Fog computing for machine translation

Pre-emptive configuration of a fog computing environment for on-demand services is provided. On-demand services are supported by service modules. Traffic related to demand for service modules is monitored and evaluated. The modules are selectively pushed to and removed from edge servers in a fog computing environment in order to efficiently service the demand for machine translation services.

BACKGROUND

The present embodiments relate to on-demand services in a fog computing environment. More specifically, the embodiments relate to selective pushing and removal of services in the fog computing environment to support real-time service support.

Fog computing is a de-centralized computing infrastructure in which computing resources and application services are logically and efficiently distributed. The goal of fog computing is to reduce data transport to a shared data resource, e.g. cloud based resource, for data processing, analysis and storage. In a fog computing environment, processing takes place local on one or more network connected devices, gateways, and edge server(s). Data is gathered, processed and stored within the network on the edge server(s).

With respect to a network of shared resources, referred to herein as the cloud, the fog computing environment extends computing resources closer to devices that produce and act on the data. Analyzing data on an edge node in the fog computing environment brings the processing in close proximity to the data, thereby minimizing latency. The edge node(s) is connected to the cloud, and as such, processing may take place local to the edge node, or it may be transported to a cloud resource for processing. Designation of processing locations may be based on characteristics associated with the data and time-sensitive requirements, if any. Accordingly, the fog computing configuration provides a processing layer that extends data processing proximal to the origin of the data.

SUMMARY

A system, computer program product, and method are provided to extend the fog computing environment to support real-time machine translation services.

In one aspect, a system is provided with a processing unit in communication with memory, and a functional unit in communication with the processing unit. The functional unit provides on-demand service management in a fog-computing environment with two or more edge servers. The functional tool monitors on-demand services and related traffic. The functional unit statistically evaluates the traffic and the service usage. More specifically, on-demand services employed in the two or more edge servers, and data traffic associated with one or more select services is evaluated. The functional unit identifies an on-demand service module and one of the edges servers to receive the on-demand service module. The functional unit selectively pushes the on-demand service module to the edge server in response to the statistical traffic evaluation and service usage. The on-demand service module provides real-time performance of a task.

In another aspect, a computer program product is provided for on-demand service management in a fog-computing environment with two or more edge servers. The computer program product includes a computer readable storage device with embodied program code that is configured to be executed by a processing unit. More specifically, program code monitors on-demand services and related traffic. Program code statistically evaluates the traffic and the service usage. More specifically, on-demand services employed in the two or more edge servers, and data traffic associated with one or more select services is evaluated. Program code identifies an on-demand service module and one of the edges servers to receive the on-demand service module. Program code selectively pushes the on-demand service module to the edge server in response to the statistical traffic evaluation and service usage. The on-demand service module provides real-time performance of a task

In yet another aspect, a method is provided for on-demand service management in a fog-computing environment with two or more edge servers. On-demand services and related traffic is monitored. The traffic and the service usage is statistically evaluated. More specifically, on-demand services employed in the two or more edge servers, and data traffic associated with one or more select services is evaluated. An on-demand service module and one of the edges servers to receive the on-demand service module is identified. The on-demand service module is selectively pushed to the edge server in response to the statistical traffic evaluation and service usage. The on-demand service module provides real-time performance of a task

These and other features and advantages will become apparent from the following detailed description of the presently preferred embodiment(s), taken in conjunction with the accompanying drawings.

DETAILED DESCRIPTION

It will be readily understood that the components of the present embodiments, as generally described and illustrated in the Figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the apparatus, system, and method of the present embodiments, as presented in the Figures, is not intended to limit the scope of the embodiments, as claimed, but is merely representative of selected embodiments.

