Patent Application
20250199834
In cloud environments, cloud providers provide the ability for virtual network interface cards to be used for virtual appliances. Virtual network interfaces cards have some specific characteristics that do not match on-premises appliances. One example of a mismatched characteristic is that when deployed, a virtual interface card has a primary internet protocol (IP) address that is immutable. because the primary IP address is immutable, a virtual interface card cannot be failed over by moving the primary IP address from a failed virtual machine to a non-failed virtual machine, for example.
In addition to dealing with the characteristics of virtual network interface cards, to deploy a cloud-based node cluster a user (e.g., customer) currently has to provide a relatively large amount of input data, and deal with many manual steps to enable features such as load balancing and dynamic network file system failover and failback of client connections across storage nodes (e.g., via DELL SMARTCONNECT) of the storage node cluster (e.g., DELL POWERSCALE). Post-deployment workflow setup and execution also involves a number of user interactions, as does lifecycle management that alters the number of virtual machines in the node cluster.
The technology described herein is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which:
Various aspects of the technology described herein are generally directed towards translating user input data, including a delegated subnet, into corresponding storage node cluster resources to be deployed. As part of automated planning for deployment of a storage node cluster, e.g., to be deployed in a cloud environment, the technology described herein divides up a delegated subnet (part of the input data) into groups of internet protocol (IP) addresses for different usages, including for use by virtual network interface cards associated with virtual machines/the nodes; (“nodes” and “virtual machines” are used interchangeably as described herein).
In one implementation, a static pool of primary IP addresses is created for association with the virtual network interface cards; these are immutable, and for example cannot be failed over to a different virtual machine. As such, the technology described herein creates a dynamic pool of secondary IP addresses for association with the virtual network interface cards. Once the node cluster is deployed, a failed virtual machine can be failed over to another, non-failed virtual, which then use the secondary IP addresses previously associated with the failed virtual machine for continued operation on the non-failed virtual machines with the secondary IP address of the virtual network interface card of the failed virtual machine. Moving the secondary IP address of the failed node onto a working-node allows clients connected to the failed node to continue communicating without perceiving an interruption (i.e., the IP address is still active, but just on a different virtual machine.
Once deployed, the node cluster can be increased in size (grown), with each new virtual machine obtaining an unused primary IP address from the delegated subnet for its virtual network interface card, along with at least two (unused) secondary IP addresses from the delegated subnet. The node cluster alternatively can be decreased in size (shrunk), with each removed virtual machine having the primary and secondary IP addresses associated with the virtual machine's virtual network interface card returned to unused status.
Reference throughout this specification to “one embodiment,” “an embodiment,” “one implementation,” “an implementation,” etc. means that a particular feature, structure, or characteristic described in connection with the embodiment/implementation is included in at least one embodiment/implementation. Thus, the appearances of such a phrase “in one embodiment,” “in an implementation,” etc. in various places throughout this specification are not necessarily all referring to the same embodiment/implementation. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments/implementations. It also should be noted that terms used herein, such as “optimize,” “optimization,” “optimal,” “optimally” and the like only represent objectives to move towards a more optimal state, rather than necessarily obtaining ideal results. For example, “optimal” placement of a subnet means selecting a more optimal subnet over another option, rather than necessarily achieving an optimal result. Similarly, “maximize” means moving towards a maximal state (e.g., up to some processing capacity limit), not necessarily achieving such a state.
The CIDR block/subnet is divided by the planner 106 into reserved address ranges based on the expected different usages of the node cluster components with respect to the client load balancing and dynamic network file system failover connections. The ranges include cloud network reserved IP addresses, service IP addresses (e.g., SSIP addresses, or SMARTCONNECT service IP addresses), primary IP addresses for virtual network interface cards (one primary IP address per node), and secondary IP addresses for virtual network interface cards. Unused IP addresses can be maintained in a data structure (e.g., a list), or can be found as needed, e.g., when adding virtual machines to the cluster as described herein.
