Data persistence for able extensions
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Michael Torchio College of Engineering and Mathematical Sciences University of Vermont Jorge Guerra Raju Rangaswami School of Computing and Information Sciences Florida International University. Data Persistence for ABLE Extensions. Self-Managing storage systems

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Data Persistence for ABLE Extensions

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Data persistence for able extensions

Michael Torchio

College of Engineering and

Mathematical Sciences

University of Vermont

Jorge Guerra

Raju Rangaswami

School of Computing and

Information Sciences

Florida International University

Data Persistence for ABLE Extensions


Able overview

Self-Managing storage systems

Use idle or cheaply available computational cycles to add intelligence to storage systems

Adapts to high-level system goal specifications

Standardized development environment allows versatile self-management extensions

ABLE Overview


Problem statement

Problem statement

  • Systems need to preserve data across reboots, power failures, and program terminations.

    • Example: history-based optimization of disk use

  • Data held by programs in volatile memory

  • Need to store data to disk, allowing updates


Definitions

Definitions

  • Memory page – fixed-length block in main memory, 4kB on Linux

  • Disk page – 4 kB area of hard disk, typical unit of read/write

  • Update – copy page(s) from memory to disk

  • Restore – reassemble data in memory from disk copy


Challenges

Challenges

  • Need more than pointer to data

  • Data structures vary greatly in size

  • Difficult to store, reassign types

    • Nested structures add complexity

  • Pointers need modification

  • Efficiently updating in-place requires data location

  • Policies for updates must be defined


Api of serializer

API of Serializer


Data persistence for able extensions

Preserve copy of memory


Persistent allocation

Persistent allocation

standard malloc()‏

Serializer

User

pmalloc()‏

no – add a block

space in any block?

yes

*pointer

Disk Manager

Storage stack

Hard disk


Updates may lead to writes

Updates may lead to writes

Serializer

User

pupdate()‏

mark_as_dirty()‏

success?

flush?

mark_as_clean()‏

Disk Manager

translate address; write

Storage stack

Hard disk


Disk address lookup

Disk address lookup

Mapping of memory page to virtual page

Mapping of virtual page to disk page

Physical memory address

Virtual page address

Disk address


Preliminary metrics with sync

Preliminary metrics – with sync()‏

  • In every case, the data was changed 1000 times.

  • With little outside disk usage:

    • No persistence (control): 1 millisecond

    • 1 pupdate(): 7 milliseconds

    • 1000 pupdate(): 1.75 seconds

  • Under heavy disk write conditions:

    • No persistence (control): 1 millisecond

    • 1 pupdate(): 20 milliseconds

    • 1000 pupdate(): 4.5 seconds

  • Under heavy disk read conditions:

    • No persistence (control): 1 millisecond

    • 1 pupdate(): 1 second

    • 1000 pupdate(): many minutes


  • Future work

    Future Work

    • Investigation into storage of mapping tables

    • Adapting the concept to allow allocations > 4k

    • Restoring from disk still requires the user to list location of each pointer

    • Further timing tests and optimizations


    References

    References

    • [1] Jorge Guerra et al. The Case for Active Block Layer Extensions. http://www.cs.fiu.edu/~raju/WWW/publications/speed2008/paper.pdf

    • [2] Troy D. Hanson. TPL Serialization Library. http://tpl.sourceforge.net/

    • This research funded by NSF IIS-0552555, “Research Experiences for Undergraduates: Autonomic Computing Research at FIU”


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