Analyzing common traits in a network assurance system

In one embodiment, a network assurance system discretizes parameter values of a plurality of time series of measurements obtained from a monitored network by assigning tags to the parameter values. The network assurance system detects occurrences of a particular type of failure event in the monitored network. The network assurance system identifies a set of the assigned tags that frequently co-occur with the occurrences of the particular type of failure event. The network assurance system determines, using a Bayesian framework, rankings for the tags in the identified set based on how well each of the tags acts as a predictor of the failure event. The network assurance system initiates performance of a corrective measure for the failure event based in part on the determined rankings for the tags in the identified set.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, more particularly, to analyzing common traits in a network assurance system.

BACKGROUND

Networks are large-scale distributed systems governed by complex dynamics and very large number of parameters. In general, network assurance involves applying analytics to captured network information, to assess the health of the network. For example, a network assurance system may track and assess metrics such as available bandwidth, packet loss, jitter, and the like, to ensure that the experiences of users of the network are not impinged. However, as networks continue to evolve, so too will the number of applications present in a given network, as well as the number of metrics available from the network.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

According to one or more embodiments of the disclosure, a network assurance system discretizes parameter values of a plurality of time series of measurements obtained from a monitored network by assigning tags to the parameter values. The network assurance system detects occurrences of a particular type of failure event in the monitored network. The network assurance system identifies a set of the assigned tags that frequently co-occur with the occurrences of the particular type of failure event. The network assurance system determines, using a Bayesian framework, rankings for the tags in the identified set based on how well each of the tags acts as a predictor of the failure event. The network assurance system initiates performance of a corrective measure for the failure event based in part on the determined rankings for the tags in the identified set.

DESCRIPTION

2.) Site Type B: 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/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/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/LTE connection).

Notably, shared-media mesh networks, such as wireless or PLC networks, etc., are often on what is referred to as Low-Power and Lossy Networks (LLNs), which are a class of network in which both the routers and their interconnect are constrained: LLN routers typically operate with constraints, e.g., processing power, memory, and/or energy (battery), and their interconnects are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen to thousands or even millions of LLN routers, and support point-to-point traffic (between devices inside the LLN), point-to-multipoint traffic (from a central control point such at the root node to a subset of devices inside the LLN), and multipoint-to-point traffic (from devices inside the LLN towards a central control point). Often, an IoT network is implemented with an LLN-like architecture. For example, as shown, local network160may be an LLN in which CE-2 operates as a root node for nodes/devices10-16in the local mesh, in some embodiments.

In contrast to traditional networks, LLNs face a number of communication challenges. First, LLNs communicate over a physical medium that is strongly affected by environmental conditions that change over time. Some examples include temporal changes in interference (e.g., other wireless networks or electrical appliances), physical obstructions (e.g., doors opening/closing, seasonal changes such as the foliage density of trees, etc.), and propagation characteristics of the physical media (e.g., temperature or humidity changes, etc.). The time scales of such temporal changes can range between milliseconds (e.g., transmissions from other transceivers) to months (e.g., seasonal changes of an outdoor environment). In addition, LLN devices typically use low-cost and low-power designs that limit the capabilities of their transceivers. In particular, LLN transceivers typically provide low throughput. Furthermore, LLN transceivers typically support limited link margin, making the effects of interference and environmental changes visible to link and network protocols. The high number of nodes in LLNs in comparison to traditional networks also makes routing, quality of service (QoS), security, network management, and traffic engineering extremely challenging, to mention a few.

Network assurance process248includes computer executable instructions that, when executed by processor(s)220, cause device200to perform network assurance functions as part of a network assurance infrastructure within the network. In general, network assurance refers to the branch of networking concerned with ensuring that the network provides an acceptable level of quality in terms of the user experience. For example, in the case of a user participating in a videoconference, the infrastructure may enforce one or more network policies regarding the videoconference traffic, as well as monitor the state of the network, to ensure that the user does not perceive potential issues in the network (e.g., the video seen by the user freezes, the audio output drops, etc.).

In some embodiments, network assurance process248may use any number of predefined health status rules, to enforce policies and to monitor the health of the network, in view of the observed conditions of the network. For example, one rule may be related to maintaining the service usage peak on a weekly and/or daily basis and specify that if the monitored usage variable exceeds more than 10% of the per day peak from the current week AND more than 10% of the last four weekly peaks, an insight alert should be triggered and sent to a user interface.

