On the Usage of Multiflow Transponders under Anycast and Unicast Traffic in Elastic Optical Networks Krzysztof Walkowiak1 and Mirosław Klinkowski2 1
Wroclaw University of Technology, Wybrzeże Wyspianskiego 27, 50-370 Wroclaw, Poland Email:
[email protected] 2 National Institute of Telecommunications, 1 Szachowa Street, 04-894 Warsaw, Poland
Abstract: The impact of anycast and unicast traffic on transponder usage in both symmetric and asymmetric lightpath provisioning scenarios in Elastic Optical Networks is studied. Acceptable costs of multiflow transponders, with respect to 100G WDM transponders, are evaluated.1 OCIS codes: (060.0060) Fiber optics and optical communications; (060.4250) Networks.
1. Introduction The multiflow transponder (MFT) has been recently proposed as a solution for efficient transport of IP traffic in Elastic Optical Networks (EONs) [1]. A single MFT is able to support a number of aggregated traffic flows by provisioning of multiple optical paths (lightpaths) for carrying these flows over an EON and by sharing flexibly the transponder processing and transmission capacity between the transported flows. Anycasting is defined as one-to-one of many transmission, in which one of the nodes of the demand is fixed while the second node can be chosen among several alternative nodes [2]. The use of anycast transmission in current networks is a consequence of an increasing interest on services like cloud computing, content delivery networks (CDNs), grids, distributed storage, VoD, etc. In all enumerated systems, a single request can be served by various data centers (DCs) available in various parts of the network. These new services lead to asymmetric traffic patterns in a backbone network [3]. As an example, in a CDN system, for the downstream connection from a content server to a client node, we have large flows of aggregated traffic related to content downloads/streaming. Meanwhile, the upstream traffic flow is relatively small and includes mostly requests to browse the content. Apart from that, the use of anycasting will produce more varying and less predictable patterns of traffic, both in time and geographical domains [4]. As a result, a common assumption regarding symmetric traffic demands in network planning and operation may be costly and not efficient in the new service context [3-4]. Aiming at efficient support of asymmetric traffic demands in EONs, we investigate three different models of lightpath provisioning, namely: two symmetric models and an asymmetric model (the details are presented in Sec. 2). The comparison between the models, presented in Sec. 3, concerns the number of MFTs required to support given anycast and unicast traffic demands. In this regards, both random and realistic traffic patterns, based on Cisco predictions, are assumed. Eventually, considering different levels of the downstream/upstream traffic asymmetry, we study acceptable costs of MFTs with respect to single-flow WDM transponders. 2. Network scenarios and models We assume that demands in the network are established in a bidirectional way, i.e., for each node pair (v,w) there is an upstream demand from v to w and a downstream demand from w to v – for ease of reference we call such two demands associated. For each unidirectional demand d between v and w a separate lightpath must be provisioned, i.e., at the origin node v the outgoing flow of the demand must be assigned to one of transponders located at node v and at the destination node w the incoming flow of demand d must be assigned to one of transponders available at node w. Three different lightpath provisioning models in terms of MFTs assignment are studied: • fully-symmetric (SYM_C) – the same transponder is used to serve both upstream and downstream demand, the requested capacity (bit-rate) of both demands is equal to the largest value, i.e., h = max(hDown,hUp); • semi-symmetric (SYM_T) – the same transponder is used to serve associated upstream/downstream demands; • asymmetric (ASYM) – full flexibility in the assignment of demands to transponders is available, i.e., at a particular node two different transponders can be selected to serve incoming and outgoing flows of the two associated upstream and downstream demands and each flow may have different bit-rate. Moreover, the demands can be of two types: unicast and anycast. In the former case both end nodes of the demand are fixed. In the latter case one of the end nodes (client node) is fixed while the second node (server) is to be 1
This work was supported by the Polish National Science Centre (NCN) under Grant DEC-2012/07/B/ST7/01215 and by the European Commission under the 7th Framework Programme, Coordination and Support Action, Grant Agreement Number 316097, ENGINE - European research centre of Network intelliGence for INnovation Enhancement.The work of Mirosław Klinkowski was supported in part by EC under the FP7 project IDEALIST, Grant Agreement Number 317999.
selected among nodes hosting DCs. Consequently, for anycast traffic, the assignment of the demand to a transponder is more flexible, as for the server node we can consider transponders available at each DC node. Fig. 1 illustrates the considered lightpath provisioning models in EONs with MFTs. Note that in the case of WDM transponders only the SYM_C scenario (i.e., the conventional one) is considered in our paper. Demand d1, h=max(h1,h2) T(1,1)
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(a) (b) (c) (d) Fig. 1. Lightpath provisioning scenarios: a) unicast SYM_C, b) unicast SYM_T, c) unicast ASYM, and d) anycast ASYM.
