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Controlling computer storage systems

a computer storage and computer technology, applied in the field of information technology, can solve the problems of inability to easily extend from single controller to system of multiple storage controllers, error-prone and expensive manual availability management in large data centers,

Inactive Publication Date: 2008-01-10
GLOBALFOUNDRIES INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The present invention provides techniques for controlling a computer storage system by obtaining information about the availability of components in the system and checking if there are any violations of availability goals. The invention also includes methods for obtaining a request for change, estimating the time required for replication to accommodate the change, and evaluating the request for change based on the estimated time. The invention can automatically manage availability and performance goals in enterprise data centers and storage consolidation and migration activities, which are standard parts of IT infrastructure lifecycle management. Overall, the invention provides a technical solution for managing computer storage systems efficiently.

Problems solved by technology

Manual availability management in large data centers can be error prone and expensive and is thus not a practical solution.
Also, they may not easily extend from single controllers to systems of multiple storage controllers.
While representing a substantial advance in the art, CHAMPS may have limitations regarding consideration of service availability and regarding data availability in distributed storage systems.
There is little prior work on automated availability management systems in environments involving multiple, heterogeneous storage controllers.
HiRAID may not be optimally goal-oriented and may focus on DASD failures only (i.e., as if DASDs attached to all storage controllers were part of a single DASD pool).
It may not take into account the additional complexity and heterogeneity of the storage controllers themselves and thus may not be appropriate in some circumstances.
Other approaches may also inadequately characterize storage controller availability.
Prior work within this framework, however, has not addressed the heterogeneity and complexity issues in large scale storage systems or the problem of storage volume placement on a set of storage controllers.
While VPA represented a substantial advance in the art, it may not have appropriate provision for availability goals.

Method used

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Examples

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example 1

[0063]With reference to FIGS. 2 and 3, consider five storage controllers A, B, C, D, E with availability μA, μB, μC, μD, μE, respectively. In this example, we allocate a dataset of size x GB with performance y IO / s and overall availability μ using the volume allocation and placement procedure described earlier. In the first (initial allocation) phase, seven volumes are allocated based on capacity and performance considerations on three controllers (A, B, C). Following initial allocation, in the second phase of the allocation algorithm, volumes from controllers B and C (presumably the controllers with the lowest availabilities μB and μC) are selected to be replicated to equal number of volumes on controllers D, E.

[0064]The estimate of the overall probability is based on the following theorem from the Theory of Probabilities, which states that for any two events A and B, the probability that either A or B or both occur is given by:

Pr{A or B}=Pr{A}+Pr{B}−Pr{A and B}  (5)

[0065]Assuming ...

example 2

[0069]Referring to FIGS. 4 and 5, consider three datasets with different availability goals (0.9999, 0.999, and 0.9 from top to bottom). Consider also seven controllers (SC1-SC7) each with a different availability outlook expressed in their timeline of known, expected outages. The timelines in FIG. 4 describe the known, expected outages for each controller. For the first six controllers we use mostly deterministic information. The last controller (SC7), for which there is no known outage, is considered suspect due to a recent firmware upgrade. Its probabilistic availability estimate is therefore low.

[0070]As in the previous example, in the first (initial allocation) phase of the algorithms, volumes for each dataset are assigned to storage controllers based on capacity and performance goals. For the first dataset, a single volume (A) is allocated on SC1. For the second dataset, three volumes (B, C, and D) are allocated on SC3-SC5. Finally, for the third dataset a single volume (E) is...

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PUM

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Abstract

Goal-based availability and change management are handled over groups of heterogeneous storage controllers. Probabilistic and deterministic methods are employed to determine the allocation and placement of storage volumes to storage controllers, as well as the degree of data redundancy necessary to achieve data availability goals. The probabilistic methods can take into account past observations of controller availability, and operator beliefs, as well as the state of storage controller configuration, in coming up with a probabilistic estimate of future availability.

Description

FIELD OF THE INVENTION[0001]The present invention generally relates to information technology, and, more particularly, to controlling computer storage systems.BACKGROUND OF THE INVENTION[0002]The need for scaling the capacity, availability, and performance of datasets across multiple direct-access storage devices (DASDs) led to the development of the Redundant Array of Inexpensive (or Independent) Disks (RAID) technology in the early 1980s, and the implementation of storage controllers that offer RAID-based logical disk abstractions. These storage controllers are typically computer servers attached to a large number of DASDs via a peripheral I / O interconnect. They form RAID arrays by combining groups of DASDs and subsequently create and export logical disk abstractions over these RAID arrays. The RAID technology protects against data loss due to DASD failure by replicating data across multiple DASDs and by transparently reconstructing lost data onto spare DASDs in case of failure. D...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): G06F11/00
CPCG06F11/008G06F2211/1004G06F11/2069
Inventor DEVARAKONDA, MURTHY V.MAGOUTIS, KONSTANTINOSVOGL, NORBERT GEORGEVORUGANTI, KALADHAR
Owner GLOBALFOUNDRIES INC
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