Another example of a health status rule may involve client transition events in a wireless network. In such cases, whenever there is a failure in any of the transition events, the wireless controller may send a reason code to the assurance system. To evaluate a rule regarding these conditions, the network assurance system may then group150failures into different “buckets” (e.g., Association, Authentication, Mobility, DHCP, WebAuth, Configuration, Infra, Delete, De-Authorization) and continue to increment these counters per service set identifier (SSID), while performing averaging every five minutes and hourly. The system may also maintain a client association request count per SSID every five minutes and hourly, as well. To trigger the rule, the system may evaluate whether the error count in any bucket has exceeded 20% of the total client association request count for one hour.

In various embodiments, network assurance process248may 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 network observations that do, or do not, violate a given network health status rule and are labeled as such. 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 in the behavior. Semi-supervised learning models take a middle ground approach that uses a greatly reduced set of labeled training data.

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 whether a network health status rule was violated. Conversely, the false negatives of the model may refer to the number of times the model predicted that a health status rule was not violated when, in fact, the rule was violated. True negatives and positives may refer to the number of times the model correctly predicted whether a rule was violated or not violated, respectively. Related to these measurements are the concepts of recall and precision. Generally, recall refers to the ratio of true positives to the sum of true positives and false negatives, which quantifies the sensitivity of the model. Similarly, precision refers to the ratio of true positives the sum of true and false positives.

FIG. 3illustrates an example network assurance system300, according to various embodiments. As shown, at the core of network assurance system300may be a cloud service302that leverages machine learning in support of cognitive analytics for the network, predictive analytics (e.g., models used to predict user experience, etc.), troubleshooting with root cause analysis, and/or trending analysis for capacity planning. Generally, architecture300may support both wireless and wired network, as well as LLNs/IoT networks.

In various embodiments, cloud service302may oversee the operations of the network of an entity (e.g., a company, school, etc.) that includes any number of local networks. For example, cloud service302may oversee the operations of the local networks of any number of branch offices (e.g., branch office306) and/or campuses (e.g., campus308) that may be associated with the entity. Data collection from the various local networks/locations may be performed by a network data collection platform304that communicates with both cloud service302and the monitored network of the entity.

The network of branch office306may include any number of wireless access points320(e.g., a first access point API through nth access point, APn) through which endpoint nodes may connect. Access points320may, in turn, be in communication with any number of wireless LAN controllers (WLCs)326(e.g., supervisory devices that provide control over APs) located in a centralized datacenter324. For example, access points320may communicate with WLCs326via a VPN322and network data collection platform304may, in turn, communicate with the devices in datacenter324to retrieve the corresponding network feature data from access points320, WLCs326, etc. In such a centralized model, access points320may be flexible access points and WLCs326may be N+1 high availability (HA) WLCs, by way of example.

Conversely, the local network of campus308may instead use any number of access points328(e.g., a first access point API through nth access point APm) that provide connectivity to endpoint nodes, in a decentralized manner. Notably, instead of maintaining a centralized datacenter, access points328may instead be connected to distributed WLCs330and switches/routers332. For example, WLCs330may be 1:1 HA WLCs and access points328may be local mode access points, in some implementations.

To support the operations of the network, there may be any number of network services and control plane functions310. For example, functions310may include routing topology and network metric collection functions such as, but not limited to, routing protocol exchanges, path computations, monitoring services (e.g., NetFlow or IPFIX exporters), etc. Further examples of functions310may include authentication functions, such as by an Identity Services Engine (ISE) or the like, mobility functions such as by a Connected Mobile Experiences (CMX) function or the like, management functions, and/or automation and control functions such as by an APIC-Enterprise Manager (APIC-EM).

During operation, network data collection platform304may receive a variety of data feeds that convey collected data334from the devices of branch office306and campus308, as well as from network services and network control plane functions310. Example data feeds may comprise, but are not limited to, management information bases (MIBS) with Simple Network Management Protocol (SNMP)v2, JavaScript Object Notation (JSON) Files (e.g., WSA wireless, etc.), NetFlow/IPFIX records, logs reporting in order to collect rich datasets related to network control planes (e.g., Wi-Fi roaming, join and authentication, routing, QoS, PHY/MAC counters, links/node failures), traffic characteristics, and other such telemetry data regarding the monitored network. As would be appreciated, network data collection platform304may receive collected data334on a push and/or pull basis, as desired. Network data collection platform304may prepare and store the collected data334for processing by cloud service302. In some cases, network data collection platform may also anonymize collected data334before providing the anonymized data336to cloud service302.

