Topology optimization in SD-WANs with path downgrading

In one embodiment, a controller for a network receives, via a user interface, a downgrade policy for the network that specifies an objective for path downgrades in the network. The controller identifies traffic of an application conveyed by an edge router in the network via a particular path in the network and using a first type of link. The controller predicts an effect of downgrading the particular path from using the first type of link to using a second type of link to convey the traffic of the application. The controller causes the edge router to convey the traffic of the application via the second type of link, when the effect predicted by the controller satisfies the objective specified by the downgrade policy.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, more particularly, to topology optimization in software-defined wide area networks (SD-WANs) with path downgrading.

BACKGROUND

Software-defined wide area networks (SD-WANs) represent the application of software-defined networking (SDN) principles to WAN connections, such as connections to cellular networks, the Internet, and Multiprotocol Label Switching (MPLS) networks. The power of SD-WAN is the ability to provide consistent service level agreement (SLA) for important application traffic transparently across various underlying tunnels of varying transport quality and allow for seamless tunnel selection based on tunnel performance characteristics that can match application SLAs and satisfy the quality of service (QoS) requirements of the traffic (e.g., in terms of delay, jitter, packet loss, etc.).

Optimizing the topology of an SD-WAN or other network is relatively straightforward, when only factors such QoS path metrics are considered. However, the notion of ‘optimization’ may also encompass other factors, as well. For instance, consider the case in which the network operator has a subjective preference for certain types of links, service providers, or the like. In this case, the subjective preferences of the network operator may even run contrary to the optimization of the QoS path metrics (e.g., a disfavored path actually offers the ‘best’ QoS metrics). Thus, topology optimization that takes into account multiple factors may actually entail ‘downgrading’ certain paths, so as to satisfy the overall objectives of the network operator.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

According to one or more embodiments of the disclosure, a controller for a network receives, via a user interface, a downgrade policy for the network that specifies an objective for path downgrades in the network. The controller identifies traffic of an application conveyed by an edge router in the network via a particular path in the network and using a first type of link. The controller predicts an effect of downgrading the particular path from using the first type of link to using a second type of link to convey the traffic of the application. The controller causes the edge router to convey the traffic of the application via the second type of link, when the effect predicted by the controller satisfies the objective specified by the downgrade policy.

DESCRIPTION

2.) Site Type B: a site connected to the network by the CE router via two primary links (e.g., from different Service Providers), with potentially a backup link (e.g., a 3G/4G/5G/LTE connection). A site of type B may itself be of different types:

2a.) Site Type B1: a site connected to the network using two MPLS VPN links (e.g., from different Service Providers), with potentially a backup link (e.g., a 3G/4G/5G/LTE connection).

2c.) Site Type B3: a site connected to the network using two links connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/5G/LTE connection).

According to various embodiments, a software-defined WAN (SD-WAN) may be used in network100to connect local network160, local network162, and data center/cloud environment150. In general, an SD-WAN uses a software defined networking (SDN)-based approach to instantiate tunnels on top of the physical network and control routing decisions, accordingly. For example, as noted above, one tunnel may connect router CE-2 at the edge of local network160to router CE-1 at the edge of data center/cloud environment150over an MPLS or Internet-based service provider network in backbone130. Similarly, a second tunnel may also connect these routers over a 4G/5G/LTE cellular service provider network. SD-WAN techniques allow the WAN functions to be virtualized, essentially forming a virtual connection between local network160and data center/cloud environment150on top of the various underlying connections. Another feature of SD-WAN is centralized management by a supervisory service that can monitor and adjust the various connections, as needed.

In general, routing process (services)244contains computer executable instructions executed by the processor220to perform functions provided by one or more routing protocols. These functions may, on capable devices, be configured to manage a routing/forwarding table (a data structure245) containing, e.g., data used to make routing/forwarding decisions. In various cases, connectivity may be discovered and known, prior to computing routes to any destination in the network, e.g., link state routing such as Open Shortest Path First (OSPF), or Intermediate-System-to-Intermediate-System (ISIS), or Optimized Link State Routing (OLSR). For instance, paths may be computed using a shortest path first (SPF) or constrained shortest path first (CSPF) approach. Conversely, neighbors may first be discovered (e.g., a priori knowledge of network topology is not known) and, in response to a needed route to a destination, send a route request into the network to determine which neighboring node may be used to reach the desired destination. Example protocols that take this approach include Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), DYnamic MANET On-demand Routing (DYMO), etc. Notably, on devices not capable or configured to store routing entries, routing process244may consist solely of providing mechanisms necessary for source routing techniques. That is, for source routing, other devices in the network can tell the less capable devices exactly where to send the packets, and the less capable devices simply forward the packets as directed.

