Browse Prior Art Database

Adaptive Multi-Media Automated-Storage-Product

IP.com Disclosure Number: IPCOM000111782D
Original Publication Date: 1994-Mar-01
Included in the Prior Art Database: 2005-Mar-26
Document File: 2 page(s) / 82K

Publishing Venue

IBM

Related People

Goldfeder, ME: AUTHOR [+4]

Abstract

This article documents a linear optimization model which would be resident in the control unit of a multi-media Automated-Storage-Product (ASP). In Fig. 1, host 10 communicates with control unit 30 via channel 20 or Local Area Network (LAN)21. Resident in control unit 30 is our linear optimization model 31. Use of a multi-tasking operating system in the control unit would allow the parallel execution of normal control unit functions and the linear optimization model.

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Adaptive Multi-Media Automated-Storage-Product

      This article documents a linear optimization model which would
be resident in the control unit of a multi-media
Automated-Storage-Product (ASP).  In Fig. 1, host 10 communicates
with control unit 30 via channel 20 or Local Area Network (LAN)21.
Resident in control unit 30 is our linear optimization model 31.  Use
of a multi-tasking operating system in the control unit would allow
the parallel execution of normal control unit functions and the
linear optimization model.

      Control unit 30 conducts I/O operations with the ASP 50 via
Small Computer Systems Interface (SCSI) 40.  Resident in ASP 50 are
DASD 51, optical 52, and tape 54 devices.  The optical and tape
devices utilize removable media which is normally stored in bins
internal to the ASP 50.  Optical media is served to the optical
devices by autochanger 53.  Similarly, the tape media is serviced to
the tape devices by autochanger 55.  Alternately, both the optical
and tape devices could be serviced by identical autochangers.  In any
case, the autochangers are controlled via interfaces 56, such as the
IEEE-488.

      Our basic linear optimization model assumes that there are
three levels in the ASP storage hierarchy, as shown in Fig. 2.  We
call these levels primary, secondary, and tertiary.  Within these
levels are one type of DASD, one type of tape storage, and one type
of optical storage.

      This linear optimization model analyzes customer data which
flows through a hierarchy.  Examples of such data are (a) monthly
accounting transactions, (b) customer invoices, and (c) accounts
payable and receivable records.  This type of data is initially
accessed frequently; however, the accesses are less frequent as the
data ages.  Thus, the required capacity of each type of storage is a
function of...