In some cases, cloud service302may include a data mapper and normalizer314that receives the collected and/or anonymized data336from network data collection platform304. In turn, data mapper and normalizer314may map and normalize the received data into a unified data model for further processing by cloud service302. For example, data mapper and normalizer314may extract certain data features from data336for input and analysis by cloud service302.

In various embodiments, cloud service302may include a machine learning (ML)-based analyzer312configured to analyze the mapped and normalized data from data mapper and normalizer314. Generally, analyzer312may comprise a power machine learning-based engine that is able to understand the dynamics of the monitored network, as well as to predict behaviors and user experiences, thereby allowing cloud service302to identify and remediate potential network issues before they happen.

Machine learning-based analyzer312may include any number of machine learning models to perform the techniques herein, such as for cognitive analytics, predictive analysis, and/or trending analytics as follows:Cognitive Analytics Model(s): The aim of cognitive analytics is to find behavioral patterns in complex and unstructured datasets. For the sake of illustration, analyzer312may be able to extract patterns of Wi-Fi roaming in the network and roaming behaviors (e.g., the “stickiness” of clients to APs320,328, “ping-pong” clients, the number of visited APs320,328, roaming triggers, etc). Analyzer312may characterize such patterns by the nature of the device (e.g., device type, OS) according to the place in the network, time of day, routing topology, type of AP/WLC, etc., and potentially correlated with other network metrics (e.g., application, QoS, etc.). In another example, the cognitive analytics model(s) may be configured to extract AP/WLC related patterns such as the number of clients, traffic throughput as a function of time, number of roaming processed, or the like, or even end-device related patterns (e.g., roaming patterns of iPhones, IoT Healthcare devices, etc.).Predictive Analytics Model(s): These model(s) may be configured to predict user experiences, which is a significant paradigm shift from reactive approaches to network health. For example, in a Wi-Fi network, analyzer312may be configured to build predictive models for the joining/roaming time by taking into account a large plurality of parameters/observations (e.g., RF variables, time of day, number of clients, traffic load, DHCP/DNS/Radius time, AP/WLC loads, etc.). From this, analyzer312can detect potential network issues before they happen. Furthermore, should abnormal joining time be predicted by analyzer312, cloud service312will be able to identify the major root cause of this predicted condition, thus allowing cloud service302to remedy the situation before it occurs. The predictive analytics model(s) of analyzer312may also be able to predict other metrics such as the expected throughput for a client using a specific application. In yet another example, the predictive analytics model(s) may predict the user experience for voice/video quality using network variables (e.g., a predicted user rating of 1-5 stars for a given session, etc.), as function of the network state. As would be appreciated, this approach may be far superior to traditional approaches that rely on a mean opinion score (MOS). In contrast, cloud service302may use the predicted user experiences from analyzer312to provide information to a network administrator or architect in real-time and enable closed loop control over the network by cloud service302, accordingly. For example, cloud service302may signal to a particular type of endpoint node in branch office306or campus308(e.g., an iPhone, an IoT healthcare device, etc.) that better QoS will be achieved if the device switches to a different AP320or328.Trending Analytics Model(s): The trending analytics model(s) may include multivariate models that can predict future states of the network, thus separating noise from actual network trends. Such predictions can be used, for example, for purposes of capacity planning and other “what-if” scenarios.

Machine learning-based analyzer312may be specifically tailored for use cases in which machine learning is the only viable approach due to the high dimensionality of the dataset and patterns cannot otherwise be understood and learned. For example, finding a pattern so as to predict the actual user experience of a video call, while taking into account the nature of the application, video CODEC parameters, the states of the network (e.g., data rate, RF, etc.), the current observed load on the network, destination being reached, etc., is simply impossible using predefined rules in a rule-based system.

