Patent Publication Number: US-11659695-B2

Title: Telemetry system supporting identification of data center zones

Description:
FIELD 
     The present disclosure generally relates to Information Handling Systems (IHSs), and, more particularly, to telemetry systems used by IHSs. 
     BACKGROUND 
     As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option available to users is Information Handling Systems (IHSs). An IHS generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, IHSs may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in IHSs allow for IHSs to be general or configured for a specific user or specific use, such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, IHSs may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems. 
     The operation of an IHS may be characterized by metrics that provide a measurable aspect of the IHS&#39;s operation. For instance, an IHS metric may provide environmental sensor readings, such a temperature sensor measurement, or an operational sensor reading, such as the amps being drawn by a component of the IHS. An IHS metric may also provide discrete information, such as the operational state of a component. An IHS metric may also provide a logical rather than physical sensor measurement, such as a digital counter measuring the amount of data transferred by a networking component of the IHS. An IHS may utilize a telemetry system in order to configure and manage the collection of metric reports from various sources of metric data within the IHS. Using the metric data collected by a telemetry system, the operation of an IHS may be monitored and managed remotely. 
     In a data center environment, rack-mounted server IHSs may utilize telemetry systems that collect metric data from a variety of different sources. Administrators may utilize the data collected by such telemetry systems in diagnosing errors or other events of interest related to an IHS. A data center may include a large number of IHSs, such as servers that are installed within chassis and stacked within slots provided by racks. A data center may include large numbers of such racks that may be organized into aisles with racks lining each side. Data centers organized in this manner may be designed to provide administrators with a uniform environment, but certain conditions may vary significantly within a data center. 
     SUMMARY 
     In various embodiments, methods are provided for utilizing telemetry data to identify zones within a data center comprised of a plurality of IHSs (Information Handling Systems). The methods may include: collecting metric data from the plurality of IHSs, wherein the metric data collected from each of the respective IHSs identifies a location of the respective IHS within the data center; analyzing the collected metric data to identify a first metric that is correlated with locations within the data center; within the first metric data, identifying a first zone of the data center that comprises a subset of the plurality of IHSs that reported anomalous first metric data relative to neighboring IHSs; and adjusting operations of the data center within the first zone in order to address the anomalous readings of the first metric data by the subset of IHSs. 
     In additional method embodiments, the metric data is collected by a remote access controller operating within each of the plurality of IHSs. In additional method embodiments, the remote access controller collects the metric data from IHS components via sideband management connections with the IHS components. In additional method embodiments, the correlation of the first metric with data center locations is identified based on principal component analysis of the collected metric data. In additional method embodiments, the first zone of the data center that includes a subset of IHSs reporting anomalous first metric data relative to neighboring IHSs comprises a rack housing the subset of IHSs. In additional method embodiments, the first zone of the data center that includes a subset of IHSs reporting anomalous first metric data relative to neighboring IHSs comprises one or more rows that span a plurality of adjacent racks of the data center. In additional embodiments, the methods may further include segmenting the first metric data to identify intervals of variations in the first metric data. In additional method embodiments, the first metric data comprises a temperature metric and wherein the adjustment to data center operations comprises increasing the cooling delivered by the data center within the first zone. In additional method embodiments, the first metric data comprises a power metric and wherein the first zone comprises a rack housing the subset of IHSs. In additional method embodiments, the first metric data comprises a network metric and wherein the first zone comprises a rack housing the subset of IHSs. 
     In various additional embodiments, systems are provided for utilizing telemetry data to identify zones within a data center. The systems may include: a plurality of IHSs (Information Handling Systems), each respective IHS comprising a remote access controller providing remote management of the respective IHS, wherein the plurality of IHSs are configured to report metric data, and wherein the metric data reported by each respective IHS identifies a location of the respective IHS within the data center; and a remote management application configured to: analyze the reported metric data to identify a first metric that is correlated with locations within the data center; within the first metric data, identify a first zone of the data center that comprises a subset of the plurality of IHSs that reported anomalous first metric data relative to neighboring IHSs; and adjust operations of the data center within the first zone in order to address the anomalous readings of the first metric data by the subset of IHSs. 
     In additional system embodiments, the remote access controller of each respective IHS collects the metric data from IHS components via sideband management connections with the IHS components. In additional system embodiments, the correlation of the first metric with data center locations is identified based on principal component analysis of the collected metric data. In additional system embodiments, the first zone of the data center that includes a subset of IHSs reporting anomalous first metric data relative to neighboring IHSs comprises a rack housing the subset of IHSs. In additional system embodiments, the first zone of the data center that includes a subset of IHSs reporting anomalous first metric data relative to neighboring IHSs comprises one or more rows that span a plurality of adjacent racks of the data center. 
     In various additional embodiments, computer-readable storage devices include instructions stored thereon for utilizing telemetry data to identify zones within a data center comprised of a plurality of IHSs (Information Handling Systems). Upon execution by one or more processors, the may instructions cause the one or more processors to: analyze metric data collected from the plurality of IHSs, wherein the metric data collected from each of the respective IHSs identifies a location of the respective IHS within the data center, and wherein the collected metric data is analyzed to identify a first metric that is correlated with locations within the data center; within the first metric data, identify a first zone of the data center that comprises a subset of the plurality of IHSs that reported anomalous first metric data relative to neighboring IHSs; and adjust operations of the data center within the first zone in order to address the anomalous readings of the first metric data by the subset of IHSs. 
     In additional storage device embodiments, a remote access controller of each respective IHS collects the metric data from IHS components via sideband management connections with the IHS components. In additional storage device embodiments, the correlation of the first metric with data center locations is identified based on principal component analysis of the collected metric data. In additional storage device embodiments, the first zone of the data center that includes a subset of IHSs reporting anomalous first metric data relative to neighboring IHSs comprises a rack housing the subset of IHSs. In additional storage device embodiments, the first zone of the data center that includes a subset of IHSs reporting anomalous first metric data relative to neighboring IHSs comprises one or more rows that span a plurality of adjacent racks of the data center. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention(s) is/are illustrated by way of example and is/are not limited by the accompanying figures. Elements in the figures are illustrated for simplicity and clarity, and have not necessarily been drawn to scale. 