The performance of a machine learning model can be evaluated in a number of ways based on the number of true positives, false positives, true negatives, and/or false negatives of the model. For example, consider the case of a model that predicts whether the QoS of a path will satisfy the service level agreement (SLA) of the traffic on that path. In such a case, the false positives of the model may refer to the number of times the model incorrectly predicted that the QoS of a particular network path will not satisfy the SLA of the traffic on that path. Conversely, the false negatives of the model may refer to the number of times the model incorrectly predicted that the QoS of the path would be acceptable. True negatives and positives may refer to the number of times the model correctly predicted acceptable path performance or an SLA violation, respectively. Related to these measurements are the concepts of recall and precision. Generally, recall refers to the ratio of true positives to the sum of true positives and false negatives, which quantifies the sensitivity of the model. Similarly, precision refers to the ratio of true positives the sum of true and false positives.

As noted above, in software defined WANs (SD-WANs), traffic between individual sites are sent over tunnels. The tunnels are configured to use different switching fabrics, such as MPLS, Internet, 4G or 5G, etc. Often, the different switching fabrics provide different QoS at varied costs. For example, an MPLS fabric typically provides high QoS when compared to the Internet, but is also more expensive than traditional Internet. Some applications requiring high QoS (e.g., video conferencing, voice calls, etc.) are traditionally sent over the more costly fabrics (e.g., MPLS), while applications not needing strong guarantees are sent over cheaper fabrics, such as the Internet.

Traditionally, network policies map individual applications to Service Level Agreements (SLAs), which define the satisfactory performance metric(s) for an application, such as loss, latency, or jitter. Similarly, a tunnel is also mapped to the type of SLA that is satisfies, based on the switching fabric that it uses. During runtime, the SD-WAN edge router then maps the application traffic to an appropriate tunnel. Currently, the mapping of SLAs between applications and tunnels is performed manually by an expert, based on their experiences and/or reports on the prior performances of the applications and tunnels.

The emergence of infrastructure as a service (IaaS) and software as a service (SaaS) is having a dramatic impact of the overall Internet due to the extreme virtualization of services and shift of traffic load in many large enterprises. Consequently, a branch office or a campus can trigger massive loads on the network.

FIGS.3A-3Billustrate example network deployments300,310, respectively. As shown, a router110(e.g., a device200) located at the edge of a remote site302may provide connectivity between a local area network (LAN) of the remote site302and one or more cloud-based, SaaS providers308. For example, in the case of an SD-WAN, router110may provide connectivity to SaaS provider(s)308via tunnels across any number of networks306. This allows clients located in the LAN of remote site302to access cloud applications (e.g., Office 365™, Dropbox™, etc.) served by SaaS provider(s)308.

As would be appreciated, SD-WANs allow for the use of a variety of different pathways between an edge device and an SaaS provider. For example, as shown in example network deployment300inFIG.3A, router110may utilize two Direct Internet Access (DIA) connections to connect with SaaS provider(s)308. More specifically, a first interface of router110(e.g., a network interface210, described previously), Int1, may establish a first communication path (e.g., a tunnel) with SaaS provider(s)308via a first Internet Service Provider (ISP)306a, denoted ISP1inFIG.3A. Likewise, a second interface of router110, Int2, may establish a backhaul path with SaaS provider(s)308via a second ISP306b, denoted ISP2inFIG.3A.

FIG.3Billustrates another example network deployment310in which Int1of router110at the edge of remote site302establishes a first path to SaaS provider(s)308via ISP1and Int2establishes a second path to SaaS provider(s)308via a second ISP306b. In contrast to the example inFIG.3A, Int3of router110may establish a third path to SaaS provider(s)308via a private corporate network306c(e.g., an MPLS network) to a private data center or regional hub304which, in turn, provides connectivity to SaaS provider(s)308via another network, such as a third ISP306d.

