Methods and systems for selection of remote services

Embodiments for selecting a remote service for a core program are provided. A request for a remote service is received. Information associated with each of a plurality of remote services is received from at least one information source. A score for each of the plurality of remote services is calculated based on the information associated with each of the plurality of remote services and at least one remote service evaluation criteria.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates in general to computing systems, and more particularly, to various embodiments for selection of remote services.

Description of the Related Art

Generally, distributing computing (or distributed computing systems) refers to computing systems (e.g., cloud systems, “edge” systems, etc.) and/or programs that utilize multiple components (e.g., remote services, nodes, containers, etc.) that are implemented on different networked computing devices that coordinate their actions by sending various types of communications (e.g., requests, data, etc.) to one another. Often, this activity is performed in order to achieve a common goal (i.e., the components are working together to jointly perform a functionality or task). For example, the components (e.g., a core program and remote services, such as microservices) may jointly run a distributed program. The implementation of such systems is becoming increasingly common as many organizations (e.g., companies, service providers, etc.) are transitioning from monolithic applications towards utilizing, for example, multiple microservices and packaged containers.

In some instances, over time, multiple providers are able to provide the same remote service (e.g., microservice). In such cases, the core program (and/or a human responder) may require a method to select the appropriate (and/or the “best”) provider. As such, a remote services selection engine (and/or method) may become a fundamental control point of remote services, in the same way search engines have become such for the Internet in general.

SUMMARY OF THE INVENTION

Various embodiments for selecting a remote service for a core program, by a processor, are provided. A request for a remote service is received. Information associated with each of a plurality of remote services is received from at least one information source. A score for each of the plurality of remote services is calculated based on the information associated with each of the plurality of remote services and at least one remote service evaluation criteria.

In addition to the foregoing exemplary embodiment, various other system and computer program product embodiments are provided and supply related advantages. The foregoing Summary has been provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background.

DETAILED DESCRIPTION OF THE DRAWINGS

As discussed above, generally, distributing computing (or distributed computing systems) refers to computing systems (e.g., cloud systems, “edge” systems, etc.) and/or programs that utilize multiple components (e.g., remote services, nodes, containers, etc.) that are implemented on different networked computing devices that coordinate their actions by sending various types of communications (e.g., requests, data, etc.) to one another. Often, this activity is performed in order to achieve a common goal (i.e., the components are working together to jointly perform a functionality or task). For example, the components (e.g., a core program and remote services, such as microservices) may jointly run a distributed program. The implementation of such systems is becoming increasingly common as many organizations (e.g., companies, service providers, etc.) are transitioning from monolithic applications towards utilizing, for example, multiple microservices and packaged containers.

More specifically, in recent years, the Internet has transformed the formation of applications from monolithic applications (e.g., single code modules composed of multiple libraries to perform various functions) to core programs that utilize web (or web-based) or “remote” services, such as microservices. A properly enabled program written in this internet microservice approach may utilize tens to thousands of evocations of a particular microservice.

In some instances, over time, multiple providers are able to provide the same remote service (e.g., microservice). For example, if a core program requires the current temperature at a particular location, such information may be available through many different online channels (or microservices, providers, etc.). In such cases, the core program (and/or a human responder) may require a method to select the appropriate (and/or the “best”) provider. As such, a remote services selection engine (and/or method) may become a fundamental control point of remote services, in the same way search engines have become such for the Internet in general.

To address these needs and/or the shortcomings in the prior art, in some embodiments described herein, methods and/or systems are disclosed that facilitate (and/or provide for) the selection of a remote service (and/or the provider of a remote service). It should be noted that there may be hundreds of selection actions for a single program, each different and each potentially resulting in selection of a different remote service (e.g., microservice) and/or provider. It may be difficult and/or expensive to development software and/or updates thereof in such a way to enhance core programs with the ability to select remote services and/or providers. As such, in some embodiments, the functionality described herein (e.g., scoring, selecting, etc. remote services and/or providers) may be provided by a “remote services selection engine” and/or another node (e.g., a centralized system/server) that is separate from (or external to) the core program (or primary computing system).

