WO2016155974A1

WO2016155974A1 - Stability- and capacity-aware time-dependent routing in transport networks

Info

Publication number: WO2016155974A1
Application number: PCT/EP2016/054677
Authority: WO
Inventors: Johannes LESSMANN; Yong Cheng
Original assignee: Nec Europe Ltd.
Priority date: 2015-03-31
Filing date: 2016-03-04
Publication date: 2016-10-06

Abstract

A method for time-dependent routing in a transport network (3), the method comprising: calculating traffic demands for each network link and for different time slots based on historic data and/or prediction, leasing network link capacity from an infrastructure operator for each network link and for each of said different time slots, a paths computation engine (9) determining routing paths between the network nodes (4) of said transport network (3) for each traffic demand in each of said time slots and provisioning said routing paths into the network nodes (4), wherein said paths computation engine (9) determines said routing paths in such a way that, while taking into account the existing capacity leasing granularity and keeping the stability of resource consumption of each network link across different of said time slots within configurable boundaries, the total leased network link capacity of all time slots is minimized.

Description

STABILITY- AND CAPACITY-AWARE TIME-DEPENDENT ROUTING

IN TRANSPORT NETWORKS

The present invention relates to a method and a systenn for time-dependent routing in a transport network.

Traffic demands in transport networks (e.g., mobile backhaul networks) are time- varying and, typically, the traffic load variations are reappearing following certain patterns. For example, as shown in Fig. 1 , the traffic demands of cellular backhaul networks in residential areas (Fig. 1 a) and in commercial areas (Fig. 1 b) change over time during one typical working day and this variation is usually repeating following the same pattern on every working day (for reference see, for instance, Juha Salmelin, and Esa Metsala, "Mobile Backhaul," John Wiley & Sons, May, 2012).

Such time-varying traffic demands can be characterized with multiple traffic matrices of different time slots during one day (for reference see, for instance, Fabio Ricciato, Ugo Monaco: "Routing demands with time-varying bandwidth profiles on a MPLS network", in Computer Networks 47(1 ): 47-61 , 2005). The multiple traffic matrices can be obtained based on historic data and/or traffic prediction. Fig. 2 shows an exemplary transport network topology with six routers, R1 , R2, and R6. Tab. I and Tab. II show the traffic matrix (i.e., traffic demands) between the six routers in Fig. 2 in the 7-th time slot, with high quality-of-service (QoS) class traffic and low QoS class traffic, respectively. For instance, the high QoS class traffic demand from Router R1 to Router R3 in the /7-th time slot is 15 Mbps, as listed in Tab. I. Generally, more detailed traffic/service differentiation and more QoS classes are defined in practice.

Tab. I Example of traffic matrix in n-th time slot - High QoS class traffic

Tab. II Example of traffic matrix in /7-th time slot - Low QoS class traffic

To improve link utilization and network throughput, time-dependent routing schemes are exploited to accommodate the multiple traffic matrices of different time slots in transport networks. Because of the time-varying traffic demands (i.e., time-dependent traffic matrices as shown in Tab. I and Tab. II), the resource consumption of a network link also varies across time slots in time-dependent routing. For example, Fig. 3 shows that the link capacity has to be upgraded at a time slot boundary to accommodate additional traffic in the next time slot. Further, it is possible to downgrade the (leased) capacity of a link in a given time slot when the traffic load is lower in that time slot, as shown in Fig. 4.

