Patent Publication Number: US-11398958-B2

Title: Reverting routing decisions made based on incorrect network predictions

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to computer networks, and, more particularly, to reverting routing decisions made based on incorrect network predictions. 
     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. 
     Failure detection in a network has traditionally been reactive, meaning that the failure must first be detected before rerouting the traffic along a secondary (backup) path. In general, failure detection leverages either explicit signaling from the lower network layers or using a keep-alive mechanism that sends probes at some interval T that must be acknowledged by a receiver (e.g., a tunnel tail-end router). Typically, SD-WAN implementations leverage the keep-alive mechanisms of Bidirectional Forwarding Detection (BFD), to detect tunnel failures and to initiate rerouting the traffic onto a backup (secondary) tunnel, if such a tunnel exits. While this approach is somewhat effective at mitigating tunnel failures in an SD-WAN, reactive failure detection is also predicated on a failure first occurring. This means that traffic will be affected by the failure, until the traffic is moved to another tunnel. 
     With the recent evolution of machine learning, predictive failure detection in an SD-WAN now becomes possible through the use of machine learning techniques. This provides for the opportunity to implement proactive routing whereby traffic in the network is rerouted before an SLA violation occurs. However, machine learning itself is not infallible. Thus, an incorrect prediction can lead to traffic being rerouted onto another path in the network, needlessly. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS. 1A-1B  illustrate an example communication network; 
         FIG. 2  illustrates an example network device/node; 
         FIGS. 3A-3B  illustrate example network deployments; 
         FIGS. 4A-4B  illustrate example software defined network (SDN) implementations; 
         FIG. 5  illustrates an example plot of path delays over time; 
         FIGS. 6A-6D  illustrate an example of a failsafe mechanism for predictive routing in a network; and 
         FIG. 7  illustrates an example simplified procedure to perform fast probing for a predictive routing decision. 
     
    
    
     DESCRIPTION OF EXAMPLE EMBODIMENTS 
     Overview 
     According to one or more embodiments of the disclosure, a networking device reroutes traffic in a network from a first path to a second path, based on a prediction that the first path will not satisfy a service level agreement associated with the traffic. The networking device enters a fast monitoring state during which the networking device performs fast probing of the first path and of the second path onto which the traffic was rerouted. The networking device makes, based on the fast probing, a determination as to whether the first path would have violated the service level agreement and whether the second path violates the service level agreement. The networking device enacts a routing decision for the traffic by applying a routing policy to the determination. 
     Description 
     A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers and workstations, or other devices, such as sensors, etc. Many types of networks are available, with the types ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, and others. The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol consists of a set of rules defining how the nodes interact with each other. Computer networks may be further interconnected by an intermediate network node, such as a router, to extend the effective “size” of each network. 
     Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or “AMI” applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform any other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless or PLC networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port such as PLC, a microcontroller, and an energy source, such as a battery. Often, smart object networks are considered field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth. 
       FIG. 1A  is a schematic block diagram of an example computer network  100  illustratively comprising nodes/devices, such as a plurality of routers/devices interconnected by links or networks, as shown. For example, customer edge (CE) routers  110  may be interconnected with provider edge (PE) routers  120  (e.g., PE-1, PE-2, and PE-3) in order to communicate across a core network, such as an illustrative network backbone  130 . For example, routers  110 ,  120  may be interconnected by the public Internet, a multiprotocol label switching (MPLS) virtual private network (VPN), or the like. Data packets  140  (e.g., traffic/messages) may be exchanged among the nodes/devices of the computer network  100  over links using predefined network communication protocols such as the Transmission Control Protocol/Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relay protocol, or any other suitable protocol. Those skilled in the art will understand that any number of nodes, devices, links, etc. may be used in the computer network, and that the view shown herein is for simplicity. 
     In some implementations, a router or a set of routers may be connected to a private network (e.g., dedicated leased lines, an optical network, etc.) or a virtual private network (VPN), such as an MPLS VPN thanks to a carrier network, via one or more links exhibiting very different network and service level agreement characteristics. For the sake of illustration, a given customer site may fall under any of the following categories: 
     1.) Site Type A: a site connected to the network (e.g., via a private or VPN link) using a single CE router and a single link, with potentially a backup link (e.g., a 3G/4G/5G/LTE backup connection). For example, a particular CE router  110  shown in network  100  may support a given customer site, potentially also with a backup link, such as a wireless connection. 