To objective of our work is to minimize the number of required transponders required to serve all demands. Therefore, for each of considered scenarios an ILP (Integer Linear Programing) model was devised. The below presented ILP model refers to the ASYM case, however, models for SYM_T and SYM_C are straightforward. Model minimize F = ∑t∈T zt ∑t∈Td- x-dt = 1, d∈D ∑t∈Td+ x+dt = 1, d∈D ∑d∈Dt- x-dthd ≤ ct, t∈T ∑d∈Dt+ x+dthd ≤ ct, t∈T ∑d∈Dt- x-dt ≤ ntzt, t∈T ∑d∈Dt+ x+dt ≤ ntzt, t∈T ∑t∈Td xdtv(t) = ∑t∈Tτ(d)+ x+τ(d)tv(t), d∈DDown
zt = 1, if MFT t is used in the network; 0 (1) otherwise (2) Constants (3) nt maximum number of flows allocated to MFT t (4) (5) ct capacity of MFT t (in Gbit/s) (6) hd capacity of demand d (in Gbit/s) (7) τ(d) anycast demand associated with d (8) v(t) index of a node with MFT t Sets Variables V network nodes + x dt = 1, if incoming flow of demand d is allocated T transponders (MFTs) to MFT t; 0 otherwise D demands (both unicast and anycast) x-dt = 1, if outgoing flow of demand d is allocated R network nodes with data centers (DCs) DDown anycast downstream demands to MFT t; 0 otherwise
Dt+ set of demands that can use MFT t to serve the demand incoming flow Dt- set of demands that can use MFT t to serve the outgoing flow Td+ set of MFTs feasible for incoming flow of demand d. For unicast and anycast downstream demands, Td+ includes all MFTs located at the destination node of d. If d is an anycast upstream demand, Tdin includes DCs nodes feasible for d Td- set of MFTs feasible for outgoing flow of demand d. For unicast and anycast upstream demands, Tdout includes all MFTs located at the destination node of d. If d is an anycast downstream demand, set Tdout includes DCs nodes feasible for d
3. Performance evaluation To obtain the results, ILP models were implemented in CPLEX 12.5.1. For experiments, we use the European Nobel-EU network that contains 28 nodes and 82 directed links. Data centers location is selected according to data available at http://www.datacentermap.com/. We assume the following number of DCs nodes 3, 5, 7, 9, and 11. We evaluate two scenarios: a) with random traffic demands (RND), and b) with full-mesh (FM) connectivity. In the RND scenario, traffic demands are generated between randomly selected nodes in the network. The capacity (bit-rate) of demands is also selected at random: for unicast the range is 10-200 Gbit/s, for anycast the range is 10-400 Gbit/s. The overall traffic in the network is 20 Tbit/s. In order to examine the influence of anycast traffic, we generate various traffic patterns in terms of an anycast ratio (AR) defined as the volume of anycast traffic divided by the overall network traffic. We test values of AR equal to 20%, 40%, and 60%. Another parameter that is applied in the experiments is an average asymmetry (AA) that shows for a particular demand set the average asymmetry between upstream and downstream demands. The asymmetry of one pair of associated demands is defined as max(hDown,hUp) / min(hDown,hUp) and the AA parameter is the average value over all demands. The AA parameter is used to show the potential gains of using the ASYM lightpath provisioning scenario. In the FM scenario to generate the full mesh traffic pattern, we use the same approach as in [5]. In more detail, the traffic pattern is calculated under “Cisco Visual Networking Index” and “Cisco Global Cloud Index” reports for years 2011-2016 assuming overall traffic volume of 10 Tbit/s for year 2011. Anycast demands correspond to the data center to user traffic. In this case, we assume full symmetry of the traffic (i.e., AA = 1). The maximum number of flows allocated to a MFT (parameter nt in the ILP model) is set to 4, 8 or 16 and the corresponding results are denoted as MF(4), MF(8), and MF(16). The MFT capacity is 400 Gbit/s. As a reference result we apply the number of 100Gbit/s (100G) WDM transponders required to serve the same set of demands as in the case of MFTs. The main performance metric reported in figures is the maximum acceptable cost of a MFT given in cost units (c.u.) defined as a cost of one 100G transponder [6]. For instance, value 5.0 c.u. means that if the cost of a MFT is lower than 5, then it is more profitable to use MFTs than the WDM hardware. Note that the WDM transponder supports a single data flow.
