Proactive data protection based on weather patterns and severity

Techniques can be implemented to adjust a level of data protection for a device based on predictions of the weather. A physical location of the device can be determined. A first weather prediction from a first weather source for the physical location for a first time period, and a second weather prediction from a second weather source for the physical location for a second time period can be determined. The first weather prediction and the second weather prediction can be combined to produce a combined weather prediction for a third time period. The combined weather prediction can be analyzed to determine a weather categorization. Based on the weather categorization, a level of data protection of the device can be increased for the third time period.

BACKGROUND

Administrators of data centers can strive to avoid data loss or data unavailability. Data loss can comprise a situation where data is destroyed and cannot be recovered. Data unavailability can comprise a situation where data cannot be accessed for a period of time, though the data has not been permanently lost.

SUMMARY

An example system can determine a physical location of a device. The system can determine a first weather prediction from a first weather source for the physical location for a first time period, and a second weather prediction from a second weather source for the physical location for a second time period. The system can combine the first weather prediction and the second weather prediction to produce a combined weather prediction, the combined weather prediction occurring during a third time period, the third time overlapping with the first time period or the second time period. The system can analyze the combined weather prediction to determine a weather categorization. Based on the weather categorization, the system can increase a level of data protection of the device for the third time period.

An example method can comprise determining, by a system comprising a processor, a physical location of a device. The method can further comprise determining, by the system, a first environmental prediction from a first source and applicable to the physical location for a first time period. The method can further comprise determining, by the system, a second environmental prediction from a second source and applicable to the physical location for a second time period. The method can further comprise determining, by the system, a third environmental prediction based on the first environmental prediction and the second environmental prediction, the third environmental prediction occurring during a third time period that overlaps with the first time period or the second time period. The method can further comprise categorizing, by the system, the third environmental prediction, resulting in a categorization. The method can further comprise, based on the categorization, modifying, by the system, a level of data protection of the device for the time period.

An example non-transitory computer-readable medium can comprise instructions that, in response to execution, cause a system comprising a processor to perform operations. These operations can comprise determining an environmental prediction applicable to a physical location of a computer for a time period, wherein the environmental prediction comprises a prediction that an environmental condition associated with the computer will change at a time within the time period. These operations can comprise determining a categorization based the environmental prediction. These operations can comprise based on the categorization, modifying a level of data protection of the computer for the time period.

DETAILED DESCRIPTION

Overview

Critical weather conditions can impact data centers, which can result in data loss or a loss of business continuity requirements, because data protection (e.g., snapshots, backups, or replications) can be based on a static point in time or scheduled operations. Critical weather conditions that can negatively impact data centers can include weather patterns and natural disasters such as thunderstorms, flooding, heavy rains, tornados, hurricanes, earthquakes, and tsunamis.

In a case of critical weather conditions, administrators or users can need to manually upscale protection requirements/schedules prior to the occurrence of an event. In critical conditions, it can be that an entire data center is at risk and systems within a data center lack transparency over external conditions or factors.

There are not currently systems or procedures that provide analytics and refined data sets as events or alerts to data centers or computer systems. As there are no such systems or procedures, there can be a lack of automated implementations and procedures where computer systems can be configured to take automated decisions based on policies that are predefined to mitigate against data loss, unavailability, or business continuity.

As weather patterns and events play an important role in data centers for administrators to prioritize protection needs, an improvement over current techniques can involve using analytics to provide weather events and alerts to computer systems and data centers so that these computer systems and data centers can utilize the refined events and alerts to take action based on pre-defined policies. For example, events can be categorized as RED, GREEN, or YELLOW with a predicted event timeline, and a computer system can utilize this data to activate respective policies.

A centralized information gathering system can collect and process data from different sources and accumulate weather predictions and weather alerts for a particular geographical location. This accumulated data can be analyzed based on a geographical location of a system or data center, and the data can be classified to produce an alert for a data center to use to modify its level of data protection. In some examples, the geographical location tagging for specific systems can be set as part of provisioning a system, or as part of the system registering with another system that facilitates proactive data protection based on weather patterns and severity. This geographical location information can be extracted from a weather system, and the data can be egressed through to the respective computer systems that can be backed up. These techniques can be implemented in a cloud platform that provide these critical weather condition alerts based on a standardized application programming interface (API).

