Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
  • H04L41/08—Configuration management of networks or network elements
  • H04L41/0896—Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L45/00—Routing or path finding of packets in data switching networks
  • H04L45/12—Shortest path evaluation
  • H04L45/125—Shortest path evaluation based on throughput or bandwidth
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
  • H04L41/14—Network analysis or design
  • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network

Definitions

• the present invention relates to a method and a system for supporting dynamic resource management in a backhaul network.
• a backhaul network that may also be designated as mobile backhaul network.
• Mobile backhaul networks connect the remote base stations and cell towards to the mobile operator's core networks and provide access to both the voice network and the internet.
• the mobile backhaul network may consist of a wired part and a wireless part.
• optical fiber is the predominant, but not necessarily only, technology in the middle mile and microwave radio is the predominant, but not necessarily only, technology in the last mile.
• the first and middle mile typically employ ring or mesh topologies.
• the last mile i.e. the final leg of the telecommunications networks delivering communications connectivity to enterprise customers or base stations
• most current deployments are still tree, star or chain topologies.
• ring and mesh topologies in the last mile as well, all the more so with the introduction of small cells.
• TE traffic engineering
• the aforementioned object is accomplished by a method comprising the features of claim 1.
• a method for supporting dynamic resource management in a backhaul network wherein a resource management function is provided that includes an offline component and an online component for routing data traffic in the form of pipes, wherein said pipes include a path from an ingress switch to an egress switch of said backhaul network and an assigned capacity, wherein said offline component performs offline path computation based on expected traffic demands that are determined from one or more traffic matrices in order to compute offline computed paths for said pipes being represented in said one or more traffic matrices, wherein said expected traffic demands constitute claimable resources for said pipes, wherein said offline computed paths are installed in the backhaul network in order to configure said pipes, wherein initially only a fraction of the maximum allowable capacity that corresponds to the amount of said claimable resources is allocated as capacity for said pipes, wherein said resource management function allocates capacities to said pipes dependent on current data traffic, wherein in case excess traffic demands for one or more of said pipes - excess pipes - occur, because being beyond said expected traffic demands for said excess pipes, said excess
• a system for supporting dynamic resource management in a backhaul network includes a resource management function that includes an offline component and an online component for routing data traffic in the form of pipes, wherein said pipes include a path from an ingress switch to an egress switch of said backhaul network and an assigned capacity, wherein said offline component is configured to perform offline path computation based on expected traffic demands that are determined from one or more traffic matrices in order to compute offline computed paths for said pipes being represented in said one or more traffic matrices, wherein said expected traffic demands constitute claimable resources for said pipes, wherein said offline computed paths are installed in the backhaul network in order to configure said pipes, wherein initially only a fraction of the maximum allowable capacity that corresponds to the amount of said claimable resources is allocated as capacity for said pipes, wherein said resource management function is configured to allocate capacities to said pipes dependent on current data traffic, wherein in case excess traffic demands for one or
• a resource management function is implemented that includes an offline component and an online component for routing data traffic in the form of pipes.
• a pipe represents a path from an ingress switch to an egress switch.
• the notion of ingress and egress switches depends on the direction of traffic. For upstream traffic, the first switch after the base station is the ingress switch and the access gateway to the core network is the egress switch. The reverse is true for the downstream direction. For the sake of easier explanation with regard to the description of the present invention and its preferred embodiments, it is assumed the upstream direction in the following used terminology.
• a pipe is defined by a capacity that is assigned to the path or rather to the pipe. More specifically, a pipe may be a tunnel such as, e.g., an MPLS (Multi Protocol Label Switching) LSP (Label Swichted Path) or an Ethernet EPL (Ethernet Private Line) between an ingress switch, e.g. the cell-site switch and an egress switch, e.g. the Serving Gateway in a LTE network, with a certain capacity.
• a pipe will generally bundle multiple application-level flows of the same QoS class.
• the offline component performs offline path computation based on expected traffic demands that are determined from one or more traffic matrices in order to compute offline computed paths for the pipes, wherein the pipes are represented in the one or more traffic matrices.
• these expected traffic demands constitute claimable resources for the pipes represented in the one or more traffic matrices.
