20240028225. DATA STORAGE SYSTEM WITH SELF TUNING BASED ON CLUSTER ANALYSIS OF WORKLOAD FEATURES simplified abstract (Dell Products L.P.)

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DATA STORAGE SYSTEM WITH SELF TUNING BASED ON CLUSTER ANALYSIS OF WORKLOAD FEATURES

Organization Name

Dell Products L.P.

Inventor(s)

Shaul Dar of Petach Tikva (IL)

Paras Pandya of Anand (IN)

Vamsi K. Vankamamidi of Hopkinton MA (US)

Owen Martin of Hopedale MA (US)

DATA STORAGE SYSTEM WITH SELF TUNING BASED ON CLUSTER ANALYSIS OF WORKLOAD FEATURES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240028225 titled 'DATA STORAGE SYSTEM WITH SELF TUNING BASED ON CLUSTER ANALYSIS OF WORKLOAD FEATURES

Simplified Explanation

The patent application describes a data storage system that uses a tuner to collect data samples and calculate feature measures for data storage operations. The system then applies cluster analysis to define clusters and assigns the feature measures to these clusters. It further applies classification analysis to identify dominating features of each cluster and generates workload profiles based on these features. The system automatically adjusts processing mechanisms based on the workload profiles and performance or efficiency goals.

  • The data storage system includes a tuner to collect data samples and calculate feature measures for data storage operations.
  • Cluster analysis is applied to define clusters and assign the feature measures to these clusters.
  • Classification analysis is applied to identify dominating features of each cluster.
  • Workload profiles are generated based on the dominating features.
  • Configurable processing mechanisms are automatically adjusted based on the workload profiles and performance or efficiency goals.

Potential applications of this technology:

  • Data storage optimization in cloud computing environments.
  • Performance improvement in large-scale data centers.
  • Efficient resource allocation in distributed storage systems.

Problems solved by this technology:

  • Inefficient resource allocation in data storage systems.
  • Lack of automated mechanisms for adjusting processing based on workload profiles.
  • Difficulty in identifying dominating features of different workload clusters.

Benefits of this technology:

  • Improved performance and efficiency in data storage operations.
  • Automated adjustment of processing mechanisms based on workload profiles.
  • Enhanced resource allocation and optimization in data storage systems.


Original Abstract Submitted

a data storage system includes a tuner that obtains data samples for data storage operations of workloads and calculates feature measures for a set of features of the data storage operations over aggregation intervals of an operating period. it further (1) applies a cluster analysis to the feature measures to define a set of clusters, and assigns the feature measures to the clusters, and (2) applies a classification analysis to the feature measures labelled by their clusters to identify dominating features of each cluster, and generates workload profiles for the clusters based on the dominating features, and then automatically adjusts configurable processing mechanisms (e.g., caching or tiering) based on the workload profiles and performance or efficiency goals.

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