Examples of weather events can be as follows. Predictable events can generally be events that can be gathered from different weather sources. In some examples, to reduce false positives, weather predictions can be gathered from multiple sources, and churned through analytics and techniques to provide meaningful and simplified actionable events or alerts. Examples of predictable events can include, weather data for the next seven days for a particular geographical location, a probability of flooding or inundation, a probability of thunderstorms, a probability of heavy winds, a probability of tornados, a probability of hurricanes, a probability of a blizzards or heavy snow, a probability of sand storms, and a probability of volcano eruptions.

Another type applicable to weather events can be immediate/non-predictable events Immediate/non-predictable events can comprise events that are unpredictive and have small or no lead times for taking immediate actionable procedures. In some examples, these alerts can be used in scenarios where systems or data centers have active protection implementations (e.g., synchronous replication) with robust data recovery plans. In some examples, the actions taken can involve doing a failover of systems in sync-replicated data sets. Examples of immediate/non-predictable events can include earthquakes, a tsunami probability after an earthquake, and bush fires or forest fires.

Alerts and events can be categorized, such as RED, YELLOW, or GREEN. It can be appreciated that different categorizations can be implemented, such as those that implement more or fewer than three categories, like with the RED/YELLOW/GREEN categorization. Along with categorized alerts, a system can also provide notices such as the following: an event type can have a SCHEDULE of the event embedded in a case of predicted events; an event type can include a probability of occurrence that can be derived from different weather sources (these probabilities can be, e.g., HIGH, MEDIUM, or LOW); in a case of non-predictable events, an alert can include an impact (an impact can be, e.g., HIGH, MEDIUM, or LOW), and include information such as “SCHEDULE: IMMEDIATE ATTN;” an alert can be provided in different formats, e.g., a JavaScript Object Notation (JSON) format, a YAML Ain′t Markup Language (YAML) format, or a Terse Markup Language (TML) format.

Example Architectures and Classifications

FIG.1illustrates an example system architecture100that can facilitate proactive data protection based on weather patterns and severity, in accordance with certain embodiments of this disclosure.

As depicted, system architecture100comprises data center102a-102n, communications network104, computer system106, forecast source110a-110n, and alert source112a-112n. In turn, computer system106comprises proactive data protection based on weather patterns and severity component108.

There can be examples with one or more instances each of data center102a-102n, forecast source110a-110n, and alert source112a-112n. In the example of system architecture100, there are depicted n instances of each of data center102a-102n, forecast source110a-110n, and alert source112a-112n.

Each of data center102a-102n, computer system106, forecast source110a-110n, and alert source112a-112ncan be implemented with one or more instances of computer1002ofFIG.10. In some examples, proactive data protection based on weather patterns and severity component108can be implemented with machine-executable instructions and/or part(s) of computer1002ofFIG.10.

Communications network104can comprise a computer communications network, such as the Internet, or an isolated private computer communications network.

Computer system106can communicate with each of data center102a-102n, forecast source110a-110n, and alert source112a-112nvia communications network104. In some examples, computer system106can communicate with each of data center102a-102nto determine a physical location (sometimes referred to as a geolocation) of each data center. Computer system106can communicate with forecast source110a-110n, and alert source112a-112nto determine forecasts and alerts, respectively, about the physical location of each data center102a-102n.

Proactive data protection based on weather patterns and severity component108of computer system106can analyze these forecasts and alerts to determine when to change a data protection for a particular data center. Alerts can comprise indications that severe events have recently occurred or are occurring—for example, an earthquake. Forecasts can comprise a prediction that a weather or environmental event will happen at some point in the future—for example, that there will be an 80% chance of heavy rain between 4 pm and 9 pm tomorrow.

Proactive data protection based on weather patterns and severity component108of computer system106can analyze these forecasts and alerts. For example, proactive data protection based on weather patterns and severity component108can analyze these forecasts and alerts to determine that there is a sufficient likelihood of flooding at a physical location of data center102athat will lead to data unavailability. Based on that analysis, proactive data protection based on weather patterns and severity component108can send a communication to data center102avia communications network104to increase an amount of data protection for a time that corresponds to a time that the flood prediction spans. Increasing data protection can comprise, for example, increasing a cadence at which snapshots are taken of data in data center102afrom once per 60 minutes to once per 30 minutes.