Unfortunately, there is no one-size-fits-all machine learning methodology that is capable of solving all, or even most, use cases. In the field of machine learning, this is referred to as the “No Free Lunch” theorem. Accordingly, analyzer312may rely on a set of machine learning processes that work in conjunction with one another and, when assembled, operate as a multi-layered kernel. This allows network assurance system300to operate in real-time and constantly learn and adapt to new network conditions and traffic characteristics. In other words, not only can system300compute complex patterns in highly dimensional spaces for prediction or behavioral analysis, but system300may constantly evolve according to the captured data/observations from the network.

Cloud service302may also include output and visualization interface318configured to provide sensory data to a network administrator or other user via one or more user interface devices (e.g., an electronic display, a keypad, a speaker, etc.). For example, interface318may present data indicative of the state of the monitored network, current or predicted issues in the network (e.g., the violation of a defined rule, etc.), insights or suggestions regarding a given condition or issue in the network, etc. Cloud service302may also receive input parameters from the user via interface318that control the operation of system300and/or the monitored network itself. For example, interface318may receive an instruction or other indication to adjust/retrain one of the models of analyzer312from interface318(e.g., the user deems an alert/rule violation as a false positive).

In various embodiments, cloud service302may further include an automation and feedback controller316that provides closed-loop control instructions338back to the various devices in the monitored network. For example, based on the predictions by analyzer312, the evaluation of any predefined health status rules by cloud service302, and/or input from an administrator or other user via input318, controller316may instruct an endpoint client device, networking device in branch office306or campus308, or a network service or control plane function310, to adjust its operations (e.g., by signaling an endpoint to use a particular AP320or328, etc.).

As noted above, a network assurance system, such as system300, may collect and assess telemetry data from a monitored network, to assess the health of the monitored network. As networks continue to grow in size, complexity, and usage, the number and types of failure events may also continue to increase. However, hidden relationships between the various conditions of the network may complicate the task of mitigating the failure events. Notably, the interactions of different network devices in the network (e.g., APs, WLCs, etc.) may result in a failure event, even when the individual devices appear to be operating normally.

Analyzing Common Traits in a Network Assurance System

The techniques herein allow for the identification of insights into network failures by a network assurance system, by analyzing common traits of networking devices across the network. In some aspects, networking devices (e.g., radios, APs, routers, switches, etc.) that are impacted by a type of failure event (e.g., low throughput, reboots, anomalous behavior, etc.) may be grouped based on their categorical attributes (e.g., (e.g., status of operation, OS version, the handling of a large number of hosts, etc.). If a given combination of attributes, referred to herein as a “trait,” is shared by entities impacted by an issue, performance of corrective measures can be initiated, such as presenting the combination of traits most likely to be associated with the failure event to the administrator as a potential explanation.

Specifically, according to one or more embodiments of the disclosure as described in detail below, a network assurance system discretizes parameter values of a plurality of time series of measurements obtained from a monitored network by assigning tags to the parameter values. The network assurance system detects occurrences of a particular type of failure event in the monitored network. The network assurance system identifies a set of the assigned tags that frequently co-occur with the occurrences of the particular type of failure event. The network assurance system determines, using a Bayesian framework, rankings for the tags in the identified set based on how well each of the tags acts as a predictor of the failure event. The network assurance system initiates performance of a corrective measure for the failure event based in part on the determined rankings for the tags in the identified set.

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

Operationally, the techniques herein introduce a mechanism capable of providing data-driven explanations for the reasons behind a network failure, allowing for corrective measures to be taken, such as by automatically changing network settings or allowing a network administrator to implement the change.

According to various embodiments, the techniques herein associate the occurrences of a given type of failure even with the concurrent state of the network. A failure event can take on various forms, e.g., packet failures, radio resets, or a roaming failure. In some embodiments, the network assurance system may derive key insights by considering the state of the network when a failure event occurred, and quantifying the strength of the association between a pattern in the network and the failure event. For example, the system may answer questions such as: “was the interference level unusually high at the time of packet failures, and if so, is there a (statistically significant) mutual dependence between the two?”

In general, the following terminology is used herein to describe the analysis of a failure event by the network assurance system:Failure Event: an occurrence of an unusual/unexpected event at a point in time. In many cases, a failure event refers to an event that indicates a decrease in performance of the network and/or an anomalous behavior in the network.a categorical label that a parameter takes when its (suitably discretized) value lies beyond a threshold.Trait: a pattern of states in network data that co-occurs with an event, usually associated with one or more parameters (attributes of a trait) and their tags (levels of a trait).Common trait: a trait that is shared across more than one network gear (e.g. Wireless radio), or occurs frequently in time over many networking gears (e.g. radio).Insight: an insight is information about an event that is derived from the associated trait.

