Patent Application
20230206135
The field relates generally to information processing systems, and more particularly to user monitoring techniques in such information processing systems.
It is often desirable to determine the sentiment of one or more persons. In an office environment, for example, an organization may monitor employee sentiment in order to maintain a positive work environment (e.g., to improve employee morale, retention and/or productivity).
In one embodiment, a method comprises obtaining audio sensor data and video sensor data from at least one sensor associated with at least one user; applying at least some of the audio sensor data to a first machine learning model that analyzes an audio sentiment of the at least one user to provide at least one audio sentiment score; applying at least some of the video sensor data to a second machine learning model that analyzes a video sentiment of the at least one user to provide at least one video sentiment score; applying the at least one audio sentiment score and the at least one video sentiment score to an ensemble model that determines an aggregate sentiment score based at least in part on the at least one audio sentiment score and the at least one video sentiment score; and initiating one or more automated remedial actions based at least in part on the aggregate sentiment score.
In some embodiments, an output of the ensemble model is provided to at least one feedback agent that updates the first machine learning model and/or the second machine learning model. At least some of the audio sensor data and/or the video sensor data can be preprocessed to satisfy one or more data processing criteria of the first machine learning model and/or the second machine learning model. For example, the preprocessing may comprise: (i) selecting a number of audio features to send to the first machine learning model and/or (ii) detecting one or more human faces in the video sensor data and cropping one or more image frames using the detected one or more human faces.
In at least one embodiment, at least some of the video sensor data can be processed to identify one or more user classes that are excluded from group meetings and/or group activities by evaluating pixel coordinates of at least some of the objects in a given image associated with users to identify the one or more excluded user classes.
Other illustrative embodiments include, without limitation, apparatus, systems, methods and computer program products comprising processor-readable storage media.
Illustrative embodiments of the present disclosure will be described herein with reference to exemplary communication, storage and processing devices. It is to be appreciated, however, that the disclosure is not restricted to use with the particular illustrative configurations shown. One or more embodiments of the disclosure provide methods, apparatus and computer program products for machine learning-based user sentiment prediction using audio and video sentiment analysis.
As noted above, a positive work environment can improve the morale, retention and/or productivity of employees. If employees work in a dreary office setting with unfriendly workers, for example, the employees may not have sufficient confidence to voice their concerns. A lack of proper communication among employees, for example, may indicate an unproductive work environment.
In one or more embodiments, machine learning-based techniques are provided for user sentiment prediction using audio and video sentiment analysis. Sentiment analysis is a method for determining the opinions of individuals or groups to find their attitude towards a topic. Based on a scoring mechanism, sentiment analysis monitors conversations and evaluates language and voice inflections to quantify attitudes, opinions, and emotions related, for example, to a business, product, or topic. A deep learning algorithm-based approach is employed in some embodiments to score the workplace environment using various inputs received from an image-based facial expression recognition model and an audio-based voice sentiment analysis model. The generated sentiment score may be employed to understand the office environment at a more granular level and to transform a stressful office environment into a more relaxed, enjoyable, and open workplace where people can be themselves and collaborate.
Workplace inclusion is an important aspect for an organization. Organizations often struggle, however, to obtain the complete picture within each team and to determine how the overall sentiment is affected. One or more embodiments of the disclosure may process images from at least one video sensor to identify one or more user classes that are excluded from group meetings and/or group activities, as discussed further below. In this manner, an organization may leverage knowledge about users and/or user groups that are excluded from group meetings and/or group activities, in order to reduce workplace discrimination of various forms.
Various aspects of the disclosure recognize that survey-based approaches for evaluating user sentiment demonstrate an inherent bias, due to pressure from upper management, a fear of being excluded from a group based on survey responses and other forms of peer pressure. In addition, it can be shown that such survey-based approaches have failed to reduce key problems, such as workplace discrimination, exclusion from a certain group and the freedom for people to be themselves. Further, survey results are typically obtained at a certain point in time (e.g., quarterly or annually) and are thus not produced in real-time, causing delays in identifying areas requiring improvement.