It should also be understood that although embodiments described herein may refer to the selection (and/or utilization) of “microservices,” the methods and systems described herein may be applied to the selection of other types of remote services or distributed computing components, such as procedure calls, application components, packaged containers, nodes, various types of services, etc. As such, as used herein, “remote service” may refer to any action, service, functionality, etc. (and/or hardware performing such) that is not performed by (and/or is external to) a core program (and/or primary computing system) managed, administered, etc. by a particular user (or consumer). Additionally, remote services may or may not be implemented on/utilizing computing systems different than that of the core program (e.g., a remote computing system in operable communication with a primary computing system via any suitable communications network). Examples may include, but are not limited to, remote services that provide types of information (e.g., weather related information, stock prices, etc.) and perform various types of functionality (e.g., related to accounting, shipping, engineering, software development, data storage, etc.).

In some embodiments, a user or consumer (e.g., an individual, core application, etc.) of a cloud service (e.g., remote service, microservice, application, etc.), which may be provided by a cloud service provider (CSP), identifies one or more selection criteria to a cloud broker (CB) (or other centralized service, server, system, etc.). The CB may gather information associated with one or more remote services (and/or remote service providers) from one or more information sources (IS). The CB may then apply a measurement aggregation method to the collected information and select a remote service (and/or provider) for the user. For example, the CB may calculate a score for each of the available remote services/providers and provide an indication of the remote service (and/or provider) with the highest score to the user and/or automatically cause the remote service with the highest score to be utilized (e.g., by the core program).

In some embodiments, the information (or attribute information) associated with the remote (or external) services and/or providers (e.g., CSPs) utilized to select a remote service and/or calculate scores for remote services (i.e., evaluation criteria) may include any type of information related to the remote service(s) and/or the provider(s) thereof, including technical details of the service and non-technical details related to the service and/or the provider thereof. Examples include, but are not limited to, basic information about and/or attributes of the service (e.g., functional capabilities, technical information, etc.), cost, financial information (e.g., bond rating, cash flow, etc.), social metrics (e.g., rating regarding “green” (or environmentally “friendly”) energy, presence/lack of “negative” social associations (e.g., reviews/complaints, social media activity, etc.), information technology (IT) security posture (e.g., number of security breeches in the previous year, IT security evaluation score, etc.), legal information (e.g., litigation activity, arbitration information, etc.), location (e.g., state/locale of incorporation), IT service performance and/or management (e.g., service level agreements (SLAs), mean time to repair (MTTR), support personal per customer, etc.), engagement terms (e.g., length of contract, payment information, etc.), the presence of relationships between providers and the CB, or any other relevant information related to the remote service/provider, including other types of information described herein (which may or may not be related to the user/consumer).

In some embodiments, the aggregation method includes (or utilizes or calculates) a “degree-of-trust” (or trustworthiness) factor (or measure, score, etc.) with respect to each of the information sources. For example, the user may be provided with the ability to weigh the information from a relatively trusted information source higher than that from a less trusted information source. The trustworthiness score for each of the information sources may be retrieved or received (e.g., from another information source, such as a website or database, or may be automatically set by the system) or may be selected/set by the user (e.g., via a setting/configuration functionality or included with a request for a service).

For example, in an embodiment in which higher trustworthiness scores indicate more trustworthy information sources, a respected financial rating agency (e.g., which may provide a bond rating for a provider) may be given a trustworthiness score of 5, while a less respected source (e.g., a tabloid-like website) may be given a trustworthiness score of 1. Similarly, if the information source providing the number of security incidents experienced by a provider is the provider itself, such a source may be given a low trustworthiness score compared to a governmental reporting agency providing the same type of information. Also, in some embodiments, the degree of trust associated with information sources may be established by category. For example, if an information source is related to a government considered to have little corruption, the trustworthiness score may be 10, if the information source is related to a top-rated university, the trustworthiness score may be 9, but if the information source is an entity in a country designated as having significant corruption, then the trustworthiness score may be 2.

In some embodiments, particular evaluation criteria may be selected and weighted. For example, the user may be provided with the ability to select specific evaluation criteria and apply a weighting (or weighting scheme) to the criteria (e.g., via a setting/configuration functionality or included with a request for a service). For example, the user may select the evaluation criteria to include, for example, the provider's bond rating (weighted as 5% of the score), green energy rating (weighted as 15% of the score), number of security breeches (weighted as 50% of the score), and arbitration state (weighted as 30% of the score), perhaps along with a minimum required score for selection.