         FIG.  1    is a diagram illustrating certain components of a chassis, according to some embodiments, supporting a telemetry system used for identification of zones within a data center. 
         FIG.  2    is a diagram illustrating certain components of an IHS configured as a component of chassis, according to some embodiments, supporting a telemetry system used for identification of zones within a data center. 
         FIG.  3    is a flowchart describing certain steps of a method, according to some embodiments, for supporting a telemetry system used for identification of zones within a data center. 
         FIG.  4 A  is an illustration of an example, according to various embodiments, of the use of a telemetry system for the identification of an anomalous temperature zone within rows of a data center. 
         FIG.  4 B  is an illustration of an additional example, according to various embodiments, of the use of a telemetry system for the identification of an anomalous temperature zone within racks of a data center. 
         FIG.  5 A  is an illustration of an additional example, according to various embodiments, of the use of a telemetry system for the identification of an anomalous power zone within a rack of a data center. 
         FIG.  5 B  is an illustration of an additional example, according to various embodiments, of the use of a telemetry system for the identification of an anomalous network zone within a rack of a data center. 
     
    
    
     DETAILED DESCRIPTION 
       FIG.  1    is a block diagram illustrating certain components of a chassis  100  comprising one or more compute sleds  105   a - n  and one or more storage sleds  115   a - n  that may be configured to implement the systems and methods described herein for supporting a telemetry system used for identification of zones within a data center. Chassis  100  may include one or more bays that each receive an individual sled (that may be additionally or alternatively referred to as a tray, blade, server, drive and/or node), such as compute sleds  105   a - n  and storage sleds  115   a - n . Chassis  100  may support a variety of different numbers (e.g., 4, 8, 16, 32), sizes (e.g., single-width, double-width) and physical configurations of bays. Other embodiments may include additional types of sleds that provide various types of storage and/or processing capabilities. Other types of sleds may provide power management and networking functions. Sleds may be individually installed and removed from the chassis  100 , thus allowing the computing and storage capabilities of a chassis to be reconfigured by swapping the sleds with different types of sleds, in many cases without affecting the ongoing operations of the other sleds installed in the chassis  100 . 
     Multiple chassis  100  are typically housed within a rack, with each chassis installed in one or more slots of the rack. Data centers may utilize large numbers of racks, with various different types of chassis installed in the various rack configurations. The modular architecture provided by the sleds, chassis and rack allow for certain resources, such as cooling, power and network bandwidth, to be shared by the compute sleds  105   a - n  and storage sleds  115   a - n , thus providing efficiency and supporting various types of computational loads. 
     Chassis  100  may be installed within a rack that provides all or part of the cooling utilized by chassis  100 . For airflow cooling, a rack may include one or more banks of cooling fans that may be operated to ventilate heated air from within the chassis  100  that is housed within the rack. The chassis  100  may alternatively or additionally include one or more cooling fans  130  that may be similarly operated to ventilate heated air from within the sleds  105   a - n ,  115   a - n  that are installed within the chassis. A rack and a chassis  100  installed within the rack may utilize various configurations and combinations of cooling fans to cool the sleds  105   a - n ,  115   a - n  and other components housed within chassis  100 . 
     The sleds  105   a - n ,  115   a - n  may be individually coupled to chassis  100  via connectors that correspond to connectors provided by front-facing bays of the chassis  100 , where these connectors physically and electrically couple an individual sled to a backplane  160  of the chassis, where the backplane may be additionally or alternatively be referred to as a midplane. Chassis backplane  160  may be a printed circuit board that includes electrical traces and connectors that are configured to route signals between components of chassis  100  that are connected to the backplane  160 . In various embodiments, backplane  160  may include various additional components, such as cables, wires, connectors, expansion slots, and multiplexers. In certain embodiments, backplane  160  may be a motherboard that includes various electronic components installed thereon. Such components installed on a motherboard backplane  160  may include components that implement all or part of the functions described with regard to the SAS (Serial Attached SCSI) expander  150 , I/O controllers  145 , network controller  145  and power supply unit  135 . 
     In certain embodiments, a compute sled  105   a - n  may be an IHS such as described with regard to IHS  200  of  FIG.  2   . A compute sled  105   a - n  may provide computational processing resources that may be used to support a variety of e-commerce, multimedia, business and scientific computing applications, such as services provided via a cloud implementation. Compute sleds  105   a - n  are typically configured with hardware and software that provide leading-edge computational capabilities. Accordingly, services provided using such computing capabilities are typically provided as high-availability systems that operate with minimum downtime. As described in additional detail with regard to  FIG.  2   , compute sleds  105   a - n  may be configured for general-purpose computing or may be optimized for specific computing tasks. 
     As illustrated, each compute sled  105   a - n  includes a remote access controller (RAC)  110   a - n . As described in additional detail with regard to  FIG.  2   , each remote access controller  110   a - n  provides capabilities for remote monitoring and management of compute sled  105   a - n . In support of these monitoring and management functions, remote access controllers  110   a - n  may utilize both in-band and sideband (i.e., out-of-band) communications with various components of a compute sled  105   a - n  and chassis  100 . As illustrated, each compute sled  105   a - n  may include one or more sensors  160   a - n . As described in additional detail below, the sensors  160   a - n  may generate various types of metric data that characterize aspects of the operation of a respective compute sled  105   a - n . For instance, sensors  160   a - n  may collect metric data characterizing the performance of processing, networking, power and/or memory components of a compute sled  105   a - n , as well as monitoring environmental properties, such as compute sled temperatures. Using collected metric data, each remote access controller  110   a - n  may implement various monitoring and administrative functions related to compute sleds  105   a - n . Metric data received from these components may also be stored for further analysis, in some instances by the remote access controllers  110   a - n . As described in additional detail below, the metric data collected by remote access controllers  110   a - n  may be collectively analyzed along with metric data collected from other remote access controllers operating within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     Each of the compute sleds  105   a - n  includes a storage controller  135   a - n  that may be utilized to access storage drives that are accessible via chassis  100 . Some of the individual storage controllers  135   a - n  may provide support for RAID (Redundant Array of Independent Disks) configurations of logical and physical storage drives, such as storage drives provided by storage sleds  115   a - n . In some embodiments, some or all of the individual storage controllers  135   a - n  may be HBAs (Host Bus Adapters) that provide more limited capabilities in accessing physical storage drives provided via storage sleds  115   a - n  and/or via SAS expander  150 . 