The user devices 102 may comprise, for example, host devices and/or devices such as mobile telephones, laptop computers, tablet computers, desktop computers or other types of computing devices. Such devices are examples of what are more generally referred to herein as “processing devices.” Some of these processing devices are also generally referred to herein as “computers.” The user devices 102 may comprise a network client that includes networking capabilities such as ethernet, Wi-Fi, etc. When the user devices 102 are implemented as host devices, the host devices may illustratively comprise servers or other types of computers of an enterprise computer system, cloud-based computer system or other arrangement of multiple compute nodes associated with respective users.
For example, the host devices in some embodiments illustratively provide compute services such as execution of one or more applications on behalf of each of one or more users associated with respective ones of the host devices.
In the example of
It is to be appreciated that the term “user” in this context and elsewhere herein is intended to be broadly construed so as to encompass, for example, human, hardware, software or firmware entities, as well as various combinations of such entities. Compute and/or storage services may be provided for users under a Platform-as-a-Service (PaaS) model, an Infrastructure-as-a-Service (IaaS) model, a Storage-as-a-Service (STaaS) model and/or a Function-as-a-Service (FaaS) model, although it is to be appreciated that numerous other cloud infrastructure arrangements could be used. Also, illustrative embodiments can be implemented outside of the cloud infrastructure context, as in the case of a stand-alone computing and storage system implemented within a given enterprise.
The user sentiment evaluation server 105 may be implemented, for example, on the cloud or on the premises of an enterprise or another entity. In some embodiments, the user sentiment evaluation server 105, or portions thereof, may be implemented as part of a storage system or on a host device. As also depicted in
It is to be appreciated that this particular arrangement of modules 112, 114, 116 and 118 illustrated in the user sentiment evaluation server 105 of the
At least portions of modules 112, 114, 116 and 118 may be implemented at least in part in the form of software that is stored in memory and executed by a processor. An exemplary process utilizing modules 112, 114, 116 and 118 of an example user sentiment evaluation server 105 in computer network 100 will be described in more detail with reference to the flow diagrams of, for example,
Additionally, the user sentiment evaluation server 105 can have an associated user database 106 configured to store, for example, assignments of users to work in particular locations (e.g., rooms or at specific workstations), for example, as well as historical user sentiment information, as discussed further below in conjunction with
The user database 106 in the present embodiment is implemented using one or more storage systems associated with the user sentiment evaluation server 105. Such storage systems can comprise any of a variety of different types of storage such as, network-attached storage (NAS), storage area networks (SANs), direct-attached storage (DAS) and distributed DAS, as well as combinations of these and other storage types, including software-defined storage.
The user devices 102 and the user sentiment evaluation server 105 may be implemented on a common processing platform, or on separate processing platforms. The user devices 102 are configured to interact over the network 104 with the user sentiment evaluation server 105.
The term “processing platform” as used herein is intended to be broadly construed so as to encompass, by way of illustration and without limitation, multiple sets of processing devices and associated storage systems that are configured to communicate over one or more networks. For example, distributed implementations of the system 100 are possible, in which certain components of the system reside in one data center in a first geographic location while other components of the system reside in one or more other data centers in one or more other geographic locations that are potentially remote from the first geographic location. Thus, it is possible in some implementations of the system 100 for the user devices 102 and the user sentiment evaluation server 105 to reside in different locations (e.g., data centers). Numerous other distributed implementations of the user devices 102 and the user sentiment evaluation server 105 are possible.
The network 104 is assumed to comprise a portion of a global computer network such as the Internet, although other types of networks can be part of the computer network 100, including a wide area network (WAN), a local area network (LAN), a satellite network, a telephone or cable network, a cellular network, a wireless network such as a Wi-Fi or WiMAX network, or various portions or combinations of these and other types of networks. The computer network 100 in some embodiments therefore comprises combinations of multiple different types of networks, each comprising processing devices configured to communicate using internet protocol (IP) or other related communication protocols.