When leasing network resources from an infrastructure operator, the capacity- and time granularity of link capacity leasing may be restricted to finite stepwise discrete options (for reference, see for instance Sofie Verbrugge, Didier Colle, Mario Pickavet, and Piet Demeester: "Cost Versus Flexibility of Different Capacity Leasing Approaches on the Optical Network Layer", Optical Network Design and Modeling, Lecture Notes in Computer Science, Vol. 4534, pp. 418-427, 2007; R.G. Prinz: "Leasing cost optimizations for networks with on-demand leased lines", Proc. of the 6th International Conference on Information, Communications Signal Processing (ICICSO7), Dec, 2007; or Arie M.C.A. Koster, and Xavier Muoz, Chapter 8: Optimization of OSPF Routing in IP Networks", in Graphs and Algorithms in Communication Networks: Studies in Broadband, Optical, Wireless and Ad Hoc Networks, Springer, Mar., 2009). For example, the capacity leasing granularity may be in tens of Gbps, as illustrated in Fig. 5, and thus the corresponding charging policy is a stepwise incremental function of the leased capacity. This means that upgrading a leased link capacity (by leasing more capacity from an infrastructure operator) and downgrading a leased link capacity (by de-leasing some capacity from an infrastructure operator) can only be carried out with a stepwise constant value. In addition, the time granularity of network resource leasing may vary from seconds (in the future) over minutes to months. For instance, a small amount of link capacity may be leased and de-leased for a time period in the order of minutes, while significant capacity upgrading may take days or even months due to new hardware deployment. Thus, it can be desirable to keep the resource requirements of each network link below critical capacity values in time-dependent routing so that the need for large link capacity upgrading is avoided. However, the existing time-dependent routing schemes did not consider the stability of resource consumption of each network link, nor the stepwise increments of leasing cost.

In view of the above it is an object of the present invention to improve and further develop a method and a system for time-dependent routing in a transport network in such a way that the routing is adapted as far as possible to a restriction of capacity- and time granularity of link capacity leasing to finite stepwise discrete options.

In accordance with the invention, the aforementioned object is accomplished by a method for time-dependent routing in a transport network, the method comprising: calculating traffic demands for each network link and for different time slots based on historic data and/or prediction,

leasing network link capacity from an infrastructure operator for each network link and for each of said different time slots,

a paths computation engine determining routing paths between the network nodes of said transport network for each traffic demand in each of said time slots and provisioning said routing paths into the network nodes,

wherein said paths computation engine determines said routing paths in such a way that, while taking into account the existing capacity leasing granularity and keeping the stability of resource consumption of each network link across different of said time slots within configurable boundaries, the total leased network link capacity of all time slots is minimized.

Furthermore, the above mentioned objective is accomplished by a system for time- dependent routing in a transport network, the system comprising:

a traffic database that contains traffic demands for each network link and for different time slots based on historic data and/or prediction,

a resource leasing database containing information on network link capacity leased from an infrastructure operator for each network link and for each of said different time slots, and

a paths computation engine that is configured to determine routing paths between the network nodes of said transport network for each traffic demand in each of said time slots in such a way that, while taking into account the existing capacity leasing granularity and keeping the stability of resource consumption of each network link across different of said time slots within configurable boundaries, the total leased network link capacity of all time slots is minimized.

The present invention proposes a method and a system for stability- and capacity- aware routing in transport networks that minimizes the total leased network link capacity across all time slots, thereby also minimizing the required leasing cost, and at the same time maximizes the utilization of available network resources. Embodiments of the invention assume that the dynamic traffic demands of a transport network can be obtained from historic data and/or can be estimated, and that the leasing and de-leasing of link capacity can be realized on a short timescale (e.g., within minutes, hours or - at least - days). Based thereupon, embodiments of the invention relate to determining capacity demands in a way that aggregate capacity in each link does not vary faster (i.e. the selected time slots are not shorter) than the time granularity for (de-) leasing capacity from the infrastructure operator allows. Furthermore, embodiments consider possible link capacity upgrading and downgrading in each time slot with stepwise discrete capacity and cost values of link capacity leasing, while computing an (optimal) route for each traffic demand in each time slot with the help of the link capacity leasing information, i.e. taking capacity- and time-granularity into account. According to embodiments of the present invention the resource consumption of each network link is kept as stable across different time slots as necessary in order to acknowledge the time granularity with which lease up- and downgrades can be made, while also taking into account the stepwise incremental cost associated with leasing link capacity from infrastructure operators.