In implementing proactive data protection based on weather patterns and severity, proactive data protection based on weather patterns and severity component108can implement part(s) of process flow700ofFIG.7, process flow800ofFIG.8, and/or process flow900ofFIG.9.

FIG.2illustrates another example system architecture200that can facilitate proactive data protection based on weather patterns and severity, in accordance with certain embodiments of this disclosure. System architecture200comprises public and private data202, centralized monitoring system at data center204, and system action based on events and alerts206.

Public and private data202can comprise alert and forecast data, such as alerts from alert source112a-112n, and forecasts from forecast source110a-110n.

Centralized monitoring system at data center204can be similar to proactive data protection based on weather patterns and severity component108ofFIG.1. In some examples, centralized monitoring system at data center204can actively poll information from sources such as, social media and public or private weather application programming interfaces (APIs), such as those provided as part of public and private data202. Centralized monitoring system at data center204can use this information to provide an alert to individual systems based (such as a data center of data center102a-102nofFIG.1) on a defined classification of the weather. A defined classification can comprise a severity (e.g., GREEN, YELLOW, or RED), and a time period for which the classification is in effect.

System action based on events and alerts206can comprise an action that proactive data protection based on weather patterns and severity component108ofFIG.1instructs a data center of data center102a-102nto take. A system action can include decreasing a recovery point objective (RPO) for an existing replication session; mutating a replication session from asynchronous to synchronous, or from synchronous to asynchronous; immediately triggering active backups for certain (or all) data sets or objects; depending on a severity of the events and alerts, failing over objects to a remote site where conditions are normal; and providing high priority alerts to administrators of the corresponding data center.

System architecture200can be implemented to mitigate against data loss or down time from environmental events such as harsh weather conditions. Weather patterns and conditions can be actively tracked from public and private sources for a location of a data center, and automated mechanisms can be taken to actively protect data when the weather is, or is forecast to be, sufficiently severe.

System architecture200can be implemented to data from public and/or private sources, and implement mechanism to trigger an event based on a severity to the corresponding data center. Depending on the severity, centralized monitoring system at data center204can take an action based on policies set. In some examples, data sources can be active conditions, or future predictions that are analyzed. Systems can provide ingress and egress mechanisms to actively gather and process data and publish events.

FIG.3illustrates another example system architecture300that can facilitate proactive data protection based on weather patterns and severity, in accordance with certain embodiments of this disclosure. These elements of system architecture can represent information, information flows, information processing, and computer systems. In some examples, all or part of system architecture300can be implemented with proactive data protection based on weather patterns and severity component108ofFIG.1.

As depicted, system architecture300comprises weather information source 1302a, weather information source 2302b, weather information source 3302c, system geo location304, weather service enabled?306, service systems308, weather prediction data for each geo location310, process data per geo location312, critical alert source 1314a, critical alert source 2314b, categorize data as per predefined rules316, categorization rules318, egress API/support systems320, and data center/system322.

Service systems308, which can be part of a data center, can implement a weather service that can both provide geo location (sometimes referred to as physical location) information of where that service system is physically located, and receive and implement instructions on how to modify a data protection of the service system in response to an environmental event. In some examples, service systems308can comprise a centralized service system maintained for central processing of data for weather service registered systems maintained by a service vendor (e.g., weather information source 1302a).

Service systems308can be polled by weather service enabled?306to determine whether such a weather service is enabled. If it is determined that a weather service is enabled, the physical location of the system, as system geo location304can be provided to weather prediction data for each geo location310. If it is determined that a weather service is not enabled, service systems308can continue to be periodically polled for whether a weather service is enabled.

Weather prediction data for each geo location310can use system geo location304(as well as geo location information for other systems) to query one or more of weather information source 1302a, weather information source 2302b, and weather information source 3302c. While three weather sources are depicted in system architecture300, it can be appreciated that there can be examples with more or fewer weather sources. Weather prediction data for each geo location310can use an application programming interface (API) provided by each weather source to supply the geo location of service systems308(and, in some examples, other service systems), and receive back weather forecast information via the same API.

Weather prediction data for each geo location310can provide this received weather forecast information to process data per geo location312. Process data per geo location312can also access information about alerts from critical alert source 1314aand critical alert source 2314b(there can be examples with more or fewer critical alert sources than the two critical alert sources that are depicted). This information can be accessed via an API, such as by process data per geo location312registering with or querying each of critical alert source 1314aand critical alert source 2314bfor information about alerts relating to the geo location of service systems308.