Regardless of the specific connectivity configuration for the network, a variety of access technologies may be used (e.g., ADSL, 4G, 5G, etc.) in all cases, as well as various networking technologies (e.g., public Internet, MPLS (with or without strict SLA), etc.) to connect the LAN of remote site302to SaaS provider(s)308. Other deployments scenarios are also possible, such as using Colo, accessing SaaS provider(s)308via Zscaler or Umbrella services, and the like.

FIG.4Aillustrates an example SDN implementation400, according to various embodiments. As shown, there may be a LAN core402at a particular location, such as remote site302shown previously inFIGS.3A-3B. Connected to LAN core402may be one or more routers that form an SD-WAN service point406which provides connectivity between LAN core402and SD-WAN fabric404. For instance, SD-WAN service point406may comprise routers110a-110b.

Overseeing the operations of routers110a-110bin SD-WAN service point406and SD-WAN fabric404may be an SDN controller408. In general, SDN controller408may comprise one or more devices (e.g., devices200) configured to provide a supervisory service, typically hosted in the cloud, to SD-WAN service point406and SD-WAN fabric404. For instance, SDN controller408may be responsible for monitoring the operations thereof, promulgating policies (e.g., security policies, etc.), installing or adjusting IPsec routes/tunnels between LAN core402and remote destinations such as regional hub304and/or SaaS provider(s)308inFIGS.3A-3Band the like.

As noted above, a primary networking goal may be to design and optimize the network to satisfy the requirements of the applications that it supports. So far, though, the two worlds of “applications” and “networking” have been fairly siloed. More specifically, the network is usually designed in order to provide the best SLA in terms of performance and reliability, often supporting a variety of Class of Service (CoS), but unfortunately without a deep understanding of the actual application requirements. On the application side, the networking requirements are often poorly understood even for very common applications such as voice and video for which a variety of metrics have been developed over the past two decades, with the hope of accurately representing the Quality of Experience (QoE) from the standpoint of the users of the application.

More and more applications are moving to the cloud and many do so by leveraging an SaaS model. Consequently, the number of applications that became network-centric has grown approximately exponentially with the raise of SaaS applications, such as Office 365, ServiceNow, SAP, voice, and video, to mention a few. All of these applications rely heavily on private networks and the Internet, bringing their own level of dynamicity with adaptive and fast changing workloads. On the network side, SD-WAN provides a high degree of flexibility allowing for efficient configuration management using SDN controllers with the ability to benefit from a plethora of transport access (e.g., MPLS, Internet with supporting multiple CoS, LTE, satellite links, etc.), multiple classes of service and policies to reach private and public networks via multi-cloud SaaS.

Furthermore, the level of dynamicity observed in today's network has never been so high. Millions of paths across thousands of Service Provides (SPs) and a number of SaaS applications have shown that the overall QoS(s) of the network in terms of delay, packet loss, jitter, etc. drastically vary with the region, SP, access type, as well as over time with high granularity. The immediate consequence is that the environment is highly dynamic due to:New in-house applications being deployed;New SaaS applications being deployed everywhere in the network, hosted by a number of different cloud providers;Internet, MPLS, LTE transports providing highly varying performance characteristics, across time and regions;SaaS applications themselves being highly dynamic: it is common to see new servers deployed in the network. DNS resolution allows the network for being informed of a new server deployed in the network leading to a new destination and a potentially shift of traffic towards a new destination without being even noticed.

According to various embodiments, application aware routing usually refers to the ability to rout traffic so as to satisfy the requirements of the application, as opposed to exclusively relying on the (constrained) shortest path to reach a destination IP address. Various attempts have been made to extend the notion of routing, CSPF, link state routing protocols (ISIS, OSPF, etc.) using various metrics (e.g., Multi-topology Routing) where each metric would reflect a different path attribute (e.g., delay, loss, latency, etc.), but each time with a static metric. At best, current approaches rely on SLA templates specifying the application requirements so as for a given path (e.g., a tunnel) to be “eligible” to carry traffic for the application. In turn, application SLAs are checked using regular probing. Other solutions compute a metric reflecting a particular network characteristic (e.g., delay, throughput, etc.) and then selecting the supposed ‘best path,’ according to the metric.