Generally, the present invention can be applied in scenarios where the capacity- and time granularity of a leased network link capacity offered by an infrastructure operator is confined within a few stepwise constant values and where the operator/infrastructure owner offers dynamic capacity lease. To accommodate the limited options of time granularity of leased link capacity, routing schemes in accordance with the embodiment of the present invention keep resource consumption of each network link as stable across different time slots as necessary to avoid over-large (i.e., impossible at that speed) link capacity upgrading.

Further, leased links are normally provided and charged according to stepwise incremental capacity values, as in the example shown in Fig. 5. Routing schemes in accordance with embodiments of the present invention take into account the stepwise incremental values of leased link capacity to facilitate link capacity downgrading and to concentrate link capacity upgrading to as few network links as possible. Embodiments of the invention consider a stability- and capacity-aware routing scheme to minimize the overall leased network link capacity in all time slots. It is noted that in the context of the present invention "stability" refers to the network resource consumption of each network link being kept sufficiently stable across different time slots, and "cost" relates to the stepwise incremental cost associated with leasing stepwise constant link capacity.

According to an embodiment the computed routes are provisioned into network equipment directly, for instance by the Network Management System, NMS, or via a southbound controller interface of a centralized management component that is in charge of routing paths calculation. The network equipment of the transport network, to which the computed routing paths are provision, may include switches, routers and/or gateways.

According to an embodiment the traffic demands may be provided in form of traffic demand matrices for each network link and for different time slots. The traffic demand may be categorized and determined for different QoS classes, i.e. a traffic matrix is generated for each time slot and for each QoS class. The traffic matrices may be stored in a traffic demand database, from which the path computation engine may retrieve the traffic demand information, as required.

According to an embodiment the paths computation engine may collect capacity- and time-granularity information of network resource leasing from a network resource leasing database. This information may include the stepwise incremental function of the leased capacity, which means that upgrading a leased link capacity (by leasing more capacity from an infrastructure operator) and downgrading a leased link capacity (by de-leasing some capacity from an infrastructure operator) can only be carried out with a stepwise constant value, as well as the associated stepwise incremental leasing costs. This information may be stored in a resource leasing database, from which the paths computation engine may retrieve the respective information, as required, and take this information into account, when calculating the routing paths. According to an embodiment the minimization of the total leased network capacity over all time slots may be achieved by means of integer linear programming, ILP. In this context it may be provided that a function representing the total leased network capacity over all time slots is formulated as the objective function of an ILP problem. The ILP problem may be solved by means of applying LP_SOLVE and/or the GNU Linear Programming Kit, GLPK.

According to an embodiment the leased capacity of a network link may be downgraded (i.e. by de-leasing some capacity to an infrastructure operator), when the network link is under-utilized in a time slot, thereby reducing wasted link capacity. Similarly, the leased capacity of a network link may be upgraded (by leasing more capacity from the infrastructure operator), when said network link is overloaded in a time slot, in order to accommodate the excess traffic. As required, upgrading/downgrading of the link capacity of network links based on the computed routing paths can be carried out at the time slot boundaries (or sufficiently ahead of the time slot boundaries).

According to an embodiment the routing scheme applied by the paths computation engine for determining the routing paths, in order to minimize the total leased network link capacity, may concentrate network link capacity upgrading to as few network links as possible, and as small as possible under the given time- and capacity granularity constraints. There are several ways how to design and further develop the teaching of the present invention in an advantageous way. To this end it is to be referred to the dependent patent claims on the one hand and to the following explanation of preferred embodiments of the invention by way of example, illustrated by the drawing on the other hand. In connection with the explanation of the preferred embodiments of the invention by the aid of the drawing, generally preferred embodiments and further developments of the teaching will be explained. In the drawing

Fig. 1 is a diagram illustrating time-varying traffic demands in cellular backhaul networks,

Fig. 2 is a schematic view illustrating an example of time-varying traffic demands in cellular backhaul networks, Fig. 3 is a schematic view illustrating an example of link capacity upgrading to accommodate access traffic, is a schematic view illustrating an example of link capacity downgrading to save wasted link capacity, is a schematic view illustrating an example of stepwise incremental leasing cost of leased capacity, is a schematic view illustrating the basic architecture of a system in accordance with embodiments of the present invention, is a diagram illustrating components of a system for performing stability- and capacity-aware time-dependent routing in a transport network in accordance with an embodiment of the present invention, and is a diagram illustrating method steps for performing stability- and capacity-aware time-dependent routing in a transport network in accordance with an embodiment of the present invention.