• the claimable resources of a pipe that may be dependent on the value in the traffic matrix can be assigned or allocated to this pipe and may be primarily intended for being used by this pipe.
• an enormous improvement for dynamic resource management in a backhaul network may be achieved by allocating and blocking the claimable resources only to the extent as needed. Consequently, the offline computed paths are installed in the backhaul network in order to configure the pipes and, initially, only a fraction of the maximum allowable capacity that corresponds to the amount of the claimable resources is allocated as capacity for, preferably each of, the pipes.
• the resource management function dynamically allocates capacities to the pipes dependent on current/actual data traffic, wherein in case excess traffic demands occur for one or more of the pipes which may be designated as excess pipes, because the traffic demands are more than the expected traffic demands for the excess pipes, then the excess traffic demands constitute opportunistic resources for the excess pipes.
• the online component then performs online path computation in such a way that the online component dynamically uses unblocked claimable resources of one or more pipes different from the excess pipes in order to provide the opportunistic resources for the excess pipes.
• the present invention distinguishes between resource ownership and actual allocation and allows dynamic reuse of owned but unused resources.
• the method and the system according to the invention improve the resource utilization of the backhaul links in a backhaul network and allow maximizing the amount of data traffic which the network can serve.
• the method and the system provides a solution that is highly applicable and suitable for being deployed in actual converged mobile backhaul networks comprising wired and wireless backhaul links.
• the offline computed paths and/or online computed paths may be configured by the remote management function by means of installing forwarding entries in intermediate switches without performing resource reservation.
• the intermediate switches are switches located between an ingress switch and an egress switch.
• the paths computed by the offline component may be initially only installed in terms of the required forwarding entries in the intermediate nodes.
• the capacity corresponding to the traffic matrix values may not fully be allocated immediately. Rather, only a fraction of the full capacity may be allocated initially. Only if more capacity is needed over time, the size of the pipe may be increased, ultimately until it has reached the full capacity as stated in the traffic matrix.
• the resources that are assigned to an ingress- egress pair by virtue of the traffic matrix and the offline path computation are the claimable resources. Allocating the claimable resources only as needed rather than directly blocking them allows the online component to dynamically reuse as yet unallocated resources for accommodating other pipes in case of excess traffic. To this extent, the online component may perform dynamic path computation for cases where the actual data traffic deviates from the traffic matrix, i.e. for excess traffic. It is noted that in a mobile backhaul network, path computation as well as installing and deinstalling pipes is usually done via the Network Management System (NMS), i.e. a centralized entity. Thus, the resource management function including the offline and online component may be part of the NMS or interfacing to it.
• NMS Network Management System
• offline computation may also be completely decoupled, requiring a human in the loop to setup the paths suggested by the offline component manually.
• the online component may also be handled via distributed IGP (Interior Gateway Protocol) protocols such as OSPF (Open Shortest Path First).
• IGP Interior Gateway Protocol
• OSPF Open Shortest Path First
• the resource management function may be a centralized entity.
• traffic classification for assigning the data traffic to the pipes may be performed by the ingress switches and/or by base stations located in front of the ingress switches.
• load monitoring is performed by the ingress switches and/or base stations located in front of the ingress switches, wherein results of the load monitoring are reported to the resource management function.
• the functionality for traffic classification and load monitoring may be located in the ingress switches and/or in the base stations.
• the ingress switches may have functionality for performing traffic shaping in order to enforce that only an admitted amount of data traffic is transmitted over the pipes.
• allocating capacities to the pipes in the form of increasing or decreasing the pipe capacities is triggered by base stations, wherein the base stations translate bearer requests on the mobile network layer to corresponding pipe change requests or new pipe requests on the transport network layer.
• the base stations translate bearer requests on the mobile network layer to corresponding pipe change requests or new pipe requests on the transport network layer.
• QoS Quality of Service
• allocating capacities to the pipes in the form of increasing or decreasing the pipe capacities is triggered based on thresholds that are in relation to monitored current data traffic of the pipes.
• triggering pipe size changes could be done by the first switch after the base station, i.e. by the ingress switch.
• the triggering is done based on monitoring current data traffic/load, since switches are generally oblivious of bearer management messages of the mobile network layer, unless they would do deep packet inspection, which will often not be possible, because packets are IPSec encrypted.
