HIGH FREQUENCY PROBING FOR MICRO-FAILURE DETECTION ON SENSITIVE NETWORK PATHS

In one embodiment, a device obtains telemetry data associated with application traffic for an online application. The device identifies, based on the telemetry data, a network path as potentially exhibiting transient events during which a performance metric of the network path is degraded that are undetected at a first probing frequency for the network path. The device determines a new probing frequency for the network path, to detect the transient events. The device causes probes to be sent along the network path according to the new probing frequency.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, more particularly, to high frequency probing for micro-failure detection on sensitive 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/latency, jitter, packet loss, etc.).

Application-aware routing used in SD-WANs typically relies on probing of the network paths used by traffic for a particular application, to detect and mitigate against SLA violations. To do so, a tradeoff is often made with respect to the probing frequency so as not to impinge on the application traffic sent via the path. A potential consequence of this, though, are undetected “micro-failures” in which the QoS requirements of the traffic are temporarily not met, but return to acceptable before the next path probe. While transient in nature, such micro-failures can also impact the quality of experience of the online application from the perspective of its users.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

According to one or more embodiments of the disclosure, a device obtains telemetry data associated with application traffic for an online application. The device identifies, based on the telemetry data, a network path as potentially exhibiting transient events during which a performance metric of the network path is degraded that are undetected at a first probing frequency for the network path. The device determines a new probing frequency for the network path, to detect the transient events. The device causes probes to be sent along the network path according to the new probing frequency.

Description

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 router110shown in network100may 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 network100via 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 router110connected to PE-2 and a second CE router110connected to PE-3.

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

In general, 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), Int 1, may establish a first communication path (e.g., a tunnel) with SaaS provider(s)308via a first Internet Service Provider (ISP)306a, denoted ISP 1 inFIG.3A. Likewise, a second interface of router110, Int 2, may establish a backhaul path with SaaS provider(s)308via a second ISP306b, denoted ISP 2 inFIG.3A.

FIG.3Billustrates another example network deployment310in which Int 1 of router110at the edge of remote site302establishes a first path to SaaS provider(s)308via ISP 1 and Int 2 establishes a second path to SaaS provider(s)308via a second ISP306b. In contrast to the example inFIG.3A, Int 3 of 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 would be appreciated, modern SaaS applications are delivered globally via public cloud infrastructure using cloud native services. Even though public cloud providers may have a high number of points of presence (PoPs) and use those to deliver the application, globally. Still, testing has shown that user quality of experience (QoE) may vary greatly based on the location of the user. This is because all public cloud providers are delivering services which are region-based and applications are running in specific region(s) and location(s). Indeed, even though it might seem that an online application is global (e.g., because of its use of globally-available CloudFront POPs, etc.), in reality it might run in a single region/location and user experience might vary greatly based on the location.

To determine the QoE for a particular SaaS application, various approaches are possible such as:Obtaining user feedback directly from the applicationApplying traffic analytics, such as by analyzing Netflow records that include extra metrics like Application Response Time (ART)Sending synthetic path probes to measure networking metrics to each SaaS application from each location. These probes are ‘synthetic’ in that they seek to mimic the actual characteristics of the traffic of the application under scrutiny.

The first approach above is rarely used today because of its complexity. In addition, relying on direct user feedback to drive routing decisions also requires supporting application programming interfaces (APIs) and the relevant network telemetry, in order to optimize the routing. The second and third approaches above are well-suited for use in Secure Access Service Edge (SASE)/SD-WAN implementations. In various embodiments, predictive application aware routing engine412may make use of any or all of the above approaches.

As would be appreciated, a tradeoff is often made with respect to the probing frequency so as not to impinge on the application traffic sent via the path. A potential consequence of this, though, are undetected “micro-failures” in which the QoS requirements of the traffic are temporarily not met (e.g., in terms of delay/latency, jitter, loss, throughput, etc.), but return to acceptable before the next path probe. While transient in nature, such micro-failures can also impact the quality of experience of the online application from the perspective of its users. Indeed, micro-failures are one of the importance causes of voice and video applications to provide poor QoE.

There are various potential causes for micro-failures, either due to the application itself or due to the operation of the network. In both cases, the underlying cause is often a temporary lack of adequate resources. For instance, the network path conveying the application traffic may experience a temporary lack of bandwidth, congestion, or the like. On the application side, a micro-failure could also be due to a temporary lack of compute resources, memory, or the like, at the application server.

