Abstract: A computer-implemented method, computer program product, and computing system is provided for managing quality of experience for communication sessions. In an implementation, a method may include determining a participant focus metric for each of a plurality of participants of a communication session. The method may also include identifying one of the plurality of participants of the communication session as having sufficient capacity to support the communication session based upon, at least in part, the participant focus metric for each of the plurality of participants of the communication session. The method may also include selecting the one of the plurality of participants having a sufficient capacity to support the communication session as a selected focus for the communication session. The method may further include adaptively implementing a centralized communication session architecture utilizing the selected focus.

Abstract: Embodiments for data replication in a distributed file system environment by a processor. Data replication of one or more files that are more frequently used as compared to other files in a plurality of files may be increased according to hot data detected from one or more queries to a distributed file system.

Abstract: A Voice-over-IP (VOIP) system includes a plurality of multi control units (MCUs), in which a first MCU hosts a VOIP session. A method for recommending an MCU to host the VOIP session includes receiving MCU status associated with performance of each of the first MCU and a second MCU included in the VOIP system. The method further includes determining, based on the first and the second MCU status, that hosting the VOIP session on the second MCU can improve a VOIP system metric. According to the method, a recommendation is communicated to transfer hosting the VOIP session to the second MCU.

Abstract: A computer-implemented method, computer program product, and computing system is provided for determining quality of experience for communication sessions. In an implementation, a method may include determining a plurality of intrusive quality of experience scores associated with a plurality of intrusive audio transmission samples. The method may also include determining a plurality of non-intrusive quality of experience scores associated with a plurality of non-intrusive audio transmission samples. The method may further include deriving a quality of experience coefficient based upon a relationship between the plurality of intrusive quality of experience scores and the plurality of non-intrusive quality of experience scores.

Abstract: A conference call management method, system, and computer program product include inferring an Internet Protocol (IP) address of a new user requesting to join a call including at least one other user, inferring a codec to stream an emulated network pattern for the call if the new user were to join the call, measuring a call quality perceived by the at least one other user in the call while the emulated codec is run on the call, and measuring an impact on the call quality in a case that the new user joins the call based on the perceived call quality and the network pattern.

Abstract: Dynamically identifying performance anti-patterns in a software system is based on a set of documented symptoms that are evaluated in real-time. The evaluation is based on the observed system behavior and its comparison against the documented symptoms of different types of performance issues.

Abstract: Techniques that facilitate machine learning optimization are provided. In one example, a system includes a computational resource component, a batch interval component, and a machine learning component. The computational resource component collects computational resource data associated with a group of computing devices that performs a machine learning process. The batch interval component determines, based on the computational resource data, batch interval data indicative of a time interval to collect data for the machine learning process. The machine learning component provides the batch interval data to the group of computing devices to facilitate execution of the machine learning process based on the batch interval data.

Abstract: A system and method for recognizing recurrent crowd mobility patterns in a defined geographical area are presented. A crowded area may be determined for a selected geographical region at predefined time intervals based on spatial distribution of a plurality of users collected from a social media network. A crowd footprint may be generated according to the crowded areas determined at the predefined time intervals. Recurrent crowd mobility patterns may be detected according to the crowd footprint.

Abstract: Performance regression between a first version of an application and a second version of the application are detected using a comparison between transaction response times measured at a plurality of resource levels of a computer system. A first set of transaction response times (for the first version) are used to determine lower and upper limit line graphs. A new line graph determined from second set of transaction response times (for the second version) can be compared with the lower and upper limit line graphs to detect a performance regression.

Abstract: A Voice-over-IP (VOIP) system includes a plurality of multi control units (MCUs), in which a first MCU hosts a VOIP session. A method for recommending an MCU to host the VOIP session includes receiving MCU status associated with performance of each of the first MCU and a second MCU included in the VOIP system. The method further includes determining, based on the first and the second MCU status, that hosting the VOIP session on the second MCU can improve a VOIP system metric. According to the method, a recommendation is communicated to transfer hosting the VOIP session to the second MCU.

Abstract: An embodiment includes a method for use in managing a cluster comprising a plurality of computers, each computer comprising at least one hardware processor coupled to at least one memory. The method comprises a computer-implemented manager: collecting access pattern data for the plurality of computers; based at least in part on the access pattern data, determining that at least a first one of the plurality of computers is overutilized; and responsive to determining that the first computer is overutilized, moving stored data from the first computer to at least a second one of the plurality of computers.