     As illustrated, chassis  100  also includes one or more storage sleds  115   a - n  that are coupled to the backplane  160  and installed within one or more bays of chassis  100  in a similar manner to compute sleds  105   a - n . Each of the individual storage sleds  115   a - n  may include various different numbers and types of storage devices. For instance, storage sleds  115   a - n  may include SAS (Serial Attached SCSI) magnetic disk drives, SATA (Serial Advanced Technology Attachment) magnetic disk drives, solid-state drives (SSDs) and other types of storage drives in various combinations. The storage sleds  115   a - n  may be utilized in various storage configurations by the compute sleds  105   a - n  that are coupled to chassis  100 . As illustrated, each storage sled  115   a - n  may include one or more sensors  165   a - n . The sensors  165   a - n  may generate various types of metric data that characterize aspects of the operation of a respective storage sled  115   a - n . For instance, sensors  165   a - n  may collect metric data characterizing the performance of a storage sled  115   a - n , such as data transfer rates and hard disk drive RPMs, as well as monitoring environmental properties, such as storage sled temperatures. As illustrated, each storage sleds  115   a - n  includes a remote access controller (RAC)  120   a - n . As described in additional detail below, storage sleds  115   a - n , or a storage controller  135   a - n  that manages access to storage sleds  115   a - n , may be configured to generate and report this metric data to the remote access controller  120   a - n , which may analyze and store the metric data. As described in additional detail below, the metric data collected by remote access controllers  120   a - n  may be collectively analyzed along with metric data collected from other remote access controllers operating within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     In addition to the data storage capabilities provided by storage sleds  115   a - n , chassis  100  may provide access to other storage resources that may be installed components of chassis  100  and/or may be installed elsewhere within a rack housing the chassis  100 , such as within a storage blade. In certain scenarios, such storage resources  155  may be accessed via a SAS expander  150  that is coupled to the backplane  160  of the chassis  100 . The SAS expander  150  may support connections to a number of JBOD (Just a Bunch Of Disks) storage drives  155  that may be configured and managed individually and without implementing data redundancy across the various drives  155 . The additional storage resources  155  may also be at various other locations within a datacenter in which chassis  100  is installed. Such additional storage resources  155  may also be remotely located. 
     As illustrated, the chassis  100  of  FIG.  1    includes a network controller  140  that provides network access to the sleds  105   a - n ,  115   a - n  installed within the chassis. Network controller  140  may include various switches, adapters, controllers and couplings used to connect chassis  100  to a network, either directly or via additional networking components and connections provided via a rack in which chassis  100  is installed. In some embodiments, the network bandwidth provided to chassis  100  by network controller  140  may in turn be provided via a network device of a rack in which chassis  100  is installed. For instance, the rack may include a network switch, or other network routing device, that partitions an allotment of network bandwidth to some or all of the chassis that are installed within the rack. In such instances, network controller  140  may operate using an allotment of bandwidth provided by such a rack-mounted network switch, with network controllers in other chassis installed within the same rack as chassis  100  also operating using an allotment of network bandwidth provided by the network switch. 
     As with compute sleds  105   a - n  and storage sleds  115   a - n , network controller  140  may include one or more sensors  140   a  that may include physical sensors, such as a temperature sensor providing thermal metrics, and logical sensors, such as capabilities reporting metrics of input and output data transfer rates. In some embodiments, such data transfer rates may be reported for individual ports or via logically grouped ports of the network controller. As with the sensors of compute sleds  105   a - n  and storage sleds  115   a - n , the sensors  140   a  of network controller  140  may be configured to generate and report this sensor metric data. In various embodiments, the metric data reported by network controller  140  may be collectively analyzed along with other metric data collected within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     Chassis  100  may similarly include a power supply unit  135  that provides the components of the chassis with various levels of DC power from an AC power source or from power delivered via a power system provided by a rack within which chassis  100  may be installed. In certain embodiments, power supply unit  135  may be implemented within a sled that provides chassis  100  with redundant, hot-swappable power supply units. In some embodiments, the power provided to chassis  100  by power supply unit  135  may in turn be provided via a power supply of a rack in which chassis  100  is installed. In such instances, power supply unit  135  may operate using an allotment of power provided by the power supply of a rack in which chassis  100  is installed, with power supply units of other chassis installed within the same rack as chassis  100  also operating using an allotment of power provided by the rack. 
     As illustrated, power supply unit  135  may include one or more sensors  135   a  that may include physical sensors, such as a temperature sensor providing thermal and power output metrics, and logical sensors, such as capabilities that report discrete power settings. As above, the sensors  135   a  of power supply unit  135  may be configured to generate and report metric data. In various embodiments, the metric data reported by power supply unit  135  may be collectively analyzed along with other metric data collected within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     Chassis  100  may also include various I/O controllers  145  that may support various I/O ports, such as USB ports that may be used to support keyboard and mouse inputs and/or video display capabilities. Such I/O controllers  145  may be utilized by a chassis management controller  125  to support various KVM (Keyboard, Video and Mouse)  125   a  capabilities that provide administrators with the ability to interface with the chassis  100 . In addition to providing support for KVM  125   a  capabilities for administering chassis  100 , chassis management controller  125  may support various additional functions for sharing the infrastructure resources of chassis  100 . In some scenarios, chassis management controller  125  may implement tools for managing the power  135 , network bandwidth  140  and airflow cooling  130  that are available via the chassis  100 . As described, the airflow cooling  130  utilized by chassis  100  may include an airflow cooling system that is provided by a rack in which the chassis  100  may be installed and managed by a cooling module  125   b  of the chassis management controller  125 . In some embodiments, the operations of a chassis management controller  125  may be implemented by one of the compute sled or storage sled remote access controllers  110   a - n ,  120   a - n  that has been designated and configured for managing chassis-level configurations. In some embodiments, chassis management controller  125  may receive metric reports from one or more sensors  170  that are components of chassis  100 , such as temperature sensors at various chassis locations that provide inlet and exhaust temperature measurements. In such embodiments, such chassis sensors  170  be configured to generate and report metric data. In various embodiments, the metric data reported by chassis sensors  170  may be collectively analyzed along with other metric data collected within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     For purposes of this disclosure, an IHS may include any instrumentality or aggregate of instrumentalities operable to compute, calculate, determine, classify, process, transmit, receive, retrieve, originate, switch, store, display, communicate, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes. For example, an IHS may be a personal computer (e.g., desktop or laptop), tablet computer, mobile device (e.g., Personal Digital Assistant (PDA) or smart phone), server (e.g., blade server or rack server), a network storage device, or any other suitable device and may vary in size, shape, performance, functionality, and price. An IHS may include Random Access Memory (RAM), one or more processing resources such as a Central Processing Unit (CPU) or hardware or software control logic, Read-Only Memory (ROM), and/or other types of nonvolatile memory. Additional components of an IHS may include one or more disk drives, one or more network ports for communicating with external devices as well as various I/O devices, such as a keyboard, a mouse, touchscreen, and/or a video display. As described, an IHS may also include one or more buses operable to transmit communications between the various hardware components. An example of an IHS is described in more detail below. 
