Patent ID: 12231310

DETAILED DESCRIPTION

In the following description of various illustrative embodiments, reference is made to the accompanying drawings, which form a part hereof, and in which is shown, by way of illustration, various embodiments in which the claimed subject matter may be practiced. It is to be understood that other embodiments may be utilized, and that structural and functional modifications may be made, without departing from the scope of the present claimed subject matter.

A data center may experience an emergency situation (for example, electrical power outage or fire accident). In such a situation, emergency data is last minute data recorded from various sensors. Furthermore, the order of occurrence of sensor data may be important in understanding the root cause of issue. For example, a fire caused by a fuel/diesel generator may be identified by the ordering: power outage, usage of generator, and fire alarm. If such information is lost during a power outage or fire accident, analyzing the root cause may be difficult to construct.

With some embodiments, emergency data may be transferred through a quantum dot pipeline that provides fast transmission rate at low voltage levels. Such an approach is often adequate to sufficiently operate in the environment during an emergency situation.

Embodiments are directed to a centralized computing system that interacts with a plurality of data centers, each having an edge server. Each edge server obtains sensor information from a plurality of sensors and processes the sensor information to detect an imminent shutdown and sends emergency data to a centralized processing entity when detected. In order to make a decision that an emergency is imminent, the edge server processes the sensor data based on dynamic sensor thresholds and dynamic prioritizer data by syncing with the centralized computing system.

Because of the short time duration to report emergency data before an imminent complete shutdown, an edge server may utilize a quantum data pipeline and quantum data storage as a key medium for all data transfer in a normal condition, and including at the time of emergency condition as well, for internally transporting processed sensor data and providing the emergency data to the centralized processing entity.

FIG.1illustrates a plurality of data centers102-103that provide emergency data to centralized computing system101(which may be implemented in a cloud) and reporting/resolution layer108(which may be implemented with a computing device) when a shutdown is expected in accordance with one or more example embodiments.

As shown inFIG.1, data center102comprises edge server104and sensors106.

When data center102anticipates a shutdown (for example, a loss of electrical power) based on the sensor data provided by sensors106, edge server104transmits emergency data155to reporting/resolution layer108and/or centralized computing system101.

Data centers102and103provide edge data (for example, processed sensor data and emergency data as will be discussed) via paths151and152, respectively, to centralized computing system101so that centralized computing system101can determine centralized data for data centers102and103.

Centralized data (for example, dynamic prioritization data and dynamic sensor thresholds as will be discussed) for edge servers104and105and centralized computing system101are synchronized via sync paths153and154. The values of sensor threshold and prioritization data from centralized computing system101may be calculated and then transmitted to and stored in edge server104as a dynamic value to arrive at enhanced functioning for a high alert mechanism needed for critical functions. For example, the sensor threshold data from different edge servers may be processed by centralized computing system101(for example in a “cloud” using computing cloud services) and may be added to a localized threshold value of edge server104.

Centralized computing system101processes edge data from edge servers102and103and provides centralized data157to reporting/resolution layer108about the status of data centers102and103. Additionally, data centers102and103may provide emergency data155and156, respectively) when data centers102or103encounters an outage situation (for example, electrical power or fire). With some embodiments, reporting/resolution layer108may be implemented on the same platform as centralized computing system101or on a separate computing device.

FIG.2illustrates data center102, which comprises sensors106a-106n, IoT gateway201, and edge server104.

Sensors106a-106ncomprises security sensors, fire sensors, carbon emission sensors, temperature sensors, light sensors, smoke sensors, weather forecast information, and other types of sensors that are needed. As will be discussed, sensor data from homogeneous and heterogeneous sensors may be combined via randomized hierarchical combining to obtain a decision output about an emergency situation. Also, some of sensors106a-106nmay measure the same physical characteristic (for example, fire or temperature) but may be distributed throughout different regions of data center102.

Homogeneous sensors refer to hierarchal combining of the same type of sensors, for example one temperature sensor versus another temperature sensor at different locations. Heterogeneous sensors refer to hierarchal combining of different types of sensors, for example, fire versus smoke sensors.

