Patent Application
20220229679
A virtual machine configured to implement a given application is typically considered to be healthy when the virtual machine is able to timely and efficiently fulfill, e.g., end user requests. Accordingly, a virtual machine that is not able to fulfill, e.g., end user requests may be considered unhealthy and in need of removal and/or self-healing. Typically, self-healing may include rebooting or replacement of the virtual machine. A technical problem that is addressed herein is that self-healing functionality is generally deployed on virtual machines dedicated to performing a same application may include removal of one or more virtual machines from operation in a random fashion. As a result of the random removal and/or self-healing of one or more virtual machines, a given virtual machine may be removed from operation even though the virtual machine may not be unhealthy, and/or may not be the worst (i.e., unhealthiest) virtual machine in the group in need to be removed. For example, if there are five (5) virtual machines within a group of virtual machines dedicated to implementing a given application, and only one (1) of the virtual machines can be removed and/or self-healed at a given time in order to avoid endangering the implementation of the application, the virtual machine that has the greatest need of removal and/or self-healing may not be the one that is randomly chosen. Accordingly, consistency in the health of the system constituted by a group of virtual machines may be compromised.
In one general aspect, the instant application describes a system for monitoring a health of a plurality of virtual machines operating within a group of virtual machines. The system includes a processor and a memory configured to executable instructions, which when executed by the process, cause the processor to perform functions of receiving, over a communication network, health information from each of the plurality of virtual machines, identifying one or more unhealthy virtual machines based on the received health information thereof, determining a health score for each of the plurality of virtual machines based on the received health information, establishing a priority queue ranking each of the identified one or more unhealthy virtual machines based on their health score, designating at least one of the unhealthy virtual machines to remove from the group, determining a number of remaining virtual machines based on the priority queue, comparing the number of remaining virtual machines to a safety number, the safety number indicating a minimum number of virtual machines necessary to implement an application, and, based on a result of comparing the number of remaining virtual machines to the safety number, sending a message to at least one of the unhealthy virtual machines over the communication network to remove the at least one of the unhealthy virtual machines from the group.
The above general aspect may include one or more of the following features. For example, to receive health information from a virtual machine, the memory further stores executable instructions which when executed by the processor cause the processor to perform functions of sending one of more health probes to the virtual machine over the communication network by the processor, each health probe monitoring an aspect of the virtual machine, each aspect having a health threshold and a base weight, and receiving a response to each health probe by the processor from the virtual machine over the communication network.
For another example, to determine the health score for a virtual machine, the memory further stores executable instructions which when executed by the processor cause the processor to perform functions of determining an over-threshold amount for each aspect based on the health threshold thereof and the received response, determining a probe weighted score for each aspect based on the base weight thereof and the over-threshold amount, and determining a health score of the virtual machine as a total weighted score based on a sum of the probe weighted scores of one or more of the aspects of the virtual machine.
For a further example, to determine the health score for a virtual machine, the memory further stores executable instructions which when executed by the processor cause the processor to perform functions of determining the over-threshold amount for an aspect as a difference between the obtained response and the health threshold for the aspect.
As an additional example, the memory stores instructions to cause the processor to establish the priority queue by ranking each virtual machine from highest total weighted score to lowest total weighted score. The memory may also store instructions to cause the processor to identify the one or more unhealthy virtual machines by identifying one or more unhealthy virtual machines having a total weighted score that is above a desired total weighted score. The memory may also store instructions to cause the processor to remove the identified one or more unhealthy virtual machines by removing the identified unhealthy virtual machines in inverse order of their respective total weighted scores.
For another example, to monitor the identified one or more unhealthy virtual machines during operation of the group of virtual machines, the memory stores instructions to cause the processor to determine the total weighted score thereof, designate one or more of the identified unhealthy virtual machines as new healthy virtual machines when the total weighted score thereof is better than a desired total weighted score, and include the designated new healthy virtual machines in the group of virtual machines.
In various implementations, the processor is housed on each of the virtual machines. Alternatively or additionally, the processor is housed on a terminal separate from the virtual machines, the terminal being part of the group of virtual machines, or a server separate from the virtual machines.
These general and specific aspects may be implemented using a system, a method, or a computer program, or any combination of systems, methods, and computer programs.
