Patent Description:
The disclosed techniques pertain to detecting datacenter mass outages. More specifically, the disclosed techniques pertain to using machine learning models that analyse real-time and/or offline data to detect datacenter mass outages.

A datacenter can include a plurality of computing devices (e.g., servers) configured to perform various processing tasks and associated devices to power the computing devices and connect the computing devices to external devices. Servers can be arranged in racks with a number of servers, where the servers in the rack are powered by a rack power supply. Conditions within the datacenter (e.g., temperature, humidity) can be controlled and monitored (e.g., using sensors and climate control devices) to prevent overheating or loss of functionality of the servers in the datacenter.

However, for any of a variety of reasons, an outage in the datacenter can occur. The outage can include any loss of functionality of any computing devices in the datacenter, such as a loss of functionality of an application executing on servers or overheating and shutdown of servers, for example. Such an outage can result in lower user experience in interacting with devices and/or applications executing by devices in the datacenter. Accordingly, an operator maintaining the datacenter may want to efficiently identify a source of the outage and resolve the issue causing the outage. However, as more devices and applications are implemented in a datacenter, efficiently identifying the source of the outage can become increasingly difficult. <CIT> describes identifying significant events for finding a root cause of an anomaly collecting time series data for events for each network device by detecting an anomaly in the time series data comprising an outlier on an edge of the time series data by comparing a predicted value of the event to an actual value of the event using a selected forecasting model; declaring the event to be an anomaly at a particular time if a difference between the predicted value and actual value exceed a defined threshold. <CIT> describes a system and method for the aggregation and grouping of previously identified, causally related abnormal operating condition, that are observed in a monitored environment. <CIT> describes a method for anomaly alarm consolidation. The method includes detecting a plurality of anomalies in time-series data received from an information technology infrastructure; identifying a plurality of root-cause candidates for each of the anomalies; generating, by a scenario analysis of the anomalies, a plurality of alarms, wherein the scenario analysis predicts a plurality of future expected values of the time-series data over a plurality of historical values of the time-series data using a graphical Granger causal model.

The present invention is defined by the features disclosed in the independent claims. Additional embodiments are defined in the dependent claims. The present invention relates to detecting datacenter mass outages with near real-time data using one or more models. A first exemplary embodiment provides a method as defined in claim <NUM>.

A second exemplary embodiment relates to a cloud infrastructure node as defined in claim <NUM>.

A third exemplary embodiment relates to a non-transitory computer-readable medium as defined in claim <NUM>.

Additionally, embodiments may be implemented by using a computer program product, comprising computer program/instructions which, when executed by a processor, cause the processor to perform any of the methods/techniques described in the disclosure, herein.

A datacenter can include a plurality of devices, such as computing devices, power sources providing power to the computing devices, network devices communicating data to/from the computing devices, and/or a plurality of sensors monitoring/controlling the environment in the datacenter. In many instances, an outage in the datacenter can occur, resulting in a loss of access to computing devices or associated processes implemented by the computing devices, or an inability to transfer data to/from devices in the datacenter, for example.

An outage in a datacenter can result from any of a variety of causes, such as a failure of a power source, a failure of one or more devices in the datacenter, overheating devices in the datacenter, an application failing to execute, etc. Particularly, as datacenters incorporate more devices, processing resources, and applications/services, efficiently identifying a source of the outage and performing a remedial process can be difficult, and an increased time to remedy the outage can result in lower user experience in interacting with devices/applications in the datacenter.

The present embodiments relate to data center outage detection and alert generation. Particularly, an outage detection service as described herein can process near real-time data from various sources in a datacenter and process the data using a model to determine one or more projected sources of a detected outage.

For example, an outage can be caused by a rack power source (e.g., powering a rack of servers) failing, leading to loss of functionality of the corresponding servers. In this event, the outage detection system as described herein can process the near real-time data using a model to identify one or more anomalous parameters. In this example, the model can identify a power level of the rack power source as dropping below a threshold level at a time near a time of detecting the outage or a power level of a server in the rack dropping below the threshold level. The model can process the near real-time input data using a set of rules to determine that the rack power source and/or the servers in the rack as projected sources of the outage.

An alert message can be generated to provide the projected sources of the outage and other data relevant to the outage. In the example above, the alert message can specify the rack power source and/or the servers in the rack as projected sources of the outage, the anomalous parameters identified by the model, a confidence value for each of the projected sources of the outage, etc. The alert message can provide insights into the outage and can efficiently rectify the outage.

Near real-time data can include environmental data from devices in the datacenter. Example near real-time data can include server temperatures, server/rack power usage, tickets obtained, sensor data, etc., that is stored with timestamps indicating a time of capturing the near-real time data. Responsive to an occurrence of an outage, the outage detection service can execute a model using the near-real time data and the offline data as an input to specify one or more projected sources of the outage.

The model as described herein can include one or more machine learning models incorporating a series of rules to process near-real time data and offline data and determine one or more projected sources of an outage. For instance, the model can identify one or more anomalous parameters of devices in the datacenter that have an increased likelihood of causing the outage. The model can output one or more projected sources or causes of the output, such as specifying devices, power supplies, applications, etc., that likely caused the outage and a confidence value providing an estimated confidence in each projected source causing the output. The projected sources of the outage can provide detected patterns from the near real-time data to establish a correlation for a mass outage that can be used to inform a recovery for the outage. For instance, the projected sources of the outage can provide a blueprint of how the outage (and any related issues) spread across components/applications within the datacenter. Utilizing the projected sources of the outage as a blueprint to recover from the outage can reduce an overall time of detecting and resolving the outage.

As an illustrative example, an outage can be detected in the datacenter either from an indication from an operator or automatically by the outage detection system (e.g., by detecting anomalous parameters, by detecting a number of incoming tickets specifying an outage). For example, the outage can be caused by a server rack losing functionality due to an anomalous increase in power from a rack power source in the server rack, leading to overheating servers in the server rack.

In this example, the outage detection service can obtain data (e.g., near real-time data <NUM>) relating to server temperatures (e.g., <NUM>), rack power usage (e.g., <NUM>), ticketing data (e.g., <NUM>), etc., and arrange the data by timestamps for processing by a model. The model can process the obtained data to identify anomalous parameters that can be indicative of a cause of the outage. For example, a plurality of received tickets can specify an outage occurred at a first time instance. Additionally, at the first time instance, a rack power metric for the rack power source can have an anomalous increase, and fan speeds for the servers in the rack can increase (indicative of a core server temperature increase) at the first time instance.

In this example, the model can identify that a first cause of the outage can comprise a power surge to the rack power source causing an overheating (and limited functionality) of servers in the rack. The model can use a series of rules to identify a likelihood that the cause of the outage is the power surge to the rack power source and a confidence value (e.g., as a percentage) can be assigned for the cause of the outage.

In this example, the outage detection service can provide resolution data providing one or more steps to resolve the cause of the outage. Example resolution data can specify to reset or replace the rack power source, or reset the servers in the rack. The outage detection service can output an alert comprising aspects of the outage, the projected cause of the outage, the resolution data, and/or one or more graphs illustrating anomalous parameters identified from obtained data from the datacenter.

<FIG> is a block diagram of an example datacenter <NUM>. A datacenter <NUM> can include an environment (e.g., room, building) comprising a plurality of computing devices and associated devices to power the computing devices and facilitate data communication between the computing devices and devices external to the datacenter <NUM>. The datacenter <NUM> can provide a controlled environment to maintain threshold environmental conditions (e.g., temperature, humidity) in the datacenter <NUM>.

The datacenter <NUM> can include a number of server stacks 102a-n comprising computing devices (e.g., servers 104a-l). A server stack (e.g., 102a-n) can include a rack arranging a set of servers 104a-l. Each server stack 102a-n can include one or more power supplies (e.g., power units 106a-n) and network devices (e.g., 108a-n) allowing for data transmission between devices in datacenter <NUM>. In some embodiments, the outage detection service as described herein can be implemented on one or more computing devices (e.g., servers 104a-l) or computing device(s) external to the datacenter <NUM>.

Each server 104a-l in the datacenter <NUM> can implement applications/plugins/add-ons/virtual machines/etc. that are configured to perform various processing tasks, such as maintain and update databases, for example. The servers 104a-l can include a number of sensors configured to capture data relating to each server, such as a core temperature, power usage, fan speed, state, etc., of each server 104a-l.

Each server 104a-l can be connected to one or more power units 106a-n. Each power unit can be associated with a server stack 102a-n and can provide power to servers 104a-l. The power units 106a-n can monitor a plurality of power parameters (e.g., voltage, current) provided by each power unit 106a-n that can be provided as near real-time data to the outage detection service.

The servers 104a-l can communicate data via network devices 108a-n. Network devices 108a-n can include a network switch, router, etc., that can forward data between servers 104a-l and recipient devices. In some instances, network devices 108a-n can implement a streaming service providing low-latency data communication between servers 104a-l and the outage detection service executing on a cloud infrastructure node as described herein.

The datacenter <NUM> can include a plurality of sensors 110a-n. Sensors 110a-n can monitor/control the environment of the datacenter <NUM>. Example sensors can include temperature sensors, humidity sensors, pressure sensors, etc. The data captured by sensors 110a-n can be provided as the near real-time data to the outage detection service.

<FIG> is a flow diagram <NUM> illustrating a method for generating an alert for an outage. As described below, an alert can include a notification (e.g., a message, e-mail, text notification) provided to an operator of the datacenter providing insights into the outage and potential sources of the outage. The method for generating an alert for an outage can be performed by an outage detection service as described herein.

