Abstract: Aspects of the present disclosure relate to audio/video (A/V) stream functionality verification. A stream segment of a video feed prior to transmission over a network as captured by a transmitting device within a web-based conference can be stored. A stream segment of the video feed after transmission over the network as received by a receiving device within the web-based conference can be stored. The stream segment of the video feed prior to transmission over the network can be compared with the stream segment of the video feed after transmission over the network to determine a video feed quality.

Abstract: Aspects of the present disclosure relate to audio/video (A/V) stream functionality verification. A stream segment of a video feed prior to transmission over a network as captured by a transmitting device within a web-based conference can be stored. A stream segment of the video feed after transmission over the network as received by a receiving device within the web-based conference can be stored. The stream segment of the video feed prior to transmission over the network can be compared with the stream segment of the video feed after transmission over the network to determine a video feed quality.

Abstract: A method, computer system, and a computer program product for identifying one or more item restrictions of a user. The present invention may include obtaining a plurality of item data from one or more ingested items associated with the user by utilizing a blockchain structure. The present invention may then include obtaining a plurality of symptom data associated with one or more symptoms of the user. Further, the present invention may also include predicting the one or more item restrictions associated with the user based on the obtained plurality of item data from the one or more ingested items and the obtained plurality of symptom data.

Abstract: Disclosed is a novel system and method for managing requests for an additional virtual machine. The method begins with operating at least one virtual machine accessing at least one computer resource associated with at least one physical machine within a computing cluster. One or more non-deterministic virtual machine requests for the computer resource are received. An over-utilization of the computer resource as a probability distribution function is modeled. In one example, the probability distribution function is a Beta distribution function to represent a one of a plurality of probability distribution functions. Next, an additional virtual machine on the physical machine associated with the computer resource is added in response to a probability of a utilization of the computer resource being greater than a probalistic bound on the over-utilization of the computer resource. Otherwise, the additional virtual machine is not added.

Abstract: Placing an application on a private portion and a public portion of a hybrid computing environment for processing. An application may be received for placement and processing. A primary processing objective and a split preference of the application may be determined. The split preference indicates whether the application can be processed using one or both of the private portion and the public portion of the hybrid computing environment. The application may be placed on one or both of the private portion and the public portion of the hybrid computing environment for processing, based on the primary processing objective and based on the split preference.

Abstract: Disclosed is a novel system and method for managing requests for an additional virtual machine. The method begins with operating at least one virtual machine accessing at least one computer resource associated with at least one physical machine within a computing cluster. One or more non-deterministic virtual machine requests for the computer resource are received. An over-utilization of the computer resource as a probability distribution function is modeled. In one example, the probability distribution function is a Beta distribution function to represent a one of a plurality of probability distribution functions. Next, an additional virtual machine on the physical machine associated with the computer resource is added in response to a probability of a utilization of the computer resource being greater than a probalistic bound on the over-utilization of the computer resource. Otherwise, the additional virtual machine is not added.

Abstract: Embodiments of the invention provide a method, a system and a computer program product configured to automatically auto-scale a user compute instance to multiple cloud providers while considering a multiplicity of user requirements. The method, executed on a digital data processor, includes obtaining information, via a user interface, that is descriptive of user cloud computing related preferences, including a user cloud computing budgetary preference. The method further includes sensing properties of a plurality of clouds and making decisions, based at least on the obtained information and on the sensed properties, of when to scale up or scale down the user cloud instance, of selecting one of the plurality of clouds as where to scale the user cloud instance, and determining which resource or resources of the selected cloud to add or remove from the selected cloud. The method further includes automatically executing the decisions on the selected cloud.

Abstract: Embodiments of the invention provide a method, a system and a computer program product configured to automatically auto-scale a user compute instance to multiple cloud providers while considering a multiplicity of user requirements. The method, executed on a digital data processor, includes obtaining information, via a user interface, that is descriptive of user cloud computing related preferences, including a user cloud computing budgetary preference. The method further includes sensing properties of a plurality of clouds and making decisions, based at least on the obtained information and on the sensed properties, of when to scale up or scale down the user cloud instance, of selecting one of the plurality of clouds as where to scale the user cloud instance, and determining which resource or resources of the selected cloud to add or remove from the selected cloud. The method further includes automatically executing the decisions on the selected cloud.

Abstract: Disclosed is a novel system and method for managing requests for an additional virtual machine. The method begins with operating at least one virtual machine accessing at least one computer resource associated with at least one physical machine within a computing cluster. One or more non-deterministic virtual machine requests for the computer resource are received. An over-utilization of the computer resource as a probability distribution function is modeled. In one example, the probability distribution function is a Beta distribution function to represent a one of a plurality of probability distribution functions. Next, an additional virtual machine on the physical machine associated with the computer resource is added in response to a probability of a utilization of the computer resource being greater than a probalistic bound on the over-utilization of the computer resource. Otherwise, the additional virtual machine is not added.

Abstract: Techniques for workload management in cloud computing infrastructures are provided. In one aspect, a method for allocating computing resources in a datacenter cluster is provided. The method includes the steps of: creating multiple, parallel schedulers; and automatically selecting a resource allocation method for each of the schedulers based on one or more of a workload profile, user requirements, and a state of the datacenter cluster, wherein an optimistic resource allocation method is selected for at least a first one or more of the schedulers and a pessimistic resource allocation method is selected for at least a second one or more of the schedulers. Due to optimistic resource allocation conflicts may arise. Methods to resolve such conflicts are also provided.