Additional advantages and novel features of these various implementations will be set forth in part in the description that follows, and in part will become more apparent to those skilled in the art upon examination of the following or upon learning by practice of the invention.
The drawing figures depict one or more implementations in accord with the present teachings, by way of example only, not by way of limitation. In the figures, like reference numerals refer to the same or similar elements. Furthermore, it should be understood that the drawings are not necessarily to scale.
In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant teachings. It will be apparent to persons of ordinary skill, upon reading this description, that various aspects can be practiced without such details. In other instances, well known methods, procedures, components, and/or circuitry have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present teachings.
Healing virtual machines within a group of virtual machines that is performed in a random manner presents a technical problem because removal of one or more virtual machines from operation in a random fashion may result in either removing healthy virtual machines and leaving unhealthy virtual machines in operation, or removing less unhealthy machines and leaving more unhealthy machines in operation. In one specific example, an unhealthy virtual machine is a virtual machine that is not adequately able to respond to user demands.
To address these technical problems and more, in an example, this description provides a technical solution for identifying and removing the most unhealthy virtual machine(s) from operating within the group of virtual machines. Accordingly, the above technical problem may be avoided when only the unhealthiest machines are removed from operation. When the removed unhealthiest machines are no longer unhealthy, they may be rejoined to the group of virtual machines.
In various implementations, servers, also referred to herein as virtual machines, may be healed, e.g., automatically healed, based on a weighted score that is determined on the basis of a number of health metrics of the servers or virtual machines. These health metrics may reflect, e.g., the current ability of the servers or virtual machines to fulfill end user demands. The severity of unhealthiness of a virtual machine may be determined based on a weighted algorithm. The weights in the weighted algorithm may be configurable based on the importance of the related health metric. These operations may be performed during operation of the virtual machines, in real time.
In various implementations, the removal of a virtual machine, or self-healing operation, may be entirely, or substantially entirely, automated. The determination of the self-healing decision may be done in real-time, during operation of the virtual machine. A weighted algorithm may calculate the worst virtual machine in the group of virtual machines in real-time, or during operation of the group of virtual machines. The weights on the various health metrics used to evaluate the health of a given virtual machine may be configurable and may be changed when necessary or desired. The weights may be maintained in a priority queue that may be stored in a distributed cache and configured to make the decision across all the virtual machines whether to remove or keep a virtual machine in operation. The group of virtual machines may be consistently kept in its most healthy state.
Various implementations include implementing a priority queue within a group of virtual machines infrastructure to ensure that the unhealthiest machines may be taken out of rotation or operation. Instead of randomly selecting machines to be healed or removed, adding the priority queue may increase efficiency in the self-healing or removal process by consistently focusing on removing or self-healing the unhealthiest virtual machines.
Reference will now be made in detail to implementations, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. In this regard, the present implementations may have different forms and should not be construed as being limited to the descriptions set forth herein. Accordingly, the implementations are merely described below, by referring to the figures, to explain various implementations of the present description.
The process 100 starts at S110 where a health probe is sent to each, or to at least one, of the virtual machines constituting a group of virtual machines dedicated to implementing an application, e.g., a same application. In one implementation, the health probe is sent every five (5) seconds to the virtual machines. For example, a central server sends the health probes to each of the virtual machines. Alternatively, the virtual machines may send health probes to each other via processors located therein.
In S120, the health probes are responded to by the virtual machines and sent back to, for example, the central server. Each virtual machine may receive a plurality of health probes at S110, and upon receipt of the health probes, performs a check on a separate aspect of the health of the virtual machine, and replies to each health probe at S120. For example, a health probe may be a message configured to check the health of one aspect of the virtual machine. The health probes received may be stored in a data repository such as one or more of the virtual machines.
In S130, in various implementations, a threshold is identified for each aspect of the virtual machine by a processor. The threshold for one aspect may be different from the threshold of another aspect. Example thresholds may be threshold values for CPU usage, memory usage, and the like, under which the virtual machine may be considered to be unhealthy. The threshold of a given health aspect of a virtual machine provides a reference point based on which the health of the virtual machine with respect to a single aspect is evaluated. For example, if the health of the virtual machine is above the threshold, then the virtual machine may be considered to be healthy. Alternatively, the threshold may be determined so that if the health of the virtual machine is above the threshold, then the virtual machine may be considered to be unhealthy. Furthermore, the difference between the health of a given aspect of the virtual machine and the threshold for the same aspect determines the severity of unhealthiness of the given aspect of the virtual machine.
