Patent Publication Number: US-2022229679-A1

Title: Monitoring and maintaining health of groups of virtual machines

Description:
BACKGROUND 
     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. 
     SUMMARY 
     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. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       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. 
         FIG. 1  is a flowchart illustrating a method of establishing a health score for a virtual machine, according to various implementations; 
         FIG. 2  is a flowchart illustrating a method of monitoring a health of a plurality of virtual machines operating within a group of virtual machines, according to various implementations; 
         FIG. 3  is a block diagram illustrating a group of virtual machines including a plurality of virtual machines, according to various implementations; 
         FIGS. 4A-4B  are block diagrams illustrating a group of virtual machines including a plurality of virtual machines taken out of rotation, according to various implementations; 
         FIG. 5  is a table illustrating probes and baseweights for various health parameters, according to various implementations; 
         FIG. 6  is a table illustrating weighted score examples, according to various implementations; 
         FIG. 7  is a diagram illustrating a plurality of virtual machines in various states of health, according to various implementations; 
         FIG. 8  is a diagram illustrating a health maintenance operation, according to various implementations; and 
         FIGS. 9A-9C  are diagrams illustrating a communication flow during the monitoring of the health of a plurality of virtual machines operating within a group of virtual machines, according to various implementations; 
         FIG. 10  is a block diagram illustrating an example of software architecture, various portions of which may be used in conjunction with various hardware architectures herein described; and 
         FIG. 11  is a block diagram illustrating components of an example of a machine configured to read instructions from a machine-readable medium and perform any of the features described herein. 
     
    
    
     DETAILED DESCRIPTION 
     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. 
       FIG. 1  is a flowchart illustrating a method  100  for establishing a health score for a virtual machine, according to various implementations. The various steps described below may be performed by a processor such as, for example, server  810  illustrated below in  FIG. 8 . 
     The process  100  starts at S 110  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 S 120 , 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 S 110 , 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 S 120 . 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 S 130 , 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 S 140 , 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  FIG. 6 . The amount of the OverThreshold provides an estimate of the severity of the unhealthiness of a virtual machine with respect to the specific aspect. 
     In S 150 , 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  FIG. 5  and  FIG. 6  below. The Baseweights may be stored in a data repository. 
     In S 160 , 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  FIG. 6 . The weighted score for each aspect of a virtual machine may be stored in a data repository. 
     In S 170 , 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  FIG. 6 . In the example illustrated in  FIG. 6 , the total weighted score is 8.9 and is calculated by a processor, as the sum of the individual weighted scores of each individual aspect of the same virtual machine. The total weighted score calculated as discussed above may be referred to as health score. 
       FIG. 2  is a flowchart illustrating a method  200  of monitoring a health of a plurality of virtual machines operating within a group of virtual machines, according to various implementations. The method  200  starts at S 210 , where one or more virtual machines are determined to be unhealthy, or sick, based on the responses to the health probes as discussed above with respect to S 120 . The one or more virtual machines are determined to be healthy by a processor if, e.g., their health probe result is above a certain threshold. A health probe result that is above a threshold of health may be considered to be unhealthy. Alternatively, a health probe result that is below the threshold may be considered to be unhealthy. 
     In various implementations, at S 220 , a priority queue is established for the virtual machines that have been determined at S 210  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 S 210 . The priority queue is stored in a data repository. 
     In various implementations, at S 230 , based on the priority queue, one or more of the unhealthy machines identified in S 210  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 S 240 , 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 S 240  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 S 250 , and the unhealthy virtual machine may be taken out of rotation. Subsequently to S 250 , the method continues to S 110  to continue monitoring the group of virtual machines. If at S 240  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 S 110  to continue monitoring the group of virtual machines. 
