Emc IP Holding Company LLC

Emp IP Holding Company LLC

Shai Harmelin

Amihai Savir

Idan Levy

Avitan Gefen

Amit Lieberman

Shiri Gaber

Anat Parush Tzur

Roi Gamliel

Senya Touretski

Meytal Ashkenazy

Idan Levyl

Oshry Ben-Harush

Lindsay Braine

Information technology resource forecasting based on time series analysis is described. A system creates multiple forecasts for an information technology resource by applying corresponding multiple time series models to first data associated with the information technology resource. The system selects a model of the multiple time series models by comparing the multiple forecasts for the information technology resource to second data associated with the information technology resource. The system outputs a forecast that is created by applying the selected model to third data associated with the information technology resource.

Information technology resource forecasting based on time series analysis

Described is a system and method that provides a data protection risk assessment for the overall functioning of a backup and recovery system. Accordingly, the system may provide a single overall risk assessment score that provide an operator with an 'at-a-glance' overview of the entire system. Moreover, the system may account for changes that occur over time based on leveraging statistical methods to automatically generate assessment scores for various components (e.g. application, server, network, load, etc.). In order to determine a risk assessment score, the system may utilize a predictive model based on historical data. Accordingly, residual values for newly observed data may be determined using the predictive model and the system may identify potentially anomalous or high risk indicators.

Generating a data protection risk assessment score for a backup and recovery storage system

An alert configuration system facilitates accurate and reliable configuration of alerts for data protection policy in a data protection system, including eliminating or reducing manual configuration of data protection policy. The system identifies risks through trend analysis and behavioral statistics as applied to historical data, and automatically configures alerts for the identified risks so that alerts are generated upon detection of the identified risks. After detecting differences between tracked values for a data protection system and predicted values obtained through trend analysis and behavioral statistics as applied to the historical data, the alert configuration system automatically adjusts the configuration of alerts for data protection policy in accordance with the predicted values. The tracked and predicted values include attributes of a data protection system embodied in key performance indicators associated with the identified risks based on the trend analysis and behavioral statistics as applied to historical data.

Alert configuration for data protection

One embodiment is related to a method for setting a tenant context for an application in a multi-tenancy environment, comprising: identifying a tenant identity for a tenant request, wherein the tenant identity is included in the tenant request; and setting a proper context for a first service of the application based on the identified tenant identity.

Plug and play multi tenancy support for cloud applications

Data compression optimization based on client clusters is described. A system identifies a cluster of similar client devices in a group of client devices, by comparing data compression factors that correspond to each client device in the group of client devices. The system identifies a relationship between data compression factors corresponding to the cluster and data compression ratios corresponding to the cluster. The system identifies a client device, in the cluster, which corresponds to a data compression ratio that is inefficient relative to other compression ratios corresponding to other client devices in the cluster. The system outputs a data compression recommendation for the client device, based on data compression factors corresponding to the client device and the identified relationship between the data compression factors corresponding to the cluster and the data compression ratios corresponding to the cluster.

Data compression optimization based on client clusters

Detection of a cause of an error occurring in a data center is provided. A time period between a failure event in the data center and a previous successful event is determined. A set of devices involved in the failure event is generated using a data protection advisor (DPA). Data collected for the set of devices during the determined time period is scanned to detect at least one configuration change made during the determined time period. Based on a result of the scanned collected data, the at least one configuration change is displayed as a potential root cause of the error.

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Haifa, Israel

Shai Harmelin