A system architecture in accordance with an embodiment of the present invention is depicted in Fig. 6. An integral part of the system according to this embodiment is a centralized management component 1 , for instance a Backhaul Resource Manager, BRM. The management component 1 includes a topology database 2 storing the topology of a transport network 3, which comprises a number of network nodes (switches and/or routers) 4 with routing path established between them. The management component 1 further comprises a path database 5 storing the currently allocated routing paths, as well as traffic matrices 6 indicating traffic demands (potentially differentiated by QoS class) in each time slot. Finally, the management component 1 comprises a resource leasing database 7 the functionality of which will be described in more detail below. The centralized management component 1 may be implemented as a logical functional entity and could physically be part of the NMS (Network Management System) 8 or an independent entity. In any case, all database information is fed to a paths computation engine 9, that is the paths computation engine 9 retrieves information from the databases 2, 5, 6 and 7, as required, in order to compute an optimal path for each traffic demand of the traffic matrix in each time slot, as will be described in detail hereinafter. The computed routing paths can be provisioned to the network equipment of the transport network 3, e.g. routers and switches 4, via a southbound control interface 10 or via the NMS 8 directly.

Embodiments of the invention can be applied in legacy networks using open shortest path first (OSPF) or similar routing protocols. Embodiments of the invention can also be applied in the context of software-defined networking (SDN). Fig. 7 is a diagram illustrating components of a system for performing stability- and capacity-aware time-dependent routing in a transport network in accordance with an embodiment of the present invention. In addition to the common traffic engineering performance metrics like, e.g., network throughput and link utilization ratio, the illustrated embodiment of the invention further considers minimizing the total leased network link capacity in all time slots.

The key component of this embodiment is stability- and capacity-aware time- dependent routing mechanism, as shown at 701. As illustrated in Fig. 7, the proposed routing method according to this embodiment keeps the resource consumption of each network link as stable across different time slots as necessary (that is within certain stability boundaries that enable an instantaneous or a quasi-instantaneous adaptation by leasing or de-leasing link capacity from an infrastructure operator) and takes into account the stepwise incremental cost of link capacity leasing, with the objective to minimize the overall leased network link capacity in all time slots, as indicated at 702. Particularly, both the time granularity limitations (as shown at 703) and the capacity granularity limitations (as shown at 704) of network link/capacity leasing are taken into consideration in this embodiment. Fig. 8 is a diagram illustrating method steps for performing stability- and capacity- aware time-dependent routing in a transport network in accordance with an embodiment of the present invention. As shown in Fig. 8, the method for stability- and capacity-aware time-dependent routing in transport networks according to this embodiment comprises the following operations:

As indicated at 801 , time-dependent load information is collected, e.g. derived from historic data and/or based on traffic predictions/estimations, and based on the respective dynamic traffic demands traffic matrices are constructed for each time slot as shown at 802. This step may be carried out independently for different QoS classes, i.e. there could be multiple matrices per time slot, e.g. one matrix per QoS class. For instance, a differentiation between three QoS classes may be provided, e.g., voice, video and best effort traffic. The traffic matrices may be stored in the traffic matrices database 6 of the management component 1 illustrated in Fig. 6.

Furthermore, the network resource leasing information is collected, including capacity- and time granularity and the stepwise incremental cost function of network resource leasing, as indicated at 803. This information may be stored in the resource leasing database 7 of the management component 1 illustrated in Fig. 6.

Still further, as indicated at 804, network topology and network path information is provided, which may be stored in the respective databases 2 and 5 illustrated in Fig. 6.

