Root-causing user experience anomalies to coordinate reactive policies in application-aware routing

In one embodiment, a device obtains user experience metrics for a plurality of sessions with an online application. The device detects a plurality of anomalies from among the user experience metrics. The device determines, based on a correlation between the plurality of anomalies, that a particular path entity is a root cause of the plurality of anomalies. The particular path entity comprises an egress service provider or data center of the online application. The device provides an indication of the particular path entity being the root cause of the plurality of anomalies.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, more particularly, to root-causing user experience anomalies to coordinate reactive policies in application-aware routing.

BACKGROUND

Software-as-a-Service (SaaS) applications are usually deployed across the globe in different data centers. Users from different regions connect via core Internet links to these SaaS applications. Hence, the application experience of a user of a SaaS application depends on a number of different points of failure: the endpoint device of the user, their Local Area Network (LAN), the core Internet, the SaaS endpoint(s)/data center(s), etc. Accordingly, troubleshooting poor SaaS application experience to trigger corrective measures (e.g., rerouting the application traffic) with such a wide array of possible components interacting with each other is challenging.

Traditional application-aware routing used in software-defined wide area network (SD-WANs) and other network deployments typically rely on probing of the network paths, to detect and mitigate against poor application experience by comparing the probing results to service level agreements (SLAs). For instance, latency that exceeds a. defined SLA threshold is presumed to impact the user experience with the application and may trigger a traffic reroute. Testing has shown, however, that SLA violations are not necessarily indicative of the true application experience for users of certain applications, particularly for those applications that are resilient to such violations.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

According to one or more embodiments of the disclosure, a device obtains user experience metrics for a plurality of sessions with an online application. The device detects a plurality of anomalies from among the user experience metrics. The device determines, based on a correlation between the plurality of anomalies, that a particular path entity is a root cause of the plurality of anomalies. The particular path entity comprises an egress service provider or data center of the online application. The device provides an indication of the particular path entity being the root cause of the plurality of anomalies.

Description

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

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

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

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

In general, predictive routing process248contains computer executable instructions executed by the processor220to perform routing functions in conjunction with one or more routing protocols. These functions may, on capable devices, be configured to manage a routing/forwarding table (a data structure245) containing, e.g., data used to make routing/forwarding decisions. In various cases, connectivity may be discovered and known, prior to computing routes to any destination in the network, e.g., link state routing such as Open Shortest Path First (OSPF), or Intermediate-System-to-Intermediate-System (ISIS), or Optimized Link State Routing (OLSR). For instance, paths may be computed using a shortest path first (SPF) or constrained shortest path first (CSPF) approach. Conversely, neighbors may first be discovered (e.g., a priori knowledge of network topology is not known) and, in response to a needed route to a destination, send a route request into the network to determine which neighboring node may be used to reach the desired destination. Example protocols that take this approach include Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), DYnamic MANET On-demand Routing (DYMO), etc. Notably, on devices not capable or configured to store routing entries, routing process244may consist solely of providing mechanisms necessary for source routing techniques. That is, for source routing, other devices in the network can tell the less capable devices exactly where to send the packets, and the less capable devices simply forward the packets as directed.

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

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

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

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

FIGS.3A-3Billustrate example network deployments300,310, respectively. As shown, a router110located at the edge of a remote site302may provide connectivity between a local area network (LAN) of the remote site302and one or more cloud-based, SaaS providers308. For example, in the case of an SD-WAN, router110may provide connectivity to SaaS provider(s)308via tunnels across any number of networks306. This allows clients located in the LAN of remote site302to access cloud applications (e.g., Office 365™, Dropbox™, etc.) served by SaaS provider(s)308.

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

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

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

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

Overseeing the operations of routers110a-110bin SD-WAN service point406and SD-WAN fabric404may be an SDN controller408. In general, SDN controller408may comprise one or more devices (e.g., a device200) configured to provide a supervisory service, typically hosted in the cloud, to SD-WAN service point406and SD-WAN fabric404. For instance, SDN controller408may be responsible for monitoring the operations thereof, promulgating policies (e.g., security policies, etc.), installing or adjusting IPsec routes/tunnels between LAN core402and remote destinations such as regional hub304and/or SaaS provider(s)308inFIGS.3A-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.

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

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

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

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

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

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

As noted above, issues can arise for SaaS and other online applications at various places: the device of the user, the local LAN, the service provider(s) (SPs), the SaaS data center (DC), somewhere between the source SP and the DC, etc. In some cases, issues can also arise between different SaaS DCs. For instance, consider a video conference in which different users connect to different DCs, DC1and DC2. In such a case, the DC1-DC2connection itself may be the cause of the degraded user experience.