Also associated with the user devices 102 and/or the user sentiment evaluation server 105 can be one or more input-output devices (not shown), which illustratively comprise keyboards, displays or other types of input-output devices in any combination. Such input-output devices can be used, for example, to support one or more user interfaces to the user sentiment evaluation server 105, as well as to support communication between the user sentiment evaluation server 105 and other related systems and devices not explicitly shown.
The user devices 102 and the user sentiment evaluation server 105 in the
More particularly, user devices 102 and user sentiment evaluation server 105 in this embodiment each can comprise a processor coupled to a memory and a network interface.
The processor illustratively comprises a microprocessor, a microcontroller, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other type of processing circuitry, as well as portions or combinations of such circuitry elements.
The memory illustratively comprises random access memory (RAM), read-only memory (ROM) or other types of memory, in any combination. The memory and other memories disclosed herein may be viewed as examples of what are more generally referred to as “processor-readable storage media” storing executable computer program code or other types of software programs. One or more embodiments include articles of manufacture, such as computer-readable storage media. Examples of an article of manufacture include, without limitation, a storage device such as a storage disk, a storage array or an integrated circuit containing memory, as well as a wide variety of other types of computer program products. The term “article of manufacture” as used herein should be understood to exclude transitory, propagating signals. These and other references to “disks” herein are intended to refer generally to storage devices, including SSDs, and should therefore not be viewed as limited in any way to spinning magnetic media.
The network interface allows the user devices 102 and/or the user sentiment evaluation server 105 to communicate over the network 104 with each other (as well as one or more other networked devices), and illustratively comprises one or more conventional transceivers.
It is to be understood that the particular set of elements shown in
In addition, in the example of
As shown in
In one exemplary implementation, the audio preprocessor 315 slices the audio signal into frames (e.g., each having a duration between 20-40 ms), with each frame overlapping by 10-15 ms. An MFCC format is then generated for the audio frames, and the top N (e.g., N=13) MFCC features can be selected to send to the machine learning model (e.g., the machine learning model of the audio sentiment analyzer 320, discussed below). The frequency of each saved keyword is determined and applied as an input to the machine learning model.
The preprocessed audio signal from the audio preprocessor 315 is then applied to the audio sentiment analyzer 320 that generates an audio sentiment score. In some embodiments, the generated audio sentiment score may comprise a score matrix with a probability of each predefined sentiment category.
The audio sentiment analyzer 320 may be implemented, for example, as a deep learning model. The model of the audio sentiment analyzer 320 can be based on recurrent neural networks (RNNs), such as long short-term memory (LS™) or transformers, such as Bidirectional Representation for Transformers (BERT) for sentiment analysis.
In addition, the sensor data generated by the video sensors 330 is preprocessed by a video preprocessor 335. In some embodiments, facial recognition techniques are employed to match a human face from a digital image or a video frame against a database of faces, typically employed to authenticate users through identity verification services, for example, by pinpointing and measuring facial features from a given image.
Facial recognition models typically process preprocessed input data. For example, the video preprocessor 335 may cut the video signal into frames and store the frames as images. Human faces can be detected in each image using a face detection application programming interface (API). All frames in the video signal can be cropped using the same face location so that human face images are obtained.
The preprocessed video signal is then applied to a video sentiment analyzer 340 that generates a video sentiment score. In some embodiments, the generated video sentiment score may comprise a score matrix with a probability of each predefined sentiment category. The video sentiment analyzer 340 may be implemented as a deep learning model, such as a Convolutional Neural Network (CNN) for the processing of images as well as an RNN layer due to the sequential nature of the input.