With some embodiments, at least one of sensors106a-106nmay be electrically self-powered by one or more quantum dot panels (quantum dot solar cells) embedded within the sensor. Moreover, the one or more quantum dot panels may convert heat produced inside data center102to electrical power for the at least one of sensors106a-106n. The above approach may provide additional robustness during an emergency situation when external electrical power is diminished or lost.

Sensor data is obtained via sensor bus251and conveyed through IOT Gateway201where data traffic cop (DTC)202monitors the traffic for the sensor data. Exceptions arising due to data transmission is handled through a monitoring and control engine (MCE)203. Information about the exceptions may then be transmitted to reporting and resolution layer108. Otherwise, sensor data252is sent to edge server104for processing as will be discussed.

When MCE203encounters exception253with one of sensors106a-106n, MCE203may generate a signal to the corresponding sensor in order address the exemption. For example, when the corresponding sensor is not transmitting sensor data, MCE203may ping the sensor to activate it or restart the corresponding sensor via an actuator (for example, to start a cooling fan or valve).

FIG.3illustrates edge server104of data center102as shown inFIGS.1and2according to one or more illustrative embodiments.

All of sensor data252is stored in data storage301(for example a database) at edge server104. The stored sensor data351ais then filtered by data filter302according to dynamic sensor threshold data and prioritization data from local prioritizer307. Also, stored sensor data352bis sent to local prioritizer307for validation. For example, local prioritizer307may verify whether the sensor data is above or below a threshold value depending on the sensor type. A sensor typically has a corresponding threshold value that may be determined by edge server104based on synchronization data from centralized computing system101and adjusted by edge server104based on characteristics of data center101. Different sensors typically have different threshold values, where some are constant across geographical location (for example, carbon emission) while some vary based on the geographical location (for example, weather) and local policies.

Local prioritizer307also provides prioritization data354to collaborator engine306based on synchronization data received from centralized computing system101. Prioritization data354gauges the importance of different sensor data with respect to an outage situation. For example, centralized computing system101may determine prioritization data354based on sensor data obtained previous and during an emergency situation from edge servers104and105.

Local collaborator engine306may decide to send emergency data when a shutdown of data center102is expected. This decision may be based on high priority alerts such as a fire alarm. It then sends the emergency data to the cloud (for example, central computing system101).

With some embodiments, local prioritizer307provides the priority logic (including a dynamic value with priority components added and ranked through the cloud). For example, the logic may prioritize the sensors in an order of importance.

Local prioritizer307may support priority logic, in which a dynamic value for prioritization data354is determined by priority components being added and ranked through the cloud (for through centralized computing system101).

Local prioritizer307and global prioritizer401may use emergency information, company policy, industry standards, and/or regulations. For example, some regions within a country may be prone to more earthquakes, where an earthquake occurs in Location A (corresponding to a high seismic zone). This information may be utilized by local prioritizer307. This information may then be passed to global prioritizer401to be included as lessons learnt and used for a decision making process through global FPCL403.

Filtered data353is then sent to local collaborator engine306in order to prioritize filtered data353based on the prioritization data354. Prioritized sensor data355is then sent to local fuzzy probabilistic controller logic (FPCL)303.

Local collaborator engine306may prioritize filtered data353based on polices and standards.

Local FPCL303processes prioritized sensor data355by hierarchically combining it and applying fuzzy logic, where prioritized sensor data355is based on sensor data from sensors106a-106nas shown inFIG.1. As will be discussed, sensor data from both homogeneous and heterogeneous are paired and fuzzy logic is applied to each pair in order to obtain the next layer of the hierarchical combining. These FPCL operations are repeated until localized decision output356is obtained.

Although local FPCL303may be presented with near real time sensor data, FPCL303may be, alternatively or in conjunction with, presented with historical sensor data and/or emergency data. For example, near-real time temperature sensor data may be paired with corresponding sensor data from the previous hour or day.

With some embodiments, local FPCL303may further randomize the hierarchical combining. For example, local FPCL303may select random sensor sets from both homogeneous and heterogeneous types of prioritized sensor data355.

Local FPCL303may hierarchically use real time dynamic sensor threshold data, dynamic prioritizer data, and history data to arrive at a right decision as reflected in localized decision output356.