       FIG.  2    shows an example of an IHS  200  configured to implement systems and methods described herein for supporting a telemetry system used for identification of zones within a data center. It should be appreciated that although the embodiments described herein may describe an IHS that is a compute sled, server or similar computing component that may be deployed within a rack-mounted chassis, other embodiments may be utilized with other types of IHSs. In the illustrative embodiment of  FIG.  2   , IHS  200  may be a computing component, such as compute sled  105   a - n , that is configured to share infrastructure resources provided by a chassis  100 . In some embodiments, IHS  200  may be a server, such as a 1RU (Rack Unit) server, that is installed within a slot of a chassis, such as a 2RU chassis, with another 1RU IHS server installed in the other slot of the chassis. 
     The IHS  200  of  FIG.  2    may be a compute sled, such as compute sleds  105   a - n  of  FIG.  1   , that may be installed within a chassis, that may in turn be installed within a rack. Installed in this manner, IHS  200  may utilized shared power, network and cooling resources provided by the chassis and/or rack. IHS  200  may utilize one or more processors  205 . In some embodiments, processors  205  may include a main processor and a co-processor, each of which may include a plurality of processing cores that, in certain scenarios, may be used in operating multiple virtualized computing environments. In certain embodiments, one or all of processor(s)  205  may be graphics processing units (GPUs) in scenarios where IHS  200  has been configured to support functions such as multimedia services and graphics applications. 
     In some embodiments, processor  205  may be configured to operate as a source of metric data providing physical sensor data, such as junction temperatures and power consumption. Processor  205  may also be configured to operate as a source logical sensor data, such as remaining CPU processing capacity. In some embodiments, processor  205  may be configured by remote access controller  255  to generate metrics that are reported to the remote access controller, where the configuration and reporting of this metric data may be via a PECI (Platform Environment Control Interface) bus  285  operations. Processor  205  may be configured to generate and report such metric data to remote access controller  255  for analysis and storage. As described in additional detail below, upon being stored, metric data generated by processor  205  may be collectively analyzed along with other metric data collected within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     As illustrated, processor(s)  205  includes an integrated memory controller  205   a  that may be implemented directly within the circuitry of the processor  205 , or the memory controller  205   a  may be a separate integrated circuit that is located on the same die as the processor  205 . The memory controller  205   a  may be configured to manage the transfer of data to and from the system memory  210  of the IHS  200  via a high-speed memory interface  205   b . In some embodiments, memory controller  205   a  may be configured to operate as a source of metric data capable of generating metric reports that are reported to remote access controller  255 . The metric data reported by memory controller  205   a  may include metrics such as the amount of available system memory  210  and memory transfer rates via memory interface  205   b . The metric reporting capabilities of memory controller  205   a  may be configured to generate and report such metric data, to remote access controller  255  for analysis and storage. As described in additional detail below, upon being stored, metric data generated by memory controller  205   a  may be collectively analyzed along with other metric data collected within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     The system memory  210  is coupled to processor(s)  205  via a memory bus  205   b  that provides the processor(s)  205  with high-speed memory used in the execution of computer program instructions by the processor(s)  205 . Accordingly, system memory  210  may include memory components, such as such as static RAM (SRAM), dynamic RAM (DRAM), NAND Flash memory, suitable for supporting high-speed memory operations by the processor(s)  205 . In certain embodiments, system memory  210  may combine both persistent, non-volatile memory and volatile memory. In certain embodiments, the system memory  210  may be comprised of multiple removable memory modules. The system memory  210  of the illustrated embodiment includes removable memory modules  210   a - n . Each of the removable memory modules  210   a - n  may correspond to a printed circuit board memory socket that receives a specific type of removable memory module  210   a - n , such as a DIMM (Dual In-line Memory Module), that can be coupled to the socket and then decoupled from the socket as needed, such as to upgrade memory capabilities or to replace faulty components. Other embodiments of IHS system memory  210  may be configured with memory socket interfaces that correspond to different types of removable memory module form factors, such as a Dual In-line Package (DIP) memory, a Single In-line Pin Package (SIPP) memory, a Single In-line Memory Module (SIMM), and/or a Ball Grid Array (BGA) memory. 
     IHS  200  may utilize a chipset that may be implemented by integrated circuits that are connected to each processor  205 . All or portions of the chipset may be implemented directly within the integrated circuitry of an individual processor  205 . The chipset may provide the processor(s)  205  with access to a variety of resources accessible via one or more buses  215 . Various embodiments may utilize any number of buses to provide the illustrated pathways served by bus  215 . In certain embodiments, bus  215  may include a PCIe (PCI Express) switch fabric that is accessed via a PCIe root complex. IHS  200  may also include one or more I/O ports  250 , such as PCIe ports, that may be used to couple the IHS  200  directly to other IHSs, storage resources or other peripheral components. 
     In certain embodiments, a graphics processor  235  may be comprised within one or more video or graphics cards, or an embedded controller, installed as components of the IHS  200 . In certain embodiments, graphics processor  235  may be an integrated of the remote access controller  255  and may be utilized to support the display of diagnostic and administrative interfaces related to IHS  200  via display devices that are coupled, either directly or remotely, to remote access controller  255 . 
