Patent Application
20220413931
The present disclosure relates generally to resource management for computing devices. More particularly, aspects of this disclosure relate to a system that provides an intelligent mechanism for managing resources across multiple servers.
Servers are employed in large numbers for high demand applications, such as network based systems or data centers. The emergence of the cloud for computing applications has increased the demand for data centers. Data centers have numerous servers that store data and run applications accessed by remotely connected, computer device users. A typical data center has physical chassis rack structures with attendant power and communication connections. Each rack may hold multiple computing servers and storage servers that are networked together.
The servers in a data center facilitate many services for businesses, including executing applications, providing virtualization services, and facilitating Internet commerce. Servers typically have a baseboard management controller (BMC) that manages internal operations and handles network communications with a central management station in a data center. Different networks may be used for exchanging data between servers and exchanging operational data on the operational status of the server through a management network.
Data center management is an important but complex job for daily operations. In traditional data center management methods, administrators arrange the hardware devices such as servers for a specific workload purpose. Urgent service requirements usually make the efficient scheduling and allocation of workloads difficult to be implemented. Thus, traditional data center management methods always allocate the maximum resources for peak service requirements. However, in this case, the resource utilization rate is always low because of idle resources during non-peak times and the data center fails to effectively utilize resources. Other use cases involving different resource utilization rates also result in low resource utilization rate. For example, in a 5G communication system the resource utilization in a city area and a residential area are supposed to be different. In a city area, the resource utilization in working hours is always high, which is a different situation from that of in residential area. Moreover, in a commercial area, the resource preparation is designed specifically for working hours for to facilitate user needs while for non-working hours, the resources are usually idle in the commercial area.
Service capacity can be easily scaled in or out using an intelligent engine according to current CPU networking or memory usage. With an intelligent engine, the management software (e.g., the Kubernetes and OpenStack platform) has been used to monitor the environment and automatically control the service scale. For some stateful services, launching a new service entity is time-consuming. Further, such new service entities may not be prepared to accept requests from a client immediately. For some connection-oriented services, such as gaming, VPN, and 5G connections, frequent scaling in and scaling out of services leads to the difficulty of implementing session management and decreases the quality of service. This results in service reconnection and service handover when the service is scaled in. When service is scaled out, newly-added services can be executed in a new computing system (usually a virtual machine (VM), container, or bare metal system). For scaling in, the services are gathered together to free computing system resources (usually a VM, a container, or a bare metal system). For example, a system may have three resource computing systems. When the third computing system needs to be freed up, the service running on the third computing system resource should be migrated to the other two computing system resources. The concurrent jobs on the third computing system resource will therefore encounter service/job handover issues causing reconnection. Often, data center customers will have service requests that require an increase in computing resources. In order to fulfill urgent service requirements from the customer side, automation of resource allocation is considered to be a focal feature in daily operation of servers in a data center.
Thus, there is a need for a system that allows data centers to dynamically change resource allocation to hardware in real time. There is a need for a system that allows pre-allocation of resources based on historical record, predict future requirements, and train a model to fulfill the pattern from the monitored data. There is also a need for a system that employs a policy engine that determines proper configurations based on at least one model for resource allocation in a computer system.
One disclosed example is a system for distributing resources in a computing system. The resources include hardware components in a hardware pool, a management infrastructure, and an application. A telemetry system is coupled to the resources to collect operational data from the operation of the resources. A data analytics system is coupled to the telemetry subsystem to predict a future operational data value based on the collected operational data. A policy engine is coupled to the data analytics system to determine a configuration change action for the allocation of the resources in response to the predicted future operational data value.