FIG.5shows an example of hierarchical combining by local FPCL303as well as global FPCL403as will be discussed atFIG.4. Sensor data from carbon emission and fire sensors (corresponding to heterogeneous sensors) are combined while sensor data from temperature sensors (corresponding to homogeneous sensors) are combined according to fuzzy logic to obtain an output (“no issues”, “LOW”, “MEDIUM”, or “HIGH”). A digital representation of the FPCL output is stored in quantum data storage304via quantum data pipeline303. For example, binary representations ‘00’, ‘01’, ‘10’, and ‘11’ may correspond to fuzzy logic values “no issues”, “LOW”, “MEDIUM”, and “HIGH”, respectively. However, as will be appreciated by one of ordinary skill in the art, embodiments may use additional binary bits to represent additional fuzzy logic values.

The resulting output from local FPCL303and global FPCL403may be used on a local or global basis, respectively, to initiate an appropriate action. For example, when the output of local FPCL303is “High,” edge server104may switch on fans if a heat sensor is above a threshold limit or may generate an alert if a carbon emission sensor is high.

With some embodiments, local FPCL303uses hierarchically real time dynamic sensor threshold data, dynamic prioritizer data, and history data to arrive at a decision. Processing by edge server104by local FPCL303may be used to process high critical alerts in premises (localized) enabling a quick edge decision.

The output of local FPCL303(along with possibly other edge data such as processed sensor data (for example, from local collaborator engine306and/or local prioritizer307although not explicitly shown inFIG.3) is then directed through quantum data pipeline303and then stored in quantum data storage304. Quantum data storage304may require only a very low voltage to operate properly and consumes less electrical power than typical storage devices, thus providing critical data up until the last moment of the shutdown emergency and enabling enhanced decision making through reporting and resolution layer108.

Quantum data pipeline304may comprise a quantum wire that may transmit data at a speed of 100 GB/second. Quantum storage305may comprise a quantum dot storage having a density of 1 TB/cm2, which is approximately 20 times larger than typical magnetic storage.

Quantum data pipeline304and quantum storage305may utilize quantum dots (QDs) that are synthetic nano-scale crystals that transport electrons. They are typically zero dimensional crystalline semiconducting nanoparticles with diameters less than 10 nm and may be fabricated as a metalloid crystalline core.

Storage and data transmission using quantum dots enables seamless transmission of data even in the case of low voltage of operation. This approach may also prevent loss of data when there is power shutdown that is crucial for decision making. High speed quantum dot communication through quantum data pipeline304provides real time data transfer at a high speed.

Quantum data pipeline304and quantum data storage305enables that the FPCL output (decision) be transmitted and then saved to the “cloud” (for example, centralized computing system101and/or reporting/resolution layer108), thus circumventing crucial data loss during contingency as it operates even at a low voltage.

WhileFIG.3explicitly shows only the transport of the FPCL output (localized decision output356), from quantum data pipeline304to quantum data storage305, quantum data pipeline304and quantum data storage305may provide a medium for all data transfer (corresponding to edge data151) in a normal condition and including, at the time of emergency as well, for internally transporting processed sensor data and providing the emergency data to a centralized entity such as centralized processing system101. For example, transported data may include processed sensor data and emergency data from edge server104as well as localized decision output356.

While embodiments may utilize quantum data pipeline304and quantum data storage305, some embodiments may utilize other technologies for data transport and data storage. For example, while current optical fiber may transmit at less than 10 GB of data per second, embodiments may utilize technologies (such as quantum dot storage and quantum wire) that support a recording density of 1 TB/cm2and a data transmission rate of 100 GB/second.

FIG.4illustrates centralized computing system101(which may be supported in a “cloud”) as shown inFIG.1in accordance with one or more example embodiments. For example, centralized computing system101may be completely or partially implemented using computing cloud resources/services.

Central collaborator engine402obtains edge data151and152(for example, processed sensor data and emergency data) from edge servers104and105, respectively.

Edge data from edge servers104and105across multiple locations are collated at central collaborator engine402, resulting in centralized decision making.

Centralized collaboration engine402consumes data from all edge servers (for example edge data151and152from edge servers104and105, respectively) and provides critical threshold and prioritization data. Outputs from various edge servers across multiple locations are collated by collaborator engine402and in turn leading to a centralized decision making.