The term ‘SLA failure’ refers to a situation in which the SLA for a given application, often expressed as a function of delay, loss, or jitter, is not satisfied by the current network path for the traffic of a given application. This leads to poor QoE from the standpoint of the users of the application. Modern SaaS solutions like Viptela, CloudonRamp SaaS, and the like, allow for the computation of per application QoE by sending HyperText Transfer Protocol (HTTP) probes along various paths from a branch office and then route the application's traffic along a path having the best QoE for the application. At a first sight, such an approach may solve many problems. Unfortunately, though, there are several shortcomings to this approach:The SLA for the application is ‘guessed,’ using static thresholds.Routing is still entirely reactive: decisions are made using probes that reflect the status of a path at a given time, in contrast with the notion of an informed decision.SLA failures are very common in the Internet and a good proportion of them could be avoided (e.g., using an alternate path), if predicted in advance.

In various embodiments, the techniques herein allow for a predictive application aware routing engine to be deployed, such as in the cloud, to control routing decisions in a network. For instance, the predictive application aware routing engine may be implemented as part of an SDN controller (e.g., SDN controller408) or other supervisory service, or may operate in conjunction therewith. For instance,FIG.4Billustrates an example410in which SDN controller408includes a predictive application aware routing engine412(e.g., through execution of routing process244and/or path downgrade process248). Further embodiments provide for predictive application aware routing engine412to be hosted on a router110or at any other location in the network.

During execution, predictive application aware routing engine412makes use of a high volume of network and application telemetry (e.g., from routers110a-110b, SD-WAN fabric404, etc.) so as to compute statistical and/or machine learning models to control the network with the objective of optimizing the application experience and reducing potential down times. To that end, predictive application aware routing engine412may compute a variety of models to understand application requirements, and predictably route traffic over private networks and/or the Internet, thus optimizing the application experience while drastically reducing SLA failures and downtimes.

In other words, predictive application aware routing engine412may first predict SLA violations in the network that could affect the QoE of an application (e.g., due to spikes of packet loss or delay, sudden decreases in bandwidth, etc.). In turn, predictive application aware routing engine412may then implement a corrective measure, such as rerouting the traffic of the application, prior to the predicted SLA violation. For instance, in the case of video applications, it now becomes possible to maximize throughput at any given time, which is of utmost importance to maximize the QoE of the video application. Optimized throughput can then be used as a service triggering the routing decision for specific application requiring highest throughput, in one embodiment.

As noted above, many SD-WANs and other networks now utilize different types of links, such as MPLS links, Internet links, backup cellular links, and the like. Generally, speaking, it is often assumed that certain types of links provide better performance than others. For instance, MPLS-based tunnels are often assumed to perform better than Internet-based tunnels. While this generalization may hold in some circumstances, this may not always be the case. Indeed, preliminary testing has shown that approximately 32% of the MPLS-based tunnels do not provide a significantly better SLA than that of their Internet-based counterparts, 99% of the time.

By way of example,FIGS.5A-5Cillustrates plots500,513, and520, respectively, showing the observed loss, latency, and jitter measured over time between two edge routers in a live network over an MPLS-based tunnel and over an Internet-based tunnel. As shown in plot500inFIG.5A, the Internet-based tunnel between the routers universally exhibits better latency than the MPLS-based tunnel over the measured time period. Similarly, in plot510inFIG.5B, the loss observed on the MPLS-based tunnel was almost always greater than that of the Internet-based tunnel. Finally, in plot520inFIG.5C, while the MPLS-based tunnel exhibited slightly better jitter metrics than that of the Internet-based tunnel for some time, that tunnel also exhibited a large spike in jitter towards the end of the observed period.

While topology optimization approaches typically take into account whether the QoS metrics of the available paths will best satisfy the SLAs of the application traffic, this approach often fails to satisfy the notion of ‘optimization’ held by the network operator. For instance, the network operator may prefer Internet-based tunnels over MPLS-based tunnels, may view the SLAs of certain applications more flexible than others (e.g., SLA violations for certain types of traffic may be acceptable, etc.), or like. This means that there are certainly opportunities within many networks to ‘downgrade’ paths from one type to another, while still being considered optimal from the standpoint of the network operator.

Topology Optimization in SD-WANs with Path Downgrading

The techniques introduced herein a mechanism to explore and implement network topology changes so as to satisfy a notion of optimization that takes into account multiple factors. For instance, the techniques herein may consider the SLAs associated with the application traffic in the network, an order of preference for different types of links (e.g., a preference for Internet, etc.), acceptable levels of risk in terms of probabilities of no longer satisfying an SLA, combinations thereof, or the like. In turn, the system may apply dynamic routing changes to implement the computed topologies. In further aspects, the techniques herein also propose the use of observation periods to validate that the modified topology satisfies the stated objectives and confirm whether the modification(s) should be used.