The outage detection service can obtain near real-time data <NUM> and offline data <NUM> from various sources. The near real-time data <NUM> and offline data <NUM> can be processed as input data to be processed using a model as described herein. The near real-time data <NUM> can include various data types, such as server temperature data <NUM>, server power usage data <NUM>, rack power usage data <NUM>, ticket data <NUM>, and any other data types <NUM>. The outage detection service can obtain the near real-time data <NUM> from sources within the datacenter via a streaming service to provide low latency data communication to the outage detection service.

The server temperature data <NUM> can include data relating to an internal temperature of servers in the datacenter as provided by sensors (e.g., 110a-n) or the servers (e.g., 104a-n). The server temperature data <NUM> can specify server temperatures at a time instance, which can allow for monitoring of sever temperature trends over time. As described herein, an increased server temperature of one or more servers can indicate an increased power usage or overheating of the servers, which can be a cause of an outage. In some instances, server temperature data <NUM> can include fan speed data identifying fan speeds of the servers in the datacenter, which can indicate the temperature of the servers.

The server power usage data <NUM> can specify power consumption of each server in the datacenter. Example parameters relating to the server power usage data <NUM> can include a voltage, current, power draw, production load, etc., of each server during a time period. Various sensors can be disposed within or near the server to obtain the server power usage data <NUM>.

The rack power usage data <NUM> can provide a power usage of servers (and/or accompanying devices) in a rack (e.g., server stacks 102a-n). The rack power usage can be provided by power source(s) (e.g., power units 106a-n) for the rack. The power units 106a-n can measure a plurality of electrical power parameters (e.g., voltage, current, power consumption for the rack and individual devices in the rack).

The ticket data <NUM> can include a series of tickets obtained by a ticket node (e.g., an application executing on a computing device to obtain tickets from devices in the datacenter or devices external to the datacenter). Tickets can be received for detected issues/alerts relating to devices or applications executing on devices in the datacenter. A ticket can be provided automatically by devices in communication with a device in the datacenter or manually by an operator interacting with a device in the datacenter.

As an example, a ticket can be automatically generated by a device when the device is unable to obtain data from an application executing on a first server in the dataset. As another example, a ticket can be generated by a client when the client is unable to access a database maintained by a second server in the datacenter via a client device. Tickets can be associated with a timestamp and can be used to identify an outage or a cause of an outage as described herein.

Other data <NUM> can include network data specifying data transmission characteristics of devices in the datacenter, application data parameters of applications executing on devices in the datacenter, logged changes to applications/devices in the datacenter, etc..

The outage detection service can also obtain offline data <NUM> from a data source, such as one or more databases containing static datacenter information. Examples of offline data <NUM> can include device data <NUM>, location data <NUM>, and other data <NUM>. The device data <NUM> can specify a number of devices in the datacenter, and the location data <NUM> can include a location of each device in the datacenter. The device data <NUM> and location data <NUM> can identify groupings of devices in the datacenter, such as servers grouped in a rack. Other data <NUM> can specify applications executing on each server, capabilities of each device in the datacenter, software versions of each device, device types (e.g., sensor, network device, power device) in the datacenter, etc..

At <NUM>, the near real-time data <NUM> and offline data <NUM> can be joined. This can include arranging data by datatype and storing the data in a data source (e.g., database, table) based on timestamps associated with the data. As data is acquired over time, a database/table can be populated by data type according to the time of receipt of the data. For example, server temperature data for a first server in the datacenter can be stored by time of acquiring the data, providing a temperature of the first server over a time period. As another example, a rack power usage can be stored to provide trends in the power usage of the rack over time. The trends and movements of the parameters provided in the received data can provide insights into anomalous parameters in the datacenter and potential causes of an outage in the datacenter.

In some embodiments, a predicted level for measured parameters in the datacenter can be generated based on historical levels in the datacenter. For instance, historical server temperature data can be captured over time, and a predicted temperature can be generated for each time instance. The historical server temperature is just one example of a historical level. The predicted levels can be compared with corresponding parameters to detect any deviations from the predicted levels, which can be indicative of an anomaly that can be a source of an outage.

A model <NUM> can be executed using the joined data to determine one or more projected causes of an outage. In some instances, the model can be executed responsive to detection of an outage (e.g., by manual indication by an operator, automatically detected by inspecting ticket data).

The model <NUM> can process the joined data as input parameters that can be used to detect anomalous behavior that can be indicative of a source of the outage. The model <NUM> can include a machine learning model that can incorporate a plurality of rules <NUM> to process the joined data (e.g., data joined at <NUM>) and detect one or more projected sources of the outage.

The rules <NUM> can be generated from previously-identified outages and known resolutions to the outages. For example, if a previous outage was due to a power surge at a rack power source, a new rule can include instructions monitoring for a similar power surge at any power source and similar characteristics of the outage detected due to the power surge. The rules <NUM> can also be generated based on historical datacenter data or feedback data provided in response to resolving an outage. In some examples, the historical datacenter data is datacenter data and/or metadata obtained from an earlier time.

For example, a rule <NUM> can include instructions to process an input parameter to determine whether the parameter has any anomalous characteristics at any point in time. Further, rules <NUM> can include instructions correlating an anomalous parameter with one or more devices as projected source of an outage and identify one or more devices impacted by the anomalous parameter.

In a first example, the model can use a first rule to determine whether server power data for a first server includes any anomalous characteristics. For instance, a rule <NUM> can provide instructions for the model to compare the server power data for the first server with a predicted power levels to detect any deviation between the actual power level and a predicted power level. The rule can specify that when an actual power level exceeds a threshold deviation from a predicted power level at a time instance, the model <NUM> can identify the server power level for the first server being an anomalous parameter.

As another example, a rule <NUM> can include instructions to identify any modifications to applications/software for devices in the datacenter that occurred within a threshold time of detecting the outage (e.g., receiving tickets indicating an outage). For example, if an add-on caused an outage, the rule can identify any implemented changes to software in the datacenter that occurred within a threshold time of detecting the outage. In this example, the rule can identify the add-on implemented at a similar time instance as the time of detecting the outage, thus comprising a potential source of the outage.

Subsequent rules can process the anomalous characteristics to determine one or more projected sources of the outage. A rule <NUM> can include instructions to correlate an anomalous characteristic to a corresponding device/series of devices/applications/etc. For example, when a power level of a first rack power source spikes above a predicted level, the rule can identify that servers connected to the first rack power source have an increased likelihood of causing the outage due to the increased power level potentially resulting in a loss of functionality. As another example, when a threshold number of tickets specify that a first application has failed, a rule can identify all servers implementing the first application (or a virtual machine implementing the first application) can include projected sources of the outage. A plurality of rules can be executed by the model in combination to determine projected sources of the outage.

In many instances, multiple rules can be combined using the model to determine a likelihood of each projected source comprising an actual source of the outage. The likelihood of each projected source comprising an actual source of the outage can be represented in a confidence level. The confidence level can specify, for example, but is not limited to, a strength of a correlation, a lower false positive ratio, or a higher true positive ratio between each projected source of the outage with an actual source of the outage based on the near real-time data. For instance, a confidence level can be derived for each projected source of the outage based on a number of rules identifying each source as a projected source of the outage. The confidence level can be derived based on a number of executed rules that identify each device/application as the projected source of the outage.

For example, a first projected source of the outage can include a server and a second projected source of the outage can include a network switch communicating data to/from the server. In this example, two rules implemented by the model can specify the server as the first projected source of the outage (e.g., a rule identifying an anomalous temperature level of the server, a rule identifying a loss of functionality of an application executing on the server). Further, in this example, a single rule can specify the network switch as a second projected source of the outage (e.g., a rule identifying a data communication throughput from a port corresponding with the server lower than a predicted level). In this example, the first projected source of the outage can include a higher confidence level than that of the second projected source of the outage.

At <NUM>, an outage can be detected. An outage can include any identified loss of functionality implemented by any device(s) in the datacenter. Example outages can result from overheating servers, an application executed on servers being unavailable, a lack of data communication with a server/application executing on the server, etc..

In some embodiments, the outage can be detected manually, by an operator indicating that an outage has occurred. In other embodiments, the outage can be detected automatically, such as by processing tickets or other near real-time data to detect a loss of functionality or data communication with a device/application in the datacenter, for example. A model can be configured to process input data to detect an outage. The process for deriving one or more projected sources of an outage can be performed responsive to detecting the outage.

At <NUM>, an alert can be generated. An alert can provide a notification to an operator specifying the outage, one or more potential sources of the outage, and any known resolutions to the outage. For example, an alert can provide a description of the outage, one or more potential sources of the outage (e.g., as derived from model <NUM>), any resolution data for resolving the outage, a depiction of one or more parameters evidencing the potential source of the outage, etc. An alert is discussed in greater detail with respect to <FIG>.

<FIG> is a block diagram <NUM> illustrating an example outage detection service <NUM>. As noted above, the outage detection service <NUM> can be implemented on one or more interconnected computing devices external to the datacenter. The outage detection service <NUM> can obtain input parameters (e.g., near real-time data, offline data) and process the parameters using a model to derive one or more projected sources of an outage as described herein.

The outage detection service <NUM> can obtain near real-time data from an issue detection service <NUM>. The issue detection service <NUM> can obtain near real-time data (e.g., server temperature data, rack power usage data, datacenter sensor data). The issue detection service <NUM> can provide any near real-time data <NUM> as described with respect to <FIG>. In some instances, the issue detection service <NUM> can classify the near real-time data by data type for subsequent storage and processing by the outage detection service <NUM>.

The near real-time data sent by issue detection service can be forwarded to the outage detection service <NUM> via a streaming service <NUM>. The streaming service <NUM> can allow for data transmission with reduced latency between issue detection service <NUM> and outage detection service <NUM>. For instance, streaming service <NUM> can include an API providing low latency connection between issue detection service <NUM> and outage detection service <NUM>.

A telemetry service <NUM> can generate and provide a series of power-related parameters of power sources in the datacenter to the outage detection service <NUM>. For instance, the telemetry service <NUM> can provide a plurality of power parameters (e.g., voltage, current, resistance, power) for each power source (e.g., rack power units 106a-n).