     In the illustrated embodiment, processor(s)  205  is coupled to a network controller  225 , such as provided by a Network Interface Controller (NIC) that is coupled to the IHS  200  and allows the IHS  200  to communicate via an external network, such as the Internet or a LAN. As with the network controller of  FIG.  1   , network controller  225  may operate using an allotment of bandwidth from a shared pool provided by the rack and/or chassis in which IHS  200  is installed. As illustrated, network controller  225  may be instrumented with a controller or other logic unit  225   a  that supports a sideband management connection  275   b  with remote access controller  255 . Via the sideband management connection  275   b , network controller  225  may be configured to operate as a source of metric data that may include environmental metrics, such as a temperature measurements, and logical sensors, such as metrics reporting input and output data transfer rates. Network controller  225  may be configured to generate and report such metric data to remote access controller  255  for analysis and storage. As described in additional detail below, upon being stored, metric data generated by network controller  255  may be collectively analyzed along with other metric data collected within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     Processor(s)  205  may also be coupled to a power management unit  260  that may interface with the power system unit  135  of the chassis  100  in which IHS  200  may be installed. As with network controller  225 , power management unit  260  may be instrumented with a controller or other logic unit  260   a  that supports a sideband management connection  275   e  with remote access controller  255 . Via the sideband management connection  275   e , power management unit  260  may be configured to operate as a source of metric data that may include physical sensors, such as a sensors providing temperature measurements and sensors providing power output measurements, and logical sensors, such as capabilities reporting discrete power settings. Power management unit  260  may be configured to generate and report such metric data to remote access controller  255  for analysis and storage. As described in additional detail below, upon being stored, metric data generated by power management unit  260  may be collectively analyzed along with other metric data collected within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     As illustrated, IHS  200  may include one or more FPGA (Field-Programmable Gate Array) card(s)  220 . Each FPGA card  220  supported by IHS  200  may include various processing and memory resources, in addition to an FPGA integrated circuit that may be reconfigured after deployment of IHS  200  through programming functions supported by the FPGA card  220 . FGPA card  220  may be optimized to perform specific processing tasks, such as specific signal processing, security, data mining, and artificial intelligence functions, and/or to support specific hardware coupled to IHS  200 . FPGA card  220  may include one or more physical and/or logical sensors. As specialized computing components, FPGA cards may be used to support large-scale computational tasks that may result in the FPGA card  220  generating significant amounts of heat. In order to protect specialized FPGA cards from damaging levels of heat, FPGA card  220  may be outfitted with multiple temperature sensors. FPGA card  220  may also include logical sensors that are sources of metric data, such as metrics reporting numbers of calculations performed by the programmed circuitry of the FPGA. The FPGA card  220  may also include a management controller  220   a  that may support interoperation was the remote access controller  255  via a sideband device management bus  275   a . The management controller  220   a  of FPGA card  220  may be configured to generate and report metric data to remote access controller  255  for analysis and storage. As described in additional detail below, upon being stored, metric data generated by FPGA card  220  may be collectively analyzed along with other metric data collected within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     In certain embodiments, IHS  200  may operate using a BIOS (Basic Input/Output System) that may be stored in a non-volatile memory accessible by the processor(s)  205 . The BIOS may provide an abstraction layer by which the operating system of the IHS  200  interfaces with the hardware components of the IHS. Upon powering or restarting IHS  200 , processor(s)  205  may utilize BIOS instructions to initialize and test hardware components coupled to the IHS, including both components permanently installed as components of the motherboard of IHS  200  and removable components installed within various expansion slots supported by the IHS  200 . The BIOS instructions may also load an operating system for use by the IHS  200 . In certain embodiments, IHS  200  may utilize Unified Extensible Firmware Interface (UEFI) in addition to or instead of a BIOS. In certain embodiments, the functions provided by a BIOS may be implemented, in full or in part, by the remote access controller  255 . 
     IHS  200  may include one or more storage controllers  230  that may be utilized to access storage drives  240   a - n  that are accessible via the chassis in which IHS  100  is installed. Storage controller  230  may provide support for RAID (Redundant Array of Independent Disks) configurations of logical and physical storage drives  240   a - n . In some embodiments, storage controller  230  may be an HBA (Host Bus Adapter) that provides more limited capabilities in accessing physical storage drives  240   a - n . In some embodiments, storage drives  240   a - n  may be replaceable, hot-swappable storage devices that are installed within bays provided by the chassis in which IHS  200  is installed. In some embodiments, storage drives  240   a - n  may also be accessed by other IHSs that are also installed within the same chassis as IHS  100 . In embodiments where storage drives  240   a - n  are hot-swappable devices that are received by bays of chassis, the storage drives  240   a - n  may be coupled to IHS  200  via couplings between the bays of the chassis and a midplane of IHS  200 . Storage drives  240   a - n  may include SAS (Serial Attached SCSI) magnetic disk drives, SATA (Serial Advanced Technology Attachment) magnetic disk drives, solid-state drives (SSDs) and other types of storage drives in various combinations. 
     As illustrated, storage controller  230  may be instrumented with a controller or other logic unit  230   a  that supports a sideband management connection  275   c  with remote access controller  255 . Via the sideband management connection  275   c , storage controller  230  may be configured to operate as a source of metric data regarding the operation of storage drives  240   a - n . For instance, controller  230   a  may collect metric data characterizing the performance of individual storage drives  240   a - n , such as available storage capacity and data transfer rates, as well as environmental properties, such as storage drive temperatures. A controller or other logic unit  230   a  of storage controller  230  may be configured to generate and report such metric data to remote access controller  255  for analysis and storage. As described in additional detail below, upon being stored, metric data generated by storage controller  230  may be collectively analyzed along with other metric data collected within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     In certain embodiments, remote access controller  255  may operate from a different power plane from the processors  205  and other components of IHS  200 , thus allowing the remote access controller  255  to operate, and management tasks to proceed, while the processing cores of IHS  200  are powered off. As described, various functions provided by the BIOS, including launching the operating system of the IHS  200 , may be implemented by the remote access controller  255 . In some embodiments, the remote access controller  255  may perform various functions to verify the integrity of the IHS  200  and its hardware components prior to initialization of the IHS  200  (i.e., in a bare-metal state). 