In one example implementation, the number of secondary IP addresses to reserve per virtual machine is based on the formula (floor(N/10)+2), where N is the total number of virtual machines to be deployed in the node cluster. Using this formula facilitates handling load balancing on failover. By way of example, consider a four-node cluster, where (via the above formula) each virtual machine has a virtual network interface card with two secondary IP addresses. When a virtual machine fails, clients connected to that failed node will be balanced to (e.g., two) other virtual machines that are not failed by reassigning the secondary IP addresses to the failed over virtual machines/virtual network interface cards of the non-failed nodes. As a cluster gets larger, there are more secondary IP addresses per virtual machine, allowing load balancing across more nodes; (e.g., via the above formula, a cluster of less than nine virtual machines corresponds to two secondary IP addresses per virtual machine, ten-to-nineteen virtual machines corresponds to three secondary IP addresses per virtual machine, and so on).
Once the number of IP addresses is determined, the system 100 maps the primary and secondary IP addresses to the virtual network interface cards to be deployed with the virtual machines; this information is used to bootstrap the node cluster; (e.g., once the first virtual machine boots and creates the cluster, remaining nodes simple join and have the different IP addresses assigned appropriately for their virtual network interface card). At deployment time, the virtual network interface cards can be associated with the expected primary and secondary IP addresses, as obtained by an orchestration engine 108 that handles deployment operations (block 110) and post-provisioning operations (block 112) to complete formation of the node cluster 102. Automatic cluster setup (ACS), an existing technology in one or more example implementations, can be used for setting up the node cluster, including being appropriately updated for provisioning the static and dynamic pools as described herein.
Once the node cluster is formed, the bootstrap information facilitates automatic creation of the different IP address subnet portions and sets them up based on their expected usage. In particular, the primary IP addresses are provisioned into a static network pool, and secondary IP addresses into a dynamic network pool. When deployment is complete (block 114), the client device obtains the service IP addresses that were determined by the planner engine 106 for load balancing and failover connections.
Labeled arrow three (3) of
With this information, the orchestration engine 108 deploys the cluster resources (arrow four (4)) by communicating with the cloud provider via cloud provider APIs 220. Note that the use of an orchestration engine 108 and cloud provider APIs are generally well-documented with respect to deployment of a node cluster, and are not described herein in detail. The orchestration engine 108 waits until the cluster is formed, as represented by block 222 of
As triggered via the APIs 220, the orchestration environment including automatic cluster setup allows the cluster to form itself (arrow five (5)) in the cloud network. As described herein, automatic cluster setup is enhanced to include setting up of the network pools (arrow six (6)), including a static pool (of primary IP addresses) and a dynamic pool (of secondary IP addresses) based on the information previously generated and returned by the planner at arrow three (3). Arrow seven (7) represents forming the full node cluster; thereafter block 222 represents the orchestration engine 108 detecting (e.g., via polling) that the node cluster is formed.
As described with reference to
More particularly, as part of the deployment process, the planner 106 translates the user input data into resources to be deployed. The planner 106 includes logic that takes the delegated subnet (e.g., after determining the CIDR block) and divides the address space up for each of the usages by the virtual network interface cards. In one implementation, the subnet division is a first group 331 (e.g., a consecutive range of IP addresses in the CIDR block) for cloud reserved IP addresses, a second group 332 for a reserved service IP address range, a third group 333 for a reserved primary (static) IP address range, and a fourth group 334 for a reserved secondary IP address range. Note that instead of ranges of consecutive IP addresses, the separate groups can be maintained as data structures of IP addresses that may or may not include consecutive IP address ranges.
An example is shown via operations 406 and 408 of
To move IP addresses for failover, the technology described herein uses secondary IP addresses, which are dynamic, because the primary IP addresses are immutable (static). To this end, at operation 416, the planner 106 reserves the total number of secondary IP addresses based on the number of nodes and the number of secondary addresses per node, e.g., D=N*(floor(N/10)+2). Note that although not explicitly shown, it is understood that as part of operation 406, the user can be prompted or the like with an error in the event that the delegated subnet corresponds to a bounded range of IP addresses that is not large enough for the needed nodes; (note that secondary IP addresses need to be in the same subnet as the primary IP addresses). Thus, in the above example of 18 nodes, if the specified CIDR block is not large enough (is not at least 128 IP addresses, i.e., “/25” in CIDR notation in the above example, then the system returns a failure, possibly along with a recommendation of what size/CIDR suffix is needed.