FIGS.5A-5Billustrate example plots of micro-failures in a network path, according to various embodiments. More specifically,FIG.5Aillustrates an example plot500of the latency, packet loss, jitter, and bitrate/throughput measured over time along a network path by a client of a videoconferencing application. Here, the following information is highlighted:latencymax latencymean latencytransmit (tx) high packetLossreceive (rx) end-to-end (e2e) packetLossrx high packetLosstx high jitterrx e2e jittertx media bitraterx media bitratetx available bitraterx available bitraterx concealmentTimerx max ConsecutiveConcealmentTime

Here, it can be seen that during a single timeframe502, the latency and jitter spikes. This spike lasts less than 60 seconds, but has a significant impact on the end user experience. It is not rare to have a series of such micro-failures or even many (periodic) micro-failures during the whole call. Also, most of the time, micro-failures are of a very short duration (e.g., between 500 ms and 5 seconds).

Unfortunately, most applications and network solutions are unable to detect temporary failures of short duration, simply because telemetry polling operates at a different granularity. For example, ThousandEyes sends a probe every minute. Furthermore, even when higher granularity is available, most variables are aggregated, such as by using averaged values over a specific time frame to decrease costs and resource constraints). For instance, some SD-WAN solutions measure loss, jitter, and latency on a tunnel by using a time window of ten minutes and averaging the results from six hundred probes that are sent at a probing frequency of very second.

FIG.5Billustrates a plot510of the loss percentage, latency, jitter, received octets, and transmitted octets in an SD-WAN. As can be seen, micro-failures that are 500 ms long remain undetected (averaging over 60 seconds).

As discussed above, it is not possible to probe every network path at the level of granularity/frequency required to detect every micro-failure, mostly because of costs and resource constraints.

High Frequency Probing for Micro-Failure Detection on Sensitive Network Paths

The techniques herein introduce mechanisms to perform selective, high frequency probing of specific network paths of interest that are suspected of exhibiting micro-failures that remain undetected due to the current probing frequency in use. In some aspects, the paths may be selected based on telemetry data obtained from the network and/or the online application. In further aspects, a machine learning model (e.g., a classifier) may be trained to use the telemetry data to identify which paths are likely to be exhibiting micro-failures. In turn, probing at a higher probing frequency can be initiated along those paths, so that the micro-failures can actually be detected along those paths.

Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with probing process249, which may include computer executable instructions executed by the processor220(or independent processor of interfaces210) to perform functions relating to the techniques described herein, in conjunction with predictive routing process248.

Specifically, according to various embodiments, a device obtains telemetry data associated with application traffic for an online application. The device identifies, based on the telemetry data, a network path as potentially exhibiting transient events during which a performance metric of the network path is degraded that are undetected at a first probing frequency for the network path. The device determines a new probing frequency for the network path, to detect the transient events. The device causes probes to be sent along the network path according to the new probing frequency.

Operationally,FIG.6illustrates an example architecture for detecting micro-failures by a network path, according to various embodiments. At the core of architecture600is probing process249, 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, probing process249may 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, probing process249may include any or all of the following components: a path selector602, a telemetry adequacy analyzer604, a path classifier606, a probing supervisory608, and/or a feedback analyzer610. 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 probing process249.

In various embodiments, path selector602is responsible for selecting the set of paths that are highly susceptible to experiencing micro-failures, thus requiring high frequency probing. To do so, path selector602may assess path telemetry data616and/or application telemetry data618that probing process249may obtain either on a push or pull basis. For instance, path telemetry data616may include probing results from sending probes along one or more paths in the network, such as SD-WAN probes, ThousandEyes probes, or the like, that probing process249may obtain from a networking device such as router110, another networking device, or a device in communication therewith. Such path telemetry data616may, for instance, indicate the path metrics (e.g., delay/latency, loss, jitter, throughput, etc.) measured along a given network path through probing at a certain probing frequency. Similarly, application telemetry data618may be obtained from the online application whose traffic is sent via the network path, such as from an application server or client/dedicated agent of the application.