     As described, IHS  200  may be a server that is installed within a rack of a data center that may house numerous other racks, each housing additional servers. In some embodiments, upon initial configuration of IHS  200  for operations with a particular data center, a data center management application utilized by administrators of the data center may be include capabilities for configuring remote access controller  255  for remote management of IHS  200 . In some embodiments, such a data center management application may operate on an IHS such as IHS  200  that has been designated to support administrative operations within a datacenter. As part of the initial configuration of IHS  200  for operation within a data center, the data center management application may provide remote access controller  255  with information specifying the installed location of IHS  200  within the data center. As described in additional detail below, the data center management application may provide remote access controller  255  with information identifying the rack in which IHS  200  is installed, the position of IHS  200  within this rack, the location of this rack within an aisle of racks, and the location of the aisle with a data center. As describe in additional detail below, remote access controller  255  may include this location information within metric reports collected from the components of IHS  200 . This location information may then be utilized to by the data center management application in identifying zones within the data center that include IHSs that are exhibiting deviations in reported metrics relative to the metrics reported from IHSs in neighboring zones. 
     In some embodiments, remote access controller  255  may also be directly coupled via I2C couplings  275   d  with one or more sensors  280 , such as sensors that provide measurements of ambient inlet temperatures, outlet airflow temperatures and temperatures at various locations within IHS  200 . Sensors  280  coupled directly to remote access controller  255  may also be used in implementing security protocols, such as intrusion detection sensors and user proximity sensors. Sensors  280  may include logic units or other controllers  280   a  that are be configured by remote access controller  255  to generate and report metric data, where the generated metric data may be collectively analyzed along with other metric data collected within the same data center in order to identify zones within the data center that exhibit metric data deviations. 
     Remote access controller  255  may include a service processor  255   a , or specialized microcontroller, that operates management software that supports remote monitoring and administration of IHS  200 . Remote access controller  255  may be installed on the motherboard of IHS  200  or may be coupled to IHS  200  via an expansion slot provided by the motherboard. In support of remote monitoring functions, network adapter  225   c  may support connections with remote access controller  255  using wired and/or wireless network connections via a variety of network technologies. As a non-limiting example of a remote access controller, the integrated Dell Remote Access Controller (iDRAC) from Dell® is embedded within Dell PowerEdge™ servers and provides functionality that helps information technology (IT) administrators deploy, update, monitor, and maintain servers remotely. 
     In some embodiments, remote access controller  255  may support monitoring and administration of various managed devices  220 ,  225 ,  230 ,  260 ,  280  of an IHS via a sideband bus interface. For instance, messages utilized in device management may be transmitted using I2C sideband bus connections  275   a - e  that may be individually established with each of the respective managed devices  220 ,  225 ,  230 ,  260 ,  280  through the operation of an I2C multiplexer  255   d  of the remote access controller. As illustrated, certain of the managed devices of IHS  200 , such as FPGA cards  220 , network controller  225 , storage controller  230  and power management unit  260 , are coupled to the IHS processor(s)  205  via an in-line bus  215 , such as a PCIe root complex, that is separate from the I2C sideband bus connections  275   a - e  used for device management. 
     In certain embodiments, the service processor  255   a  of remote access controller  255  may rely on an I2C co-processor  255   b  to implement sideband I2C communications between the remote access controller  255  and managed components  220 ,  225 ,  230 ,  260 ,  280  of the IHS. The I2C co-processor  255   b  may be a specialized co-processor or micro-controller that is configured to interface via a sideband I2C bus interface with the managed hardware components  220 ,  225 ,  230 ,  260 ,  280  of IHS. In some embodiments, the I2C co-processor  255   b  may be an integrated component of the service processor  255   a , such as a peripheral system-on-chip feature that may be provided by the service processor  255   a . Each I2C bus  275   a - e  is illustrated as single line in  FIG.  2   . However, each I2C bus  275   a - e  may be comprised of a clock line and data line that couple the remote access controller  255  to I2C endpoints  220   a ,  225   a ,  230   a ,  260   a ,  280   a  on each of the managed components. 
     As illustrated, the I2C co-processor  255   b  may interface with the individual managed devices  220 ,  225 ,  230 ,  260 ,  280  via individual sideband I2C buses  275   a - e  selected through the operation of an I2C multiplexer  255   d . Via switching operations by the I2C multiplexer  255   d , a sideband bus connection  275   a - e  may be established by a direct coupling between the I2C co-processor  255   b  and an individual managed device  220 ,  225 ,  230 ,  260 ,  280 . In providing sideband management capabilities, the I2C co-processor  255   b  may each interoperate with corresponding endpoint I2C controllers  220   a ,  225   a ,  230   a ,  260   a ,  280   a  that implement the I2C communications of the respective managed devices  220 ,  225 ,  230 ,  260 ,  280 . The endpoint I2C controllers  220   a ,  225   a ,  230   a ,  260   a ,  280   a  may be implemented as dedicated microcontrollers for communicating sideband I2C messages with the remote access controller  255 , or endpoint I2C controllers  220   a ,  225   a ,  230   a ,  260   a ,  280   a  may be integrated SoC functions of a processor of the respective managed device endpoints  220 ,  225 ,  230 ,  260 ,  280 . 
     In various embodiments, an IHS  200  does not include each of the components shown in  FIG.  2   . In various embodiments, an IHS  200  may include various additional components in addition to those that are shown in  FIG.  2   . Furthermore, some components that are represented as separate components in  FIG.  2    may in certain embodiments instead be integrated with other components. For example, in certain embodiments, all or a portion of the functionality provided by the illustrated components may instead be provided by components integrated into the one or more processor(s)  205  as a systems-on-a-chip. 
       FIG.  3    is a flowchart describing certain steps of a method, according to some embodiments, for supporting a telemetry system used for identification of zones within a data center. Embodiments may begin at block  300  with the initialization of an IHS, such as the server IHSs described with regard to  FIGS.  1  and  2   . Upon being initialized, a wide variety of metric data may be collected by the telemetry system of an IHS. As described, various components of an IHS may be instrumented with physical and/or logical sensors that characterize various aspects of the operation of the IHS. In some embodiments, at block  305 , a remote access controller of the IHS may receive metric data reported by the components of the IHS, such as via the sideband management connections described with regard to  FIG.  2   . Upon receipt of these metric reports, the remote access controller may evaluate some or all of the reports in order to identify conditions that warrant an immediate response. For instance, the remote access controller may evaluate reported temperature information in order to immediately identify scenarios were thermal thresholds for safe operation have been exceeded. 
