Abstract: An end host redundancy elimination system and method to provide redundancy elimination as an end system service. Embodiments of the system and method use optimization techniques that reduce server central processing unit (CPU) load and memory footprint as compared to existing approaches. For server storage, embodiments of the system and method use a suite of highly-optimized data structures for managing metadata and cached payloads. An optimized asymmetric max-match technique exploits the inherent structure in data maintained at the server and client and ensures that client processing load is negligible. A load-adaptive fingerprinting technique is used that is much faster than current fingerprinting techniques while still delivering similar compression. Load-adaptive means that embodiments of the fingerprinting technique can adapt CPU usage depending on server load.

Abstract: The problem signature extraction technique extracts problem signatures from trace data collected from an application. The technique condenses the manifestation of a network, software or hardware problem into a compact signature, which could then be used to identify instances of the same problem in other trace data. For a network configuration, the technique uses as input a network-level packet trace of an application's communication and extracts from it a set of features. During the training phase, each application run is manually labeled as GOOD or BAD, depending on whether the run was successful or not. The technique then employs a learning technique to build a classification tree not only to distinguish between GOOD and BAD runs but to also sub-classify the BAD runs into different classes of failures. Once a classification tree has been learned, problem signatures are extracted by walking the tree, from the root to each leaf.

Abstract: The problem signature extraction technique extracts problem signatures from trace data collected from an application. The technique condenses the manifestation of a network, software or hardware problem into a compact signature, which could then be used to identify instances of the same problem in other trace data. For a network configuration, the technique uses as input a network-level packet trace of an application's communication and extracts from it a set of features. During the training phase, each application run is manually labeled as GOOD or BAD, depending on whether the run was successful or not. The technique then employs a learning technique to build a classification tree not only to distinguish between GOOD and BAD runs but to also sub-classify the BAD runs into different classes of failures. Once a classification tree has been learned, problem signatures are extracted by walking the tree, from the root to each leaf.

Abstract: An end host redundancy elimination system and method to provide redundancy elimination as an end system service. Embodiments of the system and method use optimization techniques that reduce server central processing unit (CPU) load and memory footprint as compared to existing approaches. For server storage, embodiments of the system and method use a suite of highly-optimized data structures for managing metadata and cached payloads. An optimized asymmetric max-match technique exploits the inherent structure in data maintained at the server and client and ensures that client processing load is negligible. A load-adaptive fingerprinting technique is used that is much faster than current fingerprinting techniques while still delivering similar compression. Load-adaptive means that embodiments of the fingerprinting technique can adapt CPU usage depending on server load.

Abstract: The method collects configuration data about the network, compares it to known good configurations to see if a corrective configuration is available. In addition, the method will review known bad configurations and determine if any of the successful corrective configurations for the bad configuration would be appropriate for the bad configuration under consideration.