In S140, in various implementations, an amount over the threshold (“OverThreshold”) is calculated by a processor. The OverThreshold may be calculated as the ratio of the difference between the probe response and the threshold over the threshold for a given aspect of the health of a virtual machine, e.g., (response−threshold)/threshold or (threshold−response)/threshold. Example amounts of the OverThreshold are provided in Column 4 of
In S150, in various implementations, the Baseweight of each aspect for a given machine is determined by a processor. The Baseweight is specific to a given aspect, and provides an estimation of the relative importance of the health of a given aspect with respect to other aspects for the same machine. Example Baseweights are provided in
In S160, in various implementations, a weighted score is calculated for each aspect of the virtual machine by a processor. The weighted score is calculated as the product of the Baseweight and the amount over the threshold. Example weighted scores are provided in Column 6 of
In S170, in various implementations, a total weighted score is calculated for the virtual machine in its entirety by a processor. The total weighted score for a given virtual machine is calculated as the sum of the weighted scores of each aspect of the given virtual machine. The total weighted score provides an indication of the overall health of the virtual machine. An example of a total weighted score for a given virtual machine is at the row of column 660 of
In various implementations, at S220, a priority queue is established for the virtual machines that have been determined at S210 to be unhealthy, the priority queue ranking the virtual machines based on their respective total weighted scores. The priority queue is established by a processor. For example, the least healthy virtual machine may be at the top of the queue, and each subsequently ranked virtual machine is healthier, or less unhealthy, than the one ranked immediately above, until the last queued virtual machine, which is the least unhealthy virtual machine of all the unhealthy virtual machines identified in S210. The priority queue is stored in a data repository.
In various implementations, at S230, based on the priority queue, one or more of the unhealthy machines identified in S210 are designated to be removed based on their total weighted score. For example, the virtual machine showing the highest unhealthiness is selected. The unhealthiest virtual machine is selected by a processor.
In various implementations, at S240, before removing an unhealthy virtual machine, a determination is made whether the number of remaining virtual machines is equal to or greater than a safety limit. The safety limit is the minimum number of virtual machines that can be active within a group without impeding the ability of the group to implement a given application. In other words, if the remaining number of virtual machines within the group, after having removed unhealthy virtual machines, would be lower than the safety limit, then the unhealthy virtual machine is not removed. The determination whether the number of remaining virtual machines is equal to or greater than the safety limit is performed by a processor.
In various implementations, if at S240 the processor, determines that the number of remaining healthy virtual machines is equal to or greater than the safety limit, then the method continues to S250, and the unhealthy virtual machine may be taken out of rotation. Subsequently to S250, the method continues to S110 to continue monitoring the group of virtual machines. If at S240 the processor, determines that the number of remaining healthy virtual machines is less than the safety limit, then the unhealthy virtual machine is kept in the rotation, and the method continues to S110 to continue monitoring the group of virtual machines.
In various implementations, the amount over the threshold (“OverThreshold”) 640 is calculated, as discussed in S140 of the method illustrated above with respect to
As an example, one of the probes 610 is “CPUUsage” and determines the health of the CPU usage for a given virtual machine. The probe response 620 received from the virtual machine is that its CPU usage is 30. The probe threshold 630 for CPU usage for this virtual machine is 20, indicating that the CPU usage of this virtual machine may be unacceptable because the probe response 620 is greater than the probe threshold 630, and this virtual machine may be unhealthy with respect to the aspect of CPU usage. The OverThreshold amount 640 can thus be calculated as (Threshold−Response)/Threshold, which translates to (30−20)/20, which generates an OverThreshold amount of 0.5. The Baseweight 650 for this virtual machine with respect to this probe is 1, so that the probe weighted score 660, being the product of the Baseweight 650 and the OverThreshold amount 640 is equal to (1×0.5=) 0.5.