     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). 
     2b.) Site Type B2: a site connected to the network using one MPLS VPN link and one link connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/5G/LTE connection). For example, a particular customer site may be connected to network  100  via PE-3 and via a separate Internet connection, potentially also with a wireless backup link. 
     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). 
     Notably, MPLS VPN links are usually tied to a committed service level agreement, whereas Internet links may either have no service level agreement at all or a loose service level agreement (e.g., a “Gold Package” Internet service connection that guarantees a certain level of performance to a customer site). 
     3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but with more than one CE router (e.g., a first CE router connected to one link while a second CE router is connected to the other link), and potentially a backup link (e.g., a wireless 3G/4G/5G/LTE backup link). For example, a particular customer site may include a first CE router  110  connected to PE-2 and a second CE router  110  connected to PE-3. 
       FIG. 1B  illustrates an example of network  100  in greater detail, according to various embodiments. As shown, network backbone  130  may provide connectivity between devices located in different geographical areas and/or different types of local networks. For example, network  100  may comprise local/branch networks  160 ,  162  that include devices/nodes  10 - 16  and devices/nodes  18 - 20 , respectively, as well as a data center/cloud environment  150  that includes servers  152 - 154 . Notably, local networks  160 - 162  and data center/cloud environment  150  may be located in different geographic locations. 
     Servers  152 - 154  may include, in various embodiments, a network management server (NMS), a dynamic host configuration protocol (DHCP) server, a constrained application protocol (CoAP) server, an outage management system (OMS), an application policy infrastructure controller (APIC), an application server, etc. As would be appreciated, network  100  may include any number of local networks, data centers, cloud environments, devices/nodes, servers, etc. 
     In some embodiments, the techniques herein may be applied to other network topologies and configurations. For example, the techniques herein may be applied to peering points with high-speed links, data centers, etc. 
     According to various embodiments, a software-defined WAN (SD-WAN) may be used in network  100  to connect local network  160 , local network  162 , and data center/cloud environment  150 . 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 network  160  to router CE-1 at the edge of data center/cloud environment  150  over an MPLS or Internet-based service provider network in backbone  130 . 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 network  160  and data center/cloud environment  150  on 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. 
       FIG. 2  is a schematic block diagram of an example node/device  200  that may be used with one or more embodiments described herein, e.g., as any of the computing devices shown in  FIGS. 1A-1B , particularly the PE routers  120 , CE routers  110 , nodes/device  10 - 20 , servers  152 - 154  (e.g., a network controller/supervisory service located in a data center, etc.), any other computing device that supports the operations of network  100  (e.g., switches, etc.), or any of the other devices referenced below. The device  200  may also be any other suitable type of device depending upon the type of network architecture in place, such as IoT nodes, etc. Device  200  comprises one or more network interfaces  210 , one or more processors  220 , and a memory  240  interconnected by a system bus  250 , and is powered by a power supply  260 . 
     The network interfaces  210  include the mechanical, electrical, and signaling circuitry for communicating data over physical links coupled to the network  100 . The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Notably, a physical network interface  210  may also be used to implement one or more virtual network interfaces, such as for virtual private network (VPN) access, known to those skilled in the art. 
     The memory  240  comprises a plurality of storage locations that are addressable by the processor(s)  220  and the network interfaces  210  for storing software programs and data structures associated with the embodiments described herein. The processor  220  may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures  245 . An operating system  242  (e.g., the Internetworking Operating System, or IOS®, of Cisco Systems, Inc., another operating system, etc.), portions of which are typically resident in memory  240  and executed by the processor(s), functionally organizes the node by, inter alia, invoking network operations in support of software processors and/or services executing on the device. These software processors and/or services may comprise a routing process  244  and/or a fast revert process  248 , as described herein, any of which may alternatively be located within individual network interfaces. 