A resource management service <NUM> can monitor and track components in the datacenter and a location of each component in the datacenter. For instance, the resource management service <NUM> can maintain a listing of a location and identifier of each server in each rack in the datacenter and all power sources providing power to corresponding servers. The resource management service <NUM> can maintain a listing of a location of any device in the datacenter, applications executing on each server in the datacenter, all devices directly connected to other devices in the datacenter, etc..

A ticketing data service <NUM> can obtain and process tickets received relating to the datacenter. For example, responsive to an application failing to execute or provide data to external devices, tickets can be generated specifying the failure. As another example, a client can request a ticket be generated responsive to an application or a device failing to provide a specified functionality. The ticketing data service <NUM> can aggregate and identify features of each received ticket. As described herein, the ticketing data service <NUM> can parse features from each received ticket to identify specific applications/devices/etc., which can provide insights into projected causes of an outage. Data obtained from the telemetry service <NUM>, resource management service <NUM>, and ticketing data <NUM> can be stored in an object storage <NUM>. The object storage <NUM> can include a database arranging the received data by data type and a time of obtaining the data.

The outage detection service <NUM> can obtain near real-time data (e.g., temperature data <NUM>, device power data <NUM>, rack power data <NUM>, location data <NUM>) and arrange the data by data type. For example, the near-real time data can be processed to identify features associated with each portion of data, such as a data type (e.g., temperature, power), devices/components related to each portion of data, a time of acquiring the data, etc..

The outage detection service <NUM> can implement an extract, transform, and load (ETL) process <NUM> to move and transform received data (e.g., temperature data <NUM>, device power data <NUM>, rack power data <NUM>, location data <NUM>). For example, the ETL <NUM> can obtain the near real-time data and identify a data type relating to each portion of data. The ETL <NUM> can also associate devices/components with various portions of data (e.g., using a listing of devices from resource management service <NUM>) and assign timestamps to the portions of data. The processed data can be stored in a database <NUM> providing associated temperature and power data.

The outage detection service <NUM> can process the stored data (e.g., stored in database <NUM>) using a model <NUM> to derive one or more projected sources of the outage. In some embodiments, the model <NUM> can process input data and determine whether an outage has occurred and/or identify features relating to the outage (by processing ticket data, by identifying anomalous parameters of near real-time data).

The model <NUM> can be retrieved from a model store <NUM> that can store various machinelearning model types. The model <NUM> can incorporate various rules from a rule store <NUM> to be executed by the model. For instance, the rules can identify anomalous parameters (e.g., a threshold deviation of a parameter from a predicted level at a time instance), identify devices that correspond to an anomalous parameter, a device/application relating that corresponds to received tickets, etc. The model <NUM> can output one or more projected sources of the outage and a confidence level specifying a confidence that the projected source of the outage corresponds to the outage. For example, the model can process near real-time data to identify a power surge at a first power source and determine that a projected source of the outage includes the first power source using the rules from the rules store <NUM>. In some instances, data from previous outages (e.g., anomalous parameters from the outage, a known resolution to the outage) can be fed back into the model store <NUM>/rules store <NUM> to incrementally add rules for identifying sources of an outage.

In some embodiments, the outage detection service <NUM> can identify resolution data that corresponds with a projected source of the outage. For example, if the model <NUM> identifies a first power source as a projected source of the outage, the outage detection service <NUM> can retrieve resolution data from resolution data database <NUM> to obtain corresponding resolution data (e.g., reset power source, replace power source). As another example, if the model <NUM> identifies a newly modified application executing on a series of services as a projected source of the outage, the outage detection service <NUM> can retrieve resolution data from resolution data database <NUM> to obtain corresponding resolution data (e.g., revert modifications to application).

The outage detection service can generate an alert via an alert service <NUM>. The alert can provide a message describing the outage, the projected sources of the outage, a confidence value associated with each projected source of the outage, resolution data associated with each projected source of the outage, etc..

<FIG> illustrates an example alert <NUM>. The alert <NUM> can include a message (e.g., email message, text message, a graphical output on a device associated with an operator). The alert <NUM> can provide multiple sources of data relating to the outage. For example, the alert <NUM> can provide outage data <NUM> specifying features of the outage (e.g., a time of detecting the outage, devices/applications affected due to the outage). The outage data <NUM> can include data provided by a client or operator, data derived from model(s) identifying aspects of the outage, etc..

The alert <NUM> can provide projected outage source(s) <NUM> that specify one or more projected sources of the outage. For example, the alert can provide a listing of projected sources of the outage (e.g., <NUM>) and data points identifying each source as a projected source of the outage (e.g., anomalous parameters, ticket data). The alert <NUM> can also include one or more confidence value(s) <NUM> associated with each projected source of an outage specifying an estimated likelihood of each projected source of an outage actually comprising the source of the outage.

The alert <NUM> can provide a graphical representation <NUM> of one or more parameters corresponding to a projected source of the outage. For example, if a projected source of an outage is a rack power source, the alert <NUM> can include a graphical representation <NUM> of a power parameter. In this example, the graphical representation <NUM> can provide an actual power level <NUM> in comparison with a predicted power level <NUM> (e.g., derived from historical power levels) over a time duration (e.g., time instances T1-T6). In some examples, historical power levels include previous levels that were received from a previous time. Further, in this example, the graphical representation <NUM> can illustrate multiple anomalous deviations 414a-b in the power level from the predicted power level <NUM>. The multiple anomalous deviations 414a-b in the power level can provide an insight that a power source associated with the power level <NUM> was a source of the outage.

<FIG> is a block diagram <NUM> of an example method for deriving one or more projected sources of an outage in a datacenter. A cloud infrastructure node can implement an outage detection service configured to perform the method as described herein.

At block <NUM>, the method includes obtaining a set of input data providing various parameters relating to a datacenter and a listing of devices and applications executing on devices in the datacenter. The set of input data can include the near real-time data (e.g., <NUM>) and offline data (e.g., <NUM>) as described with respect to <FIG> above. In some embodiments, the set of input data can specify any of: a temperature of each server in the datacenter, a power level of each power source in each rack of the datacenter, climate data of the datacenter obtained from a series of sensors in the datacenter, obtained ticket data identifying any functionalities of the datacenter, a listing of devices in the datacenter, and a location of all devices in the datacenter.

The set of input data includes a location of each of the devices in the datacenter and a device type of each device in the datacenter. The method includes processing the set of input data to identify a data type and one or more associated devices relating to each portion of the set of input data. Example data types can include server temperature data (e.g., <NUM>), server power usage data (e.g., <NUM>), device data (e.g., <NUM>), etc. The method also assigns a timestamp indicating a time of obtaining each portion of the set of input data to each portion of the set of input data. The outage detection service can arrange data of a specific type by timestamps to derive trends in parameters over a time duration (e.g., to identify changes in a parameter over time). The method also includes storing the set of input data in a database (e.g., database <NUM>) by data type and assigned timestamp. The outage detection service can use the stored data as an input to the model to derive the projected source(s) of the outage.

At block <NUM>, the method includes detecting an outage of at least one functionality of the datacenter. The outage can result from a loss of a functionality (e.g., an application) or a loss of computing resources (e.g., lost connection to server(s), loss of power to server(s)). Detecting the outage can include obtaining an outage notification from an external computing device specifying that the outage has occurred or detecting that a threshold number of obtained tickets are received that specify a loss of at least one functionality or the portion of computing resources at the datacenter.

At block <NUM>, the method includes processing the set of input data using a model to derive one or more projected sources of the outage. The model can incorporate a plurality of rules specifying correlations between the set of input data and the devices or the applications executing on the devices as the one or more projected sources of the outage.

At block <NUM>, deriving one or more projected sources of the outage includes generating a predicted level for each parameter included in the set of input data using historical data relating to each parameter using a set of rules accessible to the model. For example, historical server temperature data for a first server can be processed to determine a predicted temperature level for the first server. The predicted levels for each parameter can be compared with detected levels to identify whether any parameter deviates from the predicted level. Such deviations can be indicative of a device or application that is a projected source of the outage.

At block <NUM>, deriving one or more projected sources of the outage includes comparing the predicted level for each parameter with an actual level of each parameter included in the set of input data to identify one or more anomalous parameters that include actual levels with a threshold deviation from each corresponding predicted level. For example, a parameter can be anomalous when an actual level of a parameter has a threshold deviation from a predicted level for that parameter. A parameter with a threshold deviation from a predicted level can be indicative of an overheating server, a power surge in a power source, a loss in network packets, etc..

At block <NUM>, deriving one or more projected sources of the outage includes identifying one or more devices and/or an application that corresponds to each of the identified anomalous parameters. Each of the identified one or more devices and/or the application is included as the one or more projected sources of the outage. For example, responsive to determining that a server temperature level of a first server suddenly rising above a predicted level, the model can identify the first server as a projected source of the outage.

In some embodiments, the set of rules are derived at least in part based on a correlation between previously-resolved outages and identified sources of each of the previously-resolved outages. In these embodiments, the method can include identifying, by the model using a first rule of the set of rules, which a first anomalous parameter relates to a first application executing on a portion of servers in the datacenter. For example, a change to an application executing on a set of servers can cause an update. In this example, the model can identify anomalous parameters relating to the application, such as an increased server temperature level, a loss in data packet transmission, etc..

In these embodiments, the method can also include identifying, by the model using a second rule of the series of rules, which a change to execution of the first application occurred within a threshold time duration of a time of detecting the outage. For example, the model can identify that the change to the application occurred within a time of detecting the outage (e.g., detecting that the change occurred less than five minutes from the time of detecting the outage). This can be indicative that the change to the application is a projected source of the outage. The first application can be included in the output notification message as a first projected source of the outage. The output notification message can further provide resolution data specifying instructions to revert the first application to a previous version to remove the change to the execution of the first application.