Weather prediction data for each geo location310can synthesize this weather forecast information (from weather information source 1302a, weather information source 2302b, and weather information source 3302c) and alert information (from critical alert source 1314aand critical alert source 2314b) to produce a prediction or status for service systems308, and provide this information to categorize data as per predefined rules316.

Categorize data as per predefined rules316can apply categorization rules318to the information received from process data per geo location312. Categorization rules318can be set by an administrator, and can be similar to classifications500ofFIG.5. Once categorized by categorize data as per predefined rules316, categorize data as per predefined rules316can provide this categorization (or an instruction for data protection based on this classification) to egress API/support systems320, which can be an API to communicate with service systems308.

Egress API/support systems320can provide a corresponding alert to data center/system322, which can implement a level of data protection based on this alert. Data center/system322can comprise a system within a data center that is configured to receive these alerts, and effectuate corresponding data protections for the data center based on the received alerts.

FIG.4illustrates an example system architecture400for processing alerts that can facilitate proactive data protection based on weather patterns and severity, in accordance with certain embodiments of this disclosure. For example, system architecture400can be implemented by data center/system322ofFIG.3, or one of data center102a-102nof FIG.1to process alerts received (such as from proactive data protection based on weather patterns and severity component108).

Alert received402can be similar to the alert sent from API/support systems320ofFIG.3to data center/system322. Process alert category404can process this alert, and both cause an indication of alert to be displayed in a user interface with display alert406, and send the processed alert category on. As depicted, the processed alert category can comprise green406a, yellow408b, or red408c. In this example, yellow408bcan be more significant than green408a, and red408ccan be more significant than yellow408b.

The processed alert—green406a, yellow408b, or red408c—can be received by process policy based on category410, which can process the alert accordingly. Process policy based on category410can utilize information from local weather prediction policies412. This information from local weather prediction policies412can be provided by user input from an administrator, and can specify a level of data protection to provide for a given alert. For example, one data center can determine that a RED alert indicates taking a snapshot every 30 minutes, while another data center can determine that a RED alert indicates taking a snapshot every 20 minutes.

After process policy based on category410processes the policy, this processed policy information can be sent to policy based action on an object/system414. For example, where process policy based on category410determines that the policy for the alert (e.g., RED) calls for changing a cadence of taking snapshots to taking a snapshot every 20 minutes, this information can be communicated to policy based action on an object/system414which can implement taking a snapshot every 20 minutes for the data center.

FIG.5illustrates example classifications500that can facilitate proactive data protection based on weather patterns and severity, in accordance with certain embodiments of this disclosure. Example classifications500can be used to classify alerts and weather forecasts, such as by proactive data protection based on weather patterns and severity component108ofFIG.1.

As depicted, example classifications500comprises five columns—alert/classification502, wind speed504, magnitude506, precipitation508, and temperature510. These columns502-510can generally comprise metrics for different types of data that, if present, mean that the classification identified in alert/classification502will be applied. It can be appreciated that there can be other metrics used for classifying alerts and forecasts.

Wind speed504can express wind speed in terms of kilometers per hour (km/h). Magnitude506can express an earthquake according to the Richter scale. Precipitation508can express rainfall in millimeters (mm). Temperature510can express temperature in degrees Celsius (C).

Then, each row of example classifications500—here depicted as row512aand row512b—can identify a classification, and values for each metric that qualify for the classification. Row512aindicates the metrics that will lead to a classification as RED, and row512bindicates the metrics that will lead to a classification as YELLOW.

In some examples, different metrics can be classified differently. For example, the wind speed within the next two days can be forecast at >150 km/h (indicating a RED alert), and the precipitation can be forecast at <120 mm in the next two days (indicating a YELLOW alert). In such examples, the highest alert indicated by a metric can be used as the alert. So, in this example where the wind speed indicates a RED alert, and the precipitation indicates a YELLOW alert, a RED alert can be used.

FIG.6illustrates an example system architecture600for synthesizing alerts and forecasts that can facilitate proactive data protection based on weather patterns and severity, in accordance with certain embodiments of this disclosure. In some examples, system architecture600can be implemented as part of proactive data protection based on weather patterns and severity component108ofFIG.1.