Central prioritizer401may obtain calculated prioritization data and sensor threshold data from the central collaborator engine402to synchronize with edge servers104and105.

The dynamic sensor threshold and prioritization are calculated by centralized computing system101and then transmitted and stored at edge server104as a dynamic value to arrive at enhanced functioning of the high alert mechanism needed for critical functions.

Different types of sensors typically have different threshold values. For example, one type has a threshold value that is constant across geographical location, for example carbon emission. A second type has a threshold value that typically changes based on the geographical location such as weather and local policies. With the first type, centralized computing system101can modify the threshold values and update them by syncing with edge servers104and105, respectively, via sync paths153and154, respectively. With the second type, centralized computing system101(for example, at central collaborator engine402) obtains edge data151and152from the edge servers and utilizes the historical data and analyzes preventive measures obtained from historical logs to determine the dynamic threshold values. With some embodiments, edge data151and152may be processed by artificial intelligence techniques such as machine learning providing a robotic decision maker or threshold setter.

With some embodiments, centralized computing system101may support a sensor value threshold and prioritization calculator in order determine dynamic value of sensor threshold/prioritization data that is derived based on learnings/emergency situations from other locations. Centralized computing system101may also support a threshold and prioritization self-mutating algorithm based the calculator.

Global FPCL403obtains collected edge data451from central collaborator engine402and processes the collected edge451similar to local FPCL303as shown inFIG.3. For example, global FPCL may hierarchically combine collected edge data451and may further randomize the combining the prioritized collected edge data.

While the functioning of global FPCL403and local FPCL303is similar, the scope is different. Local FPCL303typically uses data from the local data center102while global FPCL403uses data from across the locations and not specific to one data center.

Global decision output453(the output of global FPCL403) may then be provided to reporting/resolution layer108.

Using the historical data in global FPCL403and analyzing the preventive measures taken from a historical log can assist in determining sensor thresholds at the cloud. A portion of the interpretation may need human intervention but the robotic decision maker may be employed to do the same. Moreover, additional information such as processed data and decision making information from central prioritizer401and central collaborator engine402may also be provided to reporting/resolution108as shown inFIGS.1and8.

FIG.6illustrates processing of sensor data at an edge server such as edge server104as shown inFIG.3.

Sensor database601(for example, corresponding to sensor storage301as shown inFIG.3) stores sensor data from sensors106a-106nfor subsequent processing by local prioritizer603and local collaborator engine605.

At block602, local prioritizer603validates whether sensor data is indicative of an emergency (for example, a fire alarm indicative of a fire at data center102). If so, emergency data is sent to local FPCL606, where emergency data652may be included in the determination of the decision output and/or included in the data transported over the quantum data pipeline to quantum storage quantum dots storage608via data combiner607.

In addition, local prioritizer603determines validates sensor data with respect to the dynamic sensor threshold (either above or below a threshold depending on the type of sensor). If so, the sensor data is passed to local collaborator engine605; otherwise, the sensor data is ignored.

Sensor data to local collaborator engine605may be further partitioned into regions (for example, regions A, B, and C) so that local collaborator engine605appropriately prioritizes sensor data across different locations within a data center. For example, data center102may have a plurality of temperature sensors. Local collaborator engine605may prioritize a subset of the plurality of temperature sensors to ensure that temperature measurements are represented over all of the desired locations of the data center.

Local collaborator engine605presents prioritized collected edge data and applies fuzzy logic on the prioritized collected edge data in a randomized hierarchal manner to obtain a global decision output data. Data combiner607may then combine the global decision output data with processed sensor data and/or emergency data, so that the combined data can be stored in quantum dots storage608, which can be provided to central computing system101as edge data.

As previously discussed, local prioritizer603communicates with central computing system101via path654in order to synchronize the dynamic prioritization data and the dynamic sensor threshold data.

FIG.7illustrates processing of edge data751from one or more edge servers by centralized computing system101as shown inFIG.4.

Central collaborator engine701collects edge data751from the plurality of edge servers and prioritizes the collected edge data to form prioritized collected edge data752. Also, central collaborator engine may consume edge data751and determine the dynamic sensor threshold data and dynamic prioritization data from the edge data. Central collaborator engine701may utilize a self-mutating algorithm based calculator to determine the dynamic sensor threshold data and dynamic prioritization data.