• thresholds that are in relation to monitored current data traffic of the pipes.
• a link has a capacity of ci
• a pipe p has a capacity of c p with current traffic t p
• the switch will ask the resource management function for new resources. The resource management function then decides the amount of new resources that might depend on parameters, e.g.
• the existing pipe size the existing pipe size, the amount of open claimable resources, overall network load, fairness considerations, Quality of Service (QoS) class of p, etc., and the path. If the new resources are still within the claimable resources of that ingress-egress pair, no new path has to be computed, but the one that was computed by the offline component can be used. Otherwise, the path returned by resource management function can be the old one, in which case the pipe is just increased in capacity, or a new one, if the old path is too saturated or does not meet the availability requirements for the pipe's Quality of Service (QoS) class. Releasing resources based on r p may be performed analogously.
• increase threshold i p is set identical to capacity c p .
• traffic will burst not only exceeding the increase threshold but also the actual pipe capacity.
• This may be designated as soft-pipes.
• a soft-pipe basically defines an upper bound in terms of resources that are assigned to the soft-pipe (c p ), but this upper bound is not strictly enforced. For example, in case of a short burst of high traffic, i.e. t p > c p , the traffic could be still admitted into the pipe.
• the ingress switch may start to limit/shape the data traffic admitted into the soft- pipe. Admission control for soft-pipes is thus based on the pipe's capacity c p and the traffic t p over a preconfigured time period T p .
• long buffers may be included in the ingress switches for allowing the ingress switch to perform traffic shaping in case of high traffic over a sustained time, which may allow the network to possibly avoid unnecessary packet drops and thus stabilizes the Transmission Control Protocol (TCP) performance.
• TCP Transmission Control Protocol
• the proposed soft-pipe allows an interaction between ingress shaping and pipe size adaptation. The more aggressive the shaper, the more pipe adaptations need to take place to follow traffic patterns. On the other hand the more optimistic the shaper, the less pipe adaptations. Thus, combining over-admission, supported by the ingress shaper, and pipe adaptation represents a good trade-off between pipe management and signaling complexity and packet losses/retransmissions.
• capacities may be assigned to the pipes dependent on a modulation and coding scheme (MCS) in such a way that a minimum availability provided by the modulation and coding scheme (MCS) and required for a Quality of Service (QoS) class is ensured.
• MCS modulation and coding scheme
• QoS Quality of Service
• time slots may be employed in order to consider recurring pattern of data traffic in the backhaul network, wherein preferably one traffic matrix is captured per time slot.
• expected traffic demands determined from the traffic matrices or rather the values of the traffic matrices are closer to the actual and current data traffic demand.
• multiple traffic matrices are employed.
• Quality of Service (QoS) classes of data traffic may be considered in such a way that the number of the time slots is made dependent on the Quality of Service (QoS) class of data traffic transmitted in one or more of the pipes.
• QoS Quality of Service
• QoS Quality of Service
• QoS Quality of Service
• the number of routing configurations to be computed may be made dependent on the Quality of Service (QoS) class of data traffic transmitted in one or more of the pipes.
• QoS Quality of Service
• a single routing configuration for the pipes may be computed, wherein the single routing configuration takes into account multiple traffic matrices.
• the pipes may be rerouted at boundaries of the time slots.
• one or more or each of the pipes may bundle multiple data traffic flows of the same Quality of Service (QoS) class.
• QoS Quality of Service
• the ingress switches upon detecting too much data traffic in one or more of the pipes the ingress switches request the resource management function to allocate further resources.
• the ingress switches merely inform the remote management function that load in the pipe has critically increased. Then the resource management function decides whether further resources are allocated or which decision is to be taken.
• the offline path computation and/or the online path computation may take a splitting granularity into account, wherein the splitting granularity is derived from predetermined flow classification granularities.
• the possible traffic splitting granularity is taken into consideration. This is particularly advantageous both for offline and online pipe placement.
• the online adaptation of pipe sizes it will not be possible in all cases to just increase existing pipes, but rather the added resource share must be routed along a different path. By doing this, it is important to not change pipe sizes in units that are smaller than the possible splitting granularity. The latter is defined by the granularity by which packet flows can be distinguished. If the base station is responsible for pipe management, splitting can be done on a flow granularity.