At block <NUM>, the method includes generating an outage notification message providing the one or more projected sources of the outage. In some embodiments, the outage notification message includes a graphical representation of a first anomalous parameter and a derived predicted level of the first anomalous parameter.

In some embodiments, the method can include, for each of the one or more projected sources of the outage, deriving a confidence level based on a number of rules that correlate to parameters relating to each projected source of the outage, wherein the outage notification message includes the confidence level.

In some embodiments, the method can include, for each of the one or more projected sources of the outage, retrieving resolution data that relates to each of the one or more projected sources of the outage, the resolution data providing known methods for resolving the outage specific to each projected source of the outage, wherein the outage notification message includes the resolution data.

As noted above, infrastructure as a service (IaaS) is one particular type of cloud computing. IaaS can be configured to provide virtualized computing resources over a public network (e.g., the Internet). In an IaaS model, a cloud computing provider can host the infrastructure components (e.g., servers, storage devices, network nodes (e.g., hardware), deployment software, platform virtualization (e.g., a hypervisor layer), or the like). In some cases, an IaaS provider may also supply a variety of services to accompany those infrastructure components (e.g., billing, monitoring, logging, load balancing and clustering, etc.). Thus, as these services may be policy-driven, IaaS users may be able to implement policies to drive load balancing to maintain application availability and performance.

In some instances, IaaS customers may access resources and services through a wide area network (WAN), such as the Internet, and can use the cloud provider's services to install the remaining elements of an application stack. For example, the user can log in to the IaaS platform to create virtual machines (VMs), install operating systems (OSs) on each VM, deploy middleware such as databases, create storage buckets for workloads and backups, and even install enterprise software into that VM. Customers can then use the provider's services to perform various functions, including balancing network traffic, troubleshooting application issues, monitoring performance, managing disaster recovery, etc..

In most cases, a cloud computing model will require the participation of a cloud provider. The cloud provider may, but need not be, a third-party service that specializes in providing (e.g., offering, renting, selling) IaaS. An entity might also opt to deploy a private cloud, becoming its own provider of infrastructure services.

In some examples, IaaS deployment is the process of putting a new application, or a new version of an application, onto a prepared application server or the like. It may also include the process of preparing the server (e.g., installing libraries, daemons, etc.). This is often managed by the cloud provider, below the hypervisor layer (e.g., the servers, storage, network hardware, and virtualization). Thus, the customer may be responsible for handling (OS), middleware, and/or application deployment (e.g., on self-service virtual machines (e.g., that can be spun up on demand) or the like.

In some examples, IaaS provisioning may refer to acquiring computers or virtual hosts for use, and even installing needed libraries or services on them. In most cases, deployment does not include provisioning, and the provisioning may need to be performed first.

In some cases, there are two different challenges for IaaS provisioning. First, there is the initial challenge of provisioning the initial set of infrastructure before anything is running. Second, there is the challenge of evolving the existing infrastructure (e.g., adding new services, changing services, removing services, etc.) once everything has been provisioned. In some cases, these two challenges may be addressed by enabling the configuration of the infrastructure to be defined declaratively. In other words, the infrastructure (e.g., what components are needed and how they interact) can be defined by one or more configuration files. Thus, the overall topology of the infrastructure (e.g., what resources depend on which, and how they each work together) can be described declaratively. In some instances, once the topology is defined, a workflow can be generated that creates and/or manages the different components described in the configuration files.

In some examples, an infrastructure may have many interconnected elements. For example, there may be one or more virtual private clouds (VPCs) (e.g., a potentially on-demand pool of configurable and/or shared computing resources), also known as a core network. In some examples, there may also be one or more inbound/outbound traffic group rules provisioned to define how the inbound and/or outbound traffic of the network will be set up and one or more virtual machines (VMs). Other infrastructure elements may also be provisioned, such as a load balancer, a database, or the like. As more and more infrastructure elements are desired and/or added, the infrastructure may incrementally evolve.

In some instances, continuous deployment techniques may be employed to enable deployment of infrastructure code across various virtual computing environments. Additionally, the described techniques can enable infrastructure management within these environments. In some examples, service teams can write code that is desired to be deployed to one or more, but often many, different production environments (e.g., across various different geographic locations, sometimes spanning the entire world). However, in some examples, the infrastructure on which the code will be deployed must first be set up. In some instances, the provisioning can be done manually, a provisioning tool may be utilized to provision the resources, and/or deployment tools may be utilized to deploy the code once the infrastructure is provisioned.

<FIG> is a block diagram <NUM> illustrating an example pattern of an IaaS architecture, according to at least one embodiment. Service operators <NUM> can be communicatively coupled to a secure host tenancy <NUM> that can include a virtual cloud network (VCN) <NUM> and a secure host subnet <NUM>. In some examples, the service operators <NUM> may be using one or more client computing devices, which may be portable handheld devices (e.g., an iPhone®, cellular telephone, an iPad®, computing tablet, a personal digital assistant (PDA)) or wearable devices (e.g., a Google Glass® head mounted display), running software such as Microsoft Windows Mobile®, and/or a variety of mobile operating systems such as iOS, Windows Phone, Android, BlackBerry <NUM>, Palm OS, and the like, and being Internet, e-mail, short message service (SMS), Blackberry®, or other communication protocol enabled. Alternatively, the client computing devices can be general purpose personal computers including, by way of example, personal computers and/or laptop computers running various versions of Microsoft Windows®, Apple Macintosh®, and/or Linux operating systems. The client computing devices can be workstation computers running any of a variety of commercially-available UNIX® or UNIX-like operating systems, including without limitation the variety of GNU/Linux operating systems, such as for example, Google Chrome OS. Alternatively, or in addition, client computing devices may be any other electronic device, such as a thin-client computer, an Internet-enabled gaming system (e.g., a Microsoft Xbox gaming console with or without a Kinect® gesture input device), and/or a personal messaging device, capable of communicating over a network that can access the VCN <NUM> and/or the Internet.

The VCN <NUM> can include a local peering gateway (LPG) <NUM> that can be communicatively coupled to a secure shell (SSH) VCN <NUM> via an LPG <NUM> contained in the SSH VCN <NUM>. The SSH VCN <NUM> can include an SSH subnet <NUM>, and the SSH VCN <NUM> can be communicatively coupled to a control plane VCN <NUM> via the LPG <NUM> contained in the control plane VCN <NUM>. Also, the SSH VCN <NUM> can be communicatively coupled to a data plane VCN <NUM> via an LPG <NUM>. The control plane VCN <NUM> and the data plane VCN <NUM> can be contained in a service tenancy <NUM> that can be owned and/or operated by the IaaS provider.

The control plane VCN <NUM> can include a control plane demilitarized zone (DMZ) tier <NUM> that acts as a perimeter network (e.g., portions of a corporate network between the corporate intranet and external networks). The DMZ-based servers may have restricted responsibilities and help keep breaches contained. Additionally, the DMZ tier <NUM> can include one or more load balancer (LB) subnet(s) <NUM>, a control plane app tier <NUM> that can include app subnet(s) <NUM>, a control plane data tier <NUM> that can include database (DB) subnet(s) <NUM> (e.g., frontend DB subnet(s) and/or backend DB subnet(s)). The LB subnet(s) <NUM> contained in the control plane DMZ tier <NUM> can be communicatively coupled to the app subnet(s) <NUM> contained in the control plane app tier <NUM> and an Internet gateway <NUM> that can be contained in the control plane VCN <NUM>, and the app subnet(s) <NUM> can be communicatively coupled to the DB subnet(s) <NUM> contained in the control plane data tier <NUM> and a service gateway <NUM> and a network address translation (NAT) gateway <NUM>. The control plane VCN <NUM> can include the service gateway <NUM> and the NAT gateway <NUM>.

The control plane VCN <NUM> can include a data plane mirror app tier <NUM> that can include app subnet(s) <NUM>. The app subnet(s) <NUM> contained in the data plane mirror app tier <NUM> can include a virtual network interface controller (VNIC) <NUM> that can execute a compute instance <NUM>. The compute instance <NUM> can communicatively couple the app subnet(s) <NUM> of the data plane mirror app tier <NUM> to app subnet(s) <NUM> that can be contained in a data plane app tier <NUM>.

The data plane VCN <NUM> can include the data plane app tier <NUM>, a data plane DMZ tier <NUM>, and a data plane data tier <NUM>. The data plane DMZ tier <NUM> can include LB subnet(s) <NUM> that can be communicatively coupled to the app subnet(s) <NUM> of the data plane app tier <NUM> and the Internet gateway <NUM> of the data plane VCN <NUM>. The app subnet(s) <NUM> can be communicatively coupled to the service gateway <NUM> of the data plane VCN <NUM> and the NAT gateway <NUM> of the data plane VCN <NUM>. The data plane data tier <NUM> can also include the DB subnet(s) <NUM> that can be communicatively coupled to the app subnet(s) <NUM> of the data plane app tier <NUM>.

The Internet gateway <NUM> of the control plane VCN <NUM> and of the data plane VCN <NUM> can be communicatively coupled to a metadata management service <NUM> that can be communicatively coupled to public Internet <NUM>. Public Internet <NUM> can be communicatively coupled to the NAT gateway <NUM> of the control plane VCN <NUM> and of the data plane VCN <NUM>. The service gateway <NUM> of the control plane VCN <NUM> and of the data plane VCN <NUM> can be communicatively couple to cloud services <NUM>.

In some examples, the service gateway <NUM> of the control plane VCN <NUM> or of the data plane VCN <NUM> can make application programming interface (API) calls to cloud services <NUM> without going through public Internet <NUM>. The API calls to cloud services <NUM> from the service gateway <NUM> can be one-way: the service gateway <NUM> can make API calls to cloud services <NUM>, and cloud services <NUM> can send requested data to the service gateway <NUM>. But, cloud services <NUM> may not initiate API calls to the service gateway <NUM>.

