Patent Publication Number: US-2022231939-A1

Title: Model counterfactual scenarios of sla violations along network paths

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to computer networks, and, more particularly, to model counterfactual scenarios of service level agreement (SLA) violations along network paths. 
     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.). 
     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. 
     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, there is also no guarantee that proactively rerouting the traffic onto a new path will result in improved performance, particularly if the new path exhibits even worse QoS metrics than the original path. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which: 
         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 architecture for evaluating counterfactual routing scenarios in a network; 
         FIG. 6  illustrates an example plot of the traffic profile along a network path over time; and 
         FIG. 7  illustrates an example simplified procedure for evaluating counterfactual routing scenarios. 
     
    
    
     DESCRIPTION OF EXAMPLE EMBODIMENTS 
     Overview 
     According to one or more embodiments of the disclosure, a device obtains traffic telemetry data regarding a first path in a network and an alternate path in the network. The device predicts, based on the traffic telemetry data, an amount of traffic for an application that is expected at a particular time. The device makes, based on the traffic telemetry data and on the amount of traffic for the application that is predicted to be expected at the particular time, a counterfactual prediction as to whether the alternate path would violate a service level agreement associated with the traffic, should the traffic be routed via the alternate path at the particular time. The device causes, based on the counterfactual prediction, the traffic for the application to be rerouted from the first path in the network to the alternate path, prior to the particular time. 
     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  (e.g., an apparatus) 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 counterfactual evaluation 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 (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 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 counterfactual evaluation 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 counterfactual evaluation 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 counterfactual evaluation 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 being indicative of an acceptable QoS or an unacceptable QoS. 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 counterfactual evaluation 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 QoS of a particular network path will not satisfy the service level agreement (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-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 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 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 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 process  244  and/or 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. 
     Predictive application aware routing engine  412  may also 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 lies 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 in 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. 
     By way of example of predictive application aware routing, assume that there is application traffic that is routed along a particular network path (e.g., a tunnel in an SDN) and predicted to experience an SLA violation or, more generally, a decrease in its associated QoE. For instance, assume that path A is forecasted to violate the following SLA for voice traffic in two hours: (latency ≤150 ms, loss ≤3%, and jitter ≤30 ms). In such a case, the routing policy may be patched temporarily on the edge router so that all voice traffic is routed onto a path B, thus avoiding the predicted disruption. However, there is no guarantee that path B is indeed capable of carrying the voice traffic usually carried by path A. Indeed, the rerouting itself might then cause a violation, possibly even worse, on path B, both for the existing traffic on path B and the rerouted traffic from path A. 
     —Model Counterfactual Scenarios of SLA Violations Along Network Paths— 
     The techniques introduced herein allow for the forecasting of so-called “counterfactual” scenarios, that is, predicting what would happen under different circumstances than those actually observed. For instance, the proposed forecasting can answer questions such as “if 3.4 Mbps of voice traffic were rerouted onto path B, would the SLA of the voice traffic be violated?” In contrast to traditional forecasting, this allows for the modeling of what-if scenarios. To this end, various mechanisms and methods are introduced to collect data, train models, and forecast the outcome of counterfactual outcomes. Note that such an issue is notoriously known as being very challenging and more than one technique may be used to achieve that objective. 
     Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with counterfactual evaluation 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 device obtains traffic telemetry data regarding a first path in a network and an alternate path in the network. The device predicts, based on the traffic telemetry data, an amount of traffic for an application that is expected at a particular time. The device makes, based on the traffic telemetry data and on the amount of traffic for the application that is predicted to be expected at the particular time, a counterfactual prediction as to whether the alternate path would violate a service level agreement associated with the traffic, should the traffic be routed via the alternate path at the particular time. The device causes, based on the counterfactual prediction, the traffic for the application to be rerouted from the first path in the network to the alternate path, prior to the particular time. 
     Operationally,  FIG. 5  illustrates an example architecture  500  for evaluating counterfactual routing scenarios in a network, according to various embodiments. At the core of architecture  500  is counterfactual evaluation process  248 , which may be executed by a supervisory device of a network or another device in communication therewith. For instance, counterfactual evaluation process  248  may be executed by an SDN controller (e.g., SDN controller  408  in  FIG. 4 ), a particular networking device in the network (e.g., a router, etc.), or another device in communication therewith. As shown, counterfactual evaluation process  248  may include any or all of the following components: a counterfactual forecasting engine  502 , a traffic forecasting engine  504 , a data collection engine  506 , a counterfactual control engine  508 , and/or a monitoring engine  510 . 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 counterfactual evaluation process  248 . 