As another example, one of the probes 610 is “MemoryUsage” and determines the health of the memory usage for a given virtual machine. The probe response 620 received from the virtual machine is that its memory usage is 70. The probe threshold 630 for memory usage for this virtual machine is 50, indicating that the memory usage of this virtual machine may also be unacceptable because the probe response 620 is greater than the probe threshold 630, and this virtual machine may be unhealthy with respect to the aspect of memory usage. The OverThreshold amount 640 can thus be calculated as (Threshold−Response)/Threshold, which translates to (70−50)/50, which generates an OverThreshold amount of 0.4. The difference between the threshold and the response provides an indication of the severity of the unhealthiness of the virtual machine. The Baseweight 650 for this virtual machine with respect to this probe is 1, so that the probe weighted score 660, being the product of the Baseweight 650 and the OverThreshold amount 640 is equal to (1×0.4=) 0.4. Similarly, probe weighted scores 660 for each aspect of the health of a virtual machine may be calculated. When all the probe weighted scores 660 are calculated, a total weighted score 670, which is the sum of all the probe weighted scores 660 of all health aspects of the virtual machine, may be calculated.
The hardware layer 1004 also includes a memory/storage 1010, which also includes the executable instructions 1008 and accompanying data. The hardware layer 1004 may also include other hardware modules 1012. Instructions 1008 held by processing unit 1006 may be portions of instructions 1008 held by the memory/storage 1010.
The example software architecture 1002 may be conceptualized as layers, each providing various functionality. For example, the software architecture 1002 may include layers and components such as an operating system (OS) 1014, libraries 1016, frameworks 1018, applications 1020, and a presentation layer 1044. Operationally, the applications 1020 and/or other components within the layers may invoke API calls 1024 to other layers and receive corresponding results 1026. The layers illustrated are representative in nature and other software architectures may include additional or different layers. For example, some mobile or special purpose operating systems may not provide the frameworks/middleware 1018.
The OS 1014 may manage hardware resources and provide common services. The OS 1014 may include, for example, a kernel 1028, services 1030, and drivers 1032. The kernel 1028 may act as an abstraction layer between the hardware layer 1004 and other software layers. For example, the kernel 1028 may be responsible for memory management, processor management (for example, scheduling), component management, networking, security settings, and so on. The services 1030 may provide other common services for the other software layers. The drivers 1032 may be responsible for controlling or interfacing with the underlying hardware layer 1004. For instance, the drivers 1032 may include display drivers, camera drivers, memory/storage drivers, peripheral device drivers (for example, via Universal Serial Bus (USB)), network and/or wireless communication drivers, audio drivers, and so forth depending on the hardware and/or software configuration.
The libraries 1016 may provide a common infrastructure that may be used by the applications 1020 and/or other components and/or layers. The libraries 1016 typically provide functionality for use by other software modules to perform tasks, rather than rather than interacting directly with the OS 1014. The libraries 1016 may include system libraries 1034 (for example, C standard library) that may provide functions such as memory allocation, string manipulation, file operations. In addition, the libraries 1016 may include API libraries 1036 such as media libraries (for example, supporting presentation and manipulation of image, sound, and/or video data formats), graphics libraries (for example, an OpenGL library for rendering 2D and 3D graphics on a display), database libraries (for example, SQLite or other relational database functions), and web libraries (for example, WebKit that may provide web browsing functionality). The libraries 1016 may also include a wide variety of other libraries 1038 to provide many functions for applications 1020 and other software modules.
The frameworks 1018 (also sometimes referred to as middleware) provide a higher-level common infrastructure that may be used by the applications 1020 and/or other software modules. For example, the frameworks 1018 may provide various graphic user interface (GUI) functions, high-level resource management, or high-level location services. The frameworks 1018 may provide a broad spectrum of other APIs for applications 1020 and/or other software modules.
The applications 1020 include built-in applications 1040 and/or third-party applications 1042. Examples of built-in applications 1040 may include, but are not limited to, a contacts application, a browser application, a location application, a media application, a messaging application, and/or a game application. Third-party applications 1042 may include any applications developed by an entity other than the vendor of the particular system. The applications 1020 may use functions available via OS 1014, libraries 1016, frameworks 1018, and presentation layer 1044 to create user interfaces to interact with users.