One observation herein is that it may be possible to infer the occurrence of undetected micro-failures through the analysis of the available telemetry data. For instance, consider the example of an SD-WAN tunnel that is typically reporting an average latency of 50 ms every minute but, at some point, increases to 100 ms while seeing its jitter increasing significantly. Such symptoms might suggest that the latency increase was not distributed equally across the one minute interval but instead spiked temporarily to a value much superior to 100 ms, resulting in significant jitter.

In another example, path selector602may assess the maximum concealment time reported as part of application telemetry data618. In general, the maximum concealment time measures the longest interval during which the audio/video codec of the application could not produce a meaningful audio/video stream. Such a metric from the application is usually a clear sign of intermittent networking issues (e.g., packet loss) which, being consecutive, prevent the codec from recovering the flow. This leads to the QoE of the application being impacted, despite not being detected by measurements techniques averaged over a long period of time.

Path selector602may identify the occurrence of potential micro-failures and assign a confidence value to them. For instance, the higher the confidence value, the more path selector602is certain of the presence of one or more micro-failures along a given network path. In further embodiments, path selector602may also group the potential micro-failures into sets of different classes. For instance, each class may consist of a set of parameters or attributes of a micro-failure such as any or all of the following: min/max latency, min/max loss, min/max jitter, duration, periodicity, etc.

In yet another embodiment, path selector602may receive input from a user interface indicative which path(s) a network administrator considers important. For instance, such input may flag a given path as critical or specify the condition(s) to use to identify paths of interest. For example, a certain path may be considered important based on the type(s) of traffic that it conveys, its volume of traffic, etc. In addition, the administrator may also be able to specify the level of confidence associated with the inferences by path selector602before initiating high-frequency probing of that path. For instance, one such rule may specify that high-frequency probing should be initiated for a path X if it conveys voice/video traffic, for more than x Mbps, and the number of suspected micro-failures exceeds 0.5 micro-failures/hour).

In yet another embodiment, path selector602may send a message to the cloud indicative of which paths may be exhibiting undetected micro-failures. For example, assume that BFD probing is used in an SD-WAN every second and the resulting metric(s) later aggregated at a coarser granularity and stored. This can be achieved, for instance, by maintaining counters on the variation of a path metric at the second level for each tunnel in the SD-WAN. In turn, probing process249may send a notification regarding the identified tunnels/paths that are likely to have micro-failures for use by another device and/or for presentation to a user.

In various embodiments, telemetry adequacy analyzer604is responsible for suggesting when/where currently available telemetry (e.g., path telemetry data616and/or application telemetry data618) is not sufficient (or not) to detect suspected micro-failures. For example, path selector602may suspect the presence of a micro-failure on a specific path with a low to moderate confidence value, but without being able to confirm with the current telemetry. As a result, telemetry adequacy analyzer604may suggest reconfiguring the SD-WAN fabric to increase the telemetry granularity for a short period of time, such as by increasing the probing frequency. Note that suggestion by telemetry adequacy analyzer604could also be the opposite (e.g., a proposal to decrease the probing frequency on a certain path). In some embodiments, probing process249may automatically implement the suggestions by telemetry adequacy analyzer604. However, in other embodiments, telemetry adequacy analyzer604may first present the suggested changes for display to a network administrator or other user, to receive confirmation of the changes, before implementing them in the network (e.g., by increasing the probing frequency along a certain path).

In general, telemetry adequacy analyzer604may base its decisions on the results of analyzing a large number of paths across different tunnels, entities, or the like (e.g., based on the observation of very specific tunnel of corporation X, it is possible to generate a recommendation for similar tunnel used by this corporation). That similarity might be achieved using clustering methods using attributes specific to the tunnel like performance patterns, traffic usage, entropy, failures, events, alarms, etc. The objective here is not only to detect a micro-failure but gather enough details to be able to classify it to the right class. That is important because different classes of micro-failure will impact different applications differently. For example, 500 ms failures spiking every five seconds with latency and jitter values to 400 ms will impact WebEx more seriously then 1000 ms failures spiking every ten seconds with a latency value of 500 ms.