Simple probing (e.g., traceroute) can be used to assess network performance, such as by identifying hops that introduce the most latency. However, in practice, probing results may not be reflective of the true user experience of an online application. Thus, an application-aware routing engine, such as predictive application aware routing engine412, may also ingest user experience metrics for the application, in order to select paths that avoid disruptions to the QoE of the application. However, user experience is more of a global metric that reflects how all of the various factors contribute to whether the user experience is acceptable. As such, it is not easy to attribute disruptions or poor user experience to specific parts, such as the SP, the DC, or SP-DC/DC-DC connections,

When using simple probes (e.g., traceroute) to assess network performance, it is possible to identify hops that introduce the most latency. However, in practice, those probes might not be reflective of the true user experience. Application-aware routing engines, such as Alto, collect the user-experience from the applications in order to select paths that avoid actual user-experience disruptions. User experience is a more global metric that reflects how all factors contribute to make a service usable or not, and as such it is not easy to attribute disruptions or poor user experience to specific parts such as the SP, the DC, or SP-DC/DC-DC connections.

Root-Causing User Experience Anomalies to Coordinate Reactive Policies in Application-Aware Routing

The techniques herein examine the routes from users to data centers of an online application, to detect anomalies at different entities along the path, such as at the egress SP, a particular DC, or combinations of SPs and/or DCs. In some aspects, the techniques herein may also be used to determine the root cause of the anomaly. In further aspects, the techniques herein can also be used to predict what might happen after an anomaly is detected. Such a prediction can be used to mitigate against possible drops in user experience metrics that may result in the future at other entities.

Specifically, according to various embodiments, a device obtains user experience metrics for a plurality of sessions with an online application. The device detects a plurality of anomalies from among the user experience metrics. The device determines, based on a correlation between the plurality of anomalies, that a particular path entity is a root cause of the plurality of anomalies. The particular path entity comprises an egress service provider or data center of the online application. The device provides an indication of the particular path entity being the root cause of the plurality of anomalies.

Operationally,FIG.5illustrates an example architecture500for root-causing user experience anomalies for an application, according to various embodiments. At the core of architecture500is predictive routing process248, which may be executed by a controller for a network or another device in communication therewith. For instance, probing process249may be executed by a controller for a network (e.g., SDN controller408inFIGS.4A-4B), a particular networking device in the network (e.g., a router, etc.), another device or service in communication therewith, or the like, to provide a supervisory service to the network. More specifically, predictive routing process248may operate in conjunction with a predictive application aware routing engine, such as predictive application aware routing engine412, or directed implemented as a component thereof, in some embodiments.

As shown, predictive routing process248may include any or all of the following components: a keyed anomaly detector (KAD)502, a root cause estimator (RCE)504, an impact estimator (IE)506, and/or a reactive policy coordinator (RPC)508. 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 predictive routing process248. In further embodiments, these components may be implemented separate from that of a predictive routing engine and operate in conjunction with other networking entities, as desired.

In various embodiments, predictive routing process248may rely on user experience data for the various SaaS sessions in the network. For instance, for a given session, user experience records can consist in regular messages of the form:pathId: <identifier for the path between the client and the SaaS application>sessionId: <identifier of the session—e.g., identifier of the call and user for voice calls>window: <time interval to which the user experience metrics correspond>metrics:Score1: 10.1 # Sample user experience metric. . .

The user experience metrics may also include both the results of regular network probes (e.g., QoS probe results regarding loss, latency, or jitter), and user experience probes. A path is a description of the main hops used when going from the client to the SaaS application. For instance, a path may be described using the following form: Source edge device (where the client is).Gateway device (optional, when backhauling through a gateway for the SaaS application's traffic).Egress Service Provider (either at the source edge device, or at the gateway). In practice, a service provider can correspond to a service provider vendor as well as to some geographical location (e.g., Orange in Paris). The geographical location's granularity can be adapted based on the amount of data available.SaaS Data. Center.

During execution, keyed anomaly detector (KAD)502is responsible for detecting anomalies in user experience along different path entities such as edge device, egress service provider, or SaaS data center. In one embodiment, KAD502may define the possible anomaly sources, which are called ‘keys’ or ‘path entities’ herein. The keys can be extracted from the path identifiers. For instance, the following keys/path entities can be used:K1: The path's egress Service Provider (SP)K2: The path's SaaS Data Center (DC)K3: A combination of a path's (Egress SP, SaaS DC) as a composite key which will detect anomalies which occur to users connected to the given egress SP and a particular DC

In some embodiments, KAD502may build an anomaly detection model for each observed value of the key. For instance, we might build models for the following combinations of entities:

The anomaly detection model is estimated based on past user experience record metrics for paths that have the corresponding key value. In various implementations, KAD502may apply any of a variety of anomaly detection models such as density estimation, quantile regression, etc. For many key values, there may be limited samples if there have not been many corresponding sessions. In such cases, KAD502. may also to drop any models in order to only retain models trained on sufficient data. This can be enforced with a minimum absolute number of samples, as well as with a train/test split for validation when there are enough samples.