• IPSec Internet Protocol Security
• VLAN Virtual Local Area Network
• DSCP Differentiated services Code Point
• packets would have to be mapped to the 3 Mbps pipe according to some flow-unaware scheduling strategy, i.e. quasi- randomly. This would lead to reordering problems at the egress switch as packets from the same flow might take different paths with different latencies. For TCP flows, this would be highly detrimental.
• the splitting granularity may be automatically estimated based on monitoring of the ingress switches and/or the base station.
• discrete splitting granularities may be derived from the given flow classification granularities.
• the discrete splitting granularities may be taken into account for pipe routing, e.g. on-demand splitting.
• the offline path computation may be based on traffic matrices. Hence, it needs to be executed only once the traffic patterns have deviated to a large enough extent from the matrices on which the current routing is based. For example, this may be in the order of days, weeks, or months. In any case, offline computation does not have to happen in real-time, which is why some time to compute can be afforded.
• the offline component may take as an input the set of base stations, the backhaul topology, e.g. an adjacency matrix, with the possible link capacities - which can vary depending on the MCS in case of wireless links - and intermediate switches, the set of gateway nodes, and/or the traffic demands.
• the offline component computes routes for all ingress-egress pairs such that the required traffic demand can be accommodated and the resource utilization of the backhaul network is maximized.
• the latter could mean to maximize the minimum residual link capacity, to maximize the mean residual link capacity, etc.
• the offline path computation may be implemented by using an Integer Linear Program that takes the splitting granularity and/or a capacity variation u m n per link (m,n) into account, wherein the capacity variation is the difference between the maximum utilization and the minimum utilization of a predetermined link (m,n) across all time slots.
• the offline path computation may be implemented as a Mixed Integer Linear Program (MILP) as follows:
• MILP Mixed Integer Linear Program
• Equations (1) to (5) are classical network flow constraints.
• Variable r is a routing variable that captures the traffic demand between node i and j (as given by the traffic matrix) in time slot s is non-zero and routed over link (m,n), i.e. in that direction. Otherwise, it is zero.
• the number of fragments i.e. variable k
• ⁇ representing the splitting granularity, since it is not wanted to assume a fluid traffic model, i.e. infinite splitting granularity.
• Equation (1) says that the sum of routing variables for the k-th fragment of a traffic demand between i and j in time slot s, leaving the source node i of that demand, must amount to 1 , i.e. the full demand must leave the source node. Likewise, the full demand must enter the destination j (cf. Equation (3)). Equations (2) and (4) say that demands may not enter its source or leave its destination. Equation (5) is the classical continuity constraint on intermediate nodes for a demand. Clearly, k is bound by the splitting granularity ⁇ , i.e. the maximum number of distinct flow classes that the ingress flow classifier can distinguish (cf. Equation (14)).
• Equation (1 ) is only specified, if the traffic demand d i,j,k,s > 0 and and k ⁇ ⁇ . Similar omissions hold for other equations.
• Variable u m,n,s in Equation (6) is the consumed capacity of link (m,n) in time slot s and thus the sum of all demands that are routed over that link in either direction. Equation (7) then defines v m,n as the maximum consumed capacity of (m,n) in any time slot. This in turn is bound by the available physical link capacity C m n (Equation (8)).
• Variable c max is the maximum link utilization in the network and will be minimized as part of the objective function.
• the consumed capacity in slot s, u m,n,s is used in Equation (10) to define two other variables, namely the lower bound or minimum consumed capacity across all time slots, , and the upper bound or maximum consumed capacity across all slots, .
• the difference between the two is defined as in Equation (1 1), which represents the capacity variation per link (m,ri).
• Variable (cf. Equation (12)) is then the total capacity variation in the network.
• the capacity variation of a link is the difference between the maximum utilization and the minimum utilization of a particular link across all time slots. A high value thus means that some load goes over a certain link in time slot t 1 , while much less load goes over that same link in another time slot t2.