In some embodiments, the machine learning models associated with the audio sentiment analyzer 320 and/or the video sentiment analyzer 340 may use training data based at least in part on the following training datasets: (i) CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset for multimodal sentiment analysis and emotion recognition; and/or (ii) Interactive Emotional Dyadic Motion Capture (IEMOCAP) database comprising facial data with anchor points to aid video sentiment analysis.
The audio sentiment score and the video sentiment score generated by the audio sentiment analyzer 320 and the video sentiment analyzer 340, respectively, are applied to an ensemble model 350. In some embodiments, the ensemble model 350 is trained using the score matrices generated by the audio sentiment analyzer 320 and the video sentiment analyzer 340. The ensemble model 350 generates an aggregate sentiment score according to the applied audio sentiment score and video sentiment score.
In at least some embodiments, the ensemble model 350 comprises a deep learning model with inputs from the models of the audio sentiment analyzer 320 and the video sentiment analyzer 340 stacked over one another, and employs a stacking approach to determine the aggregate sentiment score. The stacking approach determines how to best combine the applied audio sentiment score prediction and video sentiment score prediction from the machine learning models of the audio sentiment analyzer 320 and the video sentiment analyzer 340, respectively. Among other benefits, a stacking approach can harness the capabilities of a range of well-performing models on a classification or regression task and make predictions that have better performance than any single model in the ensemble.
The exemplary ensemble model 350 employs a linear model with two hyperparameters α and β, using the following equation:
score=α*s1+β*s2+c,
where:
s1: score matrix from model of audio sentiment analyzer 320,
α: weight matrix given to s1 by ensemble model 350,
s2: score matrix from model of video sentiment analyzer 340,
β: weight matrix given to s2 by ensemble model 350, and
c: constant.
The preprocessed video signals from the video preprocessor 335 are also applied to an exclusion analyzer 345 that generates an exclusionary score. In one or more embodiments, the exclusion analyzer 345 comprises a computer vision model that will identify the exclusion of a section of the workforce from social activities or informal team gatherings like team huddles using a CNN model, such as a region-based CNN (R-CNN) model to perform object detection. The R-CNN model of the exclusion analyzer 345 will consume the preprocessed video signals from the video sensors, such as static cameras.
In some embodiments, the R-CNN model comprises multiple CNN layers followed by fully connected layers. A sigmoid layer comprising a sigmoid function can be attached in some embodiments at the end of the fully connected layers to detect if the weighted preprocessed video signals from the video sensors demonstrates an exclusion of a section of the workforce from social activities or informal team gatherings. The sigmoid function, f(t), produces similar results to a step function in that the output is typically between 0 and 1. The sigmoid function can be expressed in some embodiments as follows:
The R-CNN model of the exclusion analyzer 345 will be trained, in some embodiments, on annotated images to recognize people in a scene. The trained mask R-CNN model will be used as a base model. Transfer learning can be applied to the base model to specifically focus on people in the scene. Samples from a corporate environment (such as desks, cubicles, meeting rooms, open plan and more) will also be obtained as an additional source of training data. Each object identified in the preprocessed video images will be annotated in some embodiments with a bounding box to denote the presence of the corresponding object in an image. For example, people in a sample image can be annotated using a bounding box around each person. The annotated and preprocessed video images are then passed into the R-CNN model of the exclusion analyzer 345 for training purposes. Generally, the bounding boxes are used to identify one or more individuals that are separated from a group of persons. In some embodiments, facial recognition can be used to recognize specific individuals and specific members of a particular grouping of employees (such as a department or team). In addition, human resource records and scheduling data can be processed to identify a time and location of particular group meeting.
The exclusion analyzer 345 further comprises an inference engine that will process each frame from the R-CNN model with the preprocessed video images annotated with the bounding boxes around each object and locations of people in each image. For purposes of illustration, assume that there are two groups of people in a scene. A first group comprises multiple individuals and the second group comprises a single person. With the result from the R-CNN model, pixel coordinates can be used to identify the distance between the different bounding boxes. In this example, the single person in the second grouping would be deemed excluded from the first group.