In some examples, the secure host tenancy <NUM> can be directly connected to the service tenancy <NUM>, which may be otherwise isolated. The secure host subnet <NUM> can communicate with the SSH subnet <NUM> through an LPG <NUM> that may enable two-way communication over an otherwise isolated system. Connecting the secure host subnet <NUM> to the SSH subnet <NUM> may give the secure host subnet <NUM> access to other entities within the service tenancy <NUM>.

The control plane VCN <NUM> may allow users of the service tenancy <NUM> to set up or otherwise provision desired resources. Desired resources provisioned in the control plane VCN <NUM> may be deployed or otherwise used in the data plane VCN <NUM>. In some examples, the control plane VCN <NUM> can be isolated from the data plane VCN <NUM>, and the data plane mirror app tier <NUM> of the control plane VCN <NUM> can communicate with the data plane app tier <NUM> of the data plane VCN <NUM> via VNICs <NUM> that can be contained in the data plane mirror app tier <NUM> and the data plane app tier <NUM>.

In some examples, users of the system, or customers, can make requests, for example create, read, update, or delete (CRUD) operations, through public Internet <NUM> that can communicate the requests to the metadata management service <NUM>. The metadata management service <NUM> can communicate the request to the control plane VCN <NUM> through the Internet gateway <NUM>. The request can be received by the LB subnet(s) <NUM> contained in the control plane DMZ tier <NUM>. The LB subnet(s) <NUM> may determine that the request is valid, and in response to this determination, the LB subnet(s) <NUM> can transmit the request to app subnet(s) <NUM> contained in the control plane app tier <NUM>. If the request is validated and requires a call to public Internet <NUM>, the call to public Internet <NUM> may be transmitted to the NAT gateway <NUM> that can make the call to public Internet <NUM>. Metadata that may be desired to be stored by the request can be stored in the DB subnet(s) <NUM>.

In some examples, the data plane mirror app tier <NUM> can facilitate direct communication between the control plane VCN <NUM> and the data plane VCN <NUM>. For example, changes, updates, or other suitable modifications to configuration may be desired to be applied to the resources contained in the data plane VCN <NUM>. Via a VNIC <NUM>, the control plane VCN <NUM> can directly communicate with, and can thereby execute the changes, updates, or other suitable modifications to configuration to, resources contained in the data plane VCN <NUM>.

In some embodiments, the control plane VCN <NUM> and the data plane VCN <NUM> can be contained in the service tenancy <NUM>. In this case, the user, or the customer, of the system may not own or operate either the control plane VCN <NUM> or the data plane VCN <NUM>. Instead, the IaaS provider may own or operate the control plane VCN <NUM> and the data plane VCN <NUM>, both of which may be contained in the service tenancy <NUM>. This embodiment can enable isolation of networks that may prevent users or customers from interacting with other users', or other customers', resources. Also, this embodiment may allow users or customers of the system to store databases privately without needing to rely on public Internet <NUM>, which may not have a desired level of threat prevention, for storage.

In other embodiments, the LB subnet(s) <NUM> contained in the control plane VCN <NUM> can be configured to receive a signal from the service gateway <NUM>. In this embodiment, the control plane VCN <NUM> and the data plane VCN <NUM> may be configured to be called by a customer of the IaaS provider without calling public Internet <NUM>. Customers of the IaaS provider may desire this embodiment since database(s) that the customers use may be controlled by the IaaS provider and may be stored on the service tenancy <NUM>, which may be isolated from public Internet <NUM>.

<FIG> is a block diagram <NUM> illustrating another example pattern of an IaaS architecture, according to at least one embodiment. Service operators <NUM> (e.g. service operators <NUM> of <FIG>) can be communicatively coupled to a secure host tenancy <NUM> (e.g. the secure host tenancy <NUM> of <FIG>) that can include a virtual cloud network (VCN) <NUM> (e.g. the VCN <NUM> of <FIG>) and a secure host subnet <NUM> (e.g. the secure host subnet <NUM> of <FIG>). The VCN <NUM> can include a local peering gateway (LPG) <NUM> (e.g. the LPG <NUM> of <FIG>) that can be communicatively coupled to a secure shell (SSH) VCN <NUM> (e.g. the SSH VCN <NUM> of <FIG>) via an LPG <NUM> contained in the SSH VCN <NUM>. The SSH VCN <NUM> can include an SSH subnet <NUM> (e.g. the SSH subnet <NUM> of <FIG>), and the SSH VCN <NUM> can be communicatively coupled to a control plane VCN <NUM> (e.g. the control plane VCN <NUM> of <FIG>) via an LPG <NUM> contained in the control plane VCN <NUM>. The control plane VCN <NUM> can be contained in a service tenancy <NUM> (e.g. the service tenancy <NUM> of <FIG>), and the data plane VCN <NUM> (e.g. the data plane VCN <NUM> of <FIG>) can be contained in a customer tenancy <NUM> that may be owned or operated by users, or customers, of the system.

The control plane VCN <NUM> can include a control plane DMZ tier <NUM> (e.g. the control plane DMZ tier <NUM> of <FIG>) that can include LB subnet(s) <NUM> (e.g. LB subnet(s) <NUM> of <FIG>), a control plane app tier <NUM> (e.g. the control plane app tier <NUM> of <FIG>) that can include app subnet(s) <NUM> (e.g. app subnet(s) <NUM> of <FIG>), a control plane data tier <NUM> (e.g. the control plane data tier <NUM> of <FIG>) that can include database (DB) subnet(s) <NUM> (e.g. similar to DB subnet(s) <NUM> of <FIG>). The LB subnet(s) <NUM> contained in the control plane DMZ tier <NUM> can be communicatively coupled to the app subnet(s) <NUM> contained in the control plane app tier <NUM> and an Internet gateway <NUM> (e.g. the Internet gateway <NUM> of <FIG>) that can be contained in the control plane VCN <NUM>, and the app subnet(s) <NUM> can be communicatively coupled to the DB subnet(s) <NUM> contained in the control plane data tier <NUM> and a service gateway <NUM> (e.g. the service gateway of <FIG>) and a network address translation (NAT) gateway <NUM> (e.g. the NAT gateway <NUM> of <FIG>). The control plane VCN <NUM> can include the service gateway <NUM> and the NAT gateway <NUM>.

The control plane VCN <NUM> can include a data plane mirror app tier <NUM> (e.g. the data plane mirror app tier <NUM> of <FIG>) that can include app subnet(s) <NUM>. The app subnet(s) <NUM> contained in the data plane mirror app tier <NUM> can include a virtual network interface controller (VNIC) <NUM> (e.g. the VNIC of <NUM>) that can execute a compute instance <NUM> (e.g. similar to the compute instance <NUM> of <FIG>). The compute instance <NUM> can facilitate communication between the app subnet(s) <NUM> of the data plane mirror app tier <NUM> and the app subnet(s) <NUM> that can be contained in a data plane app tier <NUM> (e.g. the data plane app tier <NUM> of <FIG>) via the VNIC <NUM> contained in the data plane mirror app tier <NUM> and the VNIC <NUM> contained in the data plane app tier <NUM>.

The Internet gateway <NUM> contained in the control plane VCN <NUM> can be communicatively coupled to a metadata management service <NUM> (e.g. the metadata management service <NUM> of <FIG>) that can be communicatively coupled to public Internet <NUM> (e.g. public Internet <NUM> of <FIG>). Public Internet <NUM> can be communicatively coupled to the NAT gateway <NUM> contained in the control plane VCN <NUM>. The service gateway <NUM> contained in the control plane VCN <NUM> can be communicatively couple to cloud services <NUM> (e.g. cloud services <NUM> of <FIG>).

In some examples, the data plane VCN <NUM> can be contained in the customer tenancy <NUM>. In this case, the IaaS provider may provide the control plane VCN <NUM> for each customer, and the IaaS provider may, for each customer, set up a unique compute instance <NUM> that is contained in the service tenancy <NUM>. Each compute instance <NUM> may allow communication between the control plane VCN <NUM>, contained in the service tenancy <NUM>, and the data plane VCN <NUM> that is contained in the customer tenancy <NUM>. The compute instance <NUM> may allow resources, that are provisioned in the control plane VCN <NUM> that is contained in the service tenancy <NUM>, to be deployed or otherwise used in the data plane VCN <NUM> that is contained in the customer tenancy <NUM>.

In other examples, the customer of the IaaS provider may have databases that live in the customer tenancy <NUM>. In this example, the control plane VCN <NUM> can include the data plane mirror app tier <NUM> that can include app subnet(s) <NUM>. The data plane mirror app tier <NUM> can reside in the data plane VCN <NUM>, but the data plane mirror app tier <NUM> may not live in the data plane VCN <NUM>. That is, the data plane mirror app tier <NUM> may have access to the customer tenancy <NUM>, but the data plane mirror app tier <NUM> may not exist in the data plane VCN <NUM> or be owned or operated by the customer of the IaaS provider. The data plane mirror app tier <NUM> may be configured to make calls to the data plane VCN <NUM> but may not be configured to make calls to any entity contained in the control plane VCN <NUM>. The customer may desire to deploy or otherwise use resources in the data plane VCN <NUM> that are provisioned in the control plane VCN <NUM>, and the data plane mirror app tier <NUM> can facilitate the desired deployment, or other usage of resources, of the customer.

In some embodiments, the customer of the IaaS provider can apply filters to the data plane VCN <NUM>. In this embodiment, the customer can determine what the data plane VCN <NUM> can access, and the customer may restrict access to public Internet <NUM> from the data plane VCN <NUM>. The IaaS provider may not be able to apply filters or otherwise control access of the data plane VCN <NUM> to any outside networks or databases. Applying filters and controls by the customer onto the data plane VCN <NUM>, contained in the customer tenancy <NUM>, can help isolate the data plane VCN <NUM> from other customers and from public Internet <NUM>.