• Equation (16) is an important constraint which expresses that a demand should not be routed over a link (m,n) and (n,m) at the same time (i.e. in both directions of the same physical link). If this constraint was not given, a solution will often have exactly that, because thus adding a demand "twice" to the consumed capacity u m,n,s on a (not so congested) link can reduce the capacity variation on that link (without impacting the maximum link utilization value), thereby optimizing the objective function. Since the term r can be any decimal value, it cannot be used r directly in Equation (16), but rather need to introduce corresponding binary values that constitute an upper bound of the routing variables r (cf. Equation (15)), but are at the same time either 0 or 1 (cf. Equation (17)). This indirection via binary variables b allows the linear program to be computed much faster (because of so- called integer relaxation).
• the offline path computation is implemented by using a genetic algorithm that takes the splitting granularity into account.
• the offline path computation may be achieved by using a high-level genetic algorithm with, e.g. unique heuristics for population creation, mutation, recombination operators and/or taking the dynamically determined splitting granularity into account.
• the offline path computation may be implemented as a genetic algorithm as follows:
• a solution in the genetic algorithm population may be initialized as follows:
• the mutation operator for a solution in the genetic algorithm may be implemented as follows:
• the online path computation may be handled by any algorithm with fast reaction times, e.g. by a Constrained Shortest Path First approach with constraints reflecting the required capacity, QoS parameters etc.
• the Security Parameter Index may be employed for packet classification to increase the splitting granularity in such a way that new Security Parameter Indexes (SPIs) are dynamically employed in order to increase the splitting granularity, in particular once the resource management function discovers that the current splitting granularity is insufficient.
• SPIs Security Parameter Indexes
• the splitting granularity can be increased in the particularly challenging case of IPSec.
• the SPI is created by the sender of a packet and has only local significance, it could be used for the purpose of packet marking.
• One option may be that ingress switch and base station interact, potentially via the resource management function such as to adjust the packet marking on demand, e.g. when a more fine-grained pipe allocation is needed. Upon such a request for increased packet marking, the base station could adjust the number of distinct SPIs.
• Fig. 1 is a schematic view of an application scenario of an embodiment of a method or a system according to the present invention illustrating an availability-based path computation
• Fig. 2 is a diagram illustrating traffic demand over time with regard to another embodiment of a method or system according to the present invention.
• Fig. 1 schematically illustrates an application scenario of an embodiment of a method or a system according to the present invention illustrating an availability- based path computation.
• QoS Quality of Service
• MCS modulation and coding scheme
• a capacity 6 with an amount of 40 Mbps is available when applying Quadrature Phase-Shift Keying (QPSK). If 16 QAM (Quadrature Amplitude Modulation) is applied, then a capacity of 80 Mbps would be available for an availability requirement of 99,995%.
• QPSK Quadrature Phase-Shift Keying
• Fig. 2 shows a diagram illustrating traffic demand over time with regard to an embodiment of a method or system according to the present invention according to which time slots are employed in order to balance between the resource waste problem for peak provisioning and the resource congestion problem with mean provisioning.
• Data traffic in mobile backhaul networks typically has recurring pattern with fixed periodicity, e.g. a day, and n homogeneous sub-periods, e.g. morning, day, evening, night. If one traffic matrix per sub-period or rather time slot is assumed, mean based path computation, i.e. n different path configurations and one per time slot, will be much closer to the traffic demand curve and less excess traffic must be handled.
• Fig. 2 shows a data traffic demand curve 8 over time.
• a day as recurring period is divided in two time slots.
• One time slot between matrix boundaries t1 and t2 represents the time slot during the day with a traffic matrix 9.
• Another time slot between the matrix boundaries t2 and t1 represents the time slot at night with another traffic matrix 10.
• an average traffic matrix 1 1 over the whole period is depicted in Fig. 2.
• traffic matrices for high-priority traffic should be based on peak values to avoid many online path computation processes for excess traffic.
• mean-based traffic matrices with arbitrary granularity can be used.
• the number of time slots may vary depending on the QoS class. In case an operator decides to use one routing configuration per time slot, the resulting disruptions at time slot boundaries should be done as seamlessly as possible.
• resource management is a matter of optimized path computation, centralized capacity bookkeeping and careful admission control / shaping / policing at the edge devices, in particular the ingress and/or egress switches, of the backhaul network.
• new ingress shapers are configured with the new pipe capacities, burst parameters and thresholds.

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• 2013
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