In some embodiments, properties associated with each camera stream can be processed to evaluate the amount of distance between groups in order to identify separate groups of people. This may be important in a corporate setting, for example, where some desks are closely located making it harder to detect exclusion.
As shown in
In the example of
In the embodiment shown in
In one or more embodiments, the IoT control module 370 is implemented as a central agent that selects one or more automated remedial actions to perform using other IOT devices (not shown) on the network. The IoT control module 370 receives the output of the final sentiment score and determines a course of action (for example, in accordance with a predefined policy) in order to improve the workplace environment for one or more employees, when suggested by the final sentiment score. For example, remedial action may be appropriate when there has been a sudden drop in the final sentiment score that suggests that there is tension in the workplace environment and employees are feeling uneasy.
In at least one exemplary implementation, the IoT control module 370 can initiate one or more of the following the actions:
The action manager 380 of
Accordingly, while there are two neurons/nodes shown in the hidden layer 420 in the
In the example neural network-based sentiment analyzer 400 depicted in
The particular processing operations and other network functionality described in conjunction with the flow diagram of
In some embodiments, the process 500 may also comprise providing an output of the ensemble model to at least one feedback agent that updates the first machine learning model and/or the second machine learning model. In addition, at least some of the audio sensor data and/or the video sensor data can be preprocessed to satisfy one or more data processing criteria of the first machine learning model and/or the second machine learning model. For example, the preprocessing of at least some of the audio sensor data and/or the video sensor may comprise: (i) selecting a number audio features to send to the first machine learning model and/or (ii) detecting one or more human faces in the video sensor data and cropping one or more image frames using the detected one or more human faces.
In another example, the one or more automated remedial actions performed by the process 500 may comprise: generating a notification, adjusting a temperature of a workspace area associated with the at least one user, adjusting a lighting of the workspace area associated with the at least one user, adjusting one or more of a volume and a content of music presented in the workspace area associated with the at least one user, and/or adjusting one or more scents provided in the workspace area associated with the at least one user.
The at least one audio sentiment score and/or the at least one video sentiment score of the process 500 may comprise a score matrix indicating a probability score for each of a plurality of sentiment categories. In addition, the process 500 may also comprise processing at least some of the video sensor data to identify one or more user classes that are excluded from group meetings and/or group activities by evaluating pixel coordinates of at least some of the objects in a given image associated with users to identify the one or more user classes that are excluded from the group meetings and/or the group activities
One or more aspects of the disclosure recognize that workplace discrimination is an important issue that can prevent an organization from fully utilizing its human resources. Workplace discrimination has been categorized as a big hurdle for any organization that is expanding in various regions or parts of the world. Organizations often take a number of measures to counter workplace discrimination, such as providing proper channels to communicate and share any grievances. Nonetheless, a section of the workforce may be excluded from a group in a manner that is not very transparent, such as leaving out a person belonging to a minority group from group activities. The excluded group (or individual) may not report the exclusion because of, for example, peer pressure, manager pressure, or fear of getting terminated.
In some implementations, a portal can be provided that allows an interested person to review the generated sentiment data in accordance with a selected granularity level (e.g., floor, building, city, or country). In this manner, personal can relate changes in sentiment to recent policy changes, for example.
To address privacy concerns, one or more embodiments of the disclosure may not track individuals. For example, in considering the privacy of the workforce, one or more of the following controls may optionally be employed: (i) audio sensors may only sense specific approved keywords (e.g., already stored in the system), such as “good morning,” and “happy,” and do not record the general conversation; (ii) the audio sentiment analysis may not store the data corresponding to any individual employee, rather the frequency of the keywords is stored and used to determine the sentiment of any team workspace; (iii) the video sentiment analysis may use facial recognition to generate real-time sentiment but not persist the data (thus, only the sentiment data is stored and not the actual captured video); (iv) the audio and video sensors may only be placed in a common working space and not in a personal space, such as a relaxing room or locker room; and (v) the models are used to determine the sentiment of a team working environment or defined space.