As illustrated at 805, time-dependent routing is carried out, i.e. time-dependent routing paths are computed in a stability- and capacity-aware fashion. This computation may be performed by the paths computation engine 9 of Fig. 6. The routing scheme is configured to operate based on the existing network topology and network paths, as indicated at 804, and based on the traffic matrices, as indicated at 802, and to take into account the time- and capacity granularity for capacity (de-)leasing, as indicated at 803. The routing scheme aims at minimizing the total leased network link capacity in all time slots, as will be described in more detail below.

Finally, as illustrated at 806, the computed routing paths are provisioned to the routers/switches of the transport network.

When a network link is under-utilized in a time slot, the leased capacity of that link may possibly be downgraded to reduce wasted link capacity (as exemplarily shown in Fig. 4). On the other hand, when a network link is overloaded in a time slot, the leased capacity of that link needs to be upgraded to accommodate the excess traffic (as exemplarily shown in Fig. 3). To minimize the total leased network link capacity, the routing scheme applied by the computation engine for path calculation 9 (shown in Fig. 6) shall facilitate link capacity downgrading and concentrate link capacity upgrading to as few links as possible, and as small as possible under the given time- and capacity granularity constraints.

Here, it should be noted that link capacity upgrading and downgrading in a small amount (i.e., to a certain stepwise constant) may be realized in practice within minutes (or hours) by leasing or de-leasing more link capacity from the infrastructure operator if the time granularity of network resource leasing allows for such "quick change". However, upgrading a link capacity to a much larger value may require new hardware deployment (e.g., new optical fibre installation) and thus may take weeks (or even months). When the traffic load change across time slots of a network link stays within the "quick change" region, i.e. within a predefined stability region, link capacity downgrading and upgrading can be employed to possibly reduce overall leased network link capacity. This in turn motivates that the resource consumption of each network link shall be maintained stable across different time slots so that there will be no dramatic change of the traffic load of a link across time slots (i.e. outside of the above mentioned stability region), so to avoid significant link capacity upgrading that may be practically infeasible.

Hereinafter, a reference algorithmic solution for implementing a stability- and capacity-aware time-dependent routing solution in a transport network in accordance with an embodiment of the present invention, which is based on integer linear programming (ILP), will be described in detail.

According to this embodiment it is supposed that there are M nodes (e.g., switches and routers) in the network, and the undirected network link between the /th node and the y^'th node is represented by (ij), for i, j = 1 , 2, M. The traffic matrix in the n th time-slot for the <7 th traffic QoS class is given by ε R^MXM, for n = 1 , 2, N, and q = 1 , 2, Q. This also implies that the time slots are predetermined.

The leased capacity of each network link may take any value in the set {Ci, C2, CA}. This also specifies the capacity granularity of link capacity lease.

In accordance with embodiments of the present invention a binary decision variable a _k ε {0, 1} is introduced as an indicator, with a _k = 1 indicating that the leased capacity C_k is allocated to the network link (ij) in the n th time slot, and ^ai ,k ^{= 0} otherwise, for / /= 1 , 2, M, and n = 1 , 2, N.

Further, in accordance with embodiments of the present invention binary decision variable

= 1 indicating that the traffic demand from the m th node to the /th node in the nth time slot for the <7 th traffic QoS class, i.e., [T^'^_m. is routed over the network link (ij), and j m.i.n.q) _{= Q otherw}j_{se> for} ,; = 1 _ ₂, M, n = 1 , 2, N, and q = 1 , 2, Q. It is noted that, due to the sparsity of the network topology, most of the binary decision variables {a^} and {b^^1,1 '^η'^} take value as zero and hence the computational complexity associated with solving the routing problem is significantly reduced. In accordance with embodiments of the invention a constant indicator function f([T^in,q)]m,i) is defined as

The problem of minimizing the overall least network link capacity in all time slots can be mathematically formulated as

minimize

subject to

k=l (2)

a¾ £ {0, 1}, Vi,;, /c, n (7) _b(m,l,n,q) _{£ {Q>} ^ _i>j_{> m>} l_{> n> q (8)}

In the above problem formulation, the objective function defined in Eq. (1 ) represents the total leased link capacity in all time slots. Eq. (2) says that in each time slot, each network link can be allocated at most one leased capacity. Eq. (3) and Eq. (4) are defined for the source and destination nodes, respectively. Eq. (5) is the flow-balancing equation for each network link, i.e., input equals to output for each network link. Eq. (6) defines the link capacity constraints.