Insights are generated by measuring the strength of the co-occurrence of a failure event with the concurrent patterns observed in the network. This allows for the high interpretability of the results, while ensuring statistical rigor in identifying meaningful patterns. High interpretability may be ensured by discretizing time series data into distinct categorical levels that have relevance in a network context. For example, signal-to-noise-ratio (SNR) is a continuous-valued parameter that measures the SNR in the network. By discretizing the SNR measurement time series into categorical levels, such as “high SNR,” “medium SNR,” and “low SNR” with respect to a threshold, the association rule mining of the techniques herein can detect patterns identifying failure events with “low SNR.” This simple discretization approach leads to an insight (in the form of a trait or traits) that is easily interpretable and relevant to the network. In addition, the approach is powerful and easily generalizable to any kind of network events and prevalent state of the network, and can also be customized by the network administrator, to serve up a custom analysis of the network.

Operationally,FIG. 4illustrates an example architecture400for analyzing common traits in a network assurance system, according to various embodiments. At the core of architecture400may be the following components: a network time series discretizer (NTSD)406, a network tag generator (NTG)408, an event generation engine (EGE)410, a common traits analyzer (CTA)412, a metric calculator, a trait ranking engine (TRE)416, and/or an insight generator (IG)418. In some implementations, the components of architecture400may be implemented within a network assurance system, such as system300shown inFIG. 3. Accordingly, the components406-418of architecture400shown may be implemented as part of cloud service302(e.g., as part of machine learning-based analyzer312), as part of network data collection platform304, and/or on one or more network elements/entities404that communicate with one or more client devices402within the monitored network itself. Further, these components may be implemented in a distributed manner or implemented as its own stand-alone service, either as part of the local network under observation or as a remote service. In addition, the functionalities of the components of architecture400may be combined, omitted, or implemented as part of other processes, as desired.

In various embodiments, network time series discretizer (NTSD)406may aggregate network measurement data collected by network data collection platform304over a fixed time-window. For example, data334may include data collected from a wireless controller (e.g., a network entity404) at a sample rate of every five minutes. During operation, NTSD406may discretize this “continuous” stream of measurement data by evaluating the average or most frequent values of the measured parameter in a longer time-window (e.g., a time window of 30 minutes). The time window assessed by NTSD406can also be tuned dynamically, in some cases. As a result of its processing, NTSD406produces a discretized value for each parameter for each entity (e.g., radio, AP, wireless controller, etc.) on the network.

In various embodiments, network tag generator (NTG)408may operate in conjunction with NTSD406and perform the dual functions of:calculating thresholds on various network parameters, typically based on percentiles of their probability distributions in the data.assigning ‘tags’ or names to parameters that take on discretized values (as obtained from the NTSD) that lie beyond a specified threshold.

The output of NTG408is a series of ‘tags’ or categorical labels to identify the value taken on by a parameter within a given time window. As a result, a continuous numeric time series gets transformed into a discrete time series of categorical levels. For network time series data that is categorical, such as wireless channel number, NTG408may either keep all tags or reduce them to more meaningful groups, e.g., channel number 1-39=2.4 GHz frequency, channel number 39−onwards=5 GHz frequency. In another embodiment, such thresholds may be dynamically adjusted according to a rule-based system, user feedback, or other considerations.

FIG. 5illustrates an example plot500of discretizing a time series502of a network measurement and assigning tags. As shown, assume that the network assurance system monitors wireless interference in the network and that these measurements form the time series502in plot500. Over the course of time, such as between 9:30 AM and 12:00 PM, the parameter values of time series502may vary considerably. To discretize time series502, NTSD406may divide time series502into different time window, such as the half hour-long increments shown.

Referring again toFIG. 4, in various embodiments, events generation engine (EGE)410may monitor various Key Performance Indicators (KPIs) from the network and generate failure events upon detecting an abnormal regime or pattern related to these KPIs. Such KPIs may include the measurements from the network (e.g., if packet drops exceed a certain level or are deemed anomalous, etc.) and/or quality metrics either reported by the users of the network or computed automatically (e.g., a user-specified rating for a videoconference quality, a call quality metric computed by the system, etc.).