     It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that processes may be routines or modules within other processes. 
     In general, routing process (services)  244  contains computer executable instructions executed by the processor  220  to 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 structure  245 ) 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 (i.e., 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 process  244  may 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. 
     In various embodiments, as detailed further below, routing process  244  and/or fast revert process  248  may also include computer executable instructions that, when executed by processor(s)  220 , cause device  200  to perform the techniques described herein. To do so, in some embodiments, routing process  244  and/or fast revert process  248  may utilize machine learning. In general, machine learning is concerned with the design and the development of techniques that take as input empirical data (such as network statistics and performance indicators), and recognize complex patterns in these data. One very common pattern among machine learning techniques is the use of an underlying model M, whose parameters are optimized for minimizing the cost function associated to M, given the input data. For instance, in the context of classification, the model M may be a straight line that separates the data into two classes (e.g., labels) such that M=a*x+b*y+c and the cost function would be the number of misclassified points. The learning process then operates by adjusting the parameters a, b, c such that the number of misclassified points is minimal. After this optimization phase (or learning phase), the model M can be used very easily to classify new data points. Often, M is a statistical model, and the cost function is inversely proportional to the likelihood of M, given the input data. 
     In various embodiments, routing process  244  and/or fast revert process  248  may employ one or more supervised, unsupervised, or semi-supervised machine learning models. Generally, supervised learning entails the use of a training set of data, as noted above, that is used to train the model to apply labels to the input data. For example, the training data may include sample telemetry that has been labeled as normal or anomalous. On the other end of the spectrum are unsupervised techniques that do not require a training set of labels. Notably, while a supervised learning model may look for previously seen patterns that have been labeled as such, an unsupervised model may instead look to whether there are sudden changes or patterns in the behavior of the metrics. Semi-supervised learning models take a middle ground approach that uses a greatly reduced set of labeled training data. 
     Example machine learning techniques that routing process  244  and/or fast revert process  248  can employ may include, but are not limited to, nearest neighbor (NN) techniques (e.g., k-NN models, replicator NN models, etc.), statistical techniques (e.g., Bayesian networks, etc.), clustering techniques (e.g., k-means, mean-shift, etc.), neural networks (e.g., reservoir networks, artificial neural networks, etc.), support vector machines (SVMs), logistic or other regression, Markov models or chains, principal component analysis (PCA) (e.g., for linear models), singular value decomposition (SVD), multi-layer perceptron (MLP) artificial neural networks (ANNs) (e.g., for non-linear models), replicating reservoir networks (e.g., for non-linear models, typically for time series), random forest classification, or the like. 
     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, the false positives of the model may refer to the number of times the model incorrectly predicted that conditions in the network will result in an SLA associated with traffic for a particular application will be violated. Conversely, the false negatives of the model may refer to the number of times the model incorrectly predicted that the SLA will not be violated. True negatives and positives may refer to the number of times the model correctly predicted whether the SLA will not be violated or will be violated, 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 quality of service (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-3B  illustrate example network deployments  300 ,  310 , respectively. As shown, a router  110  (e.g., a device  200 ) located at the edge of a remote site  302  may provide connectivity between a local area network (LAN) of the remote site  302  and one or more cloud-based, SaaS providers  308 . For example, in the case of an SD-WAN, router  110  may provide connectivity to SaaS provider(s)  308  via tunnels across any number of networks  306 . This allows clients located in the LAN of remote site  302  to 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 deployment  300  in  FIG. 3A , router  110  may utilize two Direct Internet Access (DIA) connections to connect with SaaS provider(s)  308 . More specifically, a first interface of router  110  (e.g., a network interface  210 , described previously), Int 1, may establish a first communication path (e.g., a tunnel) with SaaS provider(s)  308  via a first Internet Service Provider (ISP)  306   a , denoted ISP 1 in  FIG. 3A . Likewise, a second interface of router  110 , Int 2, may establish a backhaul path with SaaS provider(s)  308  via a second ISP  306   b , denoted ISP 2 in  FIG. 3A . 