In addition to identifying the anomalies, KAD502may also determine the appropriate anomaly detection band used to detect anomalies. Such a band may define what is considered normal versus abnormal. For example,FIG.6illustrates an example plot600showing a user experience anomaly, according to various embodiments. More specifically, at each time interval, KAD502may compute the distribution of user experience scores. For instance, KAD502may compute the distribution606of user experience scores at time t, the distribution604of user experience scores at time t+1, etc. From these, KAD502may compute the anomaly band as a measure of spread, such as the inter-quartile range (IQR) or n-number of standard deviations around the mean. Here, since the user experience exhibits a sudden drop at time t, distribution606may be flagged as anomalous.

Referring again toFIG.5, in other embodiments, KAD502may update the anomaly detection models online or re-estimated, periodically. Anomaly detection models can also be adjusted by KAD502using a value function, which down-weights anomalies that are not of high impact (e.g., when the user experience is better than expected).

As new sets of user experience records are obtained, they may be used as input to the machine learning models of KAD502, to produce scored anomalies. Note that such models will learn the baseline experience for a given key value. As such, a SP or a DC that always delivers poor user experience will not lead to anomalies and only changes with respect to the baseline will. In general, the techniques herein focus on dynamic changes as opposed to static findings about the performance of a given pail.

Another potential component of predictive routing process248is root cause estimator (RCE)504, which is responsible for correlating anomalies and assigning potential causes to user experience records with poor experience. RCE504associates to each user experience anomalies that correspond to the path identifier and window. Multiple anomalies can be associated to a single record: for instance, anomalies for the SP, DC, SP-DC aggregations keys can be associated to a single record.

For example, considerFIGS.7A-7D, which illustrate example plots of user experience metrics over time. More specifically, plot700inFIG.7Ashows a detected anomaly702at time t for the user experience metrics associated with a first service provider, SP1. Likewise, plot710inFIG.7Bshows that there is also an anomaly712at time t for the user experience metrics associated with the (SP1, DC1) key. However, plot720inFIG.7Cindicates that there was no corresponding anomaly at time t for the (SP1, DC2) key. Similarly, plot730inFIG.7Dshows that there was no corresponding anomaly at time t for the DC1key.

Referring again toFIG.5, in the scenario presented inFIGS.7A-7D, RCE504may look at the anomaly score and the anomaly band (or distributions) to find the probable cause of the drop in user experience scores. Indeed, it, is evident that all users who were connected to (SP1, DC1) had much higher chances of having a bad application experience (and lower width of the band) than the users who were just connected to SP1, irrespective of the DC. Hence, RCE504may assign the key value (SP1, DC1) as the probable cause for the anomaly. For each user-session that traversed the path having the (SP1, DC1) path entity, this would present a clear reason as to why their user experience was degraded.

In another embodiment, predictive routing process248may include impact estimator (IE)506, which is responsible for identifying the impact of disruptions at a given SP, SaaS DC, or combination of those. IE506produces an impact message which can be used to drive long-term improvements of the various parts of the system, as well as to provide live visibility during disruptions about what the impact is.

For instance, plot800inFIG.8shows anomalies over time for different data centers. As shown, an anomaly may first occur at DC1and then, say an hour later, an anomaly at DC2may be detected. Note that this might be possible because of a persistent problem DC1has shifted users from DC1to the next nearest data-center DC2. If this pattern occurs commonly, then the system can predict that the anomaly at DC2would occur next and send a message to the SaaS provider, the predictive routing engine, or other component of the network for corrective measures.

Referring yet again toFIG.5, IE506may monitor the anomalies and also the number of user-sessions connected to each key-value that is seen. IE506can then correlate the anomalies at key-value with anomalies with other key-value or number of user-sessions with time-lag. IE506may also use an approach such as Time-lagged Cross Correlation (TLCC), to detect such correlations. In other embodiments, every time-series maintained by IE506can be forecasted as a function of every other time-series. At every time-step, if the forecaster predicts anomaly or high number of user sessions, and it is determined due to anomaly that has previous occurred on other key-value, IE506will send a message indicating such a possibility.