Specifically, according to various embodiments, a controller for a network receives, via a user interface, a downgrade policy for the network that specifies an objective for path downgrades in the network. The controller identifies traffic of an application conveyed by an edge router in the network via a particular path in the network and using a first type of link. The controller predicts an effect of downgrading the particular path from using the first type of link to using a second type of link to convey the traffic of the application. The controller causes the edge router to convey the traffic of the application via the second type of link, when the effect predicted by the controller satisfies the objective specified by the downgrade policy.

Operationally,FIG.6illustrates an example architecture for downgrading paths in a network, according to various embodiments. At the core of architecture600is path downgrade process248, which may be executed by a controller for a network or another device in communication therewith. For instance, path downgrade process248may be executed by a controller for a network (e.g., SDN controller408inFIGS.4A-4B), a particular networking device in the network (e.g., a router, etc.), another device or service in communication therewith, or the like.

As shown, path downgrade process248may include any or all of the following components: a site analyzer (SA)602, a downgrade link engine (DLE)604, a trend analyzer606, and/or a report generator608. As would be appreciated, the functionalities of these components may be combined or omitted, as desired. In addition, these components may be implemented on a singular device or in a distributed manner, in which case the combination of executing devices can be viewed as their own singular device for purposes of executing path downgrade process248.

According to various embodiments, path downgrade process248may communicate with one or more user interfaces612, either directly or indirectly, to receive policy data620specified by a network operator. In general, policy data620may specify a downgrade policy for paths in the network. Such a policy may indicate one or more objectives for path downgrade process248during its assessment of the network. In various embodiments, an objective may be to favor certain link types over others (e.g., an order of preference for links), SLA-related preferences (e.g., whether the SLA for a certain application should never be violated or at least some violations are acceptable), or the like. For instance, one objective may be to remove all MPLS links for a given service provider, if possible. In another example, a different objective may be to maintain all application SLAs while favoring Internet-based links. As would be appreciated, SLA templates may also be defined on a per-application basis (e.g., voice traffic should have delay <150 ms, jitter <50 ms, and loss <3%). Accordingly, in further cases, the objective of the downgrade policy may be to favor a certain type or types of links, while ensuring that the SLAs of a certain subset of applications are satisfied.

In some embodiments, site analyzer (SA)602may ingest path data614from the various routers610in the network, to identify potential paths that can be downgraded/adjusted for the various sites of the network in accordance with the downgrade policy specified in policy data620. In general, path data614may indicate, for each site, a list of links available at that site. In addition, path data614may indicate the type(s) of links available at that site, such as MPLS, MPLS-gold, MPLS-silver, etc., Internet, Internet-gold, Internet-silver, . . . , cable, symmetric digital subscriber line (SDSL), Satellite, Satellite-gold, Satellite silver, . . . , or the like, where ‘gold’ represents a link plan that promises better performance than a ‘silver’ link plan, etc. Path data614may further indicate the QoS characteristics/metrics of the various paths/links, such as link speed, committed rate, cost, etc.

In turn, SA602may use path data614to identify potential path downgrades and topology changes, based on the objective of the downgrade policy. For instance, if the objective is to favor a certain type of link over another, SA602may identify sites that support both types and are currently using the disfavored type of link.

Downgrade link engine (DLE)604may be configured to evaluate specific links among the candidates identified by SA602, according to various embodiments. To do so, DLE604may obtain traffic data616regarding the traffic observed for a particular candidate site, Si. In addition, in some embodiments, traffic data616may also indicate the various applications associated with that site. Various approaches can be employed to identify the applications, such as various routers610(or other device) employing deep packet inspection (DPI), using Network Based Application Recognition (NBAR) by Cisco Systems, Inc. or another suitable application recognition mechanism.

DLE604may map the list of applications observed for the site to the required SLA(s) specified for the site, if any. As a reminder, DLE604may be required to only ensure that the SLA for a given type of traffic e.g., voice) is satisfied, while degradation of the QoS below the SLA threshold may be tolerated for other applications. In another embodiment, the level of acceptable degradation for low priority traffic may be specified by the downgrade policy (e.g. the SLA for voice for the modified topology is required, the SLA for non-transactional traffic may be transgressed by x %, etc.). Note that an additional condition related to the volume of traffic for a given application may also be specified, optionally (e.g. comply with the SLA if the average traffic volume of application A is a least X, etc.).