     During execution, counterfactual evaluation process  248  may obtain telemetry data  514  from any number of traffic telemetry collectors  512  for the network path(s) under scrutiny. For instance, telemetry data  514  may comprise NetFlow records, IPFIX records, path probing results, such as from Bidirectional Forwarding Detection (BFD) probing, or other telemetry data indicative of the performance of a particular path (e.g., in terms of delay, jitter, packet loss, etc.). Telemetry data  514  may also include application-specific information regarding the various applications whose traffic is conveyed by a particular path in the network. In some instances, counterfactual evaluation process  248  may also provide control over the collection of telemetry data  514  by traffic telemetry collectors  512 , such as by issuing control data  516  to traffic telemetry collectors  512 . 
     As would be appreciated, a prerequisite to counterfactual modeling is the collection of relevant telemetry data  514 . To this end, two possibilities exist with respect to the collection of telemetry data  514 :
         Passive data collection whereby traffic telemetry collectors  512  collect telemetry data  514  from the network without any control by counterfactual evaluation process  248 . In this situation, actively probing a path is typically not possible, to determine whether that path could support a given traffic load under specific conditions (e.g., type of traffic, time of the day/week, etc.).   Active data collection whereby traffic telemetry collectors  512  collect telemetry data  514  from the network under the control of counterfactual evaluation process  248 , to some extent, through control data  516 . For instance, control data  516  sent by counterfactual evaluation process  248  to traffic telemetry collectors  512  may indicate that traffic telemetry collectors  512  should actively probe a particular network path (e.g., tunnel) with arbitrary traffic in a specific situation.       

     As would be appreciated, counterfactual evaluation process  248  may rely on either or both of passively collected telemetry data  514  and actively collected telemetry data  514 . Indeed, active data collection may sometimes be needed to achieve satisfactory performance of the forecasting, despite its additional resource consumption in the network. 
     In various embodiments, counterfactual evaluation process  248  may include counterfactual forecasting engine (CFE)  502  that is responsible for evaluating counterfactual routing scenarios. For instance, CFE  502  may be configured to answer the question, “Given a set of conditions C at a time T, what is the likelihood that path P violates SLA template A?” Here, the set of conditions C may be given by traffic breakdown of the form of a dictionary, such as {“voice”: 2.3 Mbps, “https”: 14 Kbps, “dns”: 234 bps}, where “voice,” “https,” and “dns” are different types of application traffic. Likewise, the time T may represent an interval given by a start and end timestamp. 
     During execution, CFE  502  may predict a probability, denoted Pr C,T [A], using a machine learning-based prediction model, in various embodiments. Such a prediction model may take the form of a liner model, neural network, or other suitable form of prediction model (e.g., a statistical model, etc.). The prediction model may, for instance, take into consideration information such as, but not limited to, any or all of the following historical information: service provider (SP) information, the location of the path, router information (e.g., its model, etc.), or the like. In a more advanced embodiment, the prediction model of CFE  502  may also take into account the specific traffic type (e.g., voice, DNS, HTTPS, etc.), coupled with the QoS support on a given interface (e.g., retrieved using the SDN controller). In this case, for instance, the prediction model of CFE  502  may compute PrC,T[A] by considering the traffic class for the set of conditions C. 
     In various embodiments, counterfactual evaluation process  248  may also include traffic forecasting engine (TFE)  504 , which is configured to predict the traffic conditions on a path at a given point in time n the future. To this end, TFE  504  may also include a machine learning-based prediction model such as a time-series model that takes the form of a linear autoregressive model, neural network model, or any other suitable form of model (e.g., statistical model, etc.). In a simple embodiment, the prediction model of TFE  504  may predict a scalar value that is the expected bitrate on a given path P at a given time T. In more complex embodiments, the prediction model of TFE  504  may predict the expected bitrate for various types of application traffic. Since traffic is typically quite seasonal, TFE  504  may also make use of historical traffic statistics, in order to infer future path usage. 
       FIG. 6  illustrates an example plot  600  of the traffic profile along a network path over time, according to various embodiments. More specifically, plot  600  shows the bitrate (in kbps) of a particular network path (e.g., an SD-WAN tunnel) over the course of two weeks: Sunday, Dec. 1, 2019 through Friday, Dec. 13, 2019. As can be seen, there are both daily and weekly seasonal patterns, with the bitrate increasing drastically during the daytime of weekdays and remaining essentially zero at night and on Sundays. 
