Smart Hybrid Storage based on Intelligent Data Access Classification

This paper talks about how data access needs vary with application usage and how storage IOPS (Input/ Output Operations per second) can be increased by bringing hard drives and solid state drives together in to a logical volume group. In SAN (Storage Area Network) not always a drive group is accessed. Based on certain application demand the drive group access varies. We consider this varying IO patter (Input/ Output), this pattern is observed and controller firmware learns to identify the drive groups which need more bandwidth. Based on the IO load requirement solid state drives are attached to the drive group. This is done dynamically to improve cache at drive group level.

Learning Objectives

This paper classifies the need for data access in to three major categories (1. Mission-critical data or High performance or Sensitive data, 2. Reliable data, 3. Reliable & Sensitive Data)
The mode of operation of this paper in term of a) User Classification of data b) Storage Pool creation c) Different RAID levels used by this paper d) Data path virtualization layer to receive SCSI IOP from initiators e) Intelligent data pattern learn logic engine with smart data access classification inside CFW f) Intelligent data pattern learn logic engine with smart data access classification inside CFW
IO transaction using SSD for Disk drive groups performance boost for different RAID levels
Advantages of this method ( a) Better reliability b) Dynamic Performance boost c) Cost Vs Performance advantage d) Usage of Hybrid drives with NAND flash integrated for disk caching can boost the performance further.)
How user can select the data access needs and how the SSDs are allocated to existing disk groups based on the learn cycles from artificial intelligent Data access engine a.k.a Artificial Intelligence Data Access Classification Module