Patent ID: 12254302

DETAILED DESCRIPTION

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 identifying binary objects using a signature of the input items used to create each binary object, such as container images.

The amount of time required to build a container image can be significant. In addition, depending on the size of the resulting container image and the speed of the connection between the device where the container image is generated and the container repository where the container image is stored, the time to upload or download a container image can be significant. A need remains for techniques for suppressing the generation of a binary object (sometimes referred to as build avoidance) or the upload/download of a binary object (sometimes referred to as upload/download avoidance) when the binary object has already been generated from the same input items (or uploaded or downloaded).

In one or more embodiments, techniques are provided for identifying binary objects using a signature of the input items used to create each binary object. The disclosed signature-based techniques, in at least some embodiments, associate the signature of the input items used to create each binary object with the binary object (e.g., by embedding the signature in the binary object). In this manner, the disclosed techniques for signature-based identification of binary objects allow a suppression of the generation of a binary object when the associated signature of the input items reveals that the binary object has already been generated from the same input items.

Among other benefits, the disclosed signature-based binary object identification techniques allow the generation of a binary object and/or the uploading/downloading of a binary object to be suppressed or avoided when the associated signature of the input items reveals that the binary object is already available from the same input items.

While one or more embodiments of the disclosure are illustrated in the context of identifying container images using a signature of the input items used to create each container image, the disclosed signature-based identification techniques can be used to identify any binary object generated from a set of input items, as would be apparent to a person of ordinary skill in the art.

FIG.1shows an information processing system100configured in accordance with an illustrative embodiment. The information processing system100comprises a container image generation tool105, a container registry125and a container generation server150. In the example ofFIG.1, the container image generation tool105may be used to generate a container image configured to execute in a container instance160. The container image generation tool105may upload the generated container image to the container registry125, for example, for storage in a container image repository130. The container image may be served from the container registry125to the container instance160, in some embodiments, using the container generation server150. The term “binary object,” as used herein, is intended to be broadly construed, so as to encompass, for example, any binary object generated by applying a transformation to a set of input items, such as a container image generated using one or more computer files corresponding to one or more software applications that execute in a software container. A container image and other binary objects may also comprise metadata and/or file properties.

The container image generation tool105may be implemented, for example, as a user device, such as a host device and/or another device such as a mobile telephone, a laptop computer, a tablet computer, a desktop computer or another type of computing device. 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.” When the container image generation tool105is implemented as a host device, the host device may illustratively comprise one or more 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.

One or more of the container image generation tool105, the container registry125and the container generation server150may be coupled to a network, where the network in this embodiment is assumed to represent a sub-network or other related portion of a larger computer network. The network 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, 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 network 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, 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 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.

One or more of the container image generation tool105, the container registry125and the container generation server150illustratively comprises processing devices of one or more processing platforms. For example, the container image generation tool105can comprise one or more processing devices each having a processor and a memory, possibly implementing virtual machines and/or containers, although numerous other configurations are possible. 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.

One or more of the container image generation tool105, the container registry125and the container generation server150can additionally or alternatively be part of cloud infrastructure such as an Amazon Web Services (AWS) system. Other examples of cloud-based systems that can be used to provide at least portions of the container image generation tool105, the container registry125and/or the container generation server150include Google Cloud Platform (GCP) and Microsoft Azure.

As shown inFIG.1, the exemplary container image generation tool105comprises a container image generation module112, a container image signature evaluator114and a container image upload module116, as discussed further below in conjunction withFIGS.2through4. In one or more embodiments, the container image generation module112may be used by a user to generate a container image. The container image signature evaluator114evaluates a signature of the inputs used to generate a given container image to determine, for example, if the container image needs to be generated. The exemplary container image upload module116coordinates with the container registry125to upload generated container images to the container image repository130.

It is to be appreciated that this particular arrangement of modules112,114,116illustrated in the container image generation tool105of theFIG.1embodiment is presented by way of example only, and alternative arrangements can be used in other embodiments. For example, the functionality associated with modules112,114,116in other embodiments can be combined into a single module, or separated across a larger number of modules. As another example, multiple distinct processors and/or memory elements can be used to implement different ones of modules112,114,116or portions thereof. At least portions of modules112,114,116may be implemented at least in part in the form of software that is stored in memory and executed by a processor.