For each modified topology Tj(Si), since there may be multiple combinations allowing for cost reductions, DLE604may generate a series of routing patches (e.g., changes/exceptions to the existing routing policy). Such patches may be computed using a function returning the probability of SLA violation, considering a set of hypotheses such as the volume of traffic of a given type sent over a given link. Such prediction models may take the form of machine learning-based or statistical models that are typically trained on a per link/tunnel/path basis. To break any ties between links, DLE604may also take into account the user-specified link type preferences. For instance, if both an MPLS link and an Internet link are predicted to satisfy the SLA of voice traffic, DLE604may favor the link type specified as preferred by the network operator via user interface612.

In turn, DLE604may determine whether the predicted probability of an SLA violation is considered an acceptable risk, according to the downgrade policy. For instance, the network operator may be comfortable with a slight degree of risk of SLA violations for traffic of a non-critical application. Conversely, the network operator may view another application as critical and not be open to any significant risk of an SLA violation. Note that the degrees of risk may specified by the user, explicitly, or may be predetermined according to some categorical risk level. Such a risk factor is denoted ‘r’ herein, to signify the level of acceptable risk of an SLA violation for a particular application or category of applications.

Optionally, DLE604may also specify a condition on the routing patches elated to the number of occurrences and number of required routing changes, in one embodiment. Indeed, consider the case of a modified topology allowing to remove an MPLS link allowing to reroute traffic onto another set of links, but at the cost or applying a high number of routing policies (patches), and rerouting traffic very often. In such a case, DLE604may consider the routing policy as too costly in terms of operation and too hard to monitor for being considered (e.g., as defined in the downgrade policy, on review by the network operator, etc.). Consequently, the system may apply conditions on the number and the nature of the patches that can be used.

If a modified topology satisfies all of the above requirements, then DLE604may generate a set of routing patches for the modified topologies Tj(Si) and enter into an active monitoring phase. The purpose of this active monitoring phase is to check whether the required SLA are met according to the prediction. In some embodiments, this may entail DLE604instructing, either directly or indirectly, a router610to active probe a link or path (e.g., tunnel), such as using RFD or HTTP probes. Optionally, DLE604may activate faster probing that is augmented with DPI, so as to check that the SLAs are still met for a certain application.

In some embodiments, the amount of time of the active monitoring phase may be controlled via a period of observation (PO) parameter, which may also be set in the downgrade policy. Indeed, the critical traffic for which the SLA MUST be preserved may vary over time in terms of patterns. If the traffic pattern for the critical traffic is known using a model to capture the dynamics of the traffic, then such a model may be used to adjust the PO parameter. For example, if the critical traffic is known to be seasonal, then the PO parameter may match the seasonal period. Note that the presence of SaaS application may also greatly influence the PO parameter. Indeed, SaaS destinations are subject to change because of workload shifts. Thus, DLE604may set the PO parameter by taking into consideration not only the type of traffic (e.g., whether the traffic is critical), and its related pattern (if known), as well as the destination of such traffic (e.g., private data center vs. SaaS). In the presence of SaaS destinations, DLE604may increase the PO parameter, to reduce the probability of SLA violation, should the workload change and the modified topology not be able to meet the SLA.

In yet another embodiment, DLE604may check the SLA for the critical traffic using different statistical moments. For example, in one implementation, DLE604may compute averages over X minute time windows may, to check the thresholds specified in the SLA for delay, loss, and/or jitter. Depending on the r factor (e.g., the level of acceptable risk), DLE604may also use higher percentiles instead of averages.

At this point, DLE604may perform some form of arbitrage between different topologies. For instance, assume that DLE604computes the following:T1(Si) with cost C[T1(Si)] and probability P1 of not satisfying SLA for critical traffic.T2(Si) with cost C[T1(Si)] and probability P2 of not satisfying SLA for critical traffic.
where cost C represents a degree of preference for the particular type of link(s) used by that topology Ti. In turn, DLE604may assess the risk factors specified in the downgrade policy and the above possible topologies, to best achieve the objective of the policy. Note that automatic strategies may be pre-configured or may be driven explicitly by a user via policy data620. For instance, in an embodiment where r represents a tradeoff between the number of sessions impacted by a violation and satisfying the order of preference of the network operator (e.g., to favor Internet links over MPLS, etc.), DLE604may pick one or the other, depending on the expected number of sessions impacted per month and a scoring based on the order of preference. This number can be estimated by DLE604, for instance, from historical usage of the site.