A system, method and computer program product to pre-emptively configure a fog computing environment for machine learning translation services is provided. Machine learning translation services are supported by machine learning modules. Each module represents a different language and/or a dialect within a specific language. In one embodiment, each module represents a different type of service required such as text translation, voice translation, and/or optical character recognition translation. The modules are maintained on a network configured server and a device, such as a client machine, in communication with the server may utilize a select module for translation support. It is understood that the server and the client machine have limitations with respect to memory and bandwidth, and as such, use of the modules are managed in view of such limitations. At the same time, it is also understood that needs are subject to change, and the selection of modules may be subject to change.

Translation support is shown and described in detail in view of the fog computing environment. By employing translation services in the fog computing environment, translation is delivered locally, thereby alleviating or mitigating issues with respect to bandwidth. More specifically, translation modules are selectively delivered to edge servers, also referred to herein as edge nodes, so that the translation is provided local to the network connected device in need of the translation. The act of supplying a translation module to an edge node is referred to as pushing, and the act of removal of the module is referred to as removing. Accordingly, translation modules are pushed and removed based on the locality of the service demand.

Referring toFIG. 1, a block diagram (100) is provided illustrating a fog computing environment and components therein that support use of translation modules. As shown, the fog computing environment (105) includes multiple edge servers shown herein as edge server0(110) and edge server1(120), and network connected devices (170). Data center (150) is shown herein as a network of shared resources, also referred to as a cloud computing environment and is in communication with the fog computing environment (105), also referred to herein as the fog layer. Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

Characteristics of the cloud model are as follows:

Service Models are as follows:

Deployment Models are as follows:

Referring now toFIG. 2, a schematic of an example of a cloud computing node is shown. Cloud computing node (210) is only one example of a suitable cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. Regardless, cloud computing node (210) is capable of being implemented and/or performing any of the functionality set forth hereinabove.

As shown inFIG. 2, computer system/server (212) in cloud computing node (210) is shown in the form of a general-purpose computing device. The components of computer system/server (212) may include, but are not limited to, one or more processors or processing units (216), system memory (228), and bus (218) that couples various system components including system memory (228) to processor (216).

Computer system/server (212) typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server (212), and it includes both volatile and non-volatile media, removable and non-removable media.

Program/utility (240), having a set (at least one) of program modules (242), may be stored in memory (228) by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules (242) generally carry out the functions and/or methodologies of embodiments as described herein.

Computer system/server (212) may also communicate with one or more external devices (214) such as a keyboard, a pointing device, a display (224), etc.; one or more devices that enable a user to interact with computer system/server (212); and/or any devices (e.g., network card, modem, etc.) that enable computer system/server (212) to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces (222). Still yet, computer system/server (212) can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter (220). As depicted, network adapter (220) communicates with the other components of computer system/server (212) via bus (218). It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server (212). Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

Referring now toFIG. 3, illustrative cloud computing environment (350) is depicted. As shown, cloud computing environment (350) comprises one or more cloud computing nodes (310) with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone (354a), desktop computer (354b), laptop computer (354c), and/or automobile computer system (354n) may communicate. Nodes (310) may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment (350) to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices (354a-n) shown inFIG. 3are intended to be illustrative only and that computing nodes10and cloud computing environment (350) can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Referring now toFIG. 4, a set of functional abstraction layers provided by cloud computing environment (350) (FIG. 3) is shown. It should be understood in advance that the components, layers, and functions shown inFIG. 4are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

Hardware and software layer (460) includes hardware and software components. Examples of hardware components include: mainframes; RISC (Reduced Instruction Set Computer) architecture based servers; servers; blade servers; storage devices; and networks and networking components. In some embodiments, software components include network application server software and database software.

Virtualization layer (470) provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers; virtual storage; virtual networks, including virtual private networks; virtual applications and operating systems; and virtual clients

Workloads layer (490) provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation; software development and lifecycle management; virtual classroom education delivery; data analytics processing; transaction processing; and machine translation in fog computing.