The formulated ILP in Eqs. (1 ) - (8) can be solved with ILP solvers such as LP_SOLVE (as described, for instance, under http://lpsolve.sourceforge. net/5.5/) and/or GLPK (as described, for instance, under http://www.gnu.org/software/glpk/).

Many modifications and other embodiments of the invention set forth herein will come to mind the one skilled in the art to which the invention pertains having the benefit of the teachings presented in the foregoing description and the associated drawings. Therefore, it is to be understood that the invention is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims

C l a i m s

1. Method for time-dependent routing in a transport network (3), the method comprising:

calculating traffic demands for each network link and for different time slots based on historic data and/or prediction,

a paths computation engine (9) determining routing paths between the network nodes (4) of said transport network (3) for each traffic demand in each of said time slots and provisioning said routing paths into the network nodes (4),

wherein said paths computation engine (9) determines said routing paths in such a way that, while taking into account the existing capacity leasing granularity and keeping the stability of resource consumption of each network link across different of said time slots within configurable boundaries, the total leased network link capacity of all time slots is minimized.

2. Method according to claim 1 , wherein the computed routes are provisioned into the network nodes (4) directly or via a southbound controller interface (10) also a centralized management component (1 ).

3. Method according to claim 1 or 2, wherein said traffic demands are provided in form of traffic demand matrices for each network link and for different time slots.

4. Method according to any of claims 1 to 3, wherein said traffic demand is categorized and determined for different QoS classes.

5. Method according to any of claims 1 to 4, wherein said paths computation engine (9) collects capacity- and time-granularity information of network resource leasing from a network resource leasing database (7).

6. Method according to any of claims 1 to 5, wherein the minimization of the total leased network capacity over all time slots is achieved by means of integer linear programming, ILP.

7. Method according to claim 6, wherein a function representing the total leased network capacity over all time slots is formulated as the objective function of an ILP problem.

8. Method according to claim 6 or 7, wherein the ILP problem is solved by means of applying LP_SOLVE and/or the GNU Linear Programming Kit, GLPK.

9. Method according to any of claims 1 to 8, wherein the leased capacity of a network link is downgraded when said network link is under-utilized in a time slot.

10. Method according to any of claims 1 to 9, wherein the leased capacity of a network link is upgraded when said network link is overloaded in a time slot.

1 1. Method according to any of claims 1 to 10, wherein the routing scheme applied by said paths computation engine (9) for determining said routing paths concentrates network link capacity upgrading to as few network links as possible.

12. System for time-dependent routing in a transport network (3), in particular for executing a method according to any of claims 1 to 1 1 , the system comprising: a traffic database (6) that contains traffic demands for each network link and for different time slots based on historic data and/or prediction,

a resource leasing database (7) containing information on network link capacity leased from an infrastructure operator for each network link and for each of said different time slots, and

a paths computation engine (9) that is configured to determine routing paths between the network nodes (4) of said transport network (3) for each traffic demand in each of said time slots in such a way that, while taking into account the existing capacity leasing granularity and keeping the stability of resource consumption of each network link across different of said time slots within configurable boundaries, the total leased network link capacity of all time slots is minimized.

13. Systenn according to claim 12, wherein said paths computation engine (9) is configured to retrieve information from said traffic database (6), from said resource leasing database (7), form a topology database (2), and/or from a path database (5).

14. System according to claim 12 or 13, comprising an interface (10) adapted for provisioning said routing paths to the respective network nodes (4).

15. System according to any of claims 12 to 14, wherein said network nodes (4) include switches, routers and/or gateways.