Some software architectures use virtual machines, as illustrated by a virtual machine 1048. The virtual machine 1048 provides an execution environment where applications/modules can execute as if they were executing on a hardware machine. The virtual machine 1048 may be hosted by a host OS (for example, OS 1014) or hypervisor, and may have a virtual machine monitor 1046 which manages operation of the virtual machine 1048 and interoperation with the host operating system. A software architecture, which may be different from software architecture 1002 outside of the virtual machine, executes within the virtual machine 1048 such as an OS 1050, libraries 1052, frameworks 1054, applications 1056, and/or a presentation layer 1058.
The machine 1100 may include processors 1110, memory 1130, and I/O components 1150, which may be communicatively coupled via, for example, a bus 1102. The bus 1102 may include multiple buses coupling various elements of machine 1100 via various bus technologies and protocols. In an example, the processors 1110 (including, for example, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an ASIC, or a suitable combination thereof) may include one or more processors 1112a to 1112n that may execute the instructions 1116 and process data. In some examples, one or more processors 1110 may execute instructions provided or identified by one or more other processors 1110. The term “processor” includes a multi-core processor including cores that may execute instructions contemporaneously. Although
The memory/storage 1130 may include a main memory 1132, a static memory 1134, or other memory, and a storage unit 1136, both accessible to the processors 1110 such as via the bus 1102. The storage unit 1136 and memory 1132, 1134 store instructions 1116 embodying any one or more of the functions described herein. The memory/storage 1130 may also store temporary, intermediate, and/or long-term data for processors 1110. The instructions 1116 may also reside, completely or partially, within the memory 1132, 1134, within the storage unit 1136, within at least one of the processors 1110 (for example, within a command buffer or cache memory), within memory at least one of I/O components 1150, or any suitable combination thereof, during execution thereof. Accordingly, the memory 1132, 1134, the storage unit 1136, memory in processors 1110, and memory in I/O components 1150 are examples of machine-readable media.
As used herein, “machine-readable medium” refers to a device able to temporarily or permanently store instructions and data that cause machine 1100 to operate in a specific fashion. The term “machine-readable medium,” as used herein, does not encompass transitory electrical or electromagnetic signals per se (such as on a carrier wave propagating through a medium); the term “machine-readable medium” may therefore be considered tangible and non-transitory. Non-limiting examples of a non-transitory, tangible machine-readable medium may include, but are not limited to, nonvolatile memory (such as flash memory or read-only memory (ROM)), volatile memory (such as a static random-access memory (RAM) or a dynamic RAM), buffer memory, cache memory, optical storage media, magnetic storage media and devices, network-accessible or cloud storage, other types of storage, and/or any suitable combination thereof. The term “machine-readable medium” applies to a single medium, or combination of multiple media, used to store instructions (for example, instructions 1116) for execution by a machine 1100 such that the instructions, when executed by one or more processors 1110 of the machine 1100, cause the machine 1100 to perform and one or more of the features described herein. Accordingly, a “machine-readable medium” may refer to a single storage device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices.
The I/O components 1150 may include a wide variety of hardware components adapted to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 1150 included in a particular machine will depend on the type and/or function of the machine. For example, mobile devices such as mobile phones may include a touch input device, whereas a headless server or IoT device may not include such a touch input device. The particular examples of I/O components illustrated in
In some examples, the I/O components 1150 may include biometric components 1156, motion components 1158, environmental components 1160 and/or position components 1162, among a wide array of other environmental sensor components. The biometric components 1156 may include, for example, components to detect body expressions (for example, facial expressions, vocal expressions, hand or body gestures, or eye tracking), measure biosignals (for example, heart rate or brain waves), and identify a person (for example, via voice-, retina-, and/or facial-based identification). The position components 1162 may include, for example, location sensors (for example, a Global Position System (GPS) receiver), altitude sensors (for example, an air pressure sensor from which altitude may be derived), and/or orientation sensors (for example, magnetometers). The motion components 1158 may include, for example, motion sensors such as acceleration and rotation sensors. The environmental components 1160 may include, for example, illumination sensors, acoustic sensors and/or temperature sensors.