A further implementation of the example system is an embodiment where the data analytics system determines the future operational data value based on a machine learning system. The operational data collected by the telemetry subsystem trains the machine learning system. Another implementation is where the machine learning system produces multiple models. Each of the multiple models predict a different scenario of the future operational data value. Another implementation is where the data analytics system selects one of the multiple models to determine the resource allocation. Another implementation is where the policy engine includes a template to translate the predicted future operational data value from the data analytics system into the resource allocation. Another implementation is where the configurations include a hardware management interface for the hardware component, a management API for the infrastructure and an application API for the application. Another implementation is where the hardware component is one of a group of processors, management controllers, storage devices, and network interface cards. Another implementation is where the resources are directed toward the execution of the application. Another implementation is where hardware components are deployed in computer servers organized in racks. Another implementation is where the future operational data value is a computational requirement at a predetermined time.
Another disclosed example is a method of allocating resources in a computing system. The resources include at least one of a hardware component, a management infrastructure, or an application. Operational data is collected from the operation of the resources via a telemetry system. A future operational data value is predicted based on the collected operational data via a data analytics system. A configuration to allocate the resources is determined in response to the predicted future operational data value.
Another implementation of the example method includes training a machine learning system from the collected data. The data analytics system determines the future operational data value from the machine learning system. Another implementation is where the method includes producing multiple models from the machine learning system. Each of the multiple models predict a different scenario of the future operational data value. Another implementation is where the data analytics system selects one of the multiple models to determine the resource allocation. Another implementation is where the policy engine includes a template to translate the predicted future operational data value from the data analytics system into the resource allocation. Another implementation is where the configurations include a hardware management interface for the hardware component, a management API for the infrastructure, and an application API for the application. Another implementation is where the hardware component is one of a group of processors, management controllers, storage devices, and network interface cards. Another implementation is where the resources are directed toward the execution of the application. Another implementation is where the hardware components are deployed in computer servers organized in racks. Another implementation is where the future operational data value is a computational requirement at a predetermined time.
The above summary is not intended to represent each embodiment or every aspect of the present disclosure. Rather, the foregoing summary merely provides an example of some of the novel aspects and features set forth herein. The above features and advantages, and other features and advantages of the present disclosure, will be readily apparent from the following detailed description of representative embodiments and modes for carrying out the present invention, when taken in connection with the accompanying drawings and the appended claims.
The disclosure will be better understood from the following description of exemplary embodiments together with reference to the accompanying drawings, in which:
The present disclosure is susceptible to various modifications and alternative forms. Some representative embodiments have been shown by way of example in the drawings and will be described in detail herein. It should be understood, however, that the invention is not intended to be limited to the particular forms disclosed. Rather, the disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.
The present inventions can be embodied in many different forms. Representative embodiments are shown in the drawings, and will herein be described in detail. The present disclosure is an example or illustration of the principles of the present disclosure, and is not intended to limit the broad aspects of the disclosure to the embodiments illustrated. To that extent, elements and limitations that are disclosed, for example, in the Abstract, Summary, and Detailed Description sections, but not explicitly set forth in the claims, should not be incorporated into the claims, singly or collectively, by implication, inference, or otherwise. For purposes of the present detailed description, unless specifically disclaimed, the singular includes the plural and vice versa; and the word “including” means “including without limitation.” Moreover, words of approximation, such as “about,” “almost,” “substantially,” “approximately,” and the like, can be used herein to mean “at,” “near,” or “nearly at,” or “within 3-5% of,” or “within acceptable manufacturing tolerances,” or any logical combination thereof, for example.
The examples disclosed herein include a system and method to allow a data center to dynamically change the hardware resource allocation to an infrastructure service (when resources are insufficient) or a virtualized resource to applications (when the resources are) in real time. The intelligent resource management system can be adopted to pre-allocate hardware resources based on the historical record of an operational data value such as bandwidth required, predict future requirements, and train a model to fulfill the pattern from the monitored data. The mechanism implements reactive scaling with low response time to expand the service capacity, and the service can be properly configured to avoid bursts of requests from servers. The reactive scaling is performed based on predictions according to analysis of historical data of operational data of the system. The current data is mapped and the future operational data value is predicted to implement the scaling process. For example, a subsequent 10-minute resource utilization need may be predicted, and the resources may be pre-allocated for future consumption for that period of time. The service scale can be proactively expanded or shrunk in relation to the available resources. At the same time, only necessary resources are allocated to run services on the servers. The energy consumption of the managed servers also become more efficient based on the intelligent resource allocation as hardware resources are more efficiently deployed or deactivated as needed.