In some embodiments, cloud services <NUM> can be called by the service gateway <NUM> to access services that may not exist on public Internet <NUM>, on the control plane VCN <NUM>, or on the data plane VCN <NUM>. The connection between cloud services <NUM> and the control plane VCN <NUM> or the data plane VCN <NUM> may not be live or continuous. Cloud services <NUM> may exist on a different network owned or operated by the IaaS provider. Cloud services <NUM> may be configured to receive calls from the service gateway <NUM> and may be configured to not receive calls from public Internet <NUM>. Some cloud services <NUM> may be isolated from other cloud services <NUM>, and the control plane VCN <NUM> may be isolated from cloud services <NUM> that may not be in the same region as the control plane VCN <NUM>. For example, the control plane VCN <NUM> may be located in "Region <NUM>," and cloud service "Deployment <NUM>," may be located in Region <NUM> and in "Region <NUM>. " If a call to Deployment <NUM> is made by the service gateway <NUM> contained in the control plane VCN <NUM> located in Region <NUM>, the call may be transmitted to Deployment <NUM> in Region <NUM>. In this example, the control plane VCN <NUM>, or Deployment <NUM> in Region <NUM>, may not be communicatively coupled to, or otherwise in communication with, Deployment <NUM> in Region <NUM>.

<FIG> is a block diagram <NUM> illustrating another example pattern of an IaaS architecture, according to at least one embodiment. Service operators <NUM> (e.g. service operators <NUM> of <FIG>) can be communicatively coupled to a secure host tenancy <NUM> (e.g. the secure host tenancy <NUM> of <FIG>) that can include a virtual cloud network (VCN) <NUM> (e.g. the VCN <NUM> of <FIG>) and a secure host subnet <NUM> (e.g. the secure host subnet <NUM> of <FIG>). The VCN <NUM> can include an LPG <NUM> (e.g. the LPG <NUM> of <FIG>) that can be communicatively coupled to an SSH VCN <NUM> (e.g. the SSH VCN <NUM> of <FIG>) via an LPG <NUM> contained in the SSH VCN <NUM>. The SSH VCN <NUM> can include an SSH subnet <NUM> (e.g. the SSH subnet <NUM> of <FIG>), and the SSH VCN <NUM> can be communicatively coupled to a control plane VCN <NUM> (e.g. the control plane VCN <NUM> of <FIG>) via an LPG <NUM> contained in the control plane VCN <NUM> and to a data plane VCN <NUM> (e.g. the data plane <NUM> of <FIG>) via an LPG <NUM> contained in the data plane VCN <NUM>. The control plane VCN <NUM> and the data plane VCN <NUM> can be contained in a service tenancy <NUM> (e.g. the service tenancy <NUM> of <FIG>).

The control plane VCN <NUM> can include a control plane DMZ tier <NUM> (e.g. the control plane DMZ tier <NUM> of <FIG>) that can include load balancer (LB) subnet(s) <NUM> (e.g. LB subnet(s) <NUM> of <FIG>), a control plane app tier <NUM> (e.g. the control plane app tier <NUM> of <FIG>) that can include app subnet(s) <NUM> (e.g. similar to app subnet(s) <NUM> of <FIG>), a control plane data tier <NUM> (e.g. the control plane data tier <NUM> of <FIG>) that can include DB subnet(s) <NUM>. The LB subnet(s) <NUM> contained in the control plane DMZ tier <NUM> can be communicatively coupled to the app subnet(s) <NUM> contained in the control plane app tier <NUM> and to an Internet gateway <NUM> (e.g. the Internet gateway <NUM> of <FIG>) that can be contained in the control plane VCN <NUM>, and the app subnet(s) <NUM> can be communicatively coupled to the DB subnet(s) <NUM> contained in the control plane data tier <NUM> and to a service gateway <NUM> (e.g. the service gateway of <FIG>) and a network address translation (NAT) gateway <NUM> (e.g. the NAT gateway <NUM> of <FIG>). The control plane VCN <NUM> can include the service gateway <NUM> and the NAT gateway <NUM>.

The data plane VCN <NUM> can include a data plane app tier <NUM> (e.g. the data plane app tier <NUM> of <FIG>), a data plane DMZ tier <NUM> (e.g. the data plane DMZ tier <NUM> of <FIG>), and a data plane data tier <NUM> (e.g. the data plane data tier <NUM> of <FIG>). The data plane DMZ tier <NUM> can include LB subnet(s) <NUM> that can be communicatively coupled to trusted app subnet(s) <NUM> and untrusted app subnet(s) <NUM> of the data plane app tier <NUM> and the Internet gateway <NUM> contained in the data plane VCN <NUM>. The trusted app subnet(s) <NUM> can be communicatively coupled to the service gateway <NUM> contained in the data plane VCN <NUM>, the NAT gateway <NUM> contained in the data plane VCN <NUM>, and DB subnet(s) <NUM> contained in the data plane data tier <NUM>. The untrusted app subnet(s) <NUM> can be communicatively coupled to the service gateway <NUM> contained in the data plane VCN <NUM> and DB subnet(s) <NUM> contained in the data plane data tier <NUM>. The data plane data tier <NUM> can include DB subnet(s) <NUM> that can be communicatively coupled to the service gateway <NUM> contained in the data plane VCN <NUM>.

The untrusted app subnet(s) <NUM> can include one or more primary VNICs <NUM>(<NUM>)-(N) that can be communicatively coupled to tenant virtual machines (VMs) <NUM>(<NUM>)-(N). Each tenant VM <NUM>(<NUM>)-(N) can be communicatively coupled to a respective app subnet <NUM>(<NUM>)-(N) that can be contained in respective container egress VCNs <NUM>(<NUM>)-(N) that can be contained in respective customer tenancies <NUM>(<NUM>)-(N). Respective secondary VNICs <NUM>(<NUM>)-(N) can facilitate communication between the untrusted app subnet(s) <NUM> contained in the data plane VCN <NUM> and the app subnet contained in the container egress VCNs <NUM>(<NUM>)-(N). Each container egress VCNs <NUM>(<NUM>)-(N) can include a NAT gateway <NUM> that can be communicatively coupled to public Internet <NUM> (e.g. public Internet <NUM> of <FIG>).

The Internet gateway <NUM> contained in the control plane VCN <NUM> and contained in the data plane VCN <NUM> can be communicatively coupled to a metadata management service <NUM> (e.g. the metadata management system <NUM> of <FIG>) that can be communicatively coupled to public Internet <NUM>. Public Internet <NUM> can be communicatively coupled to the NAT gateway <NUM> contained in the control plane VCN <NUM> and contained in the data plane VCN <NUM>. The service gateway <NUM> contained in the control plane VCN <NUM> and contained in the data plane VCN <NUM> can be communicatively couple to cloud services <NUM>.

In some embodiments, the data plane VCN <NUM> can be integrated with customer tenancies <NUM>. This integration can be useful or desirable for customers of the IaaS provider in some cases such as a case that may desire support when executing code. The customer may provide code to run that may be destructive, may communicate with other customer resources, or may otherwise cause undesirable effects. In response to this, the IaaS provider may determine whether to run code given to the IaaS provider by the customer.

In some examples, the customer of the IaaS provider may grant temporary network access to the IaaS provider and request a function to be attached to the data plane tier app <NUM>. Code to run the function may be executed in the VMs <NUM>(<NUM>)-(N), and the code may not be configured to run anywhere else on the data plane VCN <NUM>. Each VM <NUM>(<NUM>)-(N) may be connected to one customer tenancy <NUM>. Respective containers <NUM>(<NUM>)-(N) contained in the VMs <NUM>(<NUM>)-(N) may be configured to run the code. In this case, there can be a dual isolation (e.g., the containers <NUM>(<NUM>)-(N) running code, where the containers <NUM>(<NUM>)-(N) may be contained in at least the VM <NUM>(<NUM>)-(N) that are contained in the untrusted app subnet(s) <NUM>), which may help prevent incorrect or otherwise undesirable code from damaging the network of the IaaS provider or from damaging a network of a different customer. The containers <NUM>(<NUM>)-(N) may be communicatively coupled to the customer tenancy <NUM> and may be configured to transmit or receive data from the customer tenancy <NUM>. The containers <NUM>(<NUM>)-(N) may not be configured to transmit or receive data from any other entity in the data plane VCN <NUM>. Upon completion of running the code, the IaaS provider may kill or otherwise dispose of the containers <NUM>(<NUM>)-(N).

In some embodiments, the trusted app subnet(s) <NUM> may run code that may be owned or operated by the IaaS provider. In this embodiment, the trusted app subnet(s) <NUM> may be communicatively coupled to the DB subnet(s) <NUM> and be configured to execute CRUD operations in the DB subnet(s) <NUM>. The untrusted app subnet(s) <NUM> may be communicatively coupled to the DB subnet(s) <NUM>, but in this embodiment, the untrusted app subnet(s) may be configured to execute read operations in the DB subnet(s) <NUM>. The containers <NUM>(<NUM>)-(N) that can be contained in the VM <NUM>(<NUM>)-(N) of each customer and that may run code from the customer may not be communicatively coupled with the DB subnet(s) <NUM>.

In other embodiments, the control plane VCN <NUM> and the data plane VCN <NUM> may not be directly communicatively coupled. In this embodiment, there may be no direct communication between the control plane VCN <NUM> and the data plane VCN <NUM>. However, communication can occur indirectly through at least one method. An LPG <NUM> may be established by the IaaS provider that can facilitate communication between the control plane VCN <NUM> and the data plane VCN <NUM>. In another example, the control plane VCN <NUM> or the data plane VCN <NUM> can make a call to cloud services <NUM> via the service gateway <NUM>. For example, a call to cloud services <NUM> from the control plane VCN <NUM> can include a request for a service that can communicate with the data plane VCN <NUM>.