Note that, routing patches generated by DLE604may also extend beyond simply selecting the ‘best’ path. Indeed, DLE604may even consider relaying traffic between locations, such as in the case of a hub and spoke configuration of the network.

Trend analyzer606may be used to determine potential trends in traffic volume of the critical traffic along modified topologies. Indeed, it is often important not to limit the analysis of modified topologies exclusively on the instantaneous values. To that end, trend analyzer606may use various statistical tools, in order to estimate the slopes and statistical significances of potential trends. If a sufficiently steep and statistically significant trend is found, then trend analyzer606may extrapolate the predicted traffic volume for critical traffic in X months and rerun the process described above by computing the new probability of SLA violation using the patches, but now also considering the future traffic. Another alternative is to effectively generate extra traffic corresponding to the estimated traffic growth, to check whether the estimation of SLA violations on the increased traffic is correct.

Finally, report generator608may generate and send report data622to user interface(s)612. In various embodiments, report data622may indicate the set of all modified topologies, the level of risk of the SLA for a certain application not being satisfied, and the meeting of any user-specified objectives (e.g., favoring a certain type of connection). In some instances, the user may also have the option to confirm and/or reject any changes by path downgrade process248, In another embodiment, report data622may display one or more dashboards to the user, allowing them insight as to the overall performance of the network on different sites and for different r factors. The user may also make iterative adjustments to the strategies and parameters the user considers the adjusted topology ‘optimized.’ Once this is the case, the user may also decommission any links that have been deemed superfluous.

FIG.7illustrates an example simplified procedure700for downgrading a path in a network, in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device200), such as controller for a network (e.g., an SDN controller or other device in communication therewith), may perform procedure700by executing stored instructions (e.g., routing process244and/or path downgrade process248). The procedure700may start at step705, and continues to step710, where, as described in greater detail above, the controller may receive, via a user interface, a downgrade policy for the network that specifies an objective for path downgrades in the network. In some embodiments, the objective may assign an order of preference to different types of links available in the network (e.g., to favor Internet-based links over MPLS-based links, etc.). In further embodiments, the objective may specify how traffic for a particular application may be evaluated for purposes of topology optimization (e.g., indicating that the SLA of the traffic should not be violated, that the SLA of the traffic can be violated if needed, etc.).

At step715, as detailed above, the controller may identify traffic of an application conveyed by an edge router in the network via a particular path and using a first type of link. For instance, DPI or another packet inspection mechanism may be used, to identify the application(s) whose traffic is conveyed via the particular path. Associated with the application may be an SLA that specifies the minimum required QoS path metric(s) for the application.

At step720, the controller may predict an effect of downgrading the particular path from using the first type of link to using a second type of link to convey the traffic of the application, as described in greater detail above. For instance, the controller may leverage a machine learning or statistical prediction model, to assess the effects of moving the application traffic from using the first type of link to the second type of link (e.g., moving the traffic from MPLS to Internet, etc.). In some embodiments, the controller may do so in part by identifying any trends in the volume of the application traffic over the path, so as to also take into account how the volume of traffic may change over time.

At step725, as detailed above, the controller may cause the edge router to convey the traffic of the application via the second type of link, when the effect predicted by the controller satisfies the objective specified by the downgrade policy. To do so, the controller may send an instruction, either directly or indirectly, to the router that instructs the router to being sending the application traffic via a different link. Procedure700then ends at step730.

The techniques described herein, therefore, allow for the optimization of (SD-WAN) network topologies while allowing for path downgrades. Said differently, the techniques herein allow for multiple factors to be considered when attempting to optimize the topology of a network, including various user preferences of the network operator.

While there have been shown and described illustrative embodiments that provide for the optimization of network topologies with path downgrading, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, while certain embodiments are described herein with respect to using certain models for purposes of predicting application experience metrics or SLA violations, the models are not limited as such and may be used for other types of predictions, in other embodiments. In addition, while certain protocols are shown, other suitable protocols may be used, accordingly.