A remote management station 140 is coupled to a management network 142. The remote management station runs management applications to monitor and control the servers 130 through the management network 142. A data network 144 that is managed through the switches 120 allows the exchange of data between the servers 130 in a rack, such as the rack 110a, and the servers in other racks.
The servers 130 each include a baseboard management controller (BMC). The BMC includes a network interface card or network interface controller that is coupled to the management network 142. The servers 130 all include hardware components that may perform functions such as storage, computing, and switching. For example, the hardware components may be processors, memory devices, PCIe device slots, etc. The BMC in this example monitors the hardware components in the respective server and allows collection of operational and usage data through the management network 142.
The applications 214 and 216 may be applications such as eMarket or a 5G traffic forwarding system that are deployed on the K8s/OpenStack platform on servers such as the application servers 134a-134n (
A telemetry system 220 is coupled to a data analytics system 222. The data analytics system 222 produces a trained model 230 based on operational data collected by the telemetry system 220 in relation to hardware resource allocation in the computer system 100. Examples of operational data include throughput, packet count, session count values, and latency. Error count data in the form of discarded packets, over lengthy packets, and error packets may also be operational data. The trained model 230 predicts a future operational data value such as the necessary processing bandwidth for the hardware resources. The trained model 230 is loaded into an orchestration system 240 that configures a policy engine 242 that allows for allocation or reallocation of resources from the infrastructure hardware pool 210 to meet the future operational data value.
The infrastructure management software 212 in this example may include Network Functions Virtualization (NFVI) infrastructure software 250 that includes network access and service execution environment routines for the hardware resources in the hardware pools 210. The NFVI infrastructure software 250 in this example includes an Open Stack Platform (OSP) architecture 252 such as an OpenStack Commercial Version architecture or an OpenShift Container Platform (OCP) architecture 254 such as the K8s Enterprise Version. The NFVI infrastructure software 250 is a platform the partition the hardware and software layers. The hardware resources may include a server, a virtual machine (VM), a container, or an application for each of the architectures.
The container environment 354 include a container management interface 380 that includes a master node 382 and a worker node 384. The container management interface 380 builds master and worker nodes such as the master node 382 and the worker node 384. The building of master and worker nodes is a mechanism for OpenShift to facilitate container management for computing, network and storage resources. One example is a 5G Core where registration, account management, and traffic tiering are different functions tackled by CNFs. Each of the functions is executed by one or more containers. The master node 382 performs a manger role used to manage OpenShift while the worker node 384 has the role of running and monitoring containers. The container environment 354 also include physical hardware resources including a server 392, storage hardware 394, and network hardware 396.
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The infrastructure management software 212 includes components of an infrastructure controller interface 410, a network functions virtualization infrastructure (NFVI) 412. The example applications 214 and 216 in
The infrastructure controller interface 410 is an interface or library to manage hardware, OpenStack, K8s, and CNF/VNF that includes a virtual machine manager 430 and an infrastructure manager 432. The virtual machine manager 430 manages the virtual machines in the computer system 100 in
The VNF group 414 includes a broadband network gateway 434 and an evolved packet core 436. The broadband network gateway 434 is an application for facilitating broadband communication. The evolved packet core 436 is the core network for either 4G or LTE communication. The service VNF group 416 includes virtual network function (VNF) modules 438. The VNF modules 438 are applications implemented on the platform that are deployed based on requests by users. For example, a user may require a streaming data server and a Content Delivery Network (CDN) service to provide good user experience with low latency.