As shown inFIG.1, the exemplary container generation server150comprises a container repository search module152, a container image download module154and a container generation module156, as discussed further below in conjunction withFIGS.3and4. In one or more embodiments, the container repository search module152allows a user to search the container image repository130to obtain desired container images. The container image download module154coordinates with the container registry125to download desired container images from the container image repository130for the creation of container instances160. The exemplary container generation module156creates container instances160from container images obtained from the container image repository130.

It is to be appreciated that this particular arrangement of modules152,154,156illustrated in the container generation server150of theFIG.1embodiment is presented by way of example only, and alternative arrangements can be used in other embodiments. For example, the functionality associated with modules152,154,156in other embodiments can be combined into a single module, or separated across a larger number of modules. As another example, multiple distinct processors and/or memory elements can be used to implement different ones of modules152,154,156or portions thereof. At least portions of modules152,154,156may be implemented at least in part in the form of software that is stored in memory and executed by a processor.

The container image generation tool105and/or the container generation server150may further include one or more additional modules and other components typically found in conventional implementations of such devices, although such additional modules and other components are omitted from the figure for clarity and simplicity of illustration.

One or more of the container image generation tool105, the container registry125and the container generation server150in theFIG.1embodiment may be implemented on a common processing platform, or on separate processing platforms. In theFIG.1embodiment, the container image generation tool105, the container registry125and the container generation server150are assumed to be implemented using at least one processing platform, with each such processing platform comprising one or more processing devices, and each such processing device comprising a processor coupled to a memory. Such processing devices can illustratively include particular arrangements of compute, storage and network resources.

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 system100are 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 system100for one or more of the container image generation tool105, the container registry125and the container generation server150to reside in different data centers. Numerous other distributed implementations of the components of the system100are possible.

As noted above, the container registry125can have an associated container image repository130configured to store one or more container images. Although the container images are stored in the example ofFIG.1in a single container image repository130, in other embodiments, an additional or alternative instance of the container image repository130, or portions thereof, may be incorporated into the container registry125or other portions of the system100.

The container image repository130in the present embodiment is implemented using one or more storage systems. Such storage systems can comprise any of a variety of different types of storage including 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.

Also associated with the container image generation tool105, the container registry125and/or the container generation server150can 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 a user device, as well as to support communication between the container image generation tool105, the container registry125, the container generation server150and/or other related systems and devices not explicitly shown.

In some embodiments, the container registry125can be implemented using the Docker Container Registry, the interface to the container image generation tool105can be implemented using the Docker command line interface and the interface to the container registry125can be implemented using the Docker registry interface (e.g., Docker Registry HTTP API V2). The Docker Container Registry comprises an object store. Thus, only one copy of a container image is typically saved in a docker registry, regardless of the names or tags given to a container image. When a docker image is uploaded to a registry, the uploaded docker image is given a hash identifier value (known as a repository digest) for the uploaded image. The hash identifier value of the image excludes metadata about the image in the registry. When a differently named (or differently tagged) imaged is uploaded to the same registry, the new image is typically only saved if it has a unique hash identifier value in that registry. Docker registries typically only support searches for container images by an image name, a tag and/or a repository digest.

In one or more embodiments, the signature of the input items of a container image is placed into a label field in the metadata of the container image, and also in the tag field of the image in the registry. In this manner, the signature can be used in local searches to determine if the required container image has already been generated and renamed or retagged. In addition, the signature can be used when uploaded to the container registry125. In at least some embodiments, the container registry125is an object repository. Thus, only one copy of the image is saved in the container registry125, but by saving the container image with a tag representing the signature of the input items, the container registry125automatically provides access when this is requested.

The memory of one or more processing platforms 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 solid-state drives (SSDs), and should therefore not be viewed as limited in any way to spinning magnetic media.

It is to be understood that the particular set of elements shown inFIG.1for signature-based identification of binary objects is presented by way of illustrative example only, and in other embodiments additional or alternative elements may be used. Thus, another embodiment includes additional or alternative systems, devices and other network entities, as well as different arrangements of modules and other components.

FIG.2illustrates the container image generation tool105ofFIG.1in further detail according to one or more embodiments. In the example ofFIG.2, the exemplary container image generation tool105processes one or more input items210to generate a container image250, for example, by transforming the one or more input items210into the container image250. The one or more input items210may comprise, for example, one or more source files and a hash value (e.g., a git subtree hash value) or another transformation or source file-dependent value that identifies (e.g., uniquely describe) the one or more source files associated with the container image250. In addition, the one or more input items210may comprise one or more of parameters, arguments, environmental variables and configuration settings.