In various embodiments, path classifier606may take the form a machine learning model in charge of classifying a given network path. For instance, the following classes/categories may be used by path classifier606to label a given network path:High: high probability of a micro-failure along the pathMedium: medium probability of a micro-failure along the pathLow: low probability of a micro-failure along the path

Of course, fewer categories (e.g., just ‘high’ and ‘low’) or more categories could also be used, as desired. Here, a first pass may be made using all paths enabled with high frequency probing so as to build a training set of samples for each category (high, medium, low). The global dataset is then used to train the classifier of path classifier606using the path attributes with low granularity, in addition to other path characteristics such as the type of path (e.g., number of hops, types of links (if available), aggregated network KPI (delay, loss, latency), set of traversed autonomous systems/service providers, and the like. Various types of classifiers can be used to implement path classifier606such as (deep) neural networks or any other suitable form of classifier. The overall precision/recall of the model can also be used to compute the level of confidence of the classifier.

In various embodiments, probing supervisory608may be responsible for overseeing the operation of probing engines across the network. To that end, a custom probing instructions message612is introduced herein to implement the change(s) suggested by telemetry adequacy analyzer604. For instance, custom probing instructions message612may cause router110to send probes614along a certain network path at an indicated probing frequency that is greater than that currently used to probe the path.

Probing supervisory608may also send custom probing instructions message612based in part on an instruction from an administrator to do so, such as after probing process249reports the suggested changes and reasons for review (e.g., suspicion of the presence of micro-failures impacting the user experience that are not current detected by the existing telemetry). For instance, one such change may be to reconfigure the SD-WAN to use BFD probes every 100 ms on a certain path/tunnel, instead of every second.

According to various embodiments, feedback analyzer610may implement a feedback loop used to validate whether the path classification is indeed correct or whether the high frequency probing enabled to detect micro-failures by that path should be disabled. To do so, when high frequency probing is activated, feedback analyzer610may start a configurable timer in charge of monitoring the rate at which micro-failures are indeed detected according to the class for the path (e.g., High, Medium, or Low). The distribution for the micro-failure class (in pass one) may then be used by feedback analyzer610to determine whether the path indeed belongs to the class. If not, plot510may take corrective measures such as notifying the network administrator via a user interface, deactivating the high frequency probing (e.g., by sending a new custom probing instructions message612), or initiating the classification model of path classifier606.

FIG.7illustrates an example simplified procedure700(e.g., a method) procedure for identifying a network path to be probed for micro-failures, in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device200), such as controller for a network (e.g., an SDN controller or other device in communication therewith), may perform procedure700by executing stored instructions (e.g., probing process249), to provide a supervisory service to a network. The procedure700may start at step705, and continues to step710, where, as described in greater detail above, the device may obtain telemetry data associated with application traffic for an online application. In some embodiments, the telemetry data comprises results from probing the network path at the first probing frequency. For instance, the telemetry data may indicate the measured packet loss, latency, jitter, throughput, etc. of the path over a given time period. In other embodiments, the device may obtain the telemetry data from the online application (e.g., maximum concealment time information, etc.).

At step715, as detailed above, the device may identify, based on the telemetry data, a network path as potentially exhibiting transient events during which a performance metric of the network path is degraded that are undetected at a first probing frequency for the network path. In one embodiment, the device may identify the network path in part based on input from a user interface designating the network path as important (e.g., of interest, critical, based on the type of traffic carried by the path, etc.). In some embodiments, the performance metric is of a different type than one or more performance metrics in the telemetry data on which identification of the network path is based. For instance, the device may infer that undetected micro-failures are occurring in the latency of the path, based on the jitter actually observed along the path. In another example, the maximum concealment time of the application may indicate undetected micro-failures in the packet loss along the path. In additional embodiments, the device may identify the network path using a machine learning classifier to classify the network path. In a further embodiment, the device may also obtain feedback regarding the probes sent along the network path according to the new probing frequency and update the machine learning classifier based on the feedback.

At step720, the device may determine a new probing frequency for the network path, to detect the transient events, as described in greater detail above. More specifically, if the network path is suspected of exhibiting undetected micro-failures, the new probing frequency may be set so as to probe the network path more frequently than the current/first probing frequency.

At step725, as detailed above, the device may cause probes to be sent along the network path according to the new probing frequency. In some embodiments, the device may send an instruction to a networking device associated with the network path that indicates the new probing frequency. Procedure700then ends at step730.

While there have been shown and described illustrative embodiments that provide for high frequency probing for micro-failure detection on sensitive network paths, 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.