In such an example, if a provider's bond rating is “AAA,” the provider may receive a score of 10 (or 10 points) for that criterion. However, if the bond rating is “BBB,” the provider may only receive 5 points, and 0 points if the bond rating is “junk.” With respect to green energy, a provider may receive 20 points if 100% of the energy the provider utilizes is considered green, but only 5 points if 50% of the utilized energy is green. With respect to security breeches, if the provider experienced no security breeches (e.g., within the previous year, two years, etc.), the provider may receive 20 points, while 1 breach may result in a score of 0 points, 2 breaches may result in a score of −20 (i.e., a reduction of 20 points), and more than two breaches resulting in a score of −100. With respect to arbitration state, as one example, some states in the United States (or other regions/locales in other countries) may be considered to have more beneficial laws/rules regarding arbitration, and thus such providers may receive 20 points, while other providers in other states/locales may only receive 10 points. As mentioned above, in some embodiments, a minimum score may be set/selected such that if none of the providers (or microservices) receives an aggregated score above that threshold, no provider is selected (and/or utilized by the user). However, in some embodiments, such a threshold may be predetermined (e.g., based on the type of service requested).

It should be understood that at least some of the aspects of functionality described herein (e.g., calculating scores for remote services/providers, determining trustworthiness scores for information sources, etc.) may be performed utilizing a cognitive analysis. The cognitive analysis may include natural language processing (NLP) or a NLP technique, such classifying natural language, analyzing tone, and analyzing sentiment (e.g., scanning for keywords, key phrases, etc.) with respect to, for example, content and communications sent to and/or received by users or entities (e.g., providers) and/or other available data sources. In some embodiments, natural language processing (NLP), Mel-frequency cepstral coefficients (MFCCs) (e.g., for audio content), and/or region-based convolutional neural network (R-CNN) pixel mapping (e.g., for object detection/classification in images/videos), as are commonly understood, are used.

The processes described herein may utilize various information or data sources associated with users (e.g., consumers) and/or entities (e.g., remote services, providers, etc.) and/or the content of communications. The data sources may include any available information (or data) sources associated with the user and/or entities. For example, in some embodiments, a profile (e.g., a cognitive profile) for the user(s) (and/or entities) may be generated. Data sources that may be use used to generate cognitive profiles may include any appropriate data sources associated with the user/entity that are accessible by the system (perhaps with the permission or authorization of the user/entity). Examples of such data sources include, but are not limited to, communication sessions and/or the content (or communications) thereof (e.g., phone calls, video calls, text messaging, emails, in person/face-to-face conversations, etc.), a profile of (or basic information about) the user/entity (e.g., job title, place of work, length of time at current position, family role, etc.), a schedule or calendar (i.e., the items listed thereon, time frames, etc.), projects (e.g., past, current, or future work-related projects), location (e.g., previous and/or current location and/or location relative to other users), social media activity (e.g., posts, reactions, comments, groups, etc.), browsing history (e.g., web pages visited), and online purchases. The cognitive profile(s) may be utilized to, for example, evaluate remote services/providers and/or information sources.

As such, in some embodiments, the methods and/or systems described herein may utilize a “cognitive analysis,” “cognitive system,” “machine learning,” “cognitive modeling,” “predictive analytics,” and/or “data analytics,” as is commonly understood by one skilled in the art. Generally, these processes may include, for example, receiving and/or retrieving multiple sets of inputs, and the associated outputs, of one or more systems and processing the data (e.g., using a computing system and/or processor) to generate or extract models, rules, etc. that correspond to, govern, and/or estimate the operation of the system(s), or with respect to the embodiments described herein, selecting a remote service (and/or a provider of such), as described herein. Utilizing the models, the performance (or operation) of the system (e.g., utilizing/based on new inputs) may be predicted and/or the performance of the system may be optimized by investigating how changes in the input(s) effect the output(s). Feedback received from (or provided by) users and/or administrators may also be utilized, which may allow for the performance of the system to further improve with continued use.

It should be understood that as used herein, the term “computing node” (or simply “node”) may refer to a computing device, such as a mobile electronic device, desktop computer, etc. and/or an application, such a chatbot, an email application, a social media application, a web browser, etc. In other words, as used herein, examples of computing nodes include, for example, computing devices such as mobile phones, tablet devices, desktop computers, or other devices, such as appliances (IoT appliances) that are owned and/or otherwise associated with individuals (or users), and/or various applications that are utilized by the individuals on such computing devices.

In particular, in some embodiments, a method for selecting a remote service (e.g., for a core program), by a processor, is provided. A request for a remote service is received (e.g., from a user or core program). Information associated with each of a plurality of remote services is received from at least one information source. A score for each of the plurality of remote services is calculated based on the information associated with each of the plurality of remote services and at least one remote service evaluation criteria.

At least one of the plurality of remote services may be selected based on the calculating of the score for each of the plurality of remote services.

The at least one remote service evaluation criteria may be received. The receiving of the at least one remote service criteria may be performed at least one of before the receiving of the request for the remote service, during the receiving of the request for the remote service, and after the receiving of the request for the remote service.

The receiving of the information associated with each of the plurality of remote services may be performed at least one of before the receiving of the request for the remote service and after the receiving of the request for the remote service.

The at least one information source may include a plurality of information sources. The calculating of the score for each of the plurality of remote services may be based on a trustworthiness measure associated with each of the plurality of information sources.

The receiving of the request for the remote service may include receiving a weighting for each of the at least one remote service evaluation criteria. The calculating of the score for each of the plurality of remote services may be based on the weighting for each of the at least one remote service evaluation criteria. The at least one remote service evaluation criteria may include a minimum score threshold for selecting at least one of the plurality of remote services.

The received information may include information associated with a provider of each of the plurality of remote services. The information associated with the provider of each of the plurality of remote services may include at least one of financial information, security information, legal information, and environmental information.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

In the context of the present invention, and as one of skill in the art will appreciate, various components depicted inFIG. 1may be located in, for example, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, mobile electronic devices such as mobile (or cellular and/or smart) phones, personal data assistants (PDAs), tablets, wearable technology devices, laptops, handheld game consoles, portable media players, etc., as well as computing systems in vehicles, such as automobiles, aircraft, watercrafts, etc. However, in some embodiments, some of the components depicted inFIG. 1may be located in a computing device in, for example, a satellite, such as a Global Position System (GPS) satellite. For example, some of the processing and data storage capabilities associated with mechanisms of the illustrated embodiments may take place locally via local processing components, while the same components are connected via a network to remotely located, distributed computing data processing and storage components to accomplish various purposes of the present invention. Again, as will be appreciated by one of ordinary skill in the art, the present illustration is intended to convey only a subset of what may be an entire connected network of distributed computing components that accomplish various inventive aspects collectively.

Referring now toFIG. 2, illustrative cloud computing environment50is depicted. As shown, cloud computing environment50comprises one or more cloud computing nodes10with which local computing devices used by cloud consumers, such as, for example, cellular (or mobile) telephone or PDA54A, desktop computer54B, laptop computer54C, and vehicular computing system (e.g., integrated within automobiles, aircraft, watercraft, etc.)54N may communicate.

Still referring toFIG. 2, nodes10may 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 environment50to 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 devices54A-N shown inFIG. 2are intended to be illustrative only and that computing nodes10and cloud computing environment50can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Device layer55as shown includes sensor52, actuator53, “learning” thermostat56with integrated processing, sensor, and networking electronics, camera57, controllable household outlet/receptacle58, and controllable electrical switch59as shown. Other possible devices may include, but are not limited to, various additional sensor devices, networking devices, electronics devices (such as a remote control device), additional actuator devices, so called “smart” appliances such as a refrigerator, washer/dryer, or air conditioning unit, and a wide variety of other possible interconnected devices/objects.

As previously mentioned, in some embodiments, methods and/or systems for selecting a remote service (e.g., a microservice) are provided. For example, in some embodiments, request for a remote service is received (e.g., from a user or core program). Information associated with each of a plurality of remote services is received from one or more one information source. A score for each of the remote services is calculated based on the information associated with the remote services and at least one remote service evaluation criteria. At least one of the remote services may be selected based on the calculating of the score for each of the plurality of remote services.

The remote service evaluation criteria may be received or retrieved (e.g., before, during, and/or after the request for the remote service). The information associated with each of the remote services may be received (or retrieved) before and/or after the request for the remote service is received. The calculating of the scores for the remote services may be based on a trustworthiness measure associated with the information source(s).

The calculating of the scores may be based on a weighting for each of the remote service evaluation criteria, which may be received with the request or predetermined. The remote service evaluation criteria may include a minimum score threshold for selecting at least one of the plurality of remote services. The received information may include information associated with a provider of each of the plurality of remote services. The information associated with the providers may include at least one of financial information, security information, legal information, and environmental information. In other words, in some embodiments, the calculated of the scores is based (at least in part) on information external to (i.e., other than) technical details about the remote services.

FIG. 4illustrates a system (and/or computing environment)400in which some of the aspects of functionality described herein may be implemented. As shown, the system400includes a cloud broker402, a consumer404, information sources406-414, and cloud service providers416-434. It should be understood thatFIG. 4is merely intended as an example, and in other embodiments, different numbers of elements, components, entities, etc. (e.g., consumers, cloud brokers, information sources, and/or cloud service providers) may be included. It should also be understood that in some embodiments the various components shown inFIG. 4may be located remotely from each other and in operable communication via an suitable communications network (e.g., the Internet, the “cloud,” etc.).

The cloud broker (or centralized server, service, etc.)402may be implemented on any suitable computing system, such as those described above, and may be configured to perform at least some of the functionality described herein (including cognitive analyses). The consumer404shown inFIG. 4may be understood to represent any suitable computing node associated with a user (or consumer), such as a core program or primary computing system. The consumer404may be controlled automatically and/or via human intervention.

Still referring toFIG. 4, the information sources406-414may include any suitable sources of information or data associated with the cloud services providers416-434. For example, the information sources406-414may include websites, web pages, databases, social media feeds/platforms, etc. which are accessible by the cloud broker402. Each of the cloud service providers416-434may represent a provider of a cloud (or remote, external, etc.) service (e.g., a business, organization, etc. and/or an associated computing system) and/or a service (e.g., remote/external service, such as a microservice) provided by such. As shown, each of the information sources406-414is in operable communication with one or more of the cloud service providers416-434, each of which may be in operable communication with more than one of the information sources406-414. In other words, each of the information sources406-414may receive information from more than one of the cloud services providers416-434, and each of the cloud service providers416-434may provide information to more than one of the information sources406-414.

It should be noted that although the information sources406-414and cloud service providers416-434are shown as separate components (or entities) inFIG. 4, in some embodiments, at least some of the information sources406-414may be (directly) associated with one or more of the cloud service providers (e.g., a website of a cloud service provider). In other words, in some embodiments, at least some of the cloud service providers may essentially be (or function as) information sources and/or otherwise provide information related to the respective service(s)/provider(s) directly to the cloud broker. An example of such is shown inFIG. 4by the dashed line formed directly between the cloud broker402and cloud service provider434.

In some embodiments, the consumer404provides a request to the cloud broker402for a selection (or recommendation) of a cloud service provider (and/or a service provided by such). The request may include one or more evaluation criteria selected by the consumer404as being relevant or important (including the type of service requested), or the criteria may be provided by the consumer404in a subsequent communication. However, in some embodiments, the evaluation criteria may be predefined (e.g., based on the type of service requested, previously selected by the consumer, etc). In some embodiments, the cloud broker402retrieves (or receives) information associated with the cloud services providers416-434(e.g., associated with/related to the evaluation criteria of the consumer404) from the information sources406-414.

However, in some embodiments, the cloud broker402may retrieve information from the information sources406-414(and/or the cloud service providers416-434) before a request is received from a user (or consumer). For example, the cloud broker402may intermittently receive or retrieve information associated with cloud service providers416-434(e.g., from the information sources406-414and/or the cloud service providers416-434), store the information (or keep the information “cached”), and then utilize the information when, for example, a request is received from a consumer, as described below.

In some embodiments, the information associated with the cloud service providers416-434(and/or the service(s) provided thereby) that is received from the information sources406-414(and/or the evaluation criteria) may include, but is not limited to, basic information about and/or attributes of the service (e.g., functional capabilities), cost, financial information (e.g., bond rating, cash flow, etc.), social metrics (e.g., rating regarding “green” (or environmentally “friendly”) energy, presence/lack of “negative” social associations, reviews/complaints, social media activity), information technology (IT) security posture (e.g., number of security breeches in the previous year, IT security evaluation score, etc.), legal information (e.g., litigation activity, arbitration information, etc.), location, IT service performance and/or management (e.g., service level agreements (SLAs), mean time to repair (MTTR), support personal per customer, etc.), engagement terms (e.g., length of contract, payment information, etc.), the presence of relationships between providers and the CB, or any other relevant information, including other types of information described herein. It should be noted that at least some of the information/criteria may be related to types of information besides technical details about the services and/or providers (e.g., financial information, legal information, etc.).

The cloud broker402may then evaluate each of the cloud service providers (and/or the service(s) provided thereby). In some embodiments, the cloud broker402first generates (or creates) a list of the cloud service providers416-434that are generally capable of performing the functionality requested by the consumer. The cloud broker402may then calculate a score (or aggregated score) for each of the cloud service providers416-434on the list utilizing the evaluation criteria (e.g., provided by the consumer404). As described above, in some embodiments, the calculating of the scores is also based on a trustworthiness measure (or factor, score, etc.) associated with the information source406-414from which each respective type of information is received. The trustworthiness measure associated with each of the information sources406-414may be, for example, defined by the consumer (e.g., and included in the request), predetermined (e.g., based on the type of service requested), and/or retrieved from another information source (e.g., one or more database, website, etc.).

The scores may be calculated as numeric scores (e.g., scalars or vectors, positive or negative, on any scale, etc.) or may be in the form of “grades” (e.g., “high,” “low,” etc.). In some embodiments, the evaluation of the cloud service providers416-434is performed utilizing a cognitive analysis.

The cloud broker402may then select the cloud service provider416-434with the highest score (or in some embodiments, the lowest score) as the cloud service provider416-434that is “best” or “preferred” for the consumer404. In some embodiments, the evaluation criteria may include a minimum threshold. In such instances, a cloud service provider416-434may only be selected (or recommended) if one or more of the cloud service providers416-434receives a score above the threshold. After a cloud service provider416-434is selected, a signal representative thereof may be generated and provided to the consumer. For example, a notification of the selected cloud service provider416-434may be generated and sent to the consumer (e.g., via email, pop-up window, or any other suitable electronic communication). In some embodiments, the selected cloud service provider416-434is caused to be automatically utilized by the consumer404(e.g., utilized by a core program administrated by the consumer404).

Turning toFIG. 5, a flowchart diagram of an exemplary method500for selecting a remote (e.g., web-based, external to a core program, etc.) service (e.g., a microservice or other distributed computing component/service) is provided. The method500begins (step502) with, for example, a plurality of remote services being made available and a user (or consumer) deciding to utilize one of the remote services (and/or determining a need for such a service).

A request for a remote service is received (or retrieved, detected, etc.) (step504). For example, the user or core program may provide a request for a selection or recommendation of a remote service and/or remote service provider. In some embodiments, at least one remote service evaluation criteria (or criterion) is received and/or included with the request. However, in some embodiments, the criteria may be previously determined or selected (e.g., based on the type of service requested) and/or provided in a subsequent communication. The at least one remote service evaluation criteria may include a weighting for each of the at least one remote service evaluation criteria. The at least one remote service evaluation criteria may include a minimum score threshold for selecting at least one of the plurality of remote services.

Information associated with each of a plurality of remote services is received from at least one information source (step506). The at least one information source may include a plurality of information sources. The information source(s) may include, for example, websites, web pages, databases, social media feeds, etc. (which may or may not be directly associated with the remote services/providers). The received information may include information associated with a provider of each of the plurality of remote services. The information associated with the provider of each of the plurality of remote services may include at least one of financial information, security information, legal information, and environmental information. The receiving of the information associated with each of the plurality of remote services may be performed before and/or after the receiving of the request for the remote service.

A score is calculated for each of the plurality of remote services based on the information associated with each of the plurality of remote services and (the) at least one remote service evaluation criteria (step508). The calculating of the score for each of the plurality of remote services may be (further) based on a trustworthiness measure associated with each of the plurality of information sources and/or the weighting for each of the at least one remote service evaluation criteria, as described above.

Method500ends (step510) with, for example, at least one of the plurality of remote services being selected based on the calculated scores. A signal representative of the selection may be generated and provided to the user (e.g., via an electronic communication). In some embodiments, feedback from users may also be utilized to improve the performance of the system over time.