An image input signature generation module220processes one or more input items210to generate a signature that identifies the one or more input items210. The signature may comprise, for example, a hash value (e.g., a SHA256 hash value), a checksum value or another transformation applied to the one or more input items210to substantially uniquely identify the one or more input items210(e.g., using a value that depends on the one or more input items210), as would be apparent to a person of ordinary skill in the art.

The exemplary container image generation tool105processes the one or more input items210and the signature of the one or more input items210generated by the image input signature generation module220to generate the container image250. The generated container image250also comprises (or is associated with) the signature generated by the image input signature generation module220.

FIG.3illustrates exemplary pseudo code for a signature-based binary object identification process300, according to one embodiment of the disclosure. As noted above, in one or more embodiments, a signature is generated of the input items210used to create a given container image250. The signature can be compared to signature values of other container images that were previously generated to determine whether to generate and/or upload the given container image. The hash value is associated with the created container image (e.g., embedded in metadata, file properties and/or as a tag field of the file name of the created container image). The hash value can be associated with the created container image at the time that the container image is created or at the time that the container image is uploaded to the container registry125.

In the example ofFIG.3, the pseudo code for the signature-based binary object identification process300comprises the following steps:(1) obtain input items;(2) calculate hash value of the input items;(3) inspect local images (e.g., using docker “inspect” command) to determine if any existing local image comprises the calculated hash value in the metadata of the local image. If a match is found: the existing local image can be renamed, if needed, and used to represent the desired output, otherwise continue to step (4);(4) request an image from the container registry125having the calculated hash value in the name and/or metadata of the image from the container registry125. If a match is found: the image from the container registry125is downloaded from the container image repository130and renamed, if needed, otherwise continue to step (5);(5) build a new image using the obtained input items; and(6) push the new image to the container registry125with the calculated hash value in the name and/or in the metadata of the new image.

FIG.4is a flow diagram illustrating an exemplary implementation of a signature-based binary object generation and identification process400, according to various embodiments. In the example ofFIG.4, the hash value of the input items210is initially calculated in step402. In step406, the process400compares the hash value of the input items calculated in step402to hash values of existing local images in a listing obtained in step404. If a match is found in step406, program control proceeds to step414, discussed below.

In step410, the process400compares the hash value of the input items calculated in step402to hash values of one or more existing images in the container registry125obtained in step408by requesting an image from the container registry125having a tag that includes the hash value calculated in step402, in accordance with the disclosed signature-based binary object identification techniques. It is noted that step408is performed, at least in some embodiments, only when the match operation is performed in step410(e.g., only if a match is not found in step406). If a match is found in step410, the matching image is downloaded in step412and the image is renamed and/or retagged, if needed, in step414. Program control then terminates.

Step418compares the hash value of the input items calculated in step402to hash values obtained from requesting an image in step416from the container registry125using a file name, in a conventional manner. It is noted that step416is performed, at least in some embodiments, only when the match operation is performed in step418(e.g., only if a match is not found in step410). If a match is found in step416, then program control terminates.

If a match is not found in step418, then the desired container image is not available in the local images, or in the container registry125. Thus, the container image is built in step420, and the generated container image and/or the generated container image with the hash value is uploaded in step422, before program control terminates. For example, two versions of the generated container image may be uploaded (e.g., a first version comprising a conventional representation of the container image and a second version comprising the container image with the calculated hash value). The second version comprising the container image with the calculated hash value allows searching of the container registry125for the container image using the calculated. hash value.

FIG.5is a flow diagram illustrating an exemplary implementation of a signature-based process500for identifying binary objects, according to various embodiments. In the example ofFIG.5, the signature-based process500obtains one or more input items in step502and generates a signature of the one or more input items in step504.

The signature-based process500then transforms the one or more input items to generate a binary object in step506and associates the signature with the binary object in step508. The transformation of the one or more input items to generate the binary object may comprise, for example, generating a first container image using the one or more input items.

The binary object is provided with the associated signature to a repository in step510, where the signature is processed to evaluate the one or more input items used to create the binary object.