       FIG. 3  is a block diagram  300  illustrating a group of virtual machines, according to various implementations. A number of virtual machines  330  are grouped together in groups  320  where the virtual machines  330  in each group  320  are configured to implement a same application. Each of the virtual machines  330  within the group  320  is configured to implement a common application, or each of the virtual machines  330  is configured to implement a portion of the application.  FIG. 3  illustrates Groups 1, 2, 3 . . . n,  320  where each of the groups  320  includes a number of virtual machines  330 . In the example illustrated in  FIG. 3 , all the groups  320  are dedicated to, for example, a given geographical region  310 . Similarly, other regions  2 ,  3  . . . n  310  may also include a number of groups  320 , each group  320  being dedicated to implementing a given application and including a number of virtual machines  330  configured to implement the given application or a portion thereof. If one of the virtual machines  330  is unhealthy, the unhealthy virtual machine  330  may be removed from the group  320  according to the method described above with respect to  FIGS. 1 and 2 . 
       FIGS. 4A-4B  are block diagrams  400  illustrating a group of virtual machines including a plurality of virtual machines taken out of rotation, according to various implementations.  FIG. 4A  illustrates a group  420  of virtual machines  410  that are in rotation. As the virtual machines  410  are in rotation, none of the virtual machines  410  has been found to be unhealthy according to the method described above with respect to  FIGS. 1 and 2 , thus none of the virtual machines  410  has been taken out of rotation.  FIG. 4B  illustrates a group  420  of virtual machines  410  that are in rotation, and one virtual machine  415 , Machine # 2 , which was in rotation in  FIG. 4A , has now been removed from the group  420  because virtual machine  415  (Machine # 2 ) has been found to be unhealthy according to the method described above with respect to  FIGS. 1 and 2 . In  FIG. 4B , virtual machine  415  (Machine # 2 ) has been taken out of rotation in group  420  because it has been determined to be unhealthy according to the method described above with respect to  FIGS. 1 and 2 . 
       FIG. 5  is a table  500  illustrating various probes  510  and their corresponding Baseweights  520  for various health parameters, according to various implementations. In the table  500 , each probe  510  includes a health check for one aspect of the virtual machine. The Baseweights  520  may include baseweights for checks for aspects of the health of the virtual machines, the aspects being or including, e.g., StsAppPool (checks whether the application pool is functioning correctly), GC Probe (garbage collection probe, which records how much memory is occupied with objects that are no longer in use by the program), SPPing (a ping that is sent to ensure that the machine is responding correctly), CPUUsage (an indication of the usage of the CPU of the virtual machine), MemoryUsage (an indication of the usage of the memory of the virtual machine), LisLogsErrorsPercentageProbe (checks how many errors are seen in the application logs), LisLogsAverageLatency (an indication of the average latency of the virtual machine), and CBSynthetic (checks whether the web application is working correctly and responding during a desired timeframe). As indicated in  FIG. 5 , each of the probes has a specific Baseweight that is independent of the others. The Baseweight assigned to each probe is exemplary and one of ordinary skill realizes that other Baseweight can be associated to each probe. 
       FIG. 6  is a table  600  illustrating weighted score examples, according to various implementations. In table  600 , a weighted score  660  is calculated for each one of the probes  610  by a processor. For each one of the probes  610 , a probe response  620  is received by a processor and is stored in a data repository. The probe response  620  may be a response to a health check. Each of the probes may have a probe threshold  630  associated therewith. The probe threshold  630  may also be stored in the data repository. 
     In various implementations, the amount over the threshold (“OverThreshold”)  640  is calculated, as discussed in S 140  of the method illustrated above with respect to  FIG. 1 . The OverThreshold  640  of each aspect of each virtual machines is representative of the level of unhealthiness of the virtual machine, e.g. the larger the amount of OverThreshold  640 , the more unhealthy the virtual machine may be. The OverThreshold  640  may be calculated by a processor and may be stored in the data repository. The Baseweights  650  for each of the probes, as determined at S 150  of the method discussed above with respect to  FIG. 1 , may be used as a basis to calculate the weighted score  660 , as indicated in S 160  of  FIG. 1 .  FIG. 6  lists all the calculated weighted scores  660  for each one of the probes  610 . The sum of all the calculated probe weighted scores  660  is calculated, as discussed with respect to S 170  in  FIG. 1 . The sum of the probe weighted scores  660  is listed as the total weighted score  670  and is illustrated on the last row of  FIG. 6 . 