     As described with regard to  FIG.  1   , remote access controllers operating in a data center environment may be configured with information specifying the installed location of the remote access controller within the data center. In some instances, this data center location information may be provided to the remote access controller operating within a server IHS upon initial installation of the server within a rack of a data center. For example, as part of installation and configuration of a server within a data center, a data center management application may configure the remote access controller of this server to conduct the described telemetry operations and may provide the remote access controller with the server&#39;s installed location within the data center. For instance, the location information may specify a name or label associated with the data center, as well as identifying a room or other area of the data center in which the server is installed. As described, rack structures that house servers may be organized into rows of racks such that aisles have racks of servers on one or both sides. Accordingly, the location information may specify an aisle of the data center. The location information may also identify the rack in which a server is installed. As described, servers are stacked within a rack structure, with multiple of these rack structures positioned side-by-side along the length of an aisle. In some instances, the location of servers that are stacked within racks may be identified based on the row (i.e. slot) of the rack in which the server is installed. For example, a server installed in the topmost slot of the rack may be identified as being installed in a top row, which may be designate as row  1 . A server installed in the bottom slot of the rack that includes seven slots may be identified as being installed in the bottom row, which may be designate as row  7 . Upon receiving metric data from various sources within a server IHS, remote access controller may add such location information to the metric reports in order to support location-based processing of the collected telemetry data. For example, the remote access controller may attach the location information as a descriptive label that is added to received metric reports. 
     Once the collected metric reports have been labeled with data center location information, at block  315 , the remote access controller may store all or part of received metric reports to a database, or to one or more logs, for use in supporting additional analysis and troubleshooting of IHS operations, include the described analysis of telemetry data for use in identify zones of interest within a data center. At block  320 , a data center management application may initiate the identification of data center zones based on the metric data collected and stored by various remote access controllers operating within the data center. In some embodiments, this data center management application may be configured to automatically initiate such processing on a periodic basis, such as once every hour, or may be configured to initiate such processing based on the detection of certain conditions, such as based on the detection of temperature thresholds being surpassed, or based on a detected error condition. 
     At block  325 , the collected metric data may be analyzed in order to identify groups of servers that have reported metric data that is correlated with respect to a location of these servers within the data center. In some embodiments, principal component analysis may be used to identify the specific types of metric data that are correlated with data center location information, thus reducing the dimensionality of identifying metrics that are exhibiting location-based characteristics. For example, principal component analysis of collected metric information may reveal that reported inlet temperatures are correlated with the locations of the servers reporting the temperature information. More specifically, reported inlet temperatures may be determined to be correlated with location information that specifies the row within a rack in which servers are installed. In another example, reported outlet temperatures may be determined to be correlated with the individual racks of the data center. On the other hand, if no such location-based correlation is present, reported inlet temperatures may vary with no observable correlation to any location information. At block  330 , the metric data that has been determined to be correlated with location information may be segmented into distinct periods, such as using a time segmentation algorithm. Based on such time segmentation analysis, distinct intervals of time with significant variations in reported metric values may be identified for further analysis and intervals without any such variations in reported metric values may be omitted from any additional analysis. 
     Once intervals of time with noteworthy metric reports that are correlated with location information have been identified, at block  355 , data center zones associated with anomalous metric values may be identified. As described in additional detail below, this analysis may identify groups of servers that are installed in distinct data center zones, where these co-located groups of servers are exhibiting deviations in reported metrics in comparison to neighboring servers. In various scenarios, the data center zones identified as including servers that are reporting anomalous metric information may be individual racks, groups of adjacent racks, aisles of racks, a row (i.e., slot) of adjacent racks in which the groups of server are each installed and/or rooms within a datacenter. Upon identification of metric information that exhibits location-based anomalies, the identified data center zones and the deviation information may be utilized in adjusting operations of the data center. As described in additional detail below, at block  340 , such adjustments may result in adjustments to the airflow cooling output within certain areas of a datacenter, configuration changes to datacenter resources such as networking and power, and/or replacement of components or systems that are determined to be the source of the identified deviations. After any such adjustments have been completed, the subsequently generated metric information may be analyzed in the described manner in order to determine if the location-based deviation in metric reports has been rectified. 
       FIG.  4 A  is an illustration of an example, according to various embodiments, of the use of a telemetry system for the identification of an anomalous temperature zone within rows of a data center. In the illustrated example, temperature telemetry data, such as an inlet temperature or an exhaust temperature, has been identified as being correlated with data center location information. As described with regard to  FIG.  3   , principal component analysis of metric information collected from servers throughout a data center may be used to identify metrics that are correlated with data center locations. In the example of  FIG.  4 A , a temperature metric, such as an inlet temperature or exhaust temperature, reported by servers has been demonstrated to vary in relation to the location of a server within an aisle comprised of seven adjacent racks. Also as described with regard to  FIG.  3   , once a location-correlated metric has been identified, a time segmentation analysis of this metric data may be used to identify periods of particular interest within this data, such as the scenario illustrated in  FIG.  4 A  where the average reported temperature over such an interval varies based on the row (i.e. slot) of the rack in which a server is installed. In some embodiments, the periods selected by the time segmentation analysis may be additionally based on events, such as error and/or warning conditions, reported by remote access controllers that have provided the metric data. For instance, a remote access controller may perform an initial analysis of metric data upon receipt in order to identify errors or other conditions that may result in notifications to administrators. In such instances, events detected by remote access controllers may be used in segmenting the reported metric data into intervals of interest. 