Referring toFIG. 5, a block diagram (500) is provided illustrating a data center in support in communication with a fog computing environment that is in support of the dynamic characteristics of application of the machine translation modules. As shown, a network of shared resources (502) is provided with data center (510). The data center (510) and the resources therein are accessible via a network connection. The data center (510) is shown with a server, shown herein as server0(520) configured with a processing unit (522) in communication with memory (526) across a bus (524). In one embodiment, data center (510) is provided with a plurality of servers, shown herein as server1(530), server2(532), server3(534), and server4(536). The quantity of servers provided is for illustration and should not be considered limiting. As shown, server0(520) is provided with a plurality of functional tools, including a machine translation edge service controller (MTESC) (560), an on-edge machine translation configuration agent (OEMTCA) (562) and in one embodiment, a machine translation traffic analyzer (MTTA) (528). The MTTA (528) monitors machine translation demand in one or more edge servers. For example, the MTTA (528) monitors the number of requests for a machine translation service in one or more edge servers, the source and target languages of the request, the source and target dialects of the request, the service type of the request, data traffic in one or more edge servers, etc. In one embodiment, the functionality of the MTTA (528) is embodied within a plurality of servers. Accordingly, the servers within the network of shared resources monitor machine translation traffic within the fog computing environment and the data center (510).

As shown, server0(520) is configured with profiles (564) in communication with memory (526). The profiles (564) are a set of online machine translation edge services rules and related settings for servers and/or devices. The rules may be defined by an online machine translation service provider. The profiles (564) may use a weighted edge pushing rule that can be applied to prioritize pushing of demanded machine translation services into the edge server. The profiles (564) can contain user information, such as native language, number of source languages for machine translation service, priority of source languages, preference on fog computing environment, default settings, etc. Accordingly, profiles may be utilized when deciding to change a configuration in the fog computing environment.

The OEMTCA (562) determines the configurations of edge servers based on profiles (564). The OEMTCA (562) receives edge server translation configuration settings and related translation profiles, and in one embodiment stores the configuration settings as profiles (564). The OEMTCA (562) selectively merges the configuration settings of one or more edge servers into merged configuration settings and selectively merges the related translation profiles of one or more edge servers into merged translation profiles and in one embodiment stores them as profiles (564). The OEMTCA (562) shares configuration settings and translation profiles with servers, edge servers and other devices within the fog computing environment. The MTESC (560) manages edge computing configuration tasks such as pushing translation modules to edge servers and removing machine translation modules from edge servers. In one embodiment, the MTESC (560) provides a graphical user interface for creating and storing edge computing rules. Accordingly, the OEMTCA (562) and the MTESC (560) work in concert to determine and change configurations of edge servers.

A machine translation service module (540) is shown operatively coupled to the processing unit (522) and memory (526). More specifically, module (540) is configured with a plurality of modules (542)-(556). The quantity of modules shown herein is for illustrative purposes, and should not be considered limiting. Each of the modules (542)-(556) is a program or set of instructions that provides a machine translation service between a source language and a target language. In one embodiment, the translation may pertain to a dialect within a specific language. In one embodiment, each module pertains to a particular type of translation service such as text translation, voice translation, and/or optical character recognition translation. The modules (542)-(556) may be individually accessed as a shared resource in the data center (510). Accordingly, one or more servers within the network of shared resources provide modules that support a machine translation service.

With respect to the fog computing environment, access of a select module is directed at pushing the select module to a designated edge server, thereby localizing use of the module. As shown herein, two edge servers, including edge server0(570) and edge server1(580), are in communication with the server0(520) across one or more network connections (570a) and (580a), respectively. Although only two edge servers are shown, this quantity should not be considered limiting. Edge server0(570) is shown with memory (576) operatively coupled to a processing unit (572) across a bus (574). In one embodiment, edge server0(570) is configured with a machine translation traffic analyzer (MTTA) (578) in communication with memory (576) and processing unit (572). Similarly, edge server1(580) is shown with memory (586) operatively coupled to a processing unit (582) across a bus (584). In one embodiment, edge server1(580) is configured with a machine translation traffic analyzer (MTTA) (588) in communication with memory (586) and processing unit (582).