     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. 
       FIG. 7  is a diagram  700  illustrating a plurality of virtual machines in various states of health, according to various implementations. The health of each of the virtual machines  701 - 707  is evaluated, as indicated, e.g., at S 210  of the method discussed above with respect to  FIG. 2 . In the example illustrated in  FIG. 7 , virtual machines  701 ,  703 ,  704  and  706  have been determined to be healthy, while virtual machines  702 ,  705  and  707  have been determined to be sick. The healthiness of the virtual machines  701 - 707  is determined by a processor. The degree of sickness of virtual machines  702 ,  705  and  707  varies, and a sorted set of virtual machines  710  is established. A processor ranks the virtual machines  702 ,  705  and  707  in order of severity of unhealthiness. 
       FIG. 8  is a diagram  800  illustrating a health maintenance operation, according to various implementations. In  FIG. 8 , a server  810  includes a data repository, and a sorted set of names or identifiers of unhealthy machines  815  is stored in the data repository. In various implementations, the server  810  may be or include a distributed cache, or may be a standalone server. Alternatively, the sorted set of unhealthy machines  815  may be stored in a memory of one or more of the virtual machines. The virtual machines  820  may include a processor and sends a communication message  818  via the processor and over a communication network to the server  810 . The server  810  stores the content of the message  818  regarding the unhealthiness of the virtual machines and replies with a message  814  to the virtual machine  820 , the message including the list of a number of virtual machines that have been found to be sick or unhealthy, e.g., the unhealthiest virtual machines ranked by the priority queue in the repository. In response to the message  814 , the virtual machine  820  can determine the most unhealthy virtual machines. The virtual machine  820  may also send a query  830  to determine whether the virtual machine  820  is listed as one of the unhealthiest virtual machines from the message  814 . If the query  830  returns a determination that the virtual machine  820  is one of the unhealthiest virtual machines, then the virtual machine  820  may be taken out of rotation. The virtual machine  820  can be taken out of rotation if the remaining number of virtual machines in the rotation is greater than the safety limit discussed above. If the query  830  returns a determination that the virtual machine  820  is not one of the unhealthiest virtual machines, then the virtual machine  820  is not taken out of rotation. Accordingly, the virtual machine  820  updates the data in the server  810  showing whether a virtual machine is out of rotation or still in rotation. 
       FIGS. 9A-9C  are diagrams  900  illustrating a communication flow during the monitoring of the health of a plurality of virtual machines operating within a group and configured to implement a given application, according to various implementations. In  FIG. 9A , a health check is performed on a virtual machine  920 . The health check is performed every five (5) seconds, in one specific example. The virtual machine sends a message  914  to a server  910  indicating the weighted score of each health probe and/or the total weighted score for the virtual machine  920 . The server  910  calculates a list of sick virtual machines via a processor. The server  910  determines, by forming a queue listing the virtual machines in rank of severity of sickness and by taking into account the safety limit of the minimum number of virtual machines that must remain active, which of the virtual machines can be taken out of circulation. The server  910  performs steps S 110 -S 170  and S 210 -S 250  discussed above with respect to  FIGS. 1 and 2 . Upon receipt of the instructions from the virtual machine  920 , the virtual machines that are designated to be removed from the rotation of the group of virtual machines are then removed from the rotation. 
       FIG. 9B  illustrates a plurality of virtual machines  920 , each providing a weighted health score to a server  910  in a message  914 . This step is similar to the step illustrated as  914  in  FIG. 9A  and corresponds to S 110  illustrated in  FIG. 1 . 
       FIG. 9C  illustrates a plurality of virtual machines  928 , each requesting health information and receiving a return message  918  from a server  910  which keeps updated health data stored therein, the message  918  including a list of unhealthy virtual machines to be taken out of rotation. Based on the message  918 , the sickest one of virtual machines  928  are taken out of rotation, leaving virtual machines  924  in the group of virtual machines. This step is similar to the step illustrated as  918  in  FIG. 9A . If there is more than one unhealthy virtual machine in rotation, the number of machines to be taken out may be determined in view of the safety limit discussed above, because the virtual machines that remain in rotation may not be lower than the safety limit. 
