Abstract: Methods and systems for agile load balancing include detecting an increased load for a first primary virtual machine (VM) on a first node that has a plurality of additional primary VMs running on a processor; deactivating one or more of the additional primary VMs, reducing said one or more deactivated VMs to a secondary state, to free resources at the first node for the first primary VM; and activating secondary VMs, located at one or more additional nodes, that correspond to the one or more deactivated VMs, raising said secondary VMs to a primary state. Activation and deactivation through micro-checkpointing may involve nodes of different CPU architectures during transient periods of peak load.

Abstract: Methods and systems for agile load balancing include detecting an increased load for a first primary virtual machine (VM) on a first node that has a plurality of additional primary VMs running on a processor; deactivating one or more of the additional primary VMs, reducing said one or more deactivated VMs to a secondary state, to free resources at the first node for the first primary VM; and activating secondary VMs, located at one or more additional nodes, that correspond to the one or more deactivated VMs, raising said secondary VMs to a primary state. Activation and deactivation through micro-checkpointing may involve nodes of different CPU architectures during transient periods of peak load.

Abstract: Methods and systems for agile load balancing include detecting an increased load for a first primary virtual machine (VM) on a first node that has a plurality of additional primary VMs running on a processor; deactivating one or more of the additional primary VMs, reducing said one or more deactivated VMs to a secondary state, to free resources at the first node for the first primary VM; and activating secondary VMs, located at one or more additional nodes, that correspond to the one or more deactivated VMs, raising said secondary VMs to a primary state. Activation and deactivation through micro-checkpointing may involve nodes of different CPU architectures during transient periods of peak load.

Abstract: Methods and systems for agile load balancing include detecting an increased load for a first primary virtual machine (VM) on a first node that has a plurality of additional primary VMs running on a processor; deactivating one or more of the additional primary VMs, reducing said one or more deactivated VMs to a secondary state, to free resources at the first node for the first primary VM; and activating secondary VMs, located at one or more additional nodes, that correspond to the one or more deactivated VMs, raising said secondary VMs to a primary state. Activation and deactivation through micro-checkpointing may involve nodes of different CPU architectures during transient periods of peak load.

Abstract: Methods and systems for agile load balancing include detecting an increased load for a first primary virtual machine (VM) on a first node that has a plurality of additional primary VMs running on a processor; deactivating one or more of the additional primary VMs, reducing said one or more deactivated VMs to a secondary state, to free resources at the first node for the first primary VM; and activating secondary VMs, located at one or more additional nodes, that correspond to the one or more deactivated VMs, raising said secondary VMs to a primary state. Activation and deactivation through micro-checkpointing may involve nodes of different CPU architectures during transient periods of peak load.

Abstract: Methods and systems for agile load balancing include detecting an increased load for a first primary virtual machine (VM) on a first node that has a plurality of additional primary VMs running on a processor; deactivating one or more of the additional primary VMs, reducing said one or more deactivated VMs to a secondary state, to free resources at the first node for the first primary VM; and activating secondary VMs, located at one or more additional nodes, that correspond to the one or more deactivated VMs, raising said secondary VMs to a primary state. Activation and deactivation through micro-checkpointing may involve nodes of different CPU architectures during transient periods of peak load.

Abstract: Methods and systems for agile load balancing include detecting an increased load for a first primary virtual machine (VM) on a first node that has a plurality of additional primary VMs running on a processor; deactivating one or more of the additional primary VMs, reducing said one or more deactivated VMs to a secondary state, to free resources at the first node for the first primary VM; and activating secondary VMs, located at one or more additional nodes, that correspond to the one or more deactivated VMs, raising said secondary VMs to a primary state. Activation and deactivation through micro-checkpointing may involve nodes of different CPU architectures during transient periods of peak load.

Abstract: Methods and systems for agile load balancing include detecting an increased load for a first primary virtual machine (VM) on a first node that has a plurality of additional primary VMs running on a processor; deactivating one or more of the additional primary VMs, reducing said one or more deactivated VMs to a secondary state, to free resources at the first node for the first primary VM; and activating secondary VMs, located at one or more additional nodes, that correspond to the one or more deactivated VMs, raising said secondary VMs to a primary state. Activation and deactivation through micro-checkpointing may involve nodes of different CPU architectures during transient periods of peak load.

Abstract: A method to predict visual quality of a DCT (discrete cosine transform) based compressed image or video stream without referring to its source. When applied to an MPEG video stream, the method is based on (1) an estimation of quantization errors using MPEG quantization scales and statistics of the inverse quantized DCT coefficients, (2) a blind estimation of the 8×8 and 16×16 blocking effect, and (3) an adaptive combination of the quantization error estimation and the blocking effect estimation using the MPEG motion vector information. The method may be used in many applications, such as network video servers, switches and multiplexers for automatic quality monitoring and control of video services, video encoders, decoders, transcoders, and statistical multiplexers for picture quality optimization.

Abstract: A system for transcoding compressed video signal, including a plurality of pictures, comprising an estimator to gather information and estimate the signal characteristics about the video signal; a decoder to completely or partially decode the compressed video signal; and an encoder to compress the reconstructed video signal according to a coding scheme devised on the estimated signal characteristics from the estimator.

Abstract: A method to predict visual quality of a DCT (discrete cosine transform) based compressed image or video stream without referring to its source. When applied to an MPEG video stream, the method is based on (1) an estimation of quantization errors using MPEG quantization scales and statistics of the inverse quantized DCT coefficients, (2) a blind estimation of the 8×8 and 16×16 blocking effect, and (3) an adaptive combination of the quantization error estimation and the blocking effect estimation using the MPEG motion vector information. The method may be used in many applications, such as network video servers, switches and multiplexers for automatic quality monitoring and control of video services, video encoders, decoders, transcoders, and statistical multiplexers for picture quality optimization.

Abstract: A system and method for generating an MPEG compliant video stream with satisfactory visual quality. The method employs re-encoding at a higher (or lower) bit rate, only the segments with unsatisfactory visual picture quality while retaining other parts of the original video stream and then merging the re-encoded segments with the remaining original portions. If the original video stream is coded at a reasonable bit rate, the number and length of those segments with unsatisfactory visual picture quality will be few and short. Re-encoding only those segments can save very significant amount of cost and only requires minimal additional storage space. Furthermore, the portions with satisfactory picture quality will be retained. The system and method is applicable to MPEG and non-MPEG compliant data streams.

Abstract: A system for transcoding compressed video signal, including a plurality of pictures, comprising an estimator to gather information and estimate the signal characteristics about the video signal; a decoder to completely or partially decode the compressed video signal; and an encoder to compress the reconstructed video signal according to a coding scheme devised on the estimated signal characteristics from the estimator.