       FIG. 3B  illustrates another example network deployment  310  in which Int 1 of router  110  at the edge of remote site  302  establishes a first path to SaaS provider(s)  308  via ISP 1 and Int 2 establishes a second path to SaaS provider(s)  308  via a second ISP  306   b . In contrast to the example in  FIG. 3A , Int 3 of router  110  may establish a third path to SaaS provider(s)  308  via a private corporate network  306   c  (e.g., an MPLS network) to a private data center or regional hub  304  which, in turn, provides connectivity to SaaS provider(s)  308  via another network, such as a third ISP  306   d.    
     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 site  302  to SaaS provider(s)  308 . Other deployments scenarios are also possible, such as using Colo, accessing SaaS provider(s)  308  via Zscaler or Umbrella services, and the like. 
       FIG. 4A  illustrates an example SDN implementation  400 , according to various embodiments. As shown, there may be a LAN core  402  at a particular location, such as remote site  302  shown previously in  FIGS. 3A-3B . Connected to LAN core  402  may be one or more routers that form an SD-WAN service point  406  which provides connectivity between LAN core  402  and SD-WAN fabric  404 . For instance, SD-WAN service point  406  may comprise routers  110   a - 110   b.    
     Overseeing the operations of routers  110   a - 110   b  in SD-WAN service point  406  and SD-WAN fabric  404  may be an SDN controller  408 . In general, SDN controller  408  may comprise one or more devices (e.g., devices  200 ) configured to provide a supervisory service, typically hosted in the cloud, to SD-WAN service point  406  and SD-WAN fabric  404 . For instance, SDN controller  408  may be responsible for monitoring the operations thereof, promulgating policies (e.g., security policies, etc.), installing or adjusting IPsec routes/tunnels between LAN core  402  and remote destinations such as regional hub  304  and/or SaaS provider(s)  308  in  FIGS. 3A-3B , and 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. 
     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&#39;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 s 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 in the Internet and a good proportion of them could be avoided (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 controller  408 ) or other supervisory service, or may operate in conjunction therewith. For instance,  FIG. 4B  illustrates an example  410  in which SDN controller  408  includes a predictive application aware routing engine  412  (e.g., through execution of routing process  244  and/or fast revert process  248 ). Further embodiments provide for predictive application aware routing engine  412  to be hosted on a router  110  or at any other location in the network. 
     During execution, predictive application aware routing engine  412  makes use of a high volume of network and application telemetry (e.g., from routers  110   a - 110   b , SD-WAN fabric  404 , 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 engine  412  may 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 engine  412  may 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 engine  412  may 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. 
     By way of example,  FIG. 5  illustrates a plot  500  of timeseries of delay observed by an edge device to reach a given destination via both the Internet (e.g., via a DIA connection) and a private data center (e.g., via traffic backhauling). Typically, the DIA connection provides the shortest delays to the destination. However, there are also times  504   a - 504   c  during which the backhauled connection via the private data center outperforms the DIA connection. Indeed, during times  504   a - 504   c , the DIA connection exhibits spikes of delay, such that SLA  502  is violated. 
     As noted above, an application aware predictive routing engine may identify trend changes in the network KPIs of a path by utilizing several probes that measure path health (e.g., loss, latency and jitter). In turn, the predictive routing engine utilizes statistical and/or machine learning techniques to predict such path deterioration in the future (e.g., predict SLA violations) and generate routing “patches” (e.g., policies) that proactively reroute application traffic before an SLA violation occurs. 
     One of the main challenges of predictive routing is in the ability to accurately perform predictions of SLA violations. Generally speaking, the SLA violation predictions should be made with high recall, for the solution to be effective. However, recall is not the only consideration. Indeed, in some instances, it might also be acceptable not to predict an SLA violation and fall back to a reactive routing approach whereby SLAs are checked thanks to probing and the traffic is rerouted only when an actual SLA violation is detected. 