The network functions virtualization infrastructure (NFVI) 412 includes the networking hardware and software supporting and connecting virtual network functions. The NFVI 412 includes OpenStack components 440a, 440b, 440c, and 440d such as the Nova, Neutron, Cinder, and Keystone components. The NFVI 412 includes a hypervisor 442 that is coupled to hardware components 444 such as servers, switches, NVMe chassis, FPGA chassis, and SSD/HDD chassis. The hardware components 444 are part of the infrastructure hardware pool 210. The hypervisor 442 supervises virtual machine creation and operation in the hardware resources.
The telemetry infrastructure 418 generally collects operational data from the management software 212 in
The VNF plug-ins 450 receives specific data, such as cache hit rates and buffered data size for a CDN application, from each of the VNF modules 438. The EPC plug-in 452 receives network traffic data from the evolved packet core 436. The BNG plug-in 454 receives broadband operational data from the broadband network gateway 434. The OpenStack plug-in 456 receives maximum virtual resource, occupied virtual resource, and running instance status data from the OpenStack components 440a-440d. In this example, the resources are components such as CPUs, memory, storage devices such as HDDs, or network interfaces. The hypervisor plug-in 458 receives operational data relating to virtual machines operated by the hypervisor 442. The hardware plug-in 460 receives operational data from the hardware components 444. Thus, the CPU may supply compute utilization data, the network interface and SSD/HHD chassis may supply port status and port utilization data, the NVMe chassis may supply NVMe status and I/O throughput, and the FPGA chassis may supply the number of used FPGAs and chip temperature.
The orchestrator lite system 422 includes a service orchestrator 470, a data analysis engine 472 and a VNF Event stream (VES) module 474. In this example, the service orchestrator 470 is composed of the orchestration system 240 and the policy engine 242 in
The OpenStack module 424 includes a time series database 480 such as Gnocchi and an alarming service 482 such Aodh. The time series database 480 stores time-series data such as CPU utilization. The alarming service 482 is used by the OpenStack module 424 to monitor stored data and send alerts to an administrator. A telemetry module, time series database, and alarm system may be provided for obtaining and storing real-time data, storing it and sending abnormal information to an administrator.
The dashboard/alert module 420 includes a Prometheus time series database 490, a time series metrics user interface 492 (Grafana) and an alert manager 494. The database 490 receives the time based operational data relating to the system resources from the data interface module 462. The user interface 492 allows the generation of an interface display that shows selected resource metrics. The alert manager 494 allows monitoring of metrics and alert notification.
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For example, data processing such as a traffic prediction model developed by vendor such as Quanta Cloud Technology (QCT) of Taipei, Taiwan may be carried out by any one or more of supervised machine learning, deep learning, a convolutional neural network, and a recurrent neural network. In addition to descriptive and predictive supervised machine learning with hand-crafted features, it is possible to implement deep learning on the machine-learning engine. In addition to descriptive and predictive supervised machine learning with hand-crafted features, it is possible to implement deep learning on the machine-learning engine. This typically relies on a larger amount of scored (labeled) data (such as many hundreds of data points collected by the telemetry system 220 from the infrastructure management software 212) for normal and abnormal conditions. This approach may implement many interconnected layers of neurons to form a neural network (“deeper” than a simple neural network), such that more and more complex features are “learned” by each layer. Machine learning can use many more variables than hand-crafted features or simple decision trees.
The inputs to the machine learning network may include the collected statistics relating to operational data for resource use, and the outputs may include future prediction statistics for operational data values. The resulting trained model 230 is the published model with inferencing abilities for resource allocation across the hardware pools 210. The orchestration system 240 manages the service/infrastructure orchestration, overlay network topology, and policies for the hardware system by deploying resources from the hardware pool 210. Moreover, the orchestration system 240 receives the events from the trained model 230. The orchestration system 240 includes a policy engine 242 that determines a predefined action to deal with the predicted operational data value determined from received events. For example, if the event leads to a prediction that a service has run out of resources or will run out of resources shortly due to an application such as the applications 214 or 216 going on line, the policy engine 242 will pick up one of the predefined scale-out actions to launch a new application and configure a new network configuration to bind the application together and share the loading among the pool of hardware resources. If applications are no longer needed, the policy engine 242 picks up one of the predefined scale-in actions.