System architecture600comprises alert(s)602, prediction(s)604, alert and prediction synthesis606, and classification608. Alert(s)602can be similar to alerts provided by critical alert source 1314aand critical alert source 2314bofFIG.3. Prediction(s) can be similar to weather prediction data provided by weather information source 1302a, weather information source 2302b, and weather information source 3302cofFIG.3. Classification608can be similar to a categorization provided by categorize data as per predefined rules316ofFIG.3.

Alert and prediction synthesis606can synthesize the various alert(s) and prediction(s) to produce a classification. In some examples, different alerts and predictions can cover different time periods, can provide conflicting information, can provide different types of information (e.g., one weather prediction source relates to temperature, and another weather prediction source relates to precipitation), or can be associated with a different reliability of predictions (e.g., one weather source can be determined to provide more reliable predictions than another weather source). From these different sources, alert and prediction synthesis606can produce on classification for a given data center and a given point in time (which can be updated over time).

In some examples where different sources provide conflicting information or provide different types of information (e.g., one source provides precipitation information and another source provides temperature information), alert and prediction synthesis606can produce a classification based on a most severe alert or prediction available for a given point in time. In other examples, alert and prediction synthesis606can base a classification on a source of alerts or predictions deemed to be most reliable. From these multiple sources, alert and prediction synthesis606can produce one classification for a given point in time.

Example Process Flows

FIG.7illustrates an example process flow700for proactive data protection based on weather patterns and severity, in accordance with certain embodiments of this disclosure. In some examples, one or more embodiments of process flow700can be implemented by proactive data protection based on weather patterns and severity component108ofFIG.1, or computing environment1000ofFIG.10.

Process flow700begins with702, and moves to operation704. Operation704depicts determining a physical location of a device. In some examples, operation704comprises receiving an identification of the physical location from the device, via a communications network. In some examples, this can comprise system geo location304ofFIG.3providing a physical location of service systems308via weather service enabled?306.

After operation704, process flow700moves to operation706.

Operation706depicts determining a first weather prediction from a first weather source for the physical location for a first time period, and a second weather prediction from a second weather source for the physical location for a second time period. In some examples, this can comprise determining a first weather prediction from weather prediction source 1302aofFIG.3, and determining a second weather prediction from weather prediction source 2302b.

After operation706, process flow700moves to operation708.

Operation708depicts combining the first weather prediction and the second weather prediction to produce a combined weather prediction, the combined weather prediction occurring during a third time period, the third time overlapping with the first time period or the second time period. Combining the weather predictions can involve producing a single weather prediction for a given physical location and a given time period.

Sources from multiple weather services (including private and public) can be normalized to increase an accuracy of prediction. That is, data can be normalized to increase a probability that a corresponding forecast or prediction occurs. In some examples, analyzing and synthesizing multiple sources of weather predictions can lead to a more accurate prediction.

In some examples where a time period for multiple weather predictions overlaps, combining two predictions can comprise selecting a more accurate prediction (based on a determined accuracy of the source), or selecting a most critical prediction (e.g., the prediction that would lead to the strongest data protection level).

In some examples, the first weather prediction and second weather prediction can cover different time periods (e.g., one is for noon-4 pm, and the other is for 5-9 pm). In such examples, combining the weather predictions can comprise using the first weather prediction for the first time period, and using the second weather prediction for the second time period.

In some examples, different weather sources can employ different approaches in predicting weather events in terms of a time of event, a length of event, a severity of event, etc. This prediction data can differ from source to source, and in some examples some sources do not normalize their respective predictions. When dealing with data from weather sources that slightly or majorly differs in terms of time, length, or severity, normalization can be applied. A normalization approach can take weather prediction data from different sources and process it to provide a higher predictability.

In an example, Source 1 can predict that the length of a weather event is four hours, from 2:00 pm to 6:00 pm, and Source 2 can predict that the length of a weather event is four hours, from noon to 4 pm. An approach to normalizing data from these two sources can be to predict a weather event for six hours, from noon to 6:00 pm.

In some examples, a severity of a weather event can be determined based on normalizing severity information from multiple weather sources. For example, where a majority of sources predict a higher severity relative to lower severity, the severity can be given the higher designation. In some examples, this approach can be taken to err on the side of safety so as not to underestimate the severity of a weather event.

In some examples, operation708comprises normalizing the first weather prediction and the second weather prediction to produce the combined weather prediction. Normalizing the predictions can comprise converting some data from a first range or format into a common range or format, so that predictions from multiple sources can be directly compared.