     Precision represents another performance metric for the SLA violation predictions, which can be particularly critical in situations in which the number of total positive examples is low (e.g., are rare events). Indeed, even a small number of false positives can strongly affect the precision, when the number of true positives is low. Furthermore, the traffic may be unnecessarily rerouted onto a path that may eventually not meet the SLA. In some embodiments, this can be mitigated against by also forecasting whether the new path will violate the SLA. However, rerouting traffic onto the new path will unavoidably change the conditions, including ways that could cause the SLA to be violated. This can be doubly problematic in situations in which the original path does not exhibit the predicted SLA violation, meaning that the predictive reroute actually made things worse. 
     Reverting Routing Decisions 
     The techniques introduced herein allow for the fast reversion of rerouting decisions on detection of a predictive routing engine incorrectly predicting an SLA violation on the primary/original path and/or traffic being rerouted onto a secondary/new path that results in a violation of the SLA. In some aspects, the techniques herein allow for a router to locally perform fast monitoring, so as to actively monitor the states of both path using various timing strategies. Then, on detection of an incorrect prediction or SLA violation on the secondary path, the rerouting decision can be quickly reverted, either by canceling the rerouting or performing another mitigation action, such as load balancing the traffic. 
     Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the fast revert process  248 , which may include computer executable instructions executed by the processor  220  (or independent processor of interfaces  210 ) to perform functions relating to the techniques described herein (e.g., in conjunction with routing process  244 ). 
     Specifically, according to various embodiments, a networking device reroutes traffic in a network from a first path to a second path, based on a prediction that the first path will not satisfy a service level agreement associated with the traffic. The networking device enters a fast monitoring state during which the networking device performs fast probing of the first path and of the second path onto which the traffic was rerouted. The networking device makes, based on the fast probing, a determination as to whether the first path would have violated the service level agreement and whether the second path violates the service level agreement. The networking device enacts a routing decision for the traffic by applying a routing policy to the determination. 
     As pointed out previously, incorrectly predicting an SLA violation by a primary path can lead to its traffic being proactively rerouted onto a secondary path. If the predicted SLA violation was a false positive, this means that the traffic was rerouted without reason. Worse, rerouting is always a costly operation, from the back-head perspective. Furthermore, rerouting may disrupt some flows because of packet re-ordering and unavoidable increase of jitter. However, the false positive can have even worse effects, if the secondary path onto which the traffic is rerouted does not meet the SLA of the traffic. 
     Operationally,  FIGS. 6A-6D  illustrate the operation of a failsafe mechanism for predictive routing, according to various embodiments. As shown, assume that a network  600  includes a router  110   c  that is to send application traffic  608  to a destination  604 , such as an SaaS provider or the like. To do so, there may be two or more possible routing paths between router  110   c  and destination  604 . For instance, router  110   c  may be able to route application traffic  608  via one or more DIA connections, MPLS backhaul connections, or the like. For simplicity, two paths, path  602   a  and path  602   b  are shown. 
     In addition, there may also be a predictive routing engine  606  that uses telemetry data collected from network  600  to predict SLA violations along path  602   a  and path  602   b  and push routing patches to router  110   c  that cause router  110   c  to proactively avoid such violations by rerouting its traffic onto another path. For instance, as shown, assume that router  110   c  routes application traffic  608  via path  602   a , which acts as the primary path for application traffic  608 . Now, assume that predictive routing engine  606  predicts that path  602   a  will violate the SLA of application traffic  608 . In such a case, application traffic  608  may generate and send a routing patch  610 , so as to reroute application traffic  608  onto path  602   b  in advance of the predicted SLA violation by path  602   a.    
     In  FIG. 6B , after receiving routing patch  610 , router  110   c  may reroute application traffic  608  onto the secondary path, path  602   b , to avoid the predicted SLA violation by path  602   a . According to various embodiments, router  110   c  may also enter into a Fast-Monitoring (FM) mode either before, shortly after, or concurrently with the rerouting of application traffic  608  onto path  602   b  at time according to routing patch  610  from predictive routing engine  606 . 
