Abstract: A method for operating a teleconference includes receiving a first video and audio signal from a first teleconference participant and receiving a second video and audio signal from a second teleconference participant. The method further includes relaying the first video and audio signal to the second teleconference participant and relaying the second video and audio signal to the first teleconference participant. The method additionally includes analyzing the first video and audio signal using a plurality of behavior analytics to determine a level of participant engagement of the first teleconference participant. The method further includes determining that the first teleconference participant is not engaged when the determined level of participant engagement is below a predetermined threshold.

Abstract: A method for operating a teleconference includes receiving a first video and audio signal from a first teleconference participant and receiving a second video and audio signal from a second teleconference participant. The method further includes relaying the first video and audio signal to the second teleconference participant and relaying the second video and audio signal to the first teleconference participant. The method additionally includes analyzing the first video and audio signal using a plurality of behavior analytics to determine a level of participant engagement of the first teleconference participant. The method further includes determining that the first teleconference participant is not engaged when the determined level of participant engagement is below a predetermined threshold.

Abstract: A method and structure for dynamic memory re-allocation for an application runtime environment (ARE) includes receiving, through an interface of an application runtime environment (ARE), a first set of internal operational metrics of the ARE executing at a current setting S1 on a processor of a computer. A first performance P1 of the ARE is determined at the current setting S1 using the received first set of internal operation metrics. The current setting S1 of the ARE is varied to a new setting S2. A second set of internal operational metrics of the ARE executing at the new setting S2 is received through the interface of the ARE. A second performance P2 of the ARE is determined at the new setting S2, using the received second set of internal operation metrics. A memory allocation for the ARE is re-allocated, based on the determined performances P1 and P2.

Abstract: An apparatus includes at least one processor executing a method for managing memory among a plurality of concurrently-running virtual machines, and a non-transitory memory device that stores a set of computer readable instructions for implementing and executing said memory management method. A memory optimization mechanism can reduce a memory usage of a virtual machine at a cost of increasing a central processing unit (CPU) usage. Information on a memory usage and a CPU usage of each virtual machine is periodically collected. When a first virtual machine exhibits high memory use, at least one second virtual machine with an extra CPU capacity is identified. A memory optimization mechanism is applied to the second virtual machine to reduce memory used by the second virtual machine, thereby providing a portion of freed memory that is then allocated to the first virtual machine.

Abstract: A method for allocating memory among a plurality of computing systems includes assigning a free memory threshold to each computing system, dynamically varying at least one free memory threshold for at least one computing system based on a past memory usage of the at least one computing system, periodically monitoring each computing system for memory usage and in response to the monitoring determining that an amount of free memory for the computer system is below the free memory threshold for the computing system, allocating memory from the free memory pool to the computing system.

Abstract: A method and structure for dynamic memory re-allocation for an application runtime environment (ARE) includes receiving, through an interface of an application runtime environment (ARE), a first set of internal operational metrics of the ARE executing at a current setting S1 on a processor of a computer. A first performance P1 of the ARE is determined at the current setting S1 using the received first set of internal operation metrics. The current setting S1 of the ARE is varied to a new setting S2. A second set of internal operational metrics of the ARE executing at the new setting S2 is received through the interface of the ARE. A second performance P2 of the ARE is determined at the new setting S2, using the received second set of internal operation metrics. A memory allocation for the ARE is re-allocated, based on the determined performances P1 and P2.

Abstract: A method (and system) for managing memory among virtual machines in a system having a plurality of virtual machines, includes providing at least one memory optimization mechanism which can reduce memory usage of a virtual machine at a cost of increasing CPU usage. Information on memory usage and CPU usage of each virtual machine is periodically collected. In response to detecting that a first virtual machine exhibits a high level of memory use, at least one second virtual machine with extra CPU capacity is identified. The at least one memory optimization mechanism is applied to the at least one second virtual machine, to reduce memory used by the at least one second virtual machine, thereby providing a portion of freed memory. The portion of freed memory is then allocated to the first virtual machine.

Abstract: An apparatus includes at least one processor executing a method for managing memory among a plurality of concurrently-running virtual machines, and a non-transitory memory device that stores a set of computer readable instructions for implementing and executing said memory management method. A memory optimization mechanism can reduce a memory usage of a virtual machine at a cost of increasing a central processing unit (CPU) usage. Information on a memory usage and a CPU usage of each virtual machine is periodically collected. When a first virtual machine exhibits high memory use, at least one second virtual machine with an extra CPU capacity is identified. A memory optimization mechanism is applied to the second virtual machine to reduce memory used by the second virtual machine, thereby providing a portion of freed memory that is then allocated to the first virtual machine.