Central collaborator engine701presents the prioritized collected edge data752to global FPCL702, which then hierarchically combines prioritized collected edge data752. As previously discussed, global FPCL702obtains global decision output753and presents it to data combiner703so that it may be combined with processed edge data. Central computing system101may present global decision output753and/or processed edge data to reporting/resolution layer108.

Central collaborator engine701may uniformly distribute prioritized collected edge data752over the plurality of edge servers in order to avoid biasing the data with respect to any particular edge server. Consequently, the resulting processed edge data will be better representative of the plurality of data centers. For example, a balanced representation over all of the edge servers helps to ensure that global decision output753generated by global FPCL702is not biased by any one particular data center.

Report generator704may then generate a report that is indicative of the data centers based on the combined data. Also, information from the report may be presented to report analyzer705. Report analyzer705may then provide modifications of the synchronization data754to central prioritizer706based on analyzing the report information.

As previously discussed, central prioritizer706synchronize synchronization data (for example, dynamic sensor threshold data and dynamic prioritization data) with a localized prioritizer of each edge server over path654.

With some embodiments, report analyzer705may apply machine learning to determine the modifications to the synchronization data.

FIG.8illustrates reporting/resolution layer108as shown inFIG.1according to one or more illustrative embodiments. Reporting/resolution layer108may be implemented by a computing device as a part of a computing system, or partially by cloud computing services.

Reporting/resolution layer108typically supports a plurality of functions. For example, reporting/resolution layer108may support inventory management801a, infra health check-up801b, alerts802, actuators803, an emission map804, a dashboard805, and co-location/fire department notification806.

Reporting/resolution layer108may monitor and present data in dashboards and reports. It may also alert a co-location data center to take over, or may alert the fire department, or alert a respective technicians.

Reporting/resolution layer108may also remotely trigger one or more actuators associated with one or more sensors106a-106nat data center102as shown inFIG.2. For example, reporting/resolution layer108may initiate one or more cooling fans associated with one of the sensors for a temperature rise.

FIG.9illustrates a computing circuit that may be incorporated into edge servers102and103, centralized computing system101, or reporting/resolution layer108as shown inFIG.1in accordance with one or more example embodiments.

Referring toFIG.1, centralized computing system101, edge servers102and103, or reporting/reporting layer108may comprise processing device901, memory device904, input interface902, and output interface903. Processing device901may execute computer-readable instructions stored at memory device304in order to execute processes to process the sensor data. Processing device901may receive sensor data from sensors601a-601nand process the sensor data. Processed sensor data, synchronization data, and/or emergency data may be exposed via output interface903.

Various aspects described herein may be embodied as a method, an apparatus, or as computer-executable instructions stored on one or more non-transitory and/or tangible computer-readable media. Accordingly, those aspects may take the form of an entirely hardware embodiment, an entirely software embodiment (which may or may not include firmware) stored on one or more non-transitory and/or tangible computer-readable media, or an embodiment combining software and hardware aspects. Any and/or all of the method steps described herein may be embodied in computer-executable instructions stored on a computer-readable medium, such as a non-transitory and/or tangible computer readable medium and/or a computer readable storage medium. Additionally or alternatively, any and/or all of the method steps described herein may be embodied in computer-readable instructions stored in the memory and/or other non-transitory and/or tangible storage medium of an apparatus that includes one or more processors, such that the apparatus is caused to perform such method steps when the one or more processors execute the computer-readable instructions. In addition, various signals representing data or events as described herein may be transferred between a source and a destination in the form of light and/or electromagnetic waves traveling through signal-conducting media such as metal wires, optical fibers, and/or wireless transmission media (for example, air and/or space).

Aspects of the disclosure have been described in terms of illustrative embodiments thereof. Numerous other embodiments, modifications, and variations within the scope and spirit of the appended claims will occur to persons of ordinary skill in the art from a review of this disclosure. For example, one of ordinary skill in the art will appreciate that the steps illustrated in the illustrative figures may be performed in other than the recited order, and that one or more steps illustrated may be optional in accordance with aspects of the disclosure.