     While in FM mode, router  110   c  may begin a fast probing cycle onto path  602   a  and path  602   b  whereby probes  612   a  and probes  612   b , respectively, are sent. Such probes  612   a - 612   b  may be of different forms, depending on the natures of paths  602   a - 602   b . For instance, probes  612   a - 612   b  may take the form of BFD probes on SD-WAN tunnels, HTTP probes for CloudOnRamp, etc. During this, router  110   c  may send probes  602   a - 602   b  every X-number of seconds where X&lt;Y (Y being the default mode). In other words, router  110   c  may send probes along paths  602   a - 602   b  at a higher frequency while in FM mode than normally. In various embodiments, X could be computed taking into account the link speed, level of congestion, level of CPU usage, or other performance metrics for router  110   c.    
     Based on the results of probing path  602   a , router  110   c  may then perform local computations to check the SLA along path  602   a . To this end, in some embodiments, predictive routing engine  606  may employ a custom extension to routing patch  610  that specifies the SLA template related to its rerouting prediction regarding path  602   a  (e.g., max delay, max loss, max jitter, etc.). 
     In a first embodiment, router  110   c  may switch application traffic  608  onto path  602   b . However, in a further embodiment, router  110   c  may also send a local shadow copy of the packets of application traffic  608  as traffic copy  608   a  onto path  602   a  with a flag indicating to destination  604  or the tail-end of the tunnel that it should be dropped on reception. The aim of sending such a copy of application traffic  608  is to check whether the SLA would have been violated had the traffic stayed on path  602   a , thus confirming whether the predicted SLA violation was a true positive or was incorrect (i.e., a false positive). Optionally, router  110   c  may send traffic copy  608   a  along path  602   a  for a shorter period of time, which may be configurable. In addition, router  110   c  may recolor as traffic copy  608   a , to assign a lower priority to it, should other non-rerouted traffic still be sent along path  602   a , to avoid QoS degradation of that traffic. 
     Router  110   c  may also perform fast probing of path  602   b , potentially with a different probing interval X′ than that of path  602   a . In turn, router  110   c  may make local computations at a higher pace, to assess whether path  602   b  violates the SLA associated with application traffic  608 . Optimally, a different statistical moment may also be used while router  110   c  is in FM mode. For instance, during normal operations, router  110   c  may usually check SLA violations during averages over ten-minute time periods. However, during FM mode, router  110   c  may check the SLA for violations at a much higher pace, such as every thirty seconds, using Max or higher percentile values. 
     In other embodiments, router  110   c  may monitor different application experience metrics, while in FM mode, in addition to any SLA violations. For example, router  110   c  may monitor the number of users affected by an SLA violation, the number of session disruptions, or even explicit application feedback information (e.g., degradation of video application traffic can be detected by changes in the resolution observed over paths). 
     As shown in  FIG. 6C , router  110   c  may send a report  612  to predictive routing engine  606  indicative of the results of its FM mode, according to various embodiments. For instance, report  612  may indicate whether the SLA was met or violated by path  602   a  or path  602   b , whether a copy of traffic  608  was sent on path  602   a , additional information related to the link status, queue status, application experience metrics, or the like. Predictive routing engine  606  may then use the information from report  612  in further predictions. 
     If the SLA of application traffic  608  was violated by path  602   a , but not by path  602   b , this is the ideal situation, meaning that predictive routing engine  606  correctly predicted that path  602   a  would violate the SLA of application traffic  608  and was able to trigger a reroute of traffic  608  onto path  602   b , proactively. Conversely, if the SLA was not violated by either of paths  602   a - 602   b , then application traffic  608  was unnecessarily rerouted but with (almost) no consequences. Finally, if the SLA was violated after rerouting application traffic  608  onto path  602   b , but no such SLA violation would have occurred on path  602   a , not only did predictive routing engine  606  make a wrong prediction (e.g., a false positive), but the decision to reroute application traffic  608  was also incorrect. Finally, if the SLA is violated on both paths  602   a - 602   b , thanks to additional information obtained by router  110   c  from fast probing, predictive routing engine  606  may be able to determine whether the rerouting decision was still beneficial to application traffic  608  by comparing the SLA on path  602   a  and path  602   b , especially in presence of traffic copy  608   a  on path  602   a.    