In various embodiments, reactive policy coordinator (RPC)508may be configured to produce reactive policies (e.g., policy changes that should be applied right away to mitigate a disruption) to apply at any or all of the following:For anomalies that include an edge router, a reactive routing policy can be pushed via SD-WAN to use an alternate path. A predictive routing engine can also be used to pick the best path based on ‘what-if scenarios’ (e.g., estimations of whether an alternate path will be able to cope with additional traffic).For anomalies that include a SaaS DC for a SaaS application that has a partnership with the routing system, DC selection hints can be pushed for the corresponding paths or users to the SaaS provider. These DC selection hints can apply for the next session initiations in the corresponding scope. The SaaS provider can also change its Domain Name System (DNS) policy to not route (or route only a small fraction) of the DNS requests to the affected SaaS data center.Secure Access Service Edge (SASE) components can also be notified in a similar manner as to the DC selection hints.

In yet another embodiment, RPC508may communicate with a user interface , to gather feedback about the probable root cause from a network administrator, so as to validate the correlation and learn the relationship for further evaluation.

When multiple choices of alternative path and reactive policy exist, RPC508can also rely on the impact reports and messages from IE506. The SaaS provider and/or routing engine can subscribe to such messages. Once a message of probable anomaly on a key-value is predicted the routing engine can change the paths to avoid the particular key, where possible. For example, if the routing engine gets a message saying that there is a possible low user-experience predicted in the next hour on all connections going on SP1, it can reroute the traffic on a different interface where the egress SP is not SP1. Similarly, the SaaS provider can redirect the loads to other data-center if it receives a message saying DC1may experience low user-experience anomalies in the next few hours.

FIG.9illustrates an example simplified procedure900(e.g., a method) procedure for determining the root cause of user experience anomalies for an application, in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device200), such as controller for a network (e.g., an SDN controller or other device in communication therewith), may perform procedure900by executing stored instructions (e.g., probing process249), to provide a supervisory service to a network. The procedure900may start at step905, and continues to step910, where, as described in greater detail above, the device may obtain user experience metrics for a plurality of sessions with an online application, such as a SaaS application. In various embodiments, the user experience metrics may be specified by users of the online application, such as via a survey or rating mechanisms integrated into the application, or through a similar mechanism that works outside of the application. In further embodiments, the device may obtain the user experience metrics from the online application.

At step915, as detailed above, the device may detect a plurality of user experience metrics from among the user experience metrics. In various embodiments, the device may do so by associating the user experience metrics with path entities for their respective sessions. Such path entities may comprise service providers, data centers of the online application, and/or connections between them. In turn, the device may use the user experience metrics and their associated path entities as input to an anomaly detection model.

At step920, the device may determine, based on a correlation between the plurality of detected anomalies, that a particular path entity is the root cause of the plurality of anomalies, as described in greater detail above. In various embodiments, the device may do so by correlating anomalies across different combinations of path entities. For instance, the device may assess whether anomalies were detected within a certain timeframe at different service providers, data centers, and links therebetween, to identify the root cause.

At step925, as detailed above, the device may provide an indication of the particular path entity being the root cause of the plurality of anomalies. In various embodiments, the device may provide the indication to an application-aware

The techniques described herein, therefore, provide system and methods to detect user experience anomalies in an application-driven routing system, as well as to determine the root causes of them. In general, root cause information can be used to quantify what the impact of certain types of events can be, to drive manual capacity or policy changes. In addition, root cause information can be used at short time scales when an anomaly is detected to try and apply reactive routing policies. Using the identified root cause, a predictive application-aware routing engine can also integrate with SD-WAN and SASE providers, or even SaaS providers, to apply policy changes at the right location so as to avoid disruptions.

For instance, when a root cause is identified to be primarily in a SaaS DC, the system can avoid unnecessary or potentially harmful routing policy changes at the edge if it can be established that these are unlikely to help. Similarly, when a root cause is identified to be due to a combination of parts (e.g., the combination of an edge path and of a given SaaS DC), coordinated policy changes could be applied to respective components to try and resolve the problem. Further aspects of the techniques herein also introduce an impact prediction mechanism that is responsible to predict the effects of an anomaly occurring at any a given entity (e.g., at a particular SaaS DC), and raise alerts, accordingly. Finally, the techniques herein also introduce reactive policy enforcement mechanisms that listen to the root-causing agent and impact predictor, and acts to alter the SaaS paths such that user experience is not compromised.

While there have been shown and described illustrative embodiments that provide for the root causing of user experience anomalies, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, while certain embodiments are described herein with respect to using certain models for purposes of predicting application experience metrics, SLA violations, or other disruptions in a network, 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.