The disclosed machine learning-based techniques for user sentiment prediction can be employed, for example, to (i) monitor a workplace environment in real-time to ensure that employees are getting an opportunity to work in a positive environment and to reach their full potential, (ii) ensure that the workplace demonstrates diversity and to reduce workplace discrimination, (iii) reduce dependence on employee surveys where employees can give false feedback under pressure from a supervisor or another colleague, and (iv) analyze whether the policies implemented to improve the workplace environment are working in the day-to-day office environment.
One or more embodiments of the disclosure provide improved methods, apparatus and computer program products for predicting user sentiment using audio and video sentiment analysis. The foregoing applications and associated embodiments should be considered as illustrative only, and numerous other embodiments can be configured using the techniques disclosed herein, in a wide variety of different applications.
It should also be understood that the disclosed user sentiment prediction techniques, as described herein, can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device such as a computer. As mentioned previously, a memory or other storage device having such program code embodied therein is an example of what is more generally referred to herein as a “computer program product.”
The disclosed machine learning-based techniques for user sentiment prediction using audio and video sentiment analysis may be implemented using one or more processing platforms. One or more of the processing modules or other components may therefore each run on a computer, storage device or other processing platform element. A given such element may be viewed as an example of what is more generally referred to herein as a “processing device.”
As noted above, illustrative embodiments disclosed herein can provide a number of significant advantages relative to conventional arrangements. It is to be appreciated that the particular advantages described above and elsewhere herein are associated with particular illustrative embodiments and need not be present in other embodiments. Also, the particular types of information processing system features and functionality as illustrated and described herein are exemplary only, and numerous other arrangements may be used in other embodiments.
In these and other embodiments, compute services can be offered to cloud infrastructure tenants or other system users as a PaaS offering, although numerous alternative arrangements are possible.
Some illustrative embodiments of a processing platform that may be used to implement at least a portion of an information processing system comprise cloud infrastructure including virtual machines implemented using a hypervisor that runs on physical infrastructure. The cloud infrastructure further comprises sets of applications running on respective ones of the virtual machines under the control of the hypervisor. It is also possible to use multiple hypervisors each providing a set of virtual machines using at least one underlying physical machine. Different sets of virtual machines provided by one or more hypervisors may be utilized in configuring multiple instances of various components of the system.
These and other types of cloud infrastructure can be used to provide what is also referred to herein as a multi-tenant environment. One or more system components such as a cloud-based user sentiment prediction engine, or portions thereof, are illustratively implemented for use by tenants of such a multi-tenant environment.
Cloud infrastructure as disclosed herein can include cloud-based systems such as AWS, GCP and Microsoft Azure. Virtual machines provided in such systems can be used to implement at least portions of a cloud-based user sentiment prediction platform in illustrative embodiments. The cloud-based systems can include object stores such as Amazon S3, GCP Cloud Storage, and Microsoft Azure Blob Storage.
In some embodiments, the cloud infrastructure additionally or alternatively comprises a plurality of containers implemented using container host devices. For example, a given container of cloud infrastructure illustratively comprises a Docker container or other type of Linux Container (LXC). The containers may run on virtual machines in a multi-tenant environment, although other arrangements are possible. The containers may be utilized to implement a variety of different types of functionality within the storage devices. For example, containers can be used to implement respective processing devices providing compute services of a cloud-based system. Again, containers may be used in combination with other virtualization infrastructure such as virtual machines implemented using a hypervisor.
Illustrative embodiments of processing platforms will now be described in greater detail with reference to
The cloud infrastructure 600 further comprises sets of applications 610-1, 610-2, . . . 610-L running on respective ones of the VMs/container sets 602-1, 602-2, . . . 602-L under the control of the virtualization infrastructure 604. The VMs/container sets 602 may comprise respective VMs, respective sets of one or more containers, or respective sets of one or more containers running in VMs.