The failure events identified by EGE410are central to the analysis since the overall mechanism described herein is designed to group network entities404that are impacted by the same type of events. Note that the term “failure event” is used herein to refer to any event in the monitored network in which performance is impacted and a given event does not necessarily require a complete loss of service to be deemed a failure event.

Upon analyzing continuous streams of data from the monitored network, EGE410may generate events of various types such as packet failures, radio resets, low throughput, etc. A multitude of sub-routines may be included in EGE410to address these different types of events. For example, EGE410may perform an explicit calculation of packet failures as a ratio of failures to successful tries to send packets, may apply anomaly detection techniques to identify low throughput by comparing with historical trends, may detect application throughput anomaly detection based on app user behavior, etc.

In various embodiments, common traits analyzer (CTA)412may build traits associated with events generated by EGE410. The events generated by EGE410are point-in-time occurrences when a failure event occurs in the network (e.g. packet failures) or when network parameters take on anomalous values (e.g. low throughput anomalies). During execution, CTA412may identify the tags applied by NTG408and NTSD406that are concurrent with failure event occurrences and build “traits” (e.g., patterns of network tags) that are significant in their association with the specific event. Said differently, CTA412may identify a set of the assigned tags, also referred to as traits, which frequently co-occur with the occurrences of the particular type of failure event.

To identify common traits of an event, CTA412may perform any or all of the following:Building transactions: a transaction is a temporal co-occurrence of discretized network data (in the form of tags) with an event. Transactions form the basis on which all associations are built, as they make explicit the relationship between network data and events at the smallest time interval (e.g., 30 minutes inFIG. 2below).Identifying frequently occurring patterns in the transactions: the most frequently occurring tags that are associated with an event are identified as traits. Any known optimization techniques to identify frequently occurring patterns in datasets can be used for this function. For example, in some implementations, CTA412may use FP Growth to find the tags that most frequently co-occur with a given event type.Identifying common traits: traits that occur on several radios or persist for an extended duration in time are denoted ‘common traits’ as they are evidenced in several radios/APs or over an extended period of time.

Referring briefly toFIG. 6, an example plot600of event traits is shown, according to various embodiments. As shown, assume that four time series are discretized using the above approach and over half-hour long time windows between 10:00 AM and 12:00 AM: 1.) an interference measurement time series, 2.) a traffic volume measurement time series, 3.) a client count measurement time series, and 4.) a time series that tracks packet failure events. From the discretized time series, CTA412may construct two sets of transactions: {High Interference+High ClientCount}and {High ClientCount}. The dashes shown in plot600indicate that no meaningful tags were generated for the remaining set of data. In other words, when the packet failure events occurred, a high client count co-occurred twice and high interference co-occurred once.

Referring again toFIG. 4, another important aspect of CTA412is the versatility of the analyses that can be performed. Analyses can be performed by CTA412on a per-radio basis (e.g., to identify issues on specific radios, that may not be prevalent across the network), as well as across all radios on a per-time-interval basis (e.g., an hourly or daily trait analysis on the entire network as a whole). Each slicing of the network data provides an added handle on network activity and provides further insight into understanding the reason behind network failures. These analyses may be further customizable to consider any combination (or subset) of radios. The final results are then synthesized, to take into account the various approaches.

Furthermore, CTA412provides highly interpretable results that are directly relevant to the network. By suitably discretizing the time series parameters into network relevant ‘tags,’ the interpretability of the model is made explicit at the start. CTA412leverages the power of association rule mining to find robust patterns in the data, based on the network relevant inputs provided to CTA412.

It is also worth noting the similarities and differences of the proposed technique with classification models (e.g., tree-based machine learning models) in the context of failure events. In tree-based models, the algorithm performs various splits on features and their values while maximizing a function, such as mutual information or cross entropy, at each node. The end result is a set of features and their split-values, with the features ranked by relative importance with respect to a purity metric, such as the Gini coefficient. These features are the key predictors in the classification of failure event occurrences. The proposed techniques differ from the classification model by making explicit the values on which to split each feature at the start (e.g., the ‘tags’). In this way, the techniques are equivalent to a tree-based model where the splits on feature values are pre-defined, and the algorithm essentially provides the relative ranking of features. Furthermore, a tree-based approach provides a single output for the entire dataset (e.g., a set of features that best optimize an objective loss function on the data). The proposed techniques provide multiple models to explain the data, relevant in different regions of the network. For example, there may be multiple sets of features (traits) that best classify the labels (failure events) for different sets of radios in the case of wireless networks. While there are benefits to this approach, a ranking mechanism may also be employed, in some embodiments, to identify the most significant traits as predictors. The trait ranking engine (TRE)416is discussed in detail below.