The control plane VCN <NUM> can include a control plane DMZ tier <NUM> (e.g. the control plane DMZ tier <NUM> of <FIG>) that can include LB subnet(s) <NUM> (e.g. LB subnet(s) <NUM> of <FIG>), a control plane app tier <NUM> (e.g. the control plane app tier <NUM> of <FIG>) that can include app subnet(s) <NUM> (e.g. app subnet(s) <NUM> of <FIG>), a control plane data tier <NUM> (e.g. the control plane data tier <NUM> of <FIG>) that can include DB subnet(s) <NUM> (e.g. DB subnet(s) <NUM> of <FIG>). The LB subnet(s) <NUM> contained in the control plane DMZ tier <NUM> can be communicatively coupled to the app subnet(s) <NUM> contained in the control plane app tier <NUM> and to an Internet gateway <NUM> (e.g. the Internet gateway <NUM> of <FIG>) that can be contained in the control plane VCN <NUM>, and the app subnet(s) <NUM> can be communicatively coupled to the DB subnet(s) <NUM> contained in the control plane data tier <NUM> and to a service gateway <NUM> (e.g. the service gateway of <FIG>) and a network address translation (NAT) gateway <NUM> (e.g. the NAT gateway <NUM> of <FIG>). The control plane VCN <NUM> can include the service gateway <NUM> and the NAT gateway <NUM>.

The data plane VCN <NUM> can include a data plane app tier <NUM> (e.g. the data plane app tier <NUM> of <FIG>), a data plane DMZ tier <NUM> (e.g. the data plane DMZ tier <NUM> of <FIG>), and a data plane data tier <NUM> (e.g. the data plane data tier <NUM> of <FIG>). The data plane DMZ tier <NUM> can include LB subnet(s) <NUM> that can be communicatively coupled to trusted app subnet(s) <NUM> (e.g. trusted app subnet(s) <NUM> of <FIG>) and untrusted app subnet(s) <NUM> (e.g. untrusted app subnet(s) <NUM> of <FIG>) of the data plane app tier <NUM> and the Internet gateway <NUM> contained in the data plane VCN <NUM>. The trusted app subnet(s) <NUM> can be communicatively coupled to the service gateway <NUM> contained in the data plane VCN <NUM>, the NAT gateway <NUM> contained in the data plane VCN <NUM>, and DB subnet(s) <NUM> contained in the data plane data tier <NUM>. The untrusted app subnet(s) <NUM> can be communicatively coupled to the service gateway <NUM> contained in the data plane VCN <NUM> and DB subnet(s) <NUM> contained in the data plane data tier <NUM>. The data plane data tier <NUM> can include DB subnet(s) <NUM> that can be communicatively coupled to the service gateway <NUM> contained in the data plane VCN <NUM>.

The untrusted app subnet(s) <NUM> can include primary VNICs <NUM>(<NUM>)-(N) that can be communicatively coupled to tenant virtual machines (VMs) <NUM>(<NUM>)-(N) residing within the untrusted app subnet(s) <NUM>. Each tenant VM <NUM>(<NUM>)-(N) can run code in a respective container <NUM>(<NUM>)-(N), and be communicatively coupled to an app subnet <NUM> that can be contained in a data plane app tier <NUM> that can be contained in a container egress VCN <NUM>. Respective secondary VNICs <NUM>(<NUM>)-(N) can facilitate communication between the untrusted app subnet(s) <NUM> contained in the data plane VCN <NUM> and the app subnet contained in the container egress VCN <NUM>. The container egress VCN can include a NAT gateway <NUM> that can be communicatively coupled to public Internet <NUM> (e.g. public Internet <NUM> of <FIG>).

In some examples, the pattern illustrated by the architecture of block diagram <NUM> of <FIG> may be considered an exception to the pattern illustrated by the architecture of block diagram <NUM> of <FIG> and may be desirable for a customer of the IaaS provider if the IaaS provider cannot directly communicate with the customer (e.g., a disconnected region). The respective containers <NUM>(<NUM>)-(N) that are contained in the VMs <NUM>(<NUM>)-(N) for each customer can be accessed in real-time by the customer. The containers <NUM>(<NUM>)-(N) may be configured to make calls to respective secondary VNICs <NUM>(<NUM>)-(N) contained in app subnet(s) <NUM> of the data plane app tier <NUM> that can be contained in the container egress VCN <NUM>. The secondary VNICs <NUM>(<NUM>)-(N) can transmit the calls to the NAT gateway <NUM> that may transmit the calls to public Internet <NUM>. In this example, the containers <NUM>(<NUM>)-(N) that can be accessed in real-time by the customer can be isolated from the control plane VCN <NUM> and can be isolated from other entities contained in the data plane VCN <NUM>. The containers <NUM>(<NUM>)-(N) may also be isolated from resources from other customers.

In other examples, the customer can use the containers <NUM>(<NUM>)-(N) to call cloud services <NUM>. In this example, the customer may run code in the containers <NUM>(<NUM>)-(N) that requests a service from cloud services <NUM>. The containers <NUM>(<NUM>)-(N) can transmit this request to the secondary VNICs <NUM>(<NUM>)-(N) that can transmit the request to the NAT gateway that can transmit the request to public Internet <NUM>. Public Internet <NUM> can transmit the request to LB subnet(s) <NUM> contained in the control plane VCN <NUM> via the Internet gateway <NUM>. In response to determining the request is valid, the LB subnet(s) can transmit the request to app subnet(s) <NUM> that can transmit the request to cloud services <NUM> via the service gateway <NUM>.

It should be appreciated that IaaS architectures <NUM>, <NUM>, <NUM>, <NUM> depicted in the figures may have other components than those depicted. Further, the embodiments shown in the figures are only some examples of a cloud infrastructure system that may incorporate an embodiment of the disclosure. In some other embodiments, the IaaS systems may have more or fewer components than shown in the figures, may combine two or more components, or may have a different configuration or arrangement of components.

In certain embodiments, the IaaS systems described herein may include a suite of applications, middleware, and database service offerings that are delivered to a customer in a self-service, subscription-based, elastically scalable, reliable, highly available, and secure manner. An example of such an IaaS system is the Oracle Cloud Infrastructure (OCI) provided by the present assignee.

<FIG> illustrates an example computer system <NUM>, in which various embodiments may be implemented. The system <NUM> may be used to implement any of the computer systems described above. As shown in the figure, computer system <NUM> includes a processing unit <NUM> that communicates with a number of peripheral subsystems via a bus subsystem <NUM>. These peripheral subsystems may include a processing acceleration unit <NUM>, an I/O subsystem <NUM>, a storage subsystem <NUM> and a communications subsystem <NUM>. Storage subsystem <NUM> includes tangible computer-readable storage media <NUM> and a system memory <NUM>.

Although bus subsystem <NUM> is shown schematically as a single bus, alternative embodiments of the bus subsystem may utilize multiple buses. Bus subsystem <NUM> may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. For example, such architectures may include an Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, which can be implemented as a Mezzanine bus manufactured to the IEEE P1386. <NUM> standard.

Processing unit <NUM>, which can be implemented as one or more integrated circuits (e.g., a conventional microprocessor or microcontroller), controls the operation of computer system <NUM>. One or more processors may be included in processing unit <NUM>. These processors may include single core or multicore processors. In certain embodiments, processing unit <NUM> may be implemented as one or more independent processing units <NUM> and/or <NUM> with single or multicore processors included in each processing unit. In other embodiments, processing unit <NUM> may also be implemented as a quad-core processing unit formed by integrating two dualcore processors into a single chip.

In various embodiments, processing unit <NUM> can execute a variety of programs in response to program code and can maintain multiple concurrently executing programs or processes. At any given time, some or all of the program code to be executed can be resident in processor(s) <NUM> and/or in storage subsystem <NUM>. Through suitable programming, processor(s) <NUM> can provide various functionalities described above. Computer system <NUM> may additionally include a processing acceleration unit <NUM>, which can include a digital signal processor (DSP), a special-purpose processor, and/or the like.

I/O subsystem <NUM> may include user interface input devices and user interface output devices. User interface input devices may include a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices. User interface input devices may include, for example, motion sensing and/or gesture recognition devices such as the Microsoft Kinect® motion sensor that enables users to control and interact with an input device, such as the Microsoft Xbox® <NUM> game controller, through a natural user interface using gestures and spoken commands. User interface input devices may also include eye gesture recognition devices such as the Google Glass® blink detector that detects eye activity (e.g., 'blinking' while taking pictures and/or making a menu selection) from users and transforms the eye gestures as input into an input device (e.g., Google Glass®). Additionally, user interface input devices may include voice recognition sensing devices that enable users to interact with voice recognition systems (e.g., Siri® navigator), through voice commands.

User interface input devices may also include, without limitation, three dimensional (3D) mice, joysticks or pointing sticks, gamepads and graphic tablets, and audio/visual devices such as speakers, digital cameras, digital camcorders, portable media players, webcams, image scanners, fingerprint scanners, barcode reader 3D scanners, 3D printers, laser rangefinders, and eye gaze tracking devices. Additionally, user interface input devices may include, for example, medical imaging input devices such as computed tomography, magnetic resonance imaging, position emission tomography, medical ultrasonography devices. User interface input devices may also include, for example, audio input devices such as MIDI keyboards, digital musical instruments and the like.

