Emc IP Holding Company LLC

Emc (benelux) B.v., S.a.r.l.

Nir Goldschmidt

Assaf Natanzon

Anat Parush Tzur

Arik Sapojnik

Nimrod Milo

Otniel Van-Handel

Saar Cohen

Sairam Veeraswamy

Ajay Potnis

Oshry Ben-Harush

Yuval Aharoni

Anat Parush-Tzur

Otniel Van Handel

Embodiments are described for detecting a data organization issue in a storage system by analyzing static and dynamic layout characteristics for data stored in the storage system, workload characteristics for applications utilizing the data, virtualization effects of the storage system on the organization of the data, input/output characteristics of processes storing the data in the storage system, and cache usage characteristics of the data. For fragmentation issues, the system uses the analysis to provide defragmentation advisories to reduce fragmentation by at least one of: re-layout or re-tiering of the data in the data storage system, or cache memory reconfiguration.

Metadata analytics for online fragmentation detection on Unix file systems and common block file systems

An apparatus comprises a plurality of storage tiers, at least one data mover module, and skew predictor. A model generator processes information characterizing input-output activity involving one or more of the storage tiers in order to obtain skew measurements indicating portions of the input-output activity directed to portions of the one or more storage tiers for respective periods of time less than at least one corresponding time granularity, and generates a predictive model from the skew measurements. The skew predictor is configured in accordance with the predictive model to convert additional skew measurements obtained for a given period of time less than a desired time granularity to corresponding skew measurements in the desired time granularity. One or more of the converted skew measurements are utilized by the data mover module in controlling transfer of data between the storage tiers. The model generator is part of a machine learning system.

Storage system with machine learning based skew prediction

Described are techniques that determine cumulative skew curves. A first model is determined that generates a predicted destination cumulative skew curve for a specified data set in a destination data storage system having a destination data movement granularity. The predicted destination cumulative skew curve is predicted by the first model in accordance with one or more inputs including a source cumulative skew curve for the specified data set in a source data storage system that uses a source data movement granularity. The source cumulative skew curve for the specified data set is determined based on observed data. First processing is performed using the first model. The first model generates as an output the predicted destination cumulative skew curve. The first processing includes providing the one or more inputs to the first model. Also described is how to generate the first model.

Techniques for determining workload skew

An apparatus comprises a plurality of storage tiers, at least one data mover module, and a machine learning system coupled to the data mover module. The machine learning system comprises a model generator and a skew predictor. The model generator processes information characterizing input-output activity involving one or more of the storage tiers in order to obtain skew measurements in different time granularities, with the skew measurements indicating portions of the input-output activity directed to portions of the storage tier(s), and generates a predictive model from the skew measurements. The skew predictor is configured in accordance with the predictive model to convert skew measurements in one of the time granularities to corresponding skew measurements in another one of the time granularities. One or more of the converted skew measurements are utilized by the data mover module in controlling transfer of data between the storage tiers.

In one aspect, a method of testing a device for use in a storage area network (SAN) system includes receiving recorded messages including messages from a host and from a storage array and messages to a host and to a storage array, sending the recorded messages from the host and the storage array to a device under test, receiving messages from the device under test in response to the recorded messages sent and determining whether the device under test functions identically to a validated device based on the messages from the device under test being substantially identical to the recorded messages.

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Herzelia, Israel

Nir Goldschmidt