In some implementations of the
An example of a hypervisor platform that may be used to implement a hypervisor within the virtualization infrastructure 604 is the VMware® vSphere® which may have an associated virtual infrastructure management system such as the VMware® vCenter™. The underlying physical machines may comprise one or more distributed processing platforms that include one or more storage systems.
In other implementations of the
As is apparent from the above, one or more of the processing modules or other components of system 100 may each run on a computer, server, storage device or other processing platform element. A given such element may be viewed as an example of what is more generally referred to herein as a “processing device.” The cloud infrastructure 600 shown in
The processing platform 700 in this embodiment comprises at least a portion of the given system and includes a plurality of processing devices, denoted 702-1, 702-2, 702-3, . . . 702-K, which communicate with one another over a network 704. The network 704 may comprise any type of network, such as a WAN, a LAN, a satellite network, a telephone or cable network, a cellular network, a wireless network such as WiFi or WiMAX, or various portions or combinations of these and other types of networks.
The processing device 702-1 in the processing platform 700 comprises a processor 710 coupled to a memory 712. The processor 710 may comprise a microprocessor, a microcontroller, an ASIC, an FPGA or other type of processing circuitry, as well as portions or combinations of such circuitry elements, and the memory 712, which may be viewed as an example of a “processor-readable storage media” storing executable program code of one or more software programs.
Articles of manufacture comprising such processor-readable storage media are considered illustrative embodiments. A given such article of manufacture may comprise, for example, a storage array, a storage disk or an integrated circuit containing RAM, ROM or other electronic memory, or any of a wide variety of other types of computer program products. The term “article of manufacture” as used herein should be understood to exclude transitory, propagating signals. Numerous other types of computer program products comprising processor-readable storage media can be used.
Also included in the processing device 702-1 is network interface circuitry 714, which is used to interface the processing device with the network 704 and other system components, and may comprise conventional transceivers.
The other processing devices 702 of the processing platform 700 are assumed to be configured in a manner similar to that shown for processing device 702-1 in the figure.
Again, the particular processing platform 700 shown in the figure is presented by way of example only, and the given system may include additional or alternative processing platforms, as well as numerous distinct processing platforms in any combination, with each such platform comprising one or more computers, storage devices or other processing devices.
Multiple elements of an information processing system may be collectively implemented on a common processing platform of the type shown in
For example, other processing platforms used to implement illustrative embodiments can comprise different types of virtualization infrastructure, in place of or in addition to virtualization infrastructure comprising virtual machines. Such virtualization infrastructure illustratively includes container-based virtualization infrastructure configured to provide Docker containers or other types of LXCs.
As another example, portions of a given processing platform in some embodiments can comprise converged infrastructure.
It should therefore be understood that in other embodiments different arrangements of additional or alternative elements may be used. At least a subset of these elements may be collectively implemented on a common processing platform, or each such element may be implemented on a separate processing platform.
Also, numerous other arrangements of computers, servers, storage devices or other components are possible in the information processing system. Such components can communicate with other elements of the information processing system over any type of network or other communication media.
As indicated previously, components of an information processing system as disclosed herein can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device. For example, at least portions of the functionality shown in one or more of the figures are illustratively implemented in the form of software running on one or more processing devices.
It should again be emphasized that the above-described embodiments are presented for purposes of illustration only. Many variations and other alternative embodiments may be used. For example, the disclosed techniques are applicable to a wide variety of other types of information processing systems. Also, the particular configurations of system and device elements and associated processing operations illustratively shown in the drawings can be varied in other embodiments. Moreover, the various assumptions made above in the course of describing the illustrative embodiments should also be viewed as exemplary rather than as requirements or limitations of the disclosure. Numerous other alternative embodiments within the scope of the appended claims will be readily apparent to those skilled in the art.