     Referring again to  FIG. 5 , the prediction model of CFE  502  may take as input telemetry data  514  that has been collected passively by traffic telemetry collectors  512 , well as actively, in some embodiments. To this end, counterfactual evaluation process  248  may also include data collection engine (DCE)  506  that is responsible for overseeing the collection of telemetry data  514  and issuing control data  516  to traffic telemetry collectors  512 , as needed. Based on telemetry data  514 , DCE  506  may keep track of the traffic per application and QoS metrics such as loss, latency, and jitter, for a given network path. Here, the overall goal of DCE  506  is to provide CFE  502  with as much variety as possible, in terms of telemetry data  514 . In particular, if a given path P is a candidate backup path for rerouting that never violates the SLA of interest, but path P also never carried any traffic, CFE  502  cannot build an accurate model of what will happen, should the application traffic be rerouted onto path P. To solve this, DCE  506  may generate routing patches that force traffic to be rerouted to path P, anyways. In various embodiments, DCE  506  can do this in multiple ways, such as by providing rerouting data  518  to routing process  244 , as follows:
         DCE  506  may ask the edge router to use path P for a subset of the application traffic.   DCE  506  may ask the router to duplicate a subset of the traffic on path P, with a marker that requests the destination to drop the duplicate traffic.   DCE  506  may ask the router to use path P as its first backup when a violation is detected on the primary path.       

     In all of the above cases, the rerouting patches generated by DCE  506  may be temporary in nature. In addition, in some embodiments, DCE  506  may create conditional routing patches, which are implemented only when some specific conditions are met (e.g., voice (raffle reaches 1 Mbps). In other embodiments, DCE  506  may rely on forecasts from TFE  504 , in order to schedule the reroutes at times where the traffic is expected to match conditions that maximize the variety of the resulting telemetry data  514 . 
     Now, as DCE  506  triggers more and more reroutes via rerouting data  518 , the variety of telemetry data  514  increases and CFE  502  will become more capable at determining the probability of SLA violation in various circumstances. 
     In various embodiments, counterfactual evaluation process  248  may also include counterfactual control engine (CCE)  508  that uses the prediction model trained by CFE  502  to make rerouting decisions. For every path P under scrutiny, CCE  508  may query TFE  504 , to check whether any traffic is expected at a given time T. If so, CCE  508  then queries CFE  502 , to check whether there is a risk of an SLA violation for a particular class of application traffic. If there is, CCE  508  may then query CFE  502  for all alternate paths P′, P″, etc. and can make a rerouting decision, if the likelihood of an SLA violation on these alternate path(s) is lower than for the primary path P. Note that CCE  508  may query CFE  502  to evaluate whether a given path can satisfy a new set of conditions C′, while taking into account potential new traffic to reroute and the existing traffic at a given time. In other words, the modeling by CFE  502  also accounts for the traffic that is expected to be rerouted onto these alternative paths, in order to evaluate the necessity of a reroute. If CCE  508  determines that a rerouting should be performed, it may initiate the rerouting by sending rerouting data  518  to routing process  244 , which carries out the rerouting operation. 
     In more complex embodiments, CCE  508  may also use CFE  502  to evaluate whether only a subset of the application traffic should be rerouted. For instance, assuming that the expected traffic on the primary path is as follows: {“voice”: 2.3 Mbps, “dropbox”: 25 Mbps, “dns”: 234 bps}, CCE  508  may query CFE  502 , to evaluate a scenario where the Dropbox traffic is defensively rerouted onto the backup path and predict whether doing so would avoid the SLA violation. In this case, CCE  508  would, therefore, protect the voice traffic by re-routing a bulk transfer on an alternate path. To achieve this, CCE  508  may use a combinatorial search that considers every application as an individual entity that it can assign to different paths. For every combination, CCE  508  may query CFE  502 , to assess the likelihood of a violation on all paths. Of course, different SLA templates may be used for different types of applications, such that the overall objective of the optimizer is to minimize the number of impacted sessions, possibly weighted by criticality of the applications. 