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In Fig. 2, we present the maximum acceptable cost given in uniform cost units (cost of one 100G transponder) as a function of average asymmetry. Figs. 2a and 2b present detailed data obtained for AR=20% and 40% for MF(4) and MF(16). Fig. 2c shows the average results for each lightpath provisioning model – each point on the figure is an average over 9 experiments (for different AR and nt). The main trend we can observe is that expanding the flexibility of lightpath provisioning increases the maximum acceptable cost, i.e., considering all 90 experiments, ASYM and SYM_T provides on average 22.5% and 20.3% gain comparing to SYM_C, respectively. Moreover, we can see that with the increase of the asymmetry, the maximum acceptable cost of ASYM and SYM_T also grows. 7.0 6.5 SYM_C
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(a) (b) (c) Fig. 2. Maximum acceptable cost in cost units (c.u.) of MFTs for RND scenario: (a) AR=20%, (b) AR=40%, (c) average results.
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In Fig. 3, we report the results obtained for the full-mesh scenario with a traffic pattern calculated according to Cisco predictions. In Figs. 3a and 3b, we show the maximum acceptable cost given in uniform cost units (cost of one 100G transponder) for consecutive years and for different number of DCs, respectively. Moreover, in Fig. 3c we present the average number of transponders required for each MF scenario and different WDM scenarios (with 400G, 100G, 40G, and 10G transponders). The average (over all cases) value of maximum acceptable cost is 4.50, 8.23, and 13.18 for MF(4), MF(8), and MF(16), respectively. According to Fig. 3a, the maximum acceptable cost of MF(4) and MF(8) increases with subsequent years, while in the case of MF(16) the trend is the opposite. This can be explained as follows. First, notice that the number of transponders in the WDM approach changes in a very little extent for subsequent years (see Fig. 3c), since the number of needed transponders mostly depends on the number of demands and only when the demand exceeds 100 Gbit/s additional transponders are needed for WDM 100G. On the other hand, for MFTs, the number of transponders grows quickly, since in subsequent years the traffic grows by about 30% , what in consequence increases the number of needed transponders. The increase of transponder number is the largest for MF(16) (see Fig. 3c). In the case of the number of DCs (Fig. 3b), for each MF scenario the maximum acceptable cost grows with the increase of DCs nodes. This follows from the fact that the MF approach enables more flexible allocation of anycast demands comparing to the (single-flow) WDM approach. DCs MF(4) MF(8) MF(16)
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(a) (b) (c) Fig. 3. Maximum acceptable cost in cost units (c.u.) of MFTs for FM scenario: (a) with 7 DCs in consecutive years, (b) average over various years as a function of DCs number (c) average number of transponders in consecutive years.
4. Conclusions In this paper, we investigated different lightpath provisioning scenarios with the use of multiflow transponders and under anycast and unicast traffic demands. The main findings from our work are that the asymmetric lightpath provisioning model brings about 20% of savings in the MFT usage and it is still profitable to have a 400G MFT supporting 16 traffic flows with its cost exceeding one order of magnitude of the cost of a 100G WDM transponder. 5. References [1] M. Jinno et al., "Multiflow Optical Transponder for Efficient Multilayer Optical Networking," Comm. Mag. 50(5), 56-65, (2012). [2] K. Walkowiak, „Anycasting in connection-oriented computer networks: models, algorithms and results,” Int. J. of Appl. Math. and Comp. Sci., 20(1), 207-220, (2010). [3] E. Palkopoulou et al., "Traffic Models for Future Backbone Networks - A Service-Oriented Approach," Eur. Trans. on Telecomm., 22(4), 137-150, (2011). [4] L.M. Contreras et al., "Toward cloud-ready transport networks," IEEE Comm. Mag., 50(9), pp. 48-55, (2012). [5] M. Klinkowski and K. Walkowiak, „On Advantages of Elastic Optical Networks for Provisioning of Cloud Computing Traffic,” IEEE Network, in press, (2013). [6] J. Vizcaino et al., "Cost evaluation for flexible-grid optical networks," in Proc. Globecom Workshops, 358-363, (2012).