The process of preparing the orchestration system 240 includes collecting data, determining interferences from the data, training the model, publishing the model, and deploying the model. A data collector in the telemetry system 220 collects operational data such as performance metrics from hardware in the hardware pool 210, the infrastructure management software 212, and the applications 214 and 216. The performance metrics may include request counts and network packet counts. The applications check the requests sent from or to user equipment and an orchestrator is implemented, depending on how many requests needs to be processed or handled. For example, in a 5G communication system, the requests relate to how many 5G Core instances are required for scaling. The collected performance metrics are stored in the telemetry system 220. The performance metrics are sent to the trained model 230. The trained model 230 produces inferences relating to resource allocation in the future and thus outputs a predictive operational data value such as the concurrent request bandwidth or the network packet counts at a predetermined time in the future. Simultaneously, the performance metrics are sent to the data analytics system 222 for model training purposes.
In this example, the data analytics system 222 periodically publishes the latest trained model. The trained model is incrementally improved to enhance the model based on latest metrics so as to ensure increased accuracy. The trained model 230 periodically sends the predictive operational data values to the orchestration system 240 according to a pre-defined schedule. The length of the time period for a predictive value is related to the amount of data. For example, predictions of operational data values in a future ten minute period are generally more precise than the prediction of resources for a future 20 minute period. In this example, the predictions are generally sufficiently accurate within a 10-minute interval, and thus the trained model periodically (every 10 minutes) sends the predictive values. Since the data is not always in a steady status, the model 230 outputs both peak hours and idle hours in terms of resources utilization.
The data length and data size influence the accuracy of the model 230. As the data size increases, the accuracy rate is relatively high. With the same model, the accuracy rate for predicting operational data values for shorter times in the future is higher than that for predicting operational data values for longer times in the future. The peak and idle hours refer to a data pattern. The pattern is changed according to the type of data resource, such as resource management for a residential area as opposed to a commercial area. Data prediction over a small interval can also result in a more accurate prediction. For example, the data pattern on a graph may show a relatively smooth curve for 10-second interval of collected data, compared with that of the data pattern on a graph for a 5 minute interval of collected data. The accuracy rate for the 10 second data collection is relatively high as well, compared to that of for the 5 minute data collection.
After receiving the predicted future operational data value from the trained model 230, the orchestration system 240 determines configuration changes of resource allocations based on the output of the policy engine 242. The orchestration system 240 sends the resulting configuration change commands to the hardware in the hardware pools 210, infrastructure management software 212, or applications 214 and 216, to change the configuration of different hardware resources and thereby deploy the resources more efficiently according to the results of the trained model 230.
The same collected data can be used for training the different models so as to generate several different trained models. In this example, the output of the models is the throughput of a 10-minute predictive matrix including throughput and packet count. Alternatively, after a model is established as sufficiently robust, the data analytics module 222 will continue to train the established model with received data to further refine the model. The trained models are periodically published to a trained model marketplace 620 that includes multiple different models. Established models are stored in a trained model marketplace 620.
For example, when any inference tasks are required, a data inference engine 630 selects a model from the model marketplace 620 and then inputs the data into the selected model to generate the interference. For example, if it is desired to determine the traffic growth trend in the future for the system 100, the inference engine 630 can check out a related model from the model marketplace 620 and then input the statistical data and timestamp. The inference engine 630 returns the predicted value of a designated time period according to the output of the selected model for traffic growth. The time interval can be any suitable period such as 10 seconds, 10 minutes, or 24 hours. The prediction is output in the form of a matrix of throughput, packet count, and session count values.
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The infrastructure controller 670 contains several kinds of applications, the infrastructure management software 212, and a set of hardware configuration abstraction APIs. When receiving the event, the infrastructure controller 620 translates related commands. The infrastructure controller 620 then sends the translated commands to the physical hardware in the hardware pools 210, the infrastructure management software 212, or the applications 214 and 216 through an appropriate communication protocol. For example, if the target of the translated commands is a hardware component, hardware management interfaces like Redfish and IPMI may be used to send the management commands. If the target of the translated commands is the infrastructure management software 212, the predefined management API of the infrastructure management software 212 can be called to execute the specific management command. If the target of the translated commands is an application, the application API can be executed to run the application-specific configuration changes.
The routine is implemented when the infrastructure controller 620 in
The policy engine 242 receives a predicted metric (such as 100G or 200G traffic after 10 minutes) required from the selected model (816). The policy engine 242 then determines the actions required to meet the predicted metric from the predictions and sends the actions to the orchestrator 240 (818). The orchestrator 240 then maps pre-selected configurations to the predicted resource requirements by accessing the appropriate service templates (820). The resulting configurations are applied to the resources of the system (822) In this example the actions are high-level commends. The actions include scaling in and scaling out resources. In this example, a new server in the hardware pool may be made available and Kubernetes or OpenStack may be deployed. A VM or a container may then be deployed and launched in the newly allocated new server hardware.
To enable user interaction with the computing device 900, an input device 920 is provided as an input mechanism. The input device 920 can comprise a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, and so forth. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the system 900. In this example, an output device 922 is also provided. The communications interface 924 can govern and manage the user input and system output.
Storage device 912 can be a non-volatile memory to store data that is accessible by a computer. The storage device 912 can be magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs) 908, read only memory (ROM) 906, and hybrids thereof.
The controller 910 can be a specialized microcontroller or processor on the system 900, such as a BMC (baseboard management controller). In some cases, the controller 910 can be part of an Intelligent Platform Management Interface (IPMI). Moreover, in some cases, the controller 910 can be embedded on a motherboard or main circuit board of the system 900. The controller 910 can manage the interface between system management software and platform hardware. The controller 910 can also communicate with various system devices and components (internal and/or external), such as controllers or peripheral components, as further described below.
The controller 910 can generate specific responses to notifications, alerts, and/or events, and communicate with remote devices or components (e.g., electronic mail message, network message, etc.) to generate an instruction or command for automatic hardware recovery procedures, etc. An administrator can also remotely communicate with the controller 910 to initiate or conduct specific hardware recovery procedures or operations, as further described below.
The controller 910 can also include a system event log controller and/or storage for managing and maintaining events, alerts, and notifications received by the controller 910. For example, the controller 910 or a system event log controller can receive alerts or notifications from one or more devices and components, and maintain the alerts or notifications in a system event log storage component.
Flash memory 932 can be an electronic non-volatile computer storage medium or chip that can be used by the system 900 for storage and/or data transfer. The flash memory 932 can be electrically erased and/or reprogrammed. Flash memory 932 can include EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), ROM, NVRAM, or CMOS (complementary metal-oxide semiconductor), for example. The flash memory 932 can store the firmware 934 executed by the system 900 when the system 900 is first powered on, along with a set of configurations specified for the firmware 934. The flash memory 932 can also store configurations used by the firmware 934.
The firmware 934 can include a Basic Input/Output System or equivalents, such as an EFI (Extensible Firmware Interface) or UEFI (Unified Extensible Firmware Interface). The firmware 934 can be loaded and executed as a sequence program each time the system 900 is started. The firmware 934 can recognize, initialize, and test hardware present in the system 800 based on the set of configurations. The firmware 934 can perform a self-test, such as a POST (Power-On-Self-Test), on the system 900. This self-test can test the functionality of various hardware components such as hard disk drives, optical reading devices, cooling devices, memory modules, expansion cards, and the like. The firmware 934 can address and allocate an area in the memory 904, ROM 906, RAM 908, and/or storage device 912, to store an operating system (OS). The firmware 834 can load a boot loader and/or OS, and give control of the system 900 to the OS.