     As illustrated, the reported temperature information exhibits a deviation in temperatures reported by servers located in rows three and four of their respective racks. In some embodiments, such location-based deviations may be identified using techniques such as univariate anomaly detection algorithms. Although data centers may employ sophisticated systems for controlling ambient temperatures throughout a data center, scenarios may nonetheless arise where airflow patterns may result in zones that are not properly ventilated. For example, in the scenario illustrated in  FIG.  4 A , a data center may provide sufficient airflow for maintaining ambient inlet temperatures below a threshold, such as below 45 degrees Celsius, for all servers in the illustrated aisle, but a draft or other airflow pathway within the data center may result in the airflow provided to the illustrated aisle being pulled downward, thus providing excessive cooling to servers in lower rows, while bypassing the servers in rows three and four. Embodiments thus provide a system for utilizing collected metric information for identifying such anomalous temperature zones within a datacenter. Without this capability provided by embodiments, administrators may be provided with individual metric reports indicating elevated inlet temperatures by some of these servers in rows three and four, but may be unable to discern that all servers in these rows are experiencing elevated ambient temperatures. Using the information provided by embodiments, administrators may adjust the output of the data centers environmental cooling system, or otherwise address the ventilation issues causing the lack of airflow to servers in these rows. Embodiments may be further utilized to evaluate whether such modifications have resulted in reduced ambient temperatures within this particular data center zone. 
       FIG.  4 B  is an illustration of an additional example, according to various embodiments, of the use of a telemetry system for the identification of an anomalous temperature zone within racks of a data center. As described, data centers may be used to support a large variety of data processing applications. One such application that may be implemented in a data center is a vSAN (virtual storage area network) that utilizes a pool of shared storage resources located within a logical cluster of servers. Although a vSAN system may utilize a logical cluster of servers, in some instances, these servers may be grouped in close physical proximity to each other within a data center. Such physical proximity may support high-speed connections between these servers. 
     In the example illustrated in  FIG.  4 B , the principal component analysis again identifies temperature metric information as being correlated with server locations within a data center. However, rather than identifying rows of servers reporting anomalous temperature data, in the example of  FIG.  4 B , the anomaly detection capabilities utilized by embodiments may identify elevated exhaust temperatures in servers located in racks three and four of the illustrated aisle of racks. In such a scenario, the servers may correspond to a cluster that is in use by a vSAN system, where the workload of this storage system and the configuration of these servers has resulted in elevated exhaust temperatures within this zone of the datacenter. Based on this information provided by embodiments, administrators may increase datacenter cooling to this aisle of the datacenter, or may adjust the airflow cooling output of these two particular racks, or may add additional servers to the cluster in use by the vSAN system in order to better distribute the workload of the system. Upon making such adjustments, administrators may utilize the capabilities provided by embodiments to evaluate whether such adjustments have addressed the anomalous exhaust temperatures in this zone of the data center. 
       FIG.  5 A  is an illustration of an additional example, according to various embodiments, of the use of a telemetry system for the identification of an anomalous power zone within a rack of a data center. In the example of  FIG.  5 B , rather than identify temperature information as being correlated with data center locations, the principal component analysis utilized by embodiments may identify a power availability metric as being correlated with location information. In particular, servers may report metrics providing a power availability, which may provide a measure of reserve power capacity that remains after the server has allocated available power. Such reserve power capacity may be used by the server to support short periods of peak power demands. However, as indicated in  FIG.  5 B , embodiments may identify servers in rack number seven as reporting abnormally low levels of reserve power capacity, thus limiting these server&#39;s ability to respond to peak power demands. As described above with regard to  FIG.  1   , servers may rely on shared power resources provided by the rack in which servers are installed. Accordingly, embodiments identifying the scenario illustrated in  FIG.  5 B  may assist administrators in identifying power capacity issues in the power supply unit of rack seven. 
       FIG.  5 B  is an illustration of an additional example, according to various embodiments, of the use of a telemetry system for the identification of an anomalous network zone within a rack of a data center. Similar to the example of  FIG.  5 A , embodiments have identified a metric discrepancy that is associated with rack number seven of the illustrated aisle of servers. However, in the example of  FIG.  5 B , reported network transmission metrics have been determined to be correlated with server locations within this illustrated aisle of servers. Servers may report metric information that relates the number of bytes of data transmitted by each port of a network controller of a server. Such network controller metric information may be aggregated for all ports and all network controllers of a server in order to determine an aggregate number of bytes being transmitted by a server over a particular time interval. The principal component analysis utilized by embodiments may identify this network transmission information as being correlated with data center location information and the anomaly detection algorithms utilized by embodiments may identify the servers in rack number seven as exhibiting anomalous network transmission data that is lower than network transmission data in neighboring racks. As described with regard to  FIG.  1   , servers installed within a rack may utilize shared network resources provided by the rack, such as network bandwidth provided by a network switch of the rack. Embodiments provide a capability for identifying scenarios where particular network characteristics are adversely affected by problems in such shared networking resources. In this example, administrators may investigate whether issues with a network switch of rack number seven are causing the servers in this rack to be provided with limited network transmission bandwidth. 
     It should be understood that various operations described herein may be implemented in software executed by logic or processing circuitry, hardware, or a combination thereof. The order in which each operation of a given method is performed may be changed, and various operations may be added, reordered, combined, omitted, modified, etc. It is intended that the invention(s) described herein embrace all such modifications and changes and, accordingly, the above description should be regarded in an illustrative rather than a restrictive sense. 
     Although the invention(s) is/are described herein with reference to specific embodiments, various modifications and changes can be made without departing from the scope of the present invention(s), as set forth in the claims below. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of the present invention(s). Any benefits, advantages, or solutions to problems that are described herein with regard to specific embodiments are not intended to be construed as a critical, required, or essential feature or element of any or all the claims. 
     Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements. The terms “coupled” or “operably coupled” are defined as connected, although not necessarily directly, and not necessarily mechanically. The terms “a” and “an” are defined as one or more unless stated otherwise. The terms “comprise” (and any form of comprise, such as “comprises” and “comprising”), “have” (and any form of have, such as “has” and “having”), “include” (and any form of include, such as “includes” and “including”) and “contain” (and any form of contain, such as “contains” and “containing”) are open-ended linking verbs. As a result, a system, device, or apparatus that “comprises,” “has,” “includes” or “contains” one or more elements possesses those one or more elements but is not limited to possessing only those one or more elements. Similarly, a method or process that “comprises,” “has,” “includes” or “contains” one or more operations possesses those one or more operations but is not limited to possessing only those one or more operations.