Each of the edge servers, edge server0(570) and edge server1(580), are in communication with at least one of the machine translation service modules pushed by the MTSEC (560) from the data center (510). The machine translation service modules (542)-(556) may be pushed in response to a configuration setting determined by the OEMTCA (562) or in response to an increase in monitored demand. In one embodiment, the increase in demand is determined by the MTTA (528). As shown herein by way of example, edge server0(570) is shown with module (542), and edge server1(580) is shown with modules (548) and (550). Module (542) is pushed to edge server0(570) by the MTSEC (562) to provide requested or specified machine learning translation in real-time to one or more of the network connected devices having a local connection or proximally positioned with respect to edge server0(570). Similarly, modules (548) and (550) are shown herein pushed to edge server1(580) by the MTSEC (562) to provide requested or specified machine learning translation in real-time to one or more network connected devices positioned proximal to edge server1(580).

As shown, a set of network connected device (512)-(519) are provided in the fog computing environment. The network connected devices may be a client machine, or in one embodiment, may be a tool with a network connection. Regardless of the form of the network connected device, it is an element in the fog computing environment that may be in need of translation services to be supported by one or more machine translation modules via a locally positioned edge server. As shown, each device (512)-(519) is in communication with the network of shared resources (502) across one or more network connections (512a)-(519a) respectively. In one embodiment, each device (512)-(515) is in communication with edge server0(570) across one or more network connections (512b)-(515b). In one embodiment, each device (516)-(519) is in communication with edge server1(580) across one or more network connections (516b)-(519b). Accordingly, a plurality of network connected devices is located throughout and in communication with the fog computing environment.

The translation modules shown ‘pushed’ to the edge servers may also be removed from the respective edge servers. The machine translation service modules (542)-(556) may be removed in response to a configuration setting determined by the OEMTCA (562) or in response to a decrease in monitored demand. In one embodiment, the decrease in demand is determined by the MTTA (528). The act of removal may be based upon a decrease in demand for the translation services by the respective edge server or by a device proximally located to the edge server. Similarly, in one embodiment, the act of removal of one or more select modules from a selected edge server may be based on an increased demand by one or more network connected devices that are positioned proximal to a different edge server. The act of pushing modules to a designated edge server proximal to one or more select devices is to mitigate expenses associated with translation. In one embodiment, the act of pushing a translation module includes copying the module from server0(520) within data center (510) to the designated edge server. Details related to determination of modules for pushing and removing is described in the flow charts discussed below.

Referring toFIG. 6, a flow diagram (600) is provided to illustrate generations of requests for machine translation services. Network connected devices are elements in the fog computing environment that may be in need of translation services to be supported by one or more machine translation modules via a locally positioned edge server. The network connected devices may proactively indicate the need for translation across a language or dialect, or in one embodiment, the translation demand related to one or more of the clients may be detected (602). Regardless of the form of ascertaining translation demand, the machine translation service requirement is determined at step (602, which is followed by determining the respective languages required to service the demand (604). More specifically, at step (604), both the source language and the target language are determined. In one embodiment, both the source dialect and target dialect are also determined at step (604). It is understood that select modules may provide translation services across select languages. For example, a select module may translate from English as a source language to German as a target language, and a separate module may translate from German as a source language to French as a target language. Similarly, in one embodiment, a single module may provide translation services across English, French and German, with the translation services being uni-directional or bi-directional. Accordingly, the determination of the source and target language may dictate the selection of translation module(s).

Following the determination of languages at step (604), the type of service(s) required to support the translation is determined or designated (606). For example, in one embodiment three types of machine translation service types are available services, including voice, text, and optical character recognition (OCR). If it was determined voice machine translation service is required then voice machine translation service is requested based on the determined languages (608). Similarly, if it was determined text machine translation service is required then text machine translation service is requested based on the determined languages (610). Similarly, if it was determined OCR machine translation service is required then OCR machine translation service is requested based on the determined languages (612). In one embodiment, the service types shown herein may be expanded to include additional or alternative service types, and as such, these example service types should not be considered limiting. At the same time, as demonstrated herein, a machine translation module may be different depending on the service type. For example, a demand for machine translation from English to German may designate a first module for the demand in the form of text, and a second module for demand in the form of voice. Accordingly, the format of the service demand as well as the source and target language designations may yield different modules for selection and pushing to a designated edge server.

Referring toFIG. 7, a flow chart (700) is provided illustrating a process for selectively pushing machine translation modules to one or more edge servers. As shown, machine translation traffic is monitored (702), including, but not limited to, languages, dialects, and translation service type. A machine translation traffic analyzer (MTTA) is invoked to investigate demand and changes in demand for the machine translation(s) and the module(s) utilized to provide the machine translation service (704). It is understood that there is a finite supply of modules, and the demand from network connected devices for the modules is dynamic. As such, the MTTA is employed to investigate the changing environment with respect to supply and demand of the machine translation modules. Accordingly, the machine translation traffic is monitored in a network and subject to further analysis to optimize the placement of one or more select modules within a fog computing environment.

As shown, the MTTA assesses demand for machine translation service modules within the fog computing system including analyzing characteristics with respect to the edge servers in the system and a profile of one or more network connected devices in the system (706). In one embodiment, each edge server has an associated characteristic profile that reflects the limitations of the edge server. For example, the profile may indicate the capacity of the edge server with respect to accommodating and servicing machine translation modules. Similarly, in one embodiment, each client device may have a separate profile that indicates device characteristics, including capacity, bandwidth, recent translation needs, etc. In one embodiment, the network connected device profile is maintained remote from the network connected device, but may be copied and synched to the respective network connected device. Accordingly, the MTTA employs both device and edge server profiles in the fog computing assessment.

Based on assessment and associated feedback from the MTTA, it is determined if fog computing for a demanded machine translation service is available on a select edge server (708). In one embodiment, there is a plurality of edge servers in the fog computing environment. The analysis at step (708) may be for a designated edge server. In one embodiment, the analysis may at step (708) may be expanded for a selection within a set of available edge servers. For example, in one embodiment, the edge servers may be organized into a hierarchical arrangement, or in one embodiment, an ordered list, with the hierarchy varying based on selective importance of server location and capacity. Based on the availability analysis at step (708), an edge server may be identified as available or not available to provide the machine translation service. In one embodiment, the availability of the edge server may be a cost analysis based on factors such as bandwidth, capacity, traffic, customer experience, etc. As such, a negative response to the determination at step (708) is followed by an assessment of the cost to set up and utilize fog computing for the demanded machine translation service with relation to the added benefit of providing the service in the fog computing environment (710). For example, in one embodiment, an available edge server may not be the optimal server for the service. If at step (710) it is determined that the cost is a barrier to delivering the demanded service in the fog computing environment, an online machine translation service is selected and utilized for the demanded service (712), which in one embodiment may be a service available as a cloud based resource. Translation output across the cloud creates a translation (714) that is communicated to an associated network connected device that requested or demanded the translation (716). Accordingly, the cloud environment may be leveraged as a backup or alternative service layer to the fog computing environment for supporting machine translation.

However, a positive response to the determination at step (710) is an indication that the cost is not a barrier for setting up and delivering the machine translation service in the fog computing environment, and preparation for fog based delivery of the service is conducted (718). A selected machine translation module is designated for pushing to one or more edge servers (720) and added as a fog service by pushing (delivering) the selected machine translation module to the designated one or more edge servers (722). It is understood that in the case of the edge server being a non-optimal selection or a less desirable selection there is an inherent expense related to delivering the service. In one embodiment, use of the non-optimal edge server is an indication that the inherent cost of performing the machine translation service in the fog computing environment is less expensive than utilizing a shared resource in the cloud environment for the translation service. Accordingly, the machine translation module is pushed to the fog environment in order to efficiently service machine translations.

Following step (722) or a positive response to the determination at step (708), the requested or demanded machine translation service is utilized on an edge server in the fog computing environment to perform the machine translation service (724). At step (724), the demanded machine translation utilizes the machine translation module pushed to the edge server. In one embodiment, the translation module is copied from a cloud based server to the edge server for delivery of the associated machine translation service from the edge server to the requesting or designated client machine(s). The fog computing environment, by utilizing the edge server, creates a translation (726) that is communicated to an associated client (716), such as a client machine or device that requested or demanded the translation. Accordingly, at least three different options are shown herein for machine translation, with two of the options being in the fog computing layer, and the third option utilizing the cloud computing layer.

As suggested inFIG. 7, selection or designation of an appropriate edge server may be complex. For example, an edge server that is located proximal to the requesting device may be less expensive than a distally positioned edge server with respect to latency. However, the capacity of the edge servers must also be utilized as a factor with respect to expense. If a proximally positioned edge server is available but near capacity, the latency may be increased by utilizing a distal edge server. Referring toFIG. 8, a flow chart (800) is provided illustrating a process for statistically evaluating traffic data related to machine translation services in the fog computing environment. The statistical evaluation of associated traffic data includes both current and historical traffic data. In one embodiment, traffic data is acquired for each of the edge servers at periodic intervals. Examples of change include pushing at least one translation module to one of the edge servers, or removing at least one translation module from one of the edge servers.

At such time as a change in the fog computing environment is detected, traffic data is acquired across each of the identified edge servers, including each edge server that has been directly or indirectly affected by the change, (802). For example, an edge server that has received a machine translation module or an edge server that has had a machine translation module removed has a direct affect from the change. Similarly, a different edge server in the fog is indirectly affected by the change. In one embodiment, traffic data is acquired for each of the edge servers whenever there is a change in the fog computing environment. Accordingly, traffic data is acquired in response to a configuration change in the fog computing environment.

Data pertaining to a current state of each of the edge servers is identified (804). Similarly, the current state data is stored so that it may be employed for historical evaluation of the state of the edge servers and the fog computing environment (806). In one embodiment, maintenance of the state data includes measuring and managing machine translation data usage in each edge server, with the usage data including quantity and type of machine translation services and data traffic for each service type. Accordingly, for each change in translation module allocation in the fog computing environment, data traffic and associated data characteristics in the fog computing environment are acquired.

The state of the fog computing environment and specifically the state of the edge servers in the fog computing environment may be assessed in its current state based on its current state data, as well as in any historic state based on past state data. For example, at such time as a machine translation service may be requested (808), the traffic in the fog computing environment is evaluated (810). This evaluation at step (810) includes a statistical traffic evaluation and assessment of the current state of the fog layer, including the state of the requested or preferably edge server to support the service request. In one embodiment, the evaluation at step (810) includes a statistical evaluation and assessment of each edge server in the fog computing environment. Similarly, the evaluation is extended to include a historical assessment of the state of the fog computing environment (812). In one embodiment, the historical assessment may be particular to the requested translation module(s), whether the request historically includes a subsequent module request, the length of prior request of the same module(s), prior traffic associated with the module and the designated server, etc. Accordingly, the statistical evaluation includes current and past request, subject and ancillary edge servers, and subject and ancillary translation modules, each associated with the selected machine translation services.

Following the evaluation at step (812), a module and edge server, or in one embodiment a selection to employ the cloud layer in place of the fog computing environment, are identified, and the module is selectively pushed to the identified edge server (814). More specifically, the selective pushing incorporates or otherwise utilizes the statistical evaluation at step (810). Machine translation services are provided in real-time to the requesting client machine. More specifically, a received input string in a source language is translated in real-time by the translation module on the designated edge server (816). The selective pushing at step (814) may be reactive based on a current state in the fog computing environment including demand of translation service requests. Similarly, the selective pushing at step (814) may be reactive based on a historical assessment of service requests of the specified machine translation service in the fog computing environment. The selective pushing at step (814) may also be proactive according to client characteristics, such as prediction of a quantity of native language clients.

It is understood that there a finite quantity of machine translation service modules, a finite quantity of edge servers, and limitations with respect to bandwidth, capacity and traffic. A machine translation module may be selectively pushed to an edge server, and at such time as demand for the service changes, the machine translation module may be selectively removed from the edge server. Referring toFIG. 9, a flow chart (900) is provided illustrating a process for selecting and removing one or more machine translation modules from an edge server. For each edge server, X, an edge server profile, Y, is maintained (902). Each edge serverXprofileYincludes a rule related to the machine translation modules which includes one or more conditions. For example, the rule may include a condition directly related to the quantity of requests received for use of one of the machine translation modules. The rule associated with serverXprofileYis determined (904). A determination is made of whether the condition of the rule has been established or is not established (906). For a module that has not already been pushed to an edge server, at such a time as the condition of the rule is determined to be established at step (906), the module is selectively pushed to the edge server (908). In one embodiment, the module is selectively pushed to the edge server in response to a minimum quantity of translation requests being met or exceeded. The minimum quantity can be dynamically determined or preset based on a parameter. Similarly, for a module that has already been pushed to an edge server, at such time as the condition of the rule is determined to be no longer established at step (906), the module is selectively removed from the edge server (910). In one embodiment, the module is selectively removed in response to a demand decreasing below a minimum quantity of translation requests.

Whether the determination of the condition is followed by selectively pushing or removal, following either of steps (908) and (910), the profile of the subject edge server is updated (912), and the update is applied across the edge servers in the fog computing environment (914). In one embodiment, the edge server profiles may be merged and shared across the fog computing environment. Similarly, in one embodiment, each machine translation module may have an associated profile, that also may be received, shared, and merged among the edge servers in the fog computing environment. The rule for one or more of the servers may be static, or in one embodiment, the rule for one or more of the edge servers may be dynamic and/or weighted, with the weighting having an increased or decreased effect on the selective pushing or removing of translation modules across the fog computing environment. Accordingly, the machine translation modules are part of a fluid system which can dynamically change configuration in order to align with demand for machine translation service requests.

In this document, the terms “computer program medium,” “computer usable medium,” and “computer readable medium” are used to generally refer to media such as main memory (228), including RAM (230), cache (232), and storage system (234), such as a removable storage drive and a hard disk installed in a hard disk drive.

Computer programs (also called computer control logic) are stored in memory (228). Computer programs may also be received via a communication interface, such as network adapter (220). Such computer programs, when run, enable the computer system to perform the features of the present embodiments as discussed herein. In particular, the computer programs, when run, enable the processing unit (216) to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.

The embodiments described herein may be implemented in a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out the embodiments described herein.

The embodiments are described herein with reference to flow chart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products. It will be understood that each block of the flow chart illustrations and/or block diagrams, and combinations of blocks in the flow chart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

It will be appreciated that, although specific embodiments have been described herein for purposes of illustration, various modifications may be made without departing from the spirit and scope of the specific embodiments described herein. Accordingly, the scope of protection is limited only by the following claims and their equivalents.

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present embodiments has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments in the form disclosed.

It will be appreciated that, although specific embodiments have been described herein for purposes of illustration, various modifications may be made without departing from the spirit and scope of the embodiments. In particular, the modules are not restricted to machine translation services modules. In one embodiment, the service modules may be a part of any modular program, software or application. Accordingly, the scope of protection of these embodiments is limited only by the following claims and their equivalents.