User interface output devices may include a display subsystem, indicator lights, or nonvisual displays such as audio output devices, etc. The display subsystem may be a cathode ray tube (CRT), a flat-panel device, such as that using a liquid crystal display (LCD) or plasma display, a projection device, a touch screen, and the like. In general, use of the term "output device" is intended to include all possible types of devices and mechanisms for outputting information from computer system <NUM> to a user or other computer. For example, user interface output devices may include, without limitation, a variety of display devices that visually convey text, graphics and audio/video information such as monitors, printers, speakers, headphones, automotive navigation systems, plotters, voice output devices, and modems.

Computer system <NUM> may comprise a storage subsystem <NUM> that comprises software elements, shown as being currently located within a system memory <NUM>. System memory <NUM> may store program instructions that are loadable and executable on processing unit <NUM>, as well as data generated during the execution of these programs.

Depending on the configuration and type of computer system <NUM>, system memory <NUM> may be volatile (such as random access memory (RAM)) and/or non-volatile (such as readonly memory (ROM), flash memory, etc.) The RAM typically contains data and/or program modules that are immediately accessible to and/or presently being operated and executed by processing unit <NUM>. In some implementations, system memory <NUM> may include multiple different types of memory, such as static random access memory (SRAM) or dynamic random access memory (DRAM). In some implementations, a basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within computer system <NUM>, such as during start-up, may typically be stored in the ROM. By way of example, and not limitation, system memory <NUM> also illustrates application programs <NUM>, which may include client applications, Web browsers, mid-tier applications, relational database management systems (RDBMS), etc., program data <NUM>, and an operating system <NUM>. By way of example, operating system <NUM> may include various versions of Microsoft Windows®, Apple Macintosh®, and/or Linux operating systems, a variety of commercially-available UNIX® or UNIX-like operating systems (including without limitation the variety of GNU/Linux operating systems, the Google Chrome® OS, and the like) and/or mobile operating systems such as iOS, Windows® Phone, Android® OS, BlackBerry® <NUM> OS, and Palm® OS operating systems.

Storage subsystem <NUM> may also provide a tangible computer-readable storage medium for storing the basic programming and data constructs that provide the functionality of some embodiments. Software (programs, code modules, instructions) that when executed by a processor provide the functionality described above may be stored in storage subsystem <NUM>. These software modules or instructions may be executed by processing unit <NUM>. Storage subsystem <NUM> may also provide a repository for storing data used in accordance with the present disclosure.

Storage subsystem <NUM> may also include a computer-readable storage media reader <NUM> that can further be connected to computer-readable storage media <NUM>. Together and, optionally, in combination with system memory <NUM>, computer-readable storage media <NUM> may comprehensively represent remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing, storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media <NUM> containing code, or portions of code, can also include any appropriate media known or used in the art, including storage media and communication media, such as but not limited to, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage and/or transmission of information. This can include tangible computer-readable storage media such as RAM, ROM, electronically erasable programmable ROM (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible computer readable media. This can also include nontangible computer-readable media, such as data signals, data transmissions, or any other medium which can be used to transmit the desired information and which can be accessed by computing system <NUM>.

By way of example, computer-readable storage media <NUM> may include a hard disk drive that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive that reads from or writes to a removable, nonvolatile magnetic disk, and an optical disk drive that reads from or writes to a removable, nonvolatile optical disk such as a CD ROM, DVD, and Blu-Ray® disk, or other optical media. Computer-readable storage media <NUM> may include, but is not limited to, Zip® drives, flash memory cards, universal serial bus (USB) flash drives, secure digital (SD) cards, DVD disks, digital video tape, and the like. Computer-readable storage media <NUM> may also include, solid-state drives (SSD) based on non-volatile memory such as flash-memory based SSDs, enterprise flash drives, solid state ROM, and the like, SSDs based on volatile memory such as solid state RAM, dynamic RAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, and hybrid SSDs that use a combination of DRAM and flash memory based SSDs. The disk drives and their associated computer-readable media may provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for computer system <NUM>.

Communications subsystem <NUM> provides an interface to other computer systems and networks. Communications subsystem <NUM> serves as an interface for receiving data from and transmitting data to other systems from computer system <NUM>. For example, communications subsystem <NUM> may enable computer system <NUM> to connect to one or more devices via the Internet. In some embodiments communications subsystem <NUM> can include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks (e.g., using cellular telephone technology, advanced data network technology, such as <NUM>, <NUM> or EDGE (enhanced data rates for global evolution), WiFi (IEEE <NUM> family standards, or other mobile communication technologies, or any combination thereof), global positioning system (GPS) receiver components, and/or other components. In some embodiments communications subsystem <NUM> can provide wired network connectivity (e.g., Ethernet) in addition to or instead of a wireless interface.

In some embodiments, communications subsystem <NUM> may also receive input communication in the form of structured and/or unstructured data feeds <NUM>, event streams <NUM>, event updates <NUM>, and the like on behalf of one or more users who may use computer system <NUM>.

By way of example, communications subsystem <NUM> may be configured to receive data feeds <NUM> in real-time from users of social networks and/or other communication services such as Twitter® feeds, Facebook® updates, web feeds such as Rich Site Summary (RSS) feeds, and/or real-time updates from one or more third party information sources.

Additionally, communications subsystem <NUM> may also be configured to receive data in the form of continuous data streams, which may include event streams <NUM> of real-time events and/or event updates <NUM>, that may be continuous or unbounded in nature with no explicit end. Examples of applications that generate continuous data may include, for example, sensor data applications, financial tickers, network performance measuring tools (e.g. network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like.

Communications subsystem <NUM> may also be configured to output the structured and/or unstructured data feeds <NUM>, event streams <NUM>, event updates <NUM>, and the like to one or more databases that may be in communication with one or more streaming data source computers coupled to computer system <NUM>.

Computer system <NUM> can be one of various types, including a handheld portable device (e.g., an iPhone® cellular phone, an iPad® computing tablet, a PDA), a wearable device (e.g., a Google Glass® head mounted display), a PC, a workstation, a mainframe, a kiosk, a server rack, or any other data processing system.

Due to the ever-changing nature of computers and networks, the description of computer system <NUM> depicted in the figure is intended only as a specific example. Many other configurations having more or fewer components than the system depicted in the figure are possible. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, firmware, software (including applets), or a combination. Further, connection to other computing devices, such as network input/output devices, may be employed. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the various embodiments.

Embodiments are not restricted to operation within certain specific data processing environments, but are free to operate within a plurality of data processing environments. Additionally, although embodiments have been described using a particular series of transactions and steps, it should be apparent to those skilled in the art that the scope of the present disclosure is not limited to the described series of transactions and steps. Various features and aspects of the above-described embodiments may be used individually or jointly.

Further, while embodiments have been described using a particular combination of hardware and software, it should be recognized that other combinations of hardware and software are also within the scope of the present disclosure. Embodiments may be implemented only in hardware, or only in software, or using combinations thereof. The various processes described herein can be implemented on the same processor or different processors in any combination. Accordingly, where components or modules are described as being configured to perform certain operations, such configuration can be accomplished, e.g., by designing electronic circuits to perform the operation, by programming programmable electronic circuits (such as microprocessors) to perform the operation, or any combination thereof. Processes can communicate using a variety of techniques including but not limited to conventional techniques for inter process communication, and different pairs of processes may use different techniques, or the same pair of processes may use different techniques at different times.

The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. Thus, although specific disclosure embodiments have been described, these are not intended to be limiting.

The use of the terms "a" and "an" and "the" and similar referents in the context of describing the disclosed embodiments (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The term "connected" is to be construed as partly or wholly contained within, attached to, or joined together, even if there is something intervening. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein and each separate value is incorporated into the specification as if it were individually recited herein. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate embodiments and does not pose a limitation on the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any nonclaimed element as essential to the practice of the disclosure.

Disjunctive language such as the phrase "at least one of X, Y, or Z," unless specifically stated otherwise, is intended to be understood within the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.

Preferred embodiments of this disclosure are described herein, including the best mode known for carrying out the disclosure. Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. Those of ordinary skill should be able to employ such variations as appropriate and the disclosure may be practiced otherwise than as specifically described herein.

In the foregoing specification, aspects of the disclosure are described with reference to specific embodiments thereof, but those skilled in the art will recognize that the disclosure is not limited thereto. Various features and aspects of the above-described disclosure may be used individually or jointly. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive.

Claim 1:
A method for deriving one or more projected sources of an outage in a datacenter, the method comprising:
obtaining (<NUM>) a set of input data providing various parameters relating to a datacenter and a listing of devices in the data center and applications executing on the devices in the datacenter;
detecting (<NUM>) an outage of at least one functionality of the datacenter based at least in part on the obtained set of input data;
responsive to detecting the outage, processing (<NUM>) the set of input data using a model to derive one or more projected sources of the outage, the model incorporating a set of rules specifying correlations between the set of input data and the devices or the applications executing on the devices as the one or more projected sources of the outage, wherein deriving the one or more projected sources of the outage includes:
generating (<NUM>), using the set of rules accessible to the model, a predicted level for each parameter included in the set of input data using historical data relating to each parameter;
comparing (<NUM>) the predicted level for each parameter with an actual level of each parameter included in the set of input data to identify one or more anomalous parameters that include actual levels with a threshold deviation from each corresponding predicted level; and
identifying (<NUM>) one or more devices and/or an application that corresponds to each of the identified anomalous parameters, wherein each of the identified one or more devices and/or the application is included as the one or more projected sources of the outage; and
generating (<NUM>) an outage notification message providing the one or more projected sources of the outage based at least in part on processing the set of input data,
wherein the set of input data includes a location of each of the devices in the datacenter and a device type of each device in the datacenter, wherein the method further comprises:
processing the set of input data to identify a data type and one or more associated devices relating to each portion of the set of input data;
assigning, to each portion of the set of input data, a timestamp indicating a time of obtaining each portion of the set of input data; and
storing the set of input data in a database by the identified data type and the assigned timestamp.