Abstract: An embodiment includes a method for use in managing a system comprising one or more computers, each computer comprising at least one hardware processor coupled to at least one memory, the method comprising a computer-implemented manager: generating a potential configuration for hardware resources of the system; determining whether the potential configuration satisfies accuracy and time constraints for a selected machine learning model; if the potential configuration satisfies the constraints, indicating the potential configuration to be the optimal configuration for the system; and if the potential configuration does not satisfy the constraints, adapting the potential configuration to satisfy the constraints. The adapting may comprise repeating the generating and determining steps. The adapting may be based at least in part on the hardware resources and the selected machine learning model.

Abstract: An embodiment includes a method for use in managing a system comprising one or more computers, each computer comprising at least one hardware processor coupled to at least one memory, the method comprising a computer-implemented manager: generating a potential configuration for hardware resources of the system; determining whether the potential configuration satisfies accuracy and time constraints for a selected machine learning model; if the potential configuration satisfies the constraints, indicating the potential configuration to be the optimal configuration for the system; and if the potential configuration does not satisfy the constraints, adapting the potential configuration to satisfy the constraints. The adapting may comprise repeating the generating and determining steps. The adapting may be based at least in part on the hardware resources and the selected machine learning model.

Abstract: An improvement to the process for identifying software problems in performance testing is achieved by dynamically adjusting workloads in real-time to stress the functionality of an application suspected of causing a software problem.

Abstract: A computer-implemented method, computer program product, and computing system is provided for determining quality of experience for communication sessions. In an implementation, a method may include determining a plurality of intrusive quality of experience scores associated with a plurality of intrusive audio transmission samples. The method may also include determining a plurality of non-intrusive quality of experience scores associated with a plurality of non-intrusive audio transmission samples. The method may further include deriving a quality of experience coefficient based upon a relationship between the plurality of intrusive quality of experience scores and the plurality of non-intrusive quality of experience scores.

Abstract: A computer-implemented method, computer program product, and computing system is provided for determining quality of experience for communication sessions. In an implementation, a method may include determining a plurality of intrusive quality of experience scores associated with a plurality of intrusive audio transmission samples. The method may also include determining a plurality of non-intrusive quality of experience scores associated with a plurality of non-intrusive audio transmission samples. The method may further include deriving a quality of experience coefficient based upon a relationship between the plurality of intrusive quality of experience scores and the plurality of non-intrusive quality of experience scores.

Abstract: A computer-implemented method, computer program product, and computing system is provided for determining quality of experience for communication sessions. In an implementation, a method may include determining a plurality of intrusive quality of experience scores associated with a plurality of intrusive audio transmission samples. The method may also include determining a plurality of non-intrusive quality of experience scores associated with a plurality of non-intrusive audio transmission samples. The method may further include deriving a quality of experience coefficient based upon a relationship between the plurality of intrusive quality of experience scores and the plurality of non-intrusive quality of experience scores.

Abstract: A Voice-over-IP (VOIP) system includes a plurality of multi control units (MCUs), in which a first MCU hosts a VOIP session. A method for recommending an MCU to host the VOIP session includes receiving MCU status associated with performance of each of the first MCU and a second MCU included in the VOIP system. The method further includes determining, based on the first and the second MCU status, that hosting the VOIP session on the second MCU can improve a VOIP system metric. According to the method, a recommendation is communicated to transfer hosting the VOIP session to the second MCU.

Abstract: A conference call management method, system, and computer program product include inferring an Internet Protocol (IP) address of a new user requesting to join a call including at least one other user, inferring a codec to stream an emulated network pattern for the call if the new user were to join the call, measuring a call quality perceived by the at least one other user in the call while the emulated codec is run on the call, and measuring an impact on the call quality in a case that the new user joins the call based on the perceived call quality and the network pattern.

Abstract: A method for dynamically and adaptively monitoring a system based on its running behavior adjusts monitoring levels of the monitored application in real-time. A rules-based mechanism dynamically adjusts monitoring levels in real-time, based on the system's performance observed during a workload run, whether in a production or test environment.