     Another aspect of the techniques herein is a fast revertive mechanism, according to various embodiments. During its FM phase, which may be dynamic, router  110   c  may perform regular checks of the SLA with respect to paths  602   a - 602   b  at a regular time interval I. In turn, router  110   c  may enforce a revertive policy, based on the results of the checks. Such policies may be sent to router  110   c  by predictive routing engine  606  or another source, or configured directly on router  110   c . For instance, the possible revertive policies may include any or all of the following:
         SLA violated on path  602   a  and not on path  602   b : Do nothing.   SLA violated on path  602   b  and not on path  602   a : In this case, router  110   c , if allowed, may trigger a revertive mode where the original application traffic  608  is switched back from path  602   b  to path  602   a . In turn, router  110   c  may also notify predictive routing engine  606  of this revertive decision (e.g., via report  612  or another notification).   SLA not violated on path  602   a  or path  602   b : Do nothing.   SLA violated on both path  602   a  and on path  602   b : In this situation, router  110   c  may be allowed to perform load balancing on the rerouted application traffic  608  onto path  602   a  and path  602   b , according to an approach governed by predictive routing engine  606  or locally configured on router  110   c . In this case, router  110   c  may report its decision to predictive routing engine  606  (e.g., via report  612  or another notification). In addition, router  110   c  may continue to monitor the situation and send additional reports to predictive routing engine  606 , along with optional parameters (e.g., Netflow parameters, queue statistics, etc.). Such information may further be used by predictive routing engine  606  to improve its predictions. In another embodiment, a further strategy may be to load balance application traffic  608  onto path  602   a  and path  602   b , according to its traffic priority.       

     For instance, as shown in  FIG. 6D , assume that the SLA was violated on path  602   b , but not on path  602   a . In such a case, router  110   c  may revert the reroute of predictive routing engine  606  from path  602   b  back onto path  602   a.    
     In yet another embodiment, router  110   c  may be allowed to perform additional trials. Under this, even if application traffic  608  is switched back to the primary path, path  602   a , router  110   c  may continue to record local conditions and may make a second rerouting attempt to move application traffic  608  back onto path  602   b , if the total rerouting period has not yet expired. For example, assume that router  110   c  determines that the link utilization suddenly decreases or had suddenly increased, prior to time T 1  (e.g., when the SLA violation was predicted to occur). That may be due, for instance, to unexpected traffic being sent via path  602   b , prior to the rerouting of application traffic  608 . In such a case, router  110   c  may make a second attempt to reroute application traffic  608  onto path  602   b  and report the results of this to predictive routing engine  606 . Optionally, router  110   c  may perform a set of N-number attempts for the reroute, each attempt being evenly spread out in time or, alternatively, separated by increasing periods of time (e.g., using dampening). 
     In yet another embodiment, the data from the N-number of rerouting attempts by router  110   c  may be analyzed statistically, to check whether rerouting onto path  602   b  is in fact significantly better than path  602   a  in terms of SLA violation. For example, router  110   c  may record the fraction of time the SLA is violated on path  602   a  and on path  602   b  for the N-number of attempts. Then, a statistical hypothesis test, such as Two sample Kolmogorov-Smirnov (KS)-Test, can be performed to check whether the above conditions can be inferred (e.g., SLA violated on path  602   a , and not on path  602   b ). 
     Note that traditional routing approaches such as ISIS, OSPF, etc., or even MPLS TE Fast Reroute where tunnel head-ends are rerouted onto the primary path upon global re-optimization, lack any form of revertive operation. 
     In further embodiments, predictive routing engine  606  may use any reports of false positives by router  110   c  to improve the prediction accuracy of its model. Optionally, a report of a false positive by router  110   c  may even trigger model retraining. 