The total number of IP addresses is thus A+B+C+D in this example, and the remainder is set aside as unused space. Note that if desired to keep consecutive ranges in each group, some unused address space may be set aside, particularly for the primary IP group, by leaving a gap before starting the secondary IP group range. For example, with a range of 18 addresses reserved for the primary IP address group when N=18, such as 192.168.1.10-192.168.1.27, instead of starting the secondary IP address group at 192.168.1.28, the secondary IP address group can be started at 192.168.1.33 (or an even higher address) to facilitate expansion of the node cluster by adding nodes, while maintained ranges of consecutive IP addresses per group.
Operation 420 represents creating the static network pool of primary IP addresses 337 (
The number of IP addresses is thus determined by the number of nodes. In one implementation, the number of primary IP addresses to reserve is one per node/virtual machine, e.g., N. The number of secondary IP addresses to reserve per virtual machine can be based on a suitable formula, such equal to the (floor(N/10)+2) where N is the total number of virtual machines being deployed in the node cluster. Using this formula facilitates handling load balancing on failover. By way of example, consider a four-node cluster, where each virtual machine has a virtual network interface card with two secondary IP addresses based on the above formula. When one virtual machine fails, clients connected to that node are balanced to two of the other virtual machines that are non-failed, including by reassigning the secondary IP addresses to the virtual network interface card on the working (“up”) nodes. For a larger cluster, there will be more secondary IP addresses per node, allowing balancing across more nodes.
After deployment, also described herein is supporting lifecycle management that alters (e.g., to grow or shrink) the number of virtual machines in the cluster in a way that ensures that resources are provisioned appropriately. When adding virtual machines to a node cluster, in one implementation the planner 106 is responsible for determining the primary IP addresses and the appropriate number of secondary IP addresses from the delegated subnet/CIDR for assignment to each virtual network interface card to be added. The planner also determines whether additional secondary IP addresses need to be added to virtual network interface cards in the cluster; for example, based on the example formula floor(N/10)+2), if the number of virtual machines is increased from less than or equal to nine to greater than or equal to ten, the number of secondary IP addresses per virtual machine increases from two to three, meaning that existing virtual machines are assigned a third secondary IP address.
The grow cluster workflow, represented as an example sequence diagram in
Example planner operations are shown in
Regardless of how primary and secondary addresses are maintained and tracked, operation 604 represents allocating Q primary IP addresses, where Q is the number of virtual machines to be added. Operation 606 represents determining a number of secondary IP addresses R=floor(R/10)+2, where R=[the number of nodes in the existing cluster, N]+[the number of nodes to be added, Q]. Based on operation 606, operation 608 determines if additional secondary IPs need to be added to the virtual network interface cards in the existing cluster, which operation 610 allocates to the existing nodes if appropriate. Operation 612 represents allocating the secondary IP addresses for the newly added nodes. Note that it is possible that the CIDR block may need to be enlarged based on the increased number of primary and secondary nodes; an error prompt or the like can be returned if doing so is not possible given the delegated subnet size.
Using the output from the planner 106, (assuming no errors occurred), the orchestration engine 108 applies changes to the existing network pools, including adding to or extending the pools based on the new IP ranges provided by the planner 106. This is represented in
When removing virtual machines from a cluster, in one implementation the planner 106 is responsible for identifying the virtual network interface card resources to be removed. Once provided with the output from the planner 106, orchestration will start a cluster shrink workflow, which will ultimately remove the resources from the cluster. Once the resources have been removed from the cluster, orchestration removes the associated IP addresses from the removed nodes from each of the different network pools. Removing the IP addresses from the network pools frees up IP addresses for the planner IP allocation determination if another grow cluster machine lifecycle management request is made.
The shrink cluster workflow, represented as an example sequence diagram in
Example planner operations with respect to virtual machine removal are shown in
Operation 806 determines whether the number of secondary IP addresses per virtual network interface card can be reduced because of the lesser number of total virtual machines, e.g., based on the floor formula described herein. If so, operation 808 reduces the number of secondary addresses of the remaining nodes. Thus, for example, if the number of virtual machines is decreased from having been greater than or equal to ten to now being less than ten, the number of secondary IP addresses per virtual machine can be decreased from three to two, meaning that remaining virtual machines will no longer be associated with a third secondary IP address.
The static pool data is represented in block 992 and includes three IP addresses (one per node in this three node cluster example), ranging from a “high” IP address of “xxx.yyy.zzz.29”, and a “low” IP address of “xxx.yyy.zzz.27”. The dynamic pool data is represented in block 994 and includes six IP addresses in this example, ranging from a “high” IP address of “xxx.yyy.zzz.42”, and a “low” IP address of “xxx.yyy.zzz.37”.
The following is an example use case in which the planner has calculated the number of nodes N to be 18. In this example, the planner divides the IP addresses as follows:
The following is another example use case in which a customer deploys a node cluster and expects to begin using the cluster once deployment is complete.
One or more aspects can be embodied in a system, such as represented in the example operations of
The node cluster can operate in a cloud environment, and further operations can include reserving a third group of cloud-reserved IP addresses from the delegated subnet, the cloud-reserved IP addresses including at least one of: network address data, default gateway address data, domain name service IP-to-virtual network space mapping address data, or network broadcast address data; the cloud-reserved IP addresses of the third group are different from the primary IP addresses of the first group and different from the secondary IP addresses of the second group. Further operations can include reserving, from the delegated subnet, a fourth group of IP addresses comprising service IP addresses, and wherein the service IP addresses of the fourth group are different from the cloud-reserved IP addresses of the third group, different from the primary IP addresses of the first group, and different from the secondary IP addresses of the second group.
The first group of primary IP addresses can include a first consecutive range of IP addresses, and the second group of secondary IP addresses can include a second consecutive range of IP addresses.
The node cluster can operate in a cloud environment, and further operations can include determining that the delegated subnet includes a sufficient amount of IP address space for the number of nodes, and in response to the determining that the delegated subnet includes a sufficient amount of IP address space, reserving, from IP address space of the delegated subnet, a third group of cloud-reserved IP addresses, and reserving, from the IP address space of the delegated subnet, a fourth group of service IP addresses, wherein the first group of primary IP addresses and the second group of secondary IP addresses are within the IP address space of the delegated subnet, and wherein each IP address in the first group, the second group, the third group and the fourth group is within only one of: the first group, the second group, the third group or the fourth group.
The first group of primary IP addresses can include a first consecutive range of IP addresses, and the second group of secondary IP addresses can include a second consecutive range of IP addresses.
Obtaining of the input data further can include obtaining a fully qualified domain name.
Further operations can include deploying node cluster resources, the deploying comprising, associating respective nodes of the nodes to deploy with respective virtual network interface cards of respective virtual machines of the respective nodes, and associating the respective virtual network interface cards with a respective primary IP address from the static network pool, and at least two respective secondary IP addresses from the dynamic network pool.
Further operations can include failing over a virtual machine of a node, the failing comprising: balancing a failed virtual machine to a non-failed virtual machine, and reassigning, to the virtual network interface card of the non-failed virtual machine, the secondary IP addresses associated with the virtual network interface card of the failed virtual machine.
Further operations can include adding a new virtual machine to the node cluster, the new virtual machine associated with a new virtual network interface card, increasing the first number of the primary IP addresses in the static network pool based on first unused addresses of the delegated subnet, increasing the second number of the secondary IP addresses in the dynamic network pool based on second unused addresses of the delegated subnet, associating the new virtual network interface card with a previously unassociated primary IP address from the static network pool, and at least two previously unassociated respective secondary IP addresses from the dynamic network pool.
Further operations can include removing a respective virtual machine from the node cluster, the removing comprising removing the respective primary IP address associated with the respective virtual machine from the static network pool, and removing the at least two respective secondary IP addresses associated with the respective virtual machine from the dynamic network pool.
Further operations can include adding the respective primary IP address and the at least two respective secondary IP addresses to a group of unused IP addresses of the delegated subnet.
One or more example aspects, such as corresponding to example operations of a method, are represented in
Further operations can include receiving, by the system, a delegated subnet as part of a request for storage capacity, and wherein the dividing of the CIDR block of the IP address space is performed in response to the request for storage capacity.
The cloud network storage node cluster can be deployed, and further operations can include failing over, by the system, a failed virtual machine to a non-failed virtual machine of the cloud network storage node cluster, the failing over comprising reassigning the secondary IP addresses associated with the virtual network interface card of the failed virtual machine to the non-failed virtual machine.
Further operations can include, in response to a request to add a virtual machine, increasing, by the system, adding to a first number of IP addresses of the static network pool, and adding to a second number of IP addresses of the dynamic network pool.
Further operations can include, in response to a request to remove a virtual machine, decreasing, by the system, removing an IP address from the static network pool, and removing an IP address from the dynamic network pool.
Further operations can include failing over a failed virtual machine of the cloud network storage node cluster to at least one non-failed virtual machine, the failing over comprising reassigning the secondary IP addresses associated with the virtual network interface card of the failed virtual machine to the at least one non-failed virtual machine.
Further operations can include at least one of: in response to a request to add a virtual machine to the cloud network storage node cluster, increasing a first size of the static network pool, and increasing a second size of the dynamic network pool, or in response to a request to remove a virtual machine from the cloud network storage node cluster, decreasing the first size of the static network pool, and decreasing the second size of the dynamic network pool.
As can be seen, the technology described herein facilitates IP assignment to enable load virtual machine balancing and failover via virtual network interface cards in a cloud environment, including for virtual network interface cards. A customer retains connectivity to the cluster in the event that a node goes down, via the secondary IP addresses that failover virtual machines without any IP address-related issues, overcoming the restrictions of existing cloud network interface cards. More specifically, via a provisioned dynamic pool using the secondary IP addresses for the virtual network interface cards on the virtual machines in the cluster, the subnet assigned for this dynamic pool provides the IP addresses used by the customer. The virtual network interface card still has a primary IP, which is tied to the static pool; however the customer does not leverage this for any connectivity, as doing so prevents failover capability.
At the same time, there is no additional work required by the end-user with the node cluster to enable load balancing and failover. Once deployment is complete, the end-user only needs to configure a DNS entry for their specified FQDN and the returned service IP addresses.
With respect to lifecycle management, the technology described herein works with and enhances the grow and shrink workflows to facilitate proper provisioning of virtual network interface cards such that the virtual network interface cards have the correct IP addresses provisioned. When adding virtual machines, the technology described herein adds to the appropriate ranges of the different network pools so that when the virtual machines join, nothing more needs to be done. When removing virtual machines, the technology described herein removes IP addresses associated to the virtual machines each of the different network pools, whereby those IP addresses become available to the planner for use if later adding virtual machines.
The technology described herein divides up the delegated subnet range within the cloud environment, which ensures that as larger clusters are deployed, more secondary IP addresses are set aside to ensure more balanced IP movement when failures occur. The technology described herein also provides an appropriate number of service IP addresses, e.g., based on the size of the cluster being deployed, which ensures that as a cluster grows, DNS functionality is maintained as virtual machine failure events occur.
The system 1300 also comprises one or more local component(s) 1320. The local component(s) 1320 can be hardware and/or software (e.g., threads, processes, computing devices). In some embodiments, local component(s) 1320 can comprise an automatic scaling component and/or programs that communicate/use the remote resources 1310, etc., connected to a remotely located distributed computing system via communication framework 1340.
One possible communication between a remote component(s) 1310 and a local component(s) 1320 can be in the form of a data packet adapted to be transmitted between two or more computer processes. Another possible communication between a remote component(s) 1310 and a local component(s) 1320 can be in the form of circuit-switched data adapted to be transmitted between two or more computer processes in radio time slots. The system 1300 comprises a communication framework 1340 that can be employed to facilitate communications between the remote component(s) 1310 and the local component(s) 1320, and can comprise an air interface, e.g., Uu interface of a UMTS network, via a long-term evolution (LTE) network, etc. Remote component(s) 1310 can be operably connected to one or more remote data store(s) 1350, such as a hard drive, solid state drive, SIM card, device memory, etc., that can be employed to store information on the remote component(s) 1310 side of communication framework 1340. Similarly, local component(s) 1320 can be operably connected to one or more local data store(s) 1330, that can be employed to store information on the local component(s) 1320 side of communication framework 1340.
In order to provide additional context for various embodiments described herein,
Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.
The illustrated embodiments of the embodiments herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.
Computing devices typically include a variety of media, which can include computer-readable storage media, machine-readable storage media, and/or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media or machine-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable or machine-readable instructions, program modules, structured data or unstructured data.
Computer-readable storage media can include, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD), Blu-ray disc (BD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible and/or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se.
Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium.
Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.
With reference again to
The system bus 1408 can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 1406 includes ROM 1410 and RAM 1412. A basic input/output system (BIOS) can be stored in a non-volatile memory such as ROM, erasable programmable read only memory (EPROM), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer 1402, such as during startup. The RAM 1412 can also include a high-speed RAM such as static RAM for caching data.
The computer 1402 further includes an internal hard disk drive (HDD) 1414 (e.g., EIDE, SATA), and can include one or more external storage devices 1416 (e.g., a magnetic floppy disk drive (FDD) 1416, a memory stick or flash drive reader, a memory card reader, etc.). While the internal HDD 1414 is illustrated as located within the computer 1402, the internal HDD 1414 can also be configured for external use in a suitable chassis (not shown). Additionally, while not shown in environment 1400, a solid state drive (SSD) could be used in addition to, or in place of, an HDD 1414.
Other internal or external storage can include at least one other storage device 1420 with storage media 1422 (e.g., a solid state storage device, a nonvolatile memory device, and/or an optical disk drive that can read or write from removable media such as a CD-ROM disc, a DVD, a BD, etc.). The external storage 1416 can be facilitated by a network virtual machine. The HDD 1414, external storage device(s) 1416 and storage device (e.g., drive) 1420 can be connected to the system bus 1408 by an HDD interface 1424, an external storage interface 1426 and a drive interface 1428, respectively.
The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer 1402, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to respective types of storage devices, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, whether presently existing or developed in the future, could also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein.
A number of program modules can be stored in the drives and RAM 1412, including an operating system 1430, one or more application programs 1432, other program modules 1434 and program data 1436. All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM 1412. The systems and methods described herein can be implemented utilizing various commercially available operating systems or combinations of operating systems.
Computer 1402 can optionally comprise emulation technologies. For example, a hypervisor (not shown) or other intermediary can emulate a hardware environment for operating system 1430, and the emulated hardware can optionally be different from the hardware illustrated in
Further, computer 1402 can be enabled with a security module, such as a trusted processing module (TPM). For instance, with a TPM, boot components hash next in time boot components, and wait for a match of results to secured values, before loading a next boot component. This process can take place at any layer in the code execution stack of computer 1402, e.g., applied at the application execution level or at the operating system (OS) kernel level, thereby enabling security at any level of code execution.
A user can enter commands and information into the computer 1402 through one or more wired/wireless input devices, e.g., a keyboard 1438, a touch screen 1440, and a pointing device, such as a mouse 1442. Other input devices (not shown) can include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller and/or virtual reality headset, a game pad, a stylus pen, an image input device, e.g., camera(s), a gesture sensor input device, a vision movement sensor input device, an emotion or facial detection device, a biometric input device, e.g., fingerprint or iris scanner, or the like. These and other input devices are often connected to the processing unit 1404 through an input device interface 1444 that can be coupled to the system bus 1408, but can be connected by other interfaces, such as a parallel port, an IEEE 1494 serial port, a game port, a USB port, an IR interface, a BLUETOOTH® interface, etc.
A monitor 1446 or other type of display device can be also connected to the system bus 1408 via an interface, such as a video adapter 1448. In addition to the monitor 1446, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.
The computer 1402 can operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s) 1450. The remote computer(s) 1450 can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer 1402, although, for purposes of brevity, only a memory/storage device 1452 is illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (LAN) 1454 and/or larger networks, e.g., a wide area network (WAN) 1456. Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet.
When used in a LAN networking environment, the computer 1402 can be connected to the local network 1454 through a wired and/or wireless communication network interface or adapter 1458. The adapter 1458 can facilitate wired or wireless communication to the LAN 1454, which can also include a wireless access point (AP) disposed thereon for communicating with the adapter 1458 in a wireless mode.
When used in a WAN networking environment, the computer 1402 can include a modem 1460 or can be connected to a communications server on the WAN 1456 via other means for establishing communications over the WAN 1456, such as by way of the Internet. The modem 1460, which can be internal or external and a wired or wireless device, can be connected to the system bus 1408 via the input device interface 1444. In a networked environment, program modules depicted relative to the computer 1402 or portions thereof, can be stored in the remote memory/storage device 1452. It will be appreciated that the network connections shown are examples and other means of establishing a communications link between the computers can be used.
When used in either a LAN or WAN networking environment, the computer 1402 can access cloud storage systems or other network-based storage systems in addition to, or in place of, external storage devices 1416 as described above. Generally, a connection between the computer 1402 and a cloud storage system can be established over a LAN 1454 or WAN 1456 e.g., by the adapter 1458 or modem 1460, respectively. Upon connecting the computer 1402 to an associated cloud storage system, the external storage interface 1426 can, with the aid of the adapter 1458 and/or modem 1460, manage storage provided by the cloud storage system as it would other types of external storage. For instance, the external storage interface 1426 can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer 1402.
The computer 1402 can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, store shelf, etc.), and telephone. This can include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices.
The above description of illustrated embodiments of the subject disclosure, comprising what is described in the Abstract, is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. While specific embodiments and examples are described herein for illustrative purposes, various modifications are possible that are considered within the scope of such embodiments and examples, as those skilled in the relevant art can recognize.
In this regard, while the disclosed subject matter has been described in connection with various embodiments and corresponding Figures, where applicable, it is to be understood that other similar embodiments can be used or modifications and additions can be made to the described embodiments for performing the same, similar, alternative, or substitute function of the disclosed subject matter without deviating therefrom. Therefore, the disclosed subject matter should not be limited to any single embodiment described herein, but rather should be construed in breadth and scope in accordance with the appended claims below.
As it employed in the subject specification, the term “processor” can refer to substantially any computing processing unit or device comprising, but not limited to comprising, single-core processors; single-processors with software multithread execution capability; multi-core processors; multi-core processors with software multithread execution capability; multi-core processors with hardware multithread technology; parallel platforms; and parallel platforms with distributed shared memory. Additionally, a processor can refer to an integrated circuit, an application specific integrated circuit, a digital signal processor, a field programmable gate array, a programmable logic controller, a complex programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Processors can exploit nano-scale architectures such as, but not limited to, molecular and quantum-dot based transistors, switches and gates, in order to optimize space usage or enhance performance of user equipment. A processor may also be implemented as a combination of computing processing units.
As used in this application, the terms “component,” “system,” “platform,” “layer,” “selector,” “interface,” and the like are intended to refer to a computer-related resource or an entity related to an operational apparatus with one or more specific functionalities, wherein the entity can be either hardware, a combination of hardware and software, software, or software in execution. As an example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration and not limitation, both an application running on a server and the server 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. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components may communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software or a firmware application executed by a processor, wherein the processor can be internal or external to the apparatus and executes at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, the electronic components can comprise a processor therein to execute software or firmware that confers at least in part the functionality of the electronic components.
In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances.
While the embodiments are susceptible to various modifications and alternative constructions, certain illustrated implementations thereof are shown in the drawings and have been described above in detail. It should be understood, however, that there is no intention to limit the various embodiments to the specific forms disclosed, but on the contrary, the intention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope.
In addition to the various implementations described herein, it is to be understood that other similar implementations can be used or modifications and additions can be made to the described implementation(s) for performing the same or equivalent function of the corresponding implementation(s) without deviating therefrom. Still further, multiple processing chips or multiple devices can share the performance of one or more functions described herein, and similarly, storage can be effected across a plurality of devices. Accordingly, the various embodiments are not to be limited to any single implementation, but rather are to be construed in breadth, spirit and scope in accordance with the appended claims.