The signature generated in step504may be compared to an additional signature of one or more additional input items to determine whether to perform the transforming in step506of the one or more additional input items to generate an additional binary object.

The signature may be associated with the binary object, for example, by including the signature in, for example, metadata embedded in the binary object, file properties associated with the binary object and/or a file name or tag of the binary object. The signature may be associated with the binary object when the binary object is created and/or when the binary object is stored in a repository.

The particular processing operations and other network functionality described in conjunction with the flow diagram ofFIG.5are presented by way of illustrative example only, and should not be construed as limiting the scope of the disclosure in any way. Alternative embodiments can use other types of processing operations for identifying binary objects using a signature of the input items used to create each binary object. For example, the ordering of the process steps may be varied in other embodiments, or certain steps may be performed concurrently with one another rather than serially. In one aspect, the process can skip one or more of the actions. In other aspects, one or more of the actions are performed simultaneously. In some aspects, additional actions can be performed.

One or more embodiments of the disclosure provide improved methods, apparatus and computer program products for identifying binary objects using a signature of the input items used to create each binary object. 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 techniques for identifying binary objects using a signature of the input items used to create each binary object, 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 techniques for identifying binary objects using a signature of the input items used to create each binary object 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, IaaS and/or a Function-as-a-Service FaaS 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 signature-based binary object identification 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 signature-based binary object identification 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 toFIGS.6and7. These platforms may also be used to implement at least portions of other information processing systems in other embodiments.

FIG.6shows an example processing platform comprising cloud infrastructure600. The cloud infrastructure600comprises a combination of physical and virtual processing resources that may be utilized to implement at least a portion of the information processing system100. The cloud infrastructure600comprises multiple virtual machines (VMs) and/or container sets602-1,602-2, . . .602-L implemented using virtualization infrastructure604. The virtualization infrastructure604runs on physical infrastructure605, and illustratively comprises one or more hypervisors and/or operating system level virtualization infrastructure. The operating system level virtualization infrastructure illustratively comprises kernel control groups of a Linux operating system or other type of operating system.

The cloud infrastructure600further comprises sets of applications610-1,610-2, . . .610-L running on respective ones of the VMs/container sets602-1,602-2, . . .602-L under the control of the virtualization infrastructure604. The VMs/container sets602may 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 theFIG.6embodiment, the VMs/container sets602comprise respective VMs implemented using virtualization infrastructure604that comprises at least one hypervisor. Such implementations can provide signature-based binary object identification functionality of the type described above for one or more processes running on a given one of the VMs. For example, each of the VMs can implement signature-based binary object identification control logic and associated functionality for suppressing the generation of binary objects for one or more processes running on that particular VM.

An example of a hypervisor platform that may be used to implement a hypervisor within the virtualization infrastructure604is 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 theFIG.6embodiment, the VMs/container sets602comprise respective containers implemented using virtualization infrastructure604that provides operating system level virtualization functionality, such as support for Docker containers running on bare metal hosts, or Docker containers running on VMs. The containers are illustratively implemented using respective kernel control groups of the operating system. Such implementations can provide signature-based binary object identification functionality of the type described above for one or more processes running on different ones of the containers. For example, a container host device supporting multiple containers of one or more container sets can implement one or more instances of signature-based binary object identification control logic and associated functionality for suppressing the generation of binary objects.

As is apparent from the above, one or more of the processing modules or other components of system100may 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 infrastructure600shown inFIG.6may represent at least a portion of one processing platform. Another example of such a processing platform is processing platform700shown inFIG.7.

The processing platform700in this embodiment comprises at least a portion of the given system and includes a plurality of processing devices, denoted702-1,702-2,702-3, . . .702-K, which communicate with one another over a network704. The network704may comprise any type of network, such as a wireless area network (WAN), a local area network (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 device702-1in the processing platform700comprises a processor710coupled to a memory712. The processor710may 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 memory712, 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 device702-1is network interface circuitry714, which is used to interface the processing device with the network704and other system components, and may comprise conventional transceivers.

The other processing devices702of the processing platform700are assumed to be configured in a manner similar to that shown for processing device702-1in the figure.

Again, the particular processing platform700shown 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 inFIG.6or7, or each such element may be implemented on a separate processing platform.

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 such as VxRail™, VxRack™, VxBlock™, or Vblock® converged infrastructure commercially available from Dell Technologies.

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.