     In addition, the false positive rate can also be used to adjust the revertive mode described previously. For instance, if the false positive rate increases, predictive routing engine  606  may adjust the FM period of router  110   c , such as by using a shorter interval between probing or a shorter period of time I to decide whether to revert. Conversely, if the false positive rate decreases, meaning that the performance of the prediction model actually increased, predictive routing engine  606  may opt to increase either or both of these periods of time on router  110   c . Similarly, predictive routing engine  606  may use metrics such as the rate of SLA violations on path  602   b  and not on path  602   a , to adapt the policy and potentially reduce the ability of the system for the automation of rerouting. Indeed, if the predictions by predictive routing engine  606  are incorrect and traffic is rerouted onto a secondary path resulting in an SLA violation, it may be desirable to restrict the ability for the system to perform close loop control, in some embodiments. Note also that the additional information provided by router  110   c  on the status along path  602   b  when rerouting application traffic  608  may be used by predictive routing engine  606  its choice of alternate path for further rerouting decisions, when it forecasts SLA violations along the primary path  602   a  (e.g., by opting to reroute further traffic along a third path instead of path  602   b , etc.). 
       FIG. 7  illustrates an example simplified procedure to perform fast probing for a predictive routing decision, in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device  200 ), such as a networking device (e.g., a router, an SDN controller for an SD-WAN, etc.), may perform procedure  700  by executing stored instructions (e.g., process  248 ). The procedure  700  may start at step  705 , and continues to step  710 , where, as described in greater detail above, the networking device may reroute traffic in a network from a first path to a second path, based on a prediction that the first path will not satisfy a SLA associated with the traffic. For instance, a machine learning-based predictive routing engine, either locally on the networking device or remote to it, may make the prediction. 
     At step  715 , as detailed above, the networking device may enter a fast monitoring state during which the networking device performs fast probing of the first path and of the second path onto which the traffic was rerouted, as described in greater detail above. In general, the fast probing may comprise sending probes along the first path and along the second path at a faster rate than a default probing rate used by the networking device. For instance, the networking device may send BFD probes, HTTP probes, or the like, along the first and second paths, to assess their performance metrics. In yet another embodiment, the fast probing may comprise sending a copy of the traffic along the first path with a flag indicating that the copy of the traffic should be dropped upon reception. 
     At step  720 , the networking device may make, based on the fast probing, a determination as to whether the first path would have violated the SLA and whether the second path violates the SLA, as described in greater detail above. In one embodiment, the determination indicates that the first path would not have violated the SLA and that the second path does violate the SLA. In another embodiment, the determination indicates that the first path would have violated the SLA and that the second path does not violate the SLA. In a further embodiment, the determination indicates that the first path would have violated the SLA and that the second path does violate the SLA. 
     At step  725 , as detailed above, the networking device may enact a routing decision for the traffic by applying a routing policy to the determination. In one embodiment, the device may do so by rerouting the traffic back onto the first path, as in the case where the determination indicates that the first path would not have violated the SLA and that the second path does violate the SLA. In such cases, the networking device may notify a machine learning-based predictive routing engine that made the prediction that the first path will not satisfy the SLA, of the determination, to improve prediction accuracy of the machine learning-based predictive routing engine. In further embodiments, the networking device may also perform a second attempt to reroute the traffic onto the second path, so as to perform an additional trial of the second path. the In another embodiment, the device may enact the routing decision by keeping the traffic on the second path, as in the case in which the determination indicates that the first path would have violated the SLA and that the second path does not violate the service level agreement. In yet another embodiment, the networking device may enact the routing decision by load balancing the traffic between the first path and the second path, as in the case in which the determination indicates that the first path would have violated the service level agreement and that the second path does violate the service level agreement. Procedure  700  then ends at step  730 . 
     It should be noted that while certain steps within procedure  700  may be optional as described above, the steps shown in  FIG. 7  are merely examples for illustration, and certain other steps may be included or excluded as desired. Further, while a particular order of the steps is shown, this ordering is merely illustrative, and any suitable arrangement of the steps may be utilized without departing from the scope of the embodiments herein. 
     The techniques described herein, therefore, introduce a safeguard mechanism for predictive routing deployments in a network. By fast probing network paths involved in a predictive routing decision, corrective measure can be taken quickly, to mitigate any harm caused by false positives. In further aspects, a feedback loop with the predictive routing engine can also be used, to help improve the prediction accuracy of it over time. 
     While there have been shown and described illustrative embodiments that provide for reverting routing decisions made based on incorrect network predictions, 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 tunnel failures, SLA violations, or the like, 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. 
     The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly, this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein.