Unravel Data Systems, Inc.

Duke University

Shivnath Babu

Kunal Agarwal

Alkiviadis Simitsis

Adrian Daniel Popescu

Herodotos Herodotou

Erik Lik Han Chu

A system and method for data application performance management is disclosed. According to one embodiment, a computer-implemented method, comprises receiving a selection of a goal for an application on a cluster of compute nodes. The goal includes one or more of a speedup goal, an efficiency goal, a reliability goal, and a service level agreement goal. The application on the cluster is executed. Data associated with the goal is collected. A recommendation to adjust one or more parameters that would allow the goal to be achieved.

System and method for data application performance management

A system and method for analyzing big data activities are disclosed. According to one embodiment, a system comprises a distributed file system for the entities and applications, wherein the applications include one or more of script applications, structured query language (SQL) applications, Not Only (NO) SQL applications, stream applications, search applications, and in-memory applications. The system further comprises a data processing platform that gathers, analyzes, and stores data relating to entities and applications. The data processing platform includes an application manager having one or more of a MapReduce Manage, a script applications manager, a structured query language (SQL) applications manager, a Not Only (NO) SQL applications manager, a stream applications manager, a search applications manager, and an in-memory applications manager. The application manager identifies if the applications are one or more of slow-running, failed, killed, unpredictable, and malfunctioning.

System and method for analyzing big data activities

Cost-based optimization of configuration parameters and cluster sizing for distributed data processing systems are disclosed. According to an aspect, a method includes receiving at least one job profile of a MapReduce job. The method also includes using the at least one job profile to predict execution of the MapReduce job within a plurality of different predetermined settings of a distributed data processing system. Further, the method includes determining one of the predetermined settings that optimizes performance of the MapReduce job. The method may also include automatically adjusting the distributed data processing system to the determined predetermined setting.

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Durham, NC, United States of America

Shivnath Babu