In addition, the techniques herein differ from classification-based approaches in the context of classification decision boundaries. The decision boundary in the proposed techniques herein is more linear than the tree-based models due to the discrete set of ‘tags’ that each parameter takes on. As a result, the feature space can only be split on a limited set of ‘tags’, as compared to the continuous set of values available to an alternative classification approach. In other words, a less non-linear decision boundary is traded for a more interpretable set of results for the end user. That being said, the proposed techniques provide results specific to different sub-regions of the dataset, as compared to a single model obtained from a tree-based classification approach.

In various embodiments, metric calculator414may quantify the degree of association between events and traits in a statistically rigorous manner. The co-occurrence of traits and events can be formulated in the language of a trait being a good “predictor” of an event. In other words, the presence of a trait “predicts” the occurrence of an event, while the absence of a trait indicates the absence of an event. To do so, in some implementations, the co-occurrence of traits and events can be quantified using a confusion matrix and metrics such as lift, conviction, or the like, can be used to characterize the strength of association between the traits and the events. As a result, traits that have high precision and high recall become good “predictors” of an event.

According to various embodiments, trait ranking engine (TRE)416is responsible for incorporating the resultant traits and metrics, obtained from CTA412and metric calculator414, and ranking them in a systematic manner in terms of their relative importance. In some embodiments, TRE416may utilize a Bayesian framework, to rank the event traits. Generally, a Bayesian analysis of parameter estimation has three components: (i.) the prior distribution of the parameter, based on previously held beliefs about its behavior, (ii.) the likelihood of the data given the parameter, (iii.) the posterior distribution of the parameter, updated with the observed data.

By definition, precision refers to the ratio of true positives the sum of true and false positives. As a “predictor” of an event, the precision of a trait denotes the rate at which an event occurs in its presence. Said differently, a high precision translates into the trait being a strong predictor of an event. As a result, ranking traits by their precision is a preferred way of finding the most important traits on the network. However, a point-estimate analysis of precision is not reliable, since it does not include the impact of the relative number of true positives and false positives for different traits.

Consider Table 1 below, where Traits A and B have the same precision, but vastly different occurrences on the network. Trait A occurs very rarely compared to Trait B. As a result, it is not clear which trait will be a better predictor on the network in the future, as a small change in either the True Positives or False Positives for Trait A will modify its precision by a large margin, relative to Trait B.

TRE416may implement a ranking system using Bayesian analysis, by quantifying the above uncertainty. More specifically, the presence or absence of a trait (and hence event occurrence, since a trait is a predictor of an event), can be modelled using a Binomial distribution Bin(k, n, p), where ‘k’ represents the number of successes (or true positives), ‘n’ represents the total number of observations and ‘p’ represents the probability of ‘k’ successes out of ‘n’ events. This is simply the definition of precision for a trait. In addition, ‘n-k’ is the number of failures (false positives). For a Binomial distribution, the conjugate prior distribution for ‘p’ is given by a Beta distribution. More formally, p˜Beta (a, b), where the Beta distribution is parameterized by two parameters: ‘a’ and ‘b’. As a result, the distribution for precision is given by a Beta distribution in this framework.

The Bayesian analysis for precision of a trait by TRE416will have three components: prior distribution, likelihood, posterior distribution. The prior distribution is based on prior beliefs about the precision of that trait. A meaningful prior that is considered is the empirical Bayes prior. Here, the prior distribution for a trait is obtained by fitting a Beta distribution to the precision of all traits i.e. prior probability(p)=Beta(a0, b0), where a0 and b0 are obtained by fitting to the distribution of precision for all traits. The likelihood is obtained by looking at the observed data, i.e., the number of true positives and false positives for each trait. Finally, the posterior distribution is obtained by updating the prior distribution with the observed data. Mathematically, this results in a simple formula for the posterior distribution for precision:
posterior probability(p)=Beta(a0+true positives,b0+false positives).