     In yet another embodiment, CCE  508  may also use CFE  502  to evaluate whether only a subset of the traffic should be rerouted, considering the impact on lower priority traffic. For example, consider the case where a traffic T 1  is expected to experience an SLA violation on path A, and there is an alternate path B such that CFE  502  predicts that no such SLA violation would occur when rerouting T 1  onto path B, except for a subset of existing traffic along B of lower priority (sharing the same QoS). In this case, it may still be acceptable and/or preferable to reroute T 1  along path B at the cost of impacting lower priority traffic already existing on path B. 
     In some embodiments, counterfactual evaluation process  248  may also include monitoring engine  510 , which is responsible for monitoring the output of CCE  508  and provide indications to DCE  506  as to which scenarios require additional exploration and telemetry data  514 . Monitoring engine  510  may, for instance, evaluate the input and output of CCE  508 , the paths suggested by CFE  502 , the predicted traffic from TFE  504 , the application, the risk of violation, and/or the ground truth (e.g., whether the SLA violation actually occurred). In one embodiment, monitoring engine  510  may first list paths, traffic regimes, and context for which CFE  502  has performed incorrect predictions, that is, that a path violates an SLA (Pr C,T [A]). Monitoring engine  510  may do so, for instance, by ranking the top &lt;paths, application and traffic-regime&gt; combinations with high incorrect predictions. Based on this, monitoring engine  510  may then instruct DCE  506  to initiate more active probing on these paths for the selected applications and traffic regimes. 
       FIG. 7  illustrates an example simplified procedure for evaluating counterfactual routing scenarios in a network, 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.), or a device in communication therewith, may perform procedure  700  by executing stored instructions (e.g., counterfactual evaluation process  248  and/or routing process  244 ). The procedure  700  may start at step  705 , and continues to step  710 , where, as described in greater detail above, the device may obtain traffic telemetry data regarding a first path and an alternate path in the network. As noted above, the device may do so in a passive manner and/or in an active manner, such as by instructing a router in the network to actively probe a path. For instance, the device may instruct the router to reroute or duplicate a portion of traffic for an application onto the alternate path. In other cases, the device may instruct the router to set the alternate path as a first backup path for the first path, so that the application traffic will be rerouted onto it, should the first path violate the SLA of the traffic. Example traffic telemetry data may indicate path QoS metrics (e.g., delay, loss, jitter, etc.), characteristics of the application traffic (e.g., the identity of the application, the bitrate of the traffic, the priority of the traffic, etc.), other path characteristics (e.g., the model of the edge router associated with the path, geographic location information for the path, etc.), combinations thereof, or the like. 
     At step  715 , as detailed above, the device may predict, based on the traffic telemetry data, an amount of the application traffic that is expected at a particular time. As noted, application traffic often exhibits seasonal profiles, such as on an hourly, daily, or weekly basis. Accordingly, the device may train and use a prediction model to predict the amount of expected application traffic at a particular time in the future. For instance, if no traffic was observed on the prior n-number of Sundays, the model may predict that there will also be no traffic observed on the upcoming Sunday. 
     At step  720 , the device may make a counterfactual prediction as to whether the alternate path would violate a service level agreement associated with the traffic, should the traffic be routed via the alternate path at the particular time, as described in greater detail above. In various embodiments, the device may base the counterfactual prediction on the traffic telemetry data and on the amount of traffic that it predicted to be expected at the particular time. In other words, the counterfactual prediction may predict the effects of rerouting the traffic onto the alternate path, even if that traffic is not currently being routed via the alternate path. In some embodiments, the device may use a machine learning-based prediction model to make such a prediction. 
     At step  725 , as detailed above, the device may cause, based on the counterfactual prediction, the traffic for the application to be rerouted from the first path to the alternate path, prior to the particular time. In some embodiments, the device may do so by opting to reroute a subset of the traffic to the alternate path. In other embodiments, the device may determine that rerouting the traffic onto the alternate path will cause an SLA associated with lower priority traffic on the alternate path to be violated, but still proceed with the rerouting, anyways. 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, dramatically improve the performance of Predictive Application Aware Routing (PAAR) engines by combining a traffic forecaster and a counterfactual forecast that is capable of estimating the likelihood of a violation on a given path for various traffic conditions. Doing so allows a control engine to make much more robust and subtle routing decisions, including defensive reroutes, to protect critical traffic instead of merely rerouting the whole traffic of a link to alternate paths that may not be able to support that much traffic. 
     While there have been shown and described illustrative embodiments that provide for modeling counterfactual routing scenarios, 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 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. 
     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.