After operation708, process flow700moves to operation710.

Operation710depicts analyzing the combined weather prediction to determine a weather categorization. In some examples, operation710can be implemented in a similar manner as process data for each geo location312ofFIG.3, and categorize data as per predefined rules316.

In some examples, the weather categorization is defined based on user input. For example, categorization rules318ofFIG.3can be defined by an administrator. In some examples, a base weather categorization can be performed by learning from multiple weather data sources for a physical location and then categorizing the combined prediction (e.g., as RED, YELLOW, or GREEN). A user can provide user input to specify rules and policies that can affect how a received category of alert (e.g., RED, YELLOW, or GREEN) can be applied to data protection of storage objects.

In some examples, operation710comprises receiving a weather alert separate from the first weather prediction and the second weather prediction, wherein the determining of the weather categorization comprises determining the weather categorization based on the weather alert, the first weather prediction, and the second weather prediction.

In some examples, the weather alert is a first weather alert, and wherein the determining of the weather categorization comprises determining the weather prediction based on the first weather alert received from a first weather alert source and a second weather alert received from a second weather alert source. That is, there can be multiple sources of weather alerts.

After operation710, process flow700moves to operation712.

Operation712depicts, based on the weather categorization, increasing a level of data protection of the device for the third time period. In some examples, this can comprise changing a rate at which data protection (e.g., a backup or snapshot) is taken for a duration of the third time period. In other examples, this can comprise failing over from the device to a second device, such as from a first replication server to a second replication server at a second physical location that is not subject to a critical weather alert.

In some examples, the level of data protection comprises a schedule for performing a data backup operation for the device, and operation712comprises decreasing an amount of time between performing iterations of the data backup, replication, or snapshot operation from a first amount of time to a second amount of time. That is, an action taken can be to increase a cadence of performing backups, snapshots, replications, or other data protection techniques.

FIG.8illustrates an example process flow800for proactive data protection based on weather patterns and severity, in accordance with certain embodiments of this disclosure. In some examples, one or more embodiments of process flow800can be implemented by proactive data protection based on weather patterns and severity component108ofFIG.1, or computing environment1000ofFIG.10.

Process flow800begins with802, and moves to operation804. Operation804depicts determining a physical location of a device. In some examples, operation804can be performed in a similar manner as operation704ofFIG.7.

After operation804, process flow800moves to operation806.

Operation806depicts determining a first environmental prediction from a first source and applicable to the physical location for a first time period. In some examples, operation806can be performed in a similar manner as operation706ofFIG.7.

In some examples, the first environmental prediction comprises a probability that an environmental event will occur at the physical location during the time period. In some examples, the first environmental prediction comprises a prediction of a time estimate within the time period representative of when an environmental event is predicted to begin at the physical location. In some examples, the first environmental prediction comprises a prediction of an amount of impact to the device from an environmental event predicted to occur at the physical location during the time period. That is, an environmental prediction can comprise a probability of occurring, a time when it is predicted to occur, and an impact if it does occur.

In some examples, operation806comprises receiving a registration message indicative of registering for environmental alert, and registering with an environmental prediction service to obtain the first environmental prediction from the environmental prediction service. For example, proactive data protection based on weather patterns and severity component108ofFIG.1can interface with both a weather source (e.g., forecast source110a) and a data center (e.g., data center102a).

After operation806, process flow800moves to operation808.

Operation808depicts determining a second environmental prediction from a second source and applicable to the physical location for a second time period. In some examples, operation808can be performed in a similar manner as operation706ofFIG.7.

After operation808, process flow800moves to operation810.

Operation810depicts determining a third environmental prediction based on the first environmental prediction and the second environmental prediction, the third environmental prediction occurring during a third time period that overlaps with the first time period or the second time period. In some examples, operation810can be performed in a similar manner as operation708ofFIG.7.

After operation810, process flow800moves to operation812.

Operation812depicts categorizing the third environmental prediction, resulting in a categorization. In some examples, operation812can be performed in a similar manner as operation710ofFIG.7.

After operation812, process flow800moves to operation810.

Operation814depicts, based on the categorization, modifying a level of data protection of the device for the time period. In some examples, operation814can be performed in a similar manner as operation712ofFIG.7.

