Abstract: A method for processing data includes monitoring, by a virtual machine (VM), a plurality of computing resources, receiving an offload request by the VM, selecting, based on the monitoring, a computing resource from the plurality of computing resources, issuing, by the VM and in response to the offload request, the processing request to the computing resource, and servicing, by the computing resource, the processing request to obtain a result, wherein the VM and the computing resource are executing on a computing device.

Abstract: Described herein is a system that facilitates recommending data compression using machine learning and statistical attributes. According to an embodiment, a system can comprise receiving a dataset, statistical attributes associated with the dataset, and a compression requirement for compression of the dataset. The system can further comprise based on the statistical attributes and the compression requirement, estimating a first compression attribute and a second compression attribute of a group of compression processes. The system can further comprise selecting a primary compression process from the group of compression processes, based on an output of an analytics component, wherein the analytics component employs a neural network to determine the primary compression process based on analysis of the statistical attributes, the compression requirement, and a compression objective.

Abstract: Extents of data are promoted or demoted between hierarchical storage tiers based on temporal locality of access captured with a primary count-min-sketch based on monitored input-output commands. Extents may also be promoted based on spatial locality of access captured with a secondary count-min-sketch based on the monitored input-output commands. The count-min-sketches may be implemented in a tiering engine of a storage array or in the flash translation layers of solid-state drives. The flash translation layers may generate tiering hints that are provided to a tiering engine.

Abstract: Allocation of storage array hardware resources between host-visible and host-hidden services is managed to ensure that sufficient hardware resources are allocated to host-visible services. Information obtained from monitoring real-world operation of the storage array is used to generate a model of the storage array. The generated model represents temporal dependencies between storage array hardware, host-visible services, and host-hidden services. Because the model includes information gathered over time and represents temporal dependencies, future occurrence of repeating variations of storage-related service usage and requirements can be predicted. The model may be used to generate hardware recommendations and dynamically re-allocate existing hardware resources to more reliably satisfy a predetermined level of measured performance.

Abstract: A fingerprint trie is used to store fingerprints for data portions stored on a storage system for use in implementing data deduplication on a storage system. The fingerprint trie may be used to compare fingerprint values to determine duplicate data portions, for example, in response to I/O operations. Leaf nodes of the fingerprint trie may be keyed by fingerprints, and a value of each leaf node may be a reference to the physical storage location of the data portion from which the fingerprint was generated. When an I/O operation is received, a fingerprint may be generated for each of one or more data portions included in the I/O operation. A fingerprint trie may be searched, for example by traversing multiple nodes of the trie according to pointers provided by the nodes, to determine whether there is any matching fingerprint specified in the fingerprint trie.

Abstract: A method of workload aware dynamic CPU processor core allocation includes the steps of predicting estimated individual workloads for each emulation in a set of emulations for each decision period of a set of decision periods over a predictive time span. The method includes using, by a Mixed Integer Programming (MIP) engine, the predicted estimated individual workloads for each emulation in the set of emulations, a set of constraints, and an optimization function, to determine sets of CPU processor cores to be allocated to each emulation during each decision period over the predictive time span. The method further includes dynamically allocating, by the host computer system, the sets of CPU processor cores to each emulation during each decision period over the predictive time span based on the output from the MIP engine.