Abstract: Techniques for workload management in cloud computing infrastructures are provided. In one aspect, a method for allocating computing resources in a datacenter cluster is provided. The method includes the steps of: creating multiple, parallel schedulers; and automatically selecting a resource allocation method for each of the schedulers based on one or more of a workload profile, user requirements, and a state of the datacenter cluster, wherein an optimistic resource allocation method is selected for at least a first one or more of the schedulers and a pessimistic resource allocation method is selected for at least a second one or more of the schedulers. Due to optimistic resource allocation conflicts may arise. Methods to resolve such conflicts are also provided.

Abstract: Disclosed is a novel system and method for managing requests for an additional virtual machine. The method begins with operating at least one virtual machine accessing at least one computer resource associated with at least one physical machine within a computing cluster. One or more non-deterministic virtual machine requests for the computer resource are received. An over-utilization of the computer resource as a probability distribution function is modeled. In one example, the probability distribution function is a Beta distribution function to represent a one of a plurality of probability distribution functions. Next, an additional virtual machine on the physical machine associated with the computer resource is added in response to a probability of a utilization of the computer resource being greater than a probalistic bound on the over-utilization of the computer resource. Otherwise, the additional virtual machine is not added.

Abstract: Disclosed is a novel system and method for managing requests for an additional virtual machine. The method begins with operating at least one virtual machine accessing at least one computer resource associated with at least one physical machine within a computing cluster. One or more non-deterministic virtual machine requests for the computer resource are received. An over-utilization of the computer resource as a probability distribution function is modeled. In one example, the probability distribution function is a Beta distribution function to represent a one of a plurality of probability distribution functions. Next, an additional virtual machine on the physical machine associated with the computer resource is added in response to a probability of a utilization of the computer resource being greater than a probabilistic bound on the over-utilization of the computer resource. Otherwise, the additional virtual machine is not added.

Abstract: Systems and methods for determining a representation of an execution trace include identifying at least one execution trace of a business process model, the business process model including parallel paths where a path influences an outcome of a decision. Path information of the business process model is determined using a processor, the path information including at least one of task execution order for each parallel path, task execution order across parallel paths, and dependency between parallel paths. A path representation for the at least one execution trace is selected based upon the path information to determine a representation of the at least one execution trace.

Abstract: Embodiments of the invention provide a method, a system and a computer program product configured to automatically auto-scale a user compute instance to multiple cloud providers while considering a multiplicity of user requirements. The method, executed on a digital data processor, includes obtaining information, via a user interface, that is descriptive of user cloud computing related preferences, including a user cloud computing budgetary preference. The method further includes sensing properties of a plurality of clouds and making decisions, based at least on the obtained information and on the sensed properties, of when to scale up or scale down the user cloud instance, of selecting one of the plurality of clouds as where to scale the user cloud instance, and determining which resource or resources of the selected cloud to add or remove from the selected cloud. The method further includes automatically executing the decisions on the selected cloud.

Abstract: Embodiments of the invention provide a method, a system and a computer program product configured to automatically auto-scale a user compute instance to multiple cloud providers while considering a multiplicity of user requirements. The method, executed on a digital data processor, includes obtaining information, via a user interface, that is descriptive of user cloud computing related preferences, including a user cloud computing budgetary preference. The method further includes sensing properties of a plurality of clouds and making decisions, based at least on the obtained information and on the sensed properties, of when to scale up or scale down the user cloud instance, of selecting one of the plurality of clouds as where to scale the user cloud instance, and determining which resource or resources of the selected cloud to add or remove from the selected cloud. The method further includes automatically executing the decisions on the selected cloud.

Abstract: Placing an application on a private portion and a public portion of a hybrid computing environment for processing. An application may be received for placement and processing. A primary processing objective and a split preference of the application may be determined. The split preference indicates whether the application can be processed using one or both of the private portion and the public portion of the hybrid computing environment. The application may be placed on one or both of the private portion and the public portion of the hybrid computing environment for processing, based on the primary processing objective and based on the split preference.

Abstract: Various embodiments select at least one service provider from a plurality of service providers in a computing environment to satisfy at least one service request. In one embodiment, a service request is received from a user. The service request includes at least a set of service requirements to be satisfied by at least one service provider. A satisfaction level is predicted for each of a plurality of service providers with respect to each of the set of service requirements. The prediction is based on a prediction satisfaction model associated with each of the plurality of service providers. At least one service provider is selected from the plurality of service providers for satisfying the service request based on the satisfaction level predicted for each of the plurality of service providers.

Abstract: Various embodiments select at least one service provider from a plurality of service providers in a computing environment. In one embodiment, a service request is received from a user. The service request comprises at least a set of service requirements to be satisfied by at least one service provider. A satisfaction level is predicted for each of a plurality of service providers with respect to each of the set of service requirements. The prediction is based on a prediction satisfaction model associated with each of the plurality of service providers. A probability of an actual observed satisfaction level being higher than at least a user defined threshold is calculated for each of the predicted satisfaction levels. At least one service provider is selected from the plurality of service providers for satisfying the service request based on the probability that has been calculated for each satisfaction level predicted.

Abstract: Systems and methods for determining a representation of an execution trace include identifying at least one execution trace of a business process model, the business process model including parallel paths where a path influences an outcome of a decision. Path information of the business process model is determined using a processor, the path information including at least one of task execution order for each parallel path, task execution order across parallel paths, and dependency between parallel paths. A path representation for the at least one execution trace is selected based upon the path information to determine a representation of the at least one execution trace.