In some examples, operation814comprises modifying the level of data protection from a first level to a second level. In such examples, operation814can comprise, after the time period concludes, modifying, by the system, the level of data protection from the second level back to the first level. That is, after the time period ends, the device can be returned to the prior, normal level of data protection.

In some examples, the level of data protection comprises a backup, a replication, or a snapshot. The level of data protection can also include a parameter of the data protection, such as a rate at which a snapshot is taken (e.g., every 30 minutes).

FIG.9illustrates an example process flow900for proactive data protection based on weather patterns and severity, in accordance with certain embodiments of this disclosure. In some examples, one or more embodiments of process flow900can be implemented by proactive data protection based on weather patterns and severity component108ofFIG.1, or computing environment1000ofFIG.10.

It can be appreciated that the operating procedures of process flow900are example operating procedures, and that there can be embodiments that implement more or fewer operating procedures than are depicted, or that implement the depicted operating procedures in a different order than as depicted. In some examples, process flow900can be implemented in conjunction with one or more embodiments of one or more of process flow700ofFIG.7and/or process flow800ofFIG.8.

Process flow900begins with902, and moves to operation904. Operation904depicts determining an environmental prediction applicable to a physical location of a computer for a time period, wherein the environmental prediction comprises a prediction that an environmental condition associated with the computer will change at a time within the time period. In some examples, operation904can be implemented in a similar manner as operations704-706ofFIG.7.

After operation904, process flow900moves to operation906.

Operation906depicts determining a categorization based the environmental prediction. In some examples, operation906can be implemented in a similar manner as operation710ofFIG.7.

In some examples, the environmental prediction comprises a prediction of at least one of a flood, a rain storm with a threshold amount of rain, a wind storm with a threshold amount of wind, a tornado, a hurricane, a derecho, a hail storm, a snow storm, a sand storm, a volcanic eruption, a critical weather condition, or a disaster.

In some examples, operation906comprises determining the categorization based on an alert of an environmental event that is currently occurring or has occurred recently not longer than a defined threshold time ago. In some examples, the environmental event comprises an earthquake, a tsunami, a power outage, or a spreading fire. That is, the environmental event can be an immediate and/or non-predictable event rather than a forecast event.

After operation906, process flow900moves to operation908.

Operation908depicts, based on the categorization, modifying a level of data protection of the computer for the time period. In some examples, operation908can be implemented in a similar manner as operation712ofFIG.7.

In some examples, the computer is a first computer. In such examples, modifying the level of data protection for the first computer can comprise prior to the time period, initiating a fail over procedure that switches a process from being performed by the first computer to being performed by a second computer. That is, modifying the data protection can comprise failing over to another computer (such as where replication servers are used).

In some examples, the physical location is a first physical location, and the fail over procedure comprises selecting the second computer based on the second computer being determined to be at a second physical location to which the environmental prediction is not applicable, or where the secondary physical location is not predicted to have a critical environmental event within a predetermined amount of time after the selecting of the second computer. That is, where modifying the data protection can comprise failing over to another computer, this other computer can be physically located out of harm's way.

Example Operating Environment

In order to provide additional context for various embodiments described herein,FIG.10and the following discussion are intended to provide a brief, general description of a suitable computing environment1000in which the various embodiments of the embodiment described herein can be implemented.

For example, parts of computing environment1000can be used to implement one or more embodiments of data center102a-102n, computer system106, forecast source110a-110n, alert source112a-112n, and/or proactive data protection based on weather patterns and severity component108ofFIG.1. In some examples, computing environment1000can implement one or more embodiments of the process flows ofFIGS.7-9to facilitate automatic identification of computer agents for throttling.

While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can be also implemented in combination with other program modules and/or as a combination of hardware and software.

CONCLUSION

In the subject specification, terms such as “data store,” data storage,” “database,” “cache,” and substantially any other information storage component relevant to operation and functionality of a component, refer to “memory components,” or entities embodied in a “memory” or components comprising the memory. It will be appreciated that the memory components, or computer-readable storage media, described herein can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. By way of illustration, and not limitation, nonvolatile memory can include ROM, programmable ROM (PROM), EPROM, EEPROM, or flash memory. Volatile memory can include RAM, which acts as external cache memory. By way of illustration and not limitation, RAM can be available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). Additionally, the disclosed memory components of systems or methods herein are intended to comprise, without being limited to comprising, these and any other suitable types of memory.