As a result, obtaining the posterior probability distribution for precision for each trait is simply a matter of updating the best-fit values a0, b0 with the observed number of true positives and false positives.

The last step in the analysis by TRE416is the relative ranking of these distributions, an example of which is shown in plot700inFIG. 7. As shown, traits are ranking in increasing order of the probability of each trait's precision distribution being larger than the expected prior probability. In this way, the uncertainty in the distribution is factored in the ranking. For the example shown, the traits are ranked in the order: trait 1>trait 3>trait 2>trait 4. This is different from the ordering that would be obtained if only the point-estimate of precision (dotted lines) would have been considered. Although trait 2 has a much higher precision, the uncertainty, due to a smaller set of true positives and false positives for trait 2, does not make it as reliable an indicator as trait 1.

Referring again toFIG. 4, insight generator (IG)418may synthesize suitably ranked traits from TRE416into natural language sentences, to provide the end user with meaningful and quantifiable insights. In turn, these insights can be provided to a user interface (UI) via output and visualization interface318. For example, the following traits may be generated for radio reset failure events:

From the above values, IG418may convert these traits into natural language insights that can be used to notify a network administrator and/or initiate automatic corrections in the monitored network. For example, IG418may generate the following insights based on the data in Tables 2-3 above:“3 radios were found to be twice as likely to have radio resets when they experienced high client count on the 5 GHz (channel=149) band in the UBCV apGroup, compared to the rest of the network.”“40 radios are 42% more likely to experience radio resets than other radios in the network when they have high client counts. These radios accounted for 749/1807 (or 40%) of the total radio reset occurrences on the network. More than 70% of these occurrences were related to high client count.”“The same 40 radios were part of the UBCV apGroup. On these radios, high client count was the most likely indicator of a radio reset, corresponding to a 17% increase in radio resets compared to any other consistent factor observed on those radios.”“5 radios, located in the default location, and in the UBCV apGroup, were found to experience a 24% increase in radio resets in the presence of high client count.”

FIG. 8illustrates an example simplified procedure for analyzing common traits in a network assurance system, in a network in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device200) may perform procedure800by executing stored instructions (e.g., process248), to implement a network assurance system. The procedure800may start at step805, and continues to step810, where, as described in greater detail above, the system may discretize parameter values of a plurality of time series of measurements obtained from a monitored network by assigning tags to the parameter values. For example, as detailed above, the network assurance service may determine whether the measurement parameter values within a given time window exceed a defined threshold and, based on this determination, apply a tag to the parameter values for that time window.

At step815, as detailed above, the network assurance system may detect occurrences of a particular type of failure event in the monitored network. In some embodiments, the system may monitor various KPIs in the network, to determine whether a failure event has occurred. In some embodiments, the network assurance system may apply anomaly detection to one or more KPIs, to determine that a failure event has occurred.

At step820, the network assurance system may identify a set of the assigned tags that frequently co-occur with the occurrences of the particular type of failure event, as described in greater detail above. Notably, the system may find the ‘traits’ of the event that describe the network measurements from the network at the time of the event.

At step825, as detailed above, the network assurance system may determine, using a Bayesian framework, rankings for the tags in the identified set based on how well each of the tags acts as a predictor of the failure event. In various embodiments, the system may do so by calculating a prior distribution, likelihood, and posterior distribution of a precision of each of the tags in the identified set, whereby the precision represents a rate of co-occurrence of the tag with the type of failure event.

At step830, the network assurance system may initiate performance of a corrective measure for the failure event based in part on the determined rankings for the tags in the identified set, as described in greater detail above. In various embodiments, this may entail sending a natural language-based insight that comprises the highest ranked tags in the set to a user interface for review by a network administrator. Such an insight may be in sentence form, in some cases, thereby allowing the administrator to easily assess the potential causes of the failure event and make changes to the network, as needed. Procedure800then ends at step835.

The techniques described herein, therefore, allow for the generation of insights into the occurrences of failure events in a monitored network. In some aspects, measurements from the network may be discretized by assigning tags to the measurement parameter values and, in turn, identifying the set of tags that frequently co-occur with the event.

While there have been shown and described illustrative embodiments that provide for insight analysis in a network assurance system, 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 anomaly detection, the models are not limited as such and may be used for other functions, in other embodiments. In addition, while certain protocols are shown, such as BGP, other suitable protocols may be used, accordingly.