The firmware 934 of the system 900 can include a firmware configuration that defines how the firmware 934 controls various hardware components in the system 900. The firmware configuration can determine the order in which the various hardware components in the system 900 are started. The firmware 934 can provide an interface, such as an UEFI, that allows a variety of different parameters to be set, which can be different from parameters in a firmware default configuration. For example, a user (e.g., an administrator) can use the firmware 934 to specify clock and bus speeds; define what peripherals are attached to the system 900; set monitoring of health (e.g., fan speeds and CPU temperature limits); and/or provide a variety of other parameters that affect overall performance and power usage of the system 900. While firmware 934 is illustrated as being stored in the flash memory 932, one of ordinary skill in the art will readily recognize that the firmware 934 can be stored in other memory components, such as memory 904 or ROM 906.
System 900 can include one or more sensors 926. The one or more sensors 926 can include, for example, one or more temperature sensors, thermal sensors, oxygen sensors, chemical sensors, noise sensors, heat sensors, current sensors, voltage detectors, air flow sensors, flow sensors, infrared thermometers, heat flux sensors, thermometers, pyrometers, etc. The one or more sensors 926 can communicate with the processor, cache 928, flash memory 932, communications interface 924, memory 904, ROM 906, RAM 908, controller 910, and storage device 912, via the bus 902, for example. The one or more sensors 926 can also communicate with other components in the system via one or more different means, such as inter-integrated circuit (I2C), general purpose input output (GPIO), and the like. Different types of sensors (e.g., sensors 926) on the system 900 can also report to the controller 910 on parameters, such as cooling fan speeds, power status, operating system (OS) status, hardware status, and so forth. A display 936 may be used by the system 900 to provide graphics related to the applications that are executed by the controller 910.
Chipset 1002 can also interface with one or more communication interfaces 1008 that can have different physical interfaces. Such communication interfaces can include interfaces for wired and wireless local area networks, for broadband wireless networks, and for personal area networks. Further, the machine can receive inputs from a user via user interface components 1006, and execute appropriate functions, such as browsing functions by interpreting these inputs using processor 1010.
Moreover, chipset 1002 can also communicate with firmware 1012, which can be executed by the computer system 1000 when powering on. The firmware 1012 can recognize, initialize, and test hardware present in the computer system 1000 based on a set of firmware configurations. The firmware 1012 can perform a self-test, such as a POST, on the system 1000. The self-test can test the functionality of the various hardware components 1002-1018. The firmware 1012 can address and allocate an area in the memory 1018 to store an OS. The firmware 1012 can load a boot loader and/or OS, and give control of the system 1000 to the OS. In some cases, the firmware 1012 can communicate with the hardware components 1002-1010 and 1014-1018. Here, the firmware 1012 can communicate with the hardware components 1002-1010 and 1014-1018 through the chipset 1002, and/or through one or more other components. In some cases, the firmware 1012 can communicate directly with the hardware components 1002-10710 and 1014-1018.
It can be appreciated that example systems 900 (in
As used in this application, the terms “component,” “module,” “system,” or the like, generally refer to a computer-related entity, either hardware (e.g., a circuit), a combination of hardware and software, software, or an entity related to an operational machine with one or more specific functionalities. For example, a component may be, but is not limited to being, a process running on a processor (e.g., digital signal processor), a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller, as well as the controller, can be a component. One or more components may reside within a process and/or thread of execution, and a component may be localized on one computer and/or distributed between two or more computers. Further, a “device” can come in the form of specially designed hardware; generalized hardware made specialized by the execution of software thereon that enables the hardware to perform specific function; software stored on a computer-readable medium; or a combination thereof.
The terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting of the invention. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, to the extent that the terms “including,” “includes,” “having,” “has,” “with,” or variants thereof, are used in either the detailed description and/or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. Furthermore, terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example only, and not limitation. Although the invention has been illustrated and described with respect to one or more implementations, equivalent alterations and modifications will occur or be known to others skilled in the art upon the reading and understanding of this specification and the annexed drawings. In addition, while a particular feature of the invention may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Thus, the breadth and scope of the present invention should not be limited by any of the above described embodiments. Rather, the scope of the invention should be defined in accordance with the following claims and their equivalents.