Abstract: A method (and system) for managing memory among virtual machines in a system having a plurality of virtual machines, includes providing at least one memory optimization mechanism which can reduce memory usage of a virtual machine at a cost of increasing CPU usage. Information on memory usage and CPU usage of each virtual machine is periodically collected. In response to detecting that a first virtual machine exhibits a high level of memory use, at least one second virtual machine with extra CPU capacity is identified. The at least one memory optimization mechanism is applied to the at least one second virtual machine, to reduce memory used by the at least one second virtual machine, thereby providing a portion of freed memory. The portion of freed memory is then allocated to the first virtual machine.

Abstract: A method for allocating memory among a plurality of computing systems includes assigning a free memory threshold to each computing system, dynamically varying at least one free memory threshold for at least one computing system based on a past memory usage of the at least one computing system, periodically monitoring each computing system for memory usage and in response to the monitoring determining that an amount of free memory for the computer system is below the free memory threshold for the computing system, allocating memory from the free memory pool to the computing system.

Abstract: A streaming engine and method includes a plurality of input types configured to pass one of samples and frames from an input stream. A processing block is configured to process the samples and frames independently of input and output types and produce any kind of output samples and frames in accordance with processing elements employed in the processing block. A plurality of output types is configured to initiate a session which determines connections between inputs and outputs so that the samples and the frames are received from the processing block in accordance with the session to appropriately direct the stream.

Abstract: A method (and system) for managing memory among virtual machines in a system having a plurality of virtual machines, includes providing at least one memory optimization mechanism which can reduce memory usage of a virtual machine at a cost of increasing CPU usage. Information on memory usage and CPU usage of each virtual machine is periodically collected. In response to detecting that a first virtual machine exhibits a high level of memory use, at least one second virtual machine with extra CPU capacity is identified. The at least one memory optimization mechanism is applied to the at least one second virtual machine, to reduce memory used by the at least one second virtual machine, thereby providing a portion of freed memory. The portion of freed memory is then allocated to the first virtual machine.

Abstract: A method (and system) for managing memory among virtual machines in a system having a plurality of virtual machines, includes providing at least one memory optimization mechanism which can reduce memory usage of a virtual machine at a cost of increasing CPU usage. Information on memory usage and CPU usage of each virtual machine is periodically collected. In response to detecting that a first virtual machine exhibits a high level of memory use, at least one second virtual machine with extra CPU capacity is identified. The at least one memory optimization mechanism is applied to the at least one second virtual machine, to reduce memory used by the at least one second virtual machine, thereby providing a portion of freed memory. The portion of freed memory is then allocated to the first virtual machine.

Abstract: A system for resource allocation in peer to peer streaming includes steps of: inferring global properties of a neighborhood made up of peer nodes, wherein the global properties are inferred from a summarization of information obtained locally at each peer node; allocating resources for each peer node to its neighbor nodes in accordance with propagated dependency information; and periodically updating the target rates as peer nodes join and leave the neighborhood.

Abstract: A method includes the steps of: detecting a request from a client to restart a streamed multimedia presentation from a new starting point; determining if an update to a source file of the streamed multimedia presentation is available; if the update is available: closing the source file, opening the update, and establishing a link to the existing data channel; locating the new starting point of the streamed multimedia presentation; and streaming the multimedia presentation from the new starting point.

Abstract: A system for resource allocation in peer to peer streaming includes steps of: inferring global properties of a neighborhood made up of peer nodes, wherein the global properties are inferred from a summarization of information obtained locally at each peer node; allocating resources for each peer node to its neighbor nodes in accordance with propagated dependency information; and periodically updating the target rates as peer nodes join and leave the neighborhood.

Abstract: A system and method for resource allocation in peer to peer streaming includes steps of: inferring global properties of a neighborhood made up of peer nodes, wherein the global properties are inferred from a summarization of information obtained locally at each peer node; allocating resources for each peer node to its neighbor nodes in accordance with propagated dependency information; and periodically updating the target rates as peer nodes join and leave the neighborhood.

Abstract: A system and method for resource allocation in peer to peer streaming includes steps of: inferring global properties of a neighborhood made up of peer nodes, wherein the global properties are inferred from a summarization of information obtained locally at each peer node; allocating resources for each peer node to its neighbor nodes in accordance with propagated dependency information; and periodically updating the target rates as peer nodes join and leave the neighborhood.

Abstract: A method includes the steps of: detecting a request from a client to restart a streamed multimedia presentation from a new starting point; determining if an update to a source file of the streamed multimedia presentation is available; if the update is available: closing the source file, opening the update, and establishing a link to the existing data channel; locating the new starting point of the streamed multimedia presentation; and streaming the multimedia presentation from the new starting point.

Abstract: A method, and program storage device apparatus for adapting multi-media presentations of various formats to a new format suitable for playback on a general scene description based playback system. More specifically, when mult-media presentations do not contain scene description components, such components appropriate to the multi-media presentation will be constructed. The multi-media presentation will then be presented together with the created scene description components to the playback system for decoding rendering.