       FIG. 10  is a block diagram  1000  illustrating an example software architecture  1002 , various portions of which may be used in conjunction with various hardware architectures herein described such as, e.g., the groups  320  of virtual machines  330 , which may implement any of the above-described features.  FIG. 10  is a non-limiting example of a software architecture and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software architecture  1002  may execute on hardware such as client devices, native application provider, web servers, server clusters, external services, and other servers. A representative hardware layer  1004  includes a processing unit  1006  and associated executable instructions  1008 . The executable instructions  1008  represent executable instructions of the software architecture  1002 , including implementation of the methods, modules and so forth described herein. 
     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 . 
       FIG. 11  illustrates components of an example machine  1100  configured to read instructions from a machine-readable medium (for example, a machine-readable storage medium) and perform any of the features described herein. In one example, the machine  1100  corresponds to virtual machine  330  shown and described in  FIG. 3 . The example machine  1100  is in a form of a computer system, within which instructions  1116  (for example, in the form of software components) for causing the machine  1100  to perform any of the features described herein may be executed. As such, the instructions  1116  may be used to implement methods or components described herein. The instructions  1116  cause unprogrammed and/or unconfigured machine  1100  to operate as a particular machine configured to carry out the described features. The machine  1100  may be configured to operate as a standalone device or may be coupled (for example, networked) to other machines. In a networked deployment, the machine  1100  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a node in a peer-to-peer or distributed network environment. Machine  1100  may be embodied as, for example, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a gaming and/or entertainment system, a smart phone, a mobile device, a wearable device (for example, a smart watch), and an Internet of Things (IoT) device. Further, although only a single machine  1100  is illustrated, the term “machine” includes a collection of virtual machines that individually or jointly execute the instructions  1116 . 
     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  1112   a  to  1112   n  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  FIG. 11  shows multiple processors, the machine  1100  may include a single processor with a single core, a single processor with multiple cores (for example, a multi-core processor), multiple processors each with a single core, multiple processors each with multiple cores, or any combination thereof. In some examples, the machine  1100  may include multiple processors distributed among multiple machines. 
     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  FIG. 11  are in no way limiting, and other types of components may be included in machine  1100 . The grouping of I/O components  1150  are merely for simplifying this discussion, and the grouping is in no way limiting. In various examples, the I/O components  1150  may include user output components  1152  and user input components  1154 . User output components  1152  may include, for example, display components for displaying information (for example, a liquid crystal display (LCD) or a projector), acoustic components (for example, speakers), haptic components (for example, a vibratory motor or force-feedback device), and/or other signal generators. User input components  1154  may include, for example, alphanumeric input components (for example, a keyboard or a touch screen), pointing components (for example, a mouse device, a touchpad, or another pointing instrument), and/or tactile input components (for example, a physical button or a touch screen that provides location and/or force of touches or touch gestures) configured for receiving various user inputs, such as user commands and/or selections. 
     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  FIGS. 1-9 ) can be implemented using software, firmware, hardware (for example, fixed logic, finite state machines, and/or other circuits), or a combination of these implementations. In the case of a software implementation, program code performs specified tasks when executed on a processor (for example, a CPU or CPUs). The program code can be stored in one or more machine-readable memory devices. The features of the techniques described herein are system-independent, meaning that the techniques may be implemented on a variety of computing systems having a variety of processors. For example, implementations may include an entity (for example, software) that causes hardware to perform operations, e.g., processors functional blocks, and so on. For example, a hardware device may include a machine-readable medium that may be configured to maintain instructions that cause the hardware device, including an operating system executed thereon and associated hardware, to perform operations. Thus, the instructions may function to configure an operating system and associated hardware to perform the operations and thereby configure or otherwise adapt a hardware device to perform functions described above. The instructions may be provided by the machine-readable medium through a variety of different configurations to hardware elements that execute the instructions. 
     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.