [8], [9]. Whenever a BV-T allows for the use of different modulation formats, RSA converts to the problem of Routing, Modulation Level, and Spectrum Allocation (RMLSA) [9]. The idea of dynamic routing of anycast requests was first introduced in [10] in the context of content delivery networks and Multiprotocol Label Switching (MPLS) protocol. However, the same idea can be used in EONs if demands of aggregated anycast traffic have to be served. By aggregated traffic we mean the traffic related to a given type of content or service (i.e., which can be served by the same data center or server) and originated in a given node of the transport network. In this context, we assume that a request to establish a lightpath carrying anycast traffic is defined by a triple: 1) source node, where the aggregation of anycast traffic to be carried through the transport network is performed, 2) bandwidth capacity (bitrate) in the downstream direction (from the server/data center node to the source node), and 3) bandwidth capacity (bitrate) in the upstream direction (from the source to the server). Thus, each anycast request includes two associated lightpath requests: downstream and upstream that must be established at the same time and they must use the same server node. The main difference between unicast and anycast dynamic requests is that to serve the anycast request– besides the routing paths and optical spectrum in both directions – also the server node must be selected. A good example where anycasting is present is cloud computing, where data centers (DCs) connected to various network nodes provide a wide range of services. Users can use any of the DCs what is currently enabled by migration capabilities following from virtualization, sharing of computing resources, and on-demand assigning or reassigning of virtual resources to applications [11], [12]. Another example is a content delivery network (CDN), where geographically spread CDN servers provide the same content and anycasting allows network users to download the data from most convenient servers [13], [14]. Note that in a given
III.
ALGORITHMS
In this Section, we present algorithms for dynamic lightpath provisioning for both unicast and anycast demands in EONs. Two approaches are studied: 1) based on the shortest path first (SPF) method (e.g., Dijkstra algorithm) which finds online the routing path for an arriving request; 2) based on the selection of routing path from a set of candidate paths (CP), which are pre-computed using a k-shortest path algorithm. Apart from route selection, both algorithms perform modulation level selection and spectrum allocation. The major goal of algorithms presented below is to minimize the requested bit-rate blocking rate expressed as the ratio of volume of rejected traffic divided by the overall volume of traffic offered to the network. To obtain this goal we construct the algorithms in order to satisfy two minor goals: (i) select a routing path as short as possible, and (ii) allocate the request to the lowest possible slice index. The first goal follows from the fact that the shorter routing path the more effective modulation format can be used and, in consequence, less spectrum resources are consumed. By the second goal, we want to reduce spectrum fragmentation and leave as much as possible of free spectrum for future requests. A. Notation We use similar notation as in [16]. The physical network is modeled as graph G(V,E,B,L) where V denotes a set of nodes, E is a set of fiber links, each fiber link can accommodate B frequency slices (slots) at most, and L = [l(1),l(2),…,l(|E|)] represents link lengths for each e ∈ E. We make an assumption that there are R data centers already located at some nodes of the network, i.e., in this work we do not address the DCs location problem. For the sake of simplicity, we assume that the network node to which the DC is connected to is equivalent with this node, what means that we do not consider the physical connection between the server and the backbone network node. Furthermore, we assume that all DCs can provide the same requested service/content. Thus, each anycast request can be assigned to any of the DCs.
Each request d can be of two types: unicast (one-to-one) and anycast (one-to-one of many). The unicast request is described by a triple: source node s(d), destination node t(d) and capacity (bitrate) c(d). The anycast request is denoted by a following triple: source (client) node s(d), downstream capacity c_down (d) and upstream capacity c_up(d). Let l(p) = Σe∈p l(e) denote the length of path p calculated as the sum of link lengths included in the path. Let P(s, t, k) denote a set of k-shortest paths for node pair (s, t). Notice that in the case of a unicast request, the set of candidate paths include exactly k paths. Concerning anycast requests, the situation is different, i.e., for each downstream (upstream) request the set of candidate paths contains of k|R| paths, since each DC node r ∈ R is considered. For instance, in the case of a downstream request d the set of candidate paths P_down(d, k) will include all paths from sets P(r, t, k) for each r ∈ R. For the sake of simplicity, we assume that paths included in each set P(s, t ,k), P_down(d, k), and P_up(d, k) are sorted according to increasing values of the path length. We assume that various modulation formats can be used in the EON. Let M denote a set of available modulation formats. For each modulation format m ∈ M we are given constant dist(m) that denotes the maximum distance that a particular modulation can support. Moreover, let n(c(d),m) denote the number of slices required to serve request of bit rate c(d) using modulation m. Without loss of generality we assume that the higher modulation format is, the higher spectral efficiency is achieved leading to lower spectrum demand, however, at the cost of shorter transmission distance (limited by dist(m)). Since in optical networks regenerators are costly, we assume that the selection of a modulation format is made in order to minimize the number of regenerators placed in the network. In particular, we compare the path distance l(p) and maximum range dist(m) of each modulation m ∈ M to find the assignment that minimizes the number of regenerators and, in the second place, maximizes the efficiency of the modulation format. Consequently, the selection of a particular modulation format for request d using path p follows directly from the path length. Let m(p) denote the modulation format selected in such a way for path p (according to the path length l(p)). B. Shortest Path First Algorithm 1 shows the pseudocode of the SPF method for a unicast request. In more detail, in line 1 the spectrum usage is calculated according to the current allocation of requests (following from all accepted requests at a given time). Next, in the main loop (lines 2-11) all modulation formats are checked, starting from the largest one that according to our assumptions provides the lowest requirement of slices but also the smallest maximum distance that the modulation supports. In line 3, the number of required slices is calculated according to the volume (bitrate) of demand d and the current modulation format m. Next, in lines 4-10, for subsequent values of the slice index b we create a virtual topology in the following way (line 5). If on a particular link e, a set of n contiguous slices starting from b are not occupied by other requests, the link metric is set as the link length l(e). Otherwise, the link metric is set to the value of infinity. In such a topology, the shortest path algorithm is run to find a path between end nodes of the
request (line 6). If a path is found and the path length is within the transmission distance of the considered modulation m (i.e., (l(p)