The I/O components 1150 may include communication components 1164, implementing a wide variety of technologies operable to couple the machine 1100 to network(s) 1170 and/or device(s) 1180 via respective communicative couplings 1172 and 1182. The communication components 1164 may include one or more network interface components or other suitable devices to interface with the network(s) 1170. The communication components 1164 may include, for example, components adapted to provide wired communication, wireless communication, cellular communication, Near Field Communication (NFC), Bluetooth communication, Wi-Fi, and/or communication via other modalities. The device(s) 1180 may include other machines or various peripheral devices (for example, coupled via USB).
In some examples, the communication components 1164 may detect identifiers or include components adapted to detect identifiers. For example, the communication components 1164 may include Radio Frequency Identification (RFID) tag readers, NFC detectors, optical sensors (for example, one- or multi-dimensional bar codes, or other optical codes), and/or acoustic detectors (for example, microphones to identify tagged audio signals). In some examples, location information may be determined based on information from the communication components 1162, such as, but not limited to, geo-location via Internet Protocol (IP) address, location via Wi-Fi, cellular, NFC, Bluetooth, or other wireless station identification and/or signal triangulation.
Generally, functions described herein (for example, the features illustrated in
While various implementations have been described, the description is intended to be exemplary, rather than limiting, and it is understood that many more implementations and implementations are possible that are within the scope of the implementations. Although many possible combinations of features are shown in the accompanying figures and discussed in this detailed description, many other combinations of the disclosed features are possible. Any feature of any implementation may be used in combination with or substituted for any other feature or element in any other implementation unless specifically restricted. Therefore, it will be understood that any of the features shown and/or discussed in the present disclosure may be implemented together in any suitable combination. Accordingly, the implementations are not to be restricted except in light of the attached claims and their equivalents. Also, various modifications and changes may be made within the scope of the attached claims.
While the foregoing has described what are considered to be the best mode and/or other examples, it is understood that various modifications may be made therein and that the subject matter disclosed herein may be implemented in various forms and examples, and that the teachings may be applied in numerous applications, only some of which have been described herein. It is intended by the following claims to claim any and all applications, modifications and variations that fall within the true scope of the present teachings.
Unless otherwise stated, all measurements, values, ratings, positions, magnitudes, sizes, and other specifications that are set forth in this specification, including in the claims that follow, are approximate, not exact. They are intended to have a reasonable range that is consistent with the functions to which they relate and with what is customary in the art to which they pertain.
The scope of protection is limited solely by the claims that now follow. That scope is intended and should be interpreted to be as broad as is consistent with the ordinary meaning of the language that is used in the claims when interpreted in light of this specification and the prosecution history that follows and to encompass all structural and functional equivalents. Notwithstanding, none of the claims are intended to embrace subject matter that fails to satisfy the requirement of Sections 101, 102, or 103 of the Patent Act, nor should they be interpreted in such a way. Any unintended embracement of such subject matter is hereby disclaimed.
Except as stated immediately above, nothing that has been stated or illustrated is intended or should be interpreted to cause a dedication of any component, step, feature, object, benefit, advantage, or equivalent to the public, regardless of whether it is or is not recited in the claims.
It will be understood that the terms and expressions used herein have the ordinary meaning as is accorded to such terms and expressions with respect to their corresponding respective areas of inquiry and study except where specific meanings have otherwise been set forth herein.
Relational terms such as first and second and the like may be used solely to distinguish one entity or action from another without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms “comprises,” “comprising,” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by “a” or “an” does not, without further constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.
The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various examples for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claims require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed example. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.
While various implementations have been described, the description is intended to be exemplary, rather than limiting, and it is understood that many more implementations and implementations are possible that are within the scope of the implementations. Although many possible combinations of features are shown in the accompanying figures and discussed in this detailed description, many other combinations of the disclosed features are possible. Any feature of any implementation may be used in combination with or substituted for any other feature or element in any other implementation unless specifically restricted. Therefore, it will be understood that any of the features shown and/or discussed in the present disclosure may be implemented together in any suitable combination. Accordingly, the implementations are not to be restricted except in light of the attached claims and their equivalents. Also, various modifications and changes may be made within the scope of the attached claims.
