Automated administration for storage system

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Automated administration for storage system

• Presentation by Amitayu Das

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Introduction

• Major challenges in storage management
• System design and configuration (device
  management)
• Capacity Planning (space management)
• Performance tuning (performance management)
• High Availability (availability management)
• Automation (all of the above, in a self-managing
  manner)

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Motivation

• Large disk arrays and networked storage lead to
  huge storage capacities and high bandwidth access
  to facilitate consolidated storage systems.
• Enterprise-scale storage systems contain hundreds
  of host computers and storage devices and up to
  tens of thousands of disks.
• Designing, deploying and runtime management of
  such systems lead to huge cost (often higher than
  procuring cost)
• Look at the problems in greater details

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Storage System life-cycle
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Storage administration functions

• Data protection
• Performance tuning
• Planning and deployment
• Monitoring and record-keeping
• Diagnosis and repair

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Few notable attempts

• System-managed storage (IBM)
• Attribute-managed storage (HP)
• Replication
• RAID
• Online snapshot support
• Remote replication
• Online archival
• Interposed request routing
• Smart file-system switches

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Designing problem

• Given a pool of resources and workload, determine
  appropriate choice of devices, configure them and
  assign the workload to the configured storage.
• Solution is not straight-forward because,
• Huge size of system and thousands of design
  choices and many choices have unforeseen
  circumstances.
• Personnel with detailed knowledge of
  applications storage behavior are in short
  supply and hence, are quite expensive.
• Design process is tedious and complicated to do
  by hand, usually leading to solutions that are
  grossly over-provisioned, substantially
  under-performing or, in the worst case, both.
• Once a design is in place, implementing it is
  time-consuming, tedious and error-prone.
• A mistake in any of these steps is difficult to
  identify and can result in a failure to meet the
  performance requirements.

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Storage System life-cycle design/configuration
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System design and assignment problem
Application
Application
Application
Application
Workload requirements
Workload
Storage
Assignment engine
Storage System
System configuration
Storage device abilities
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Initial system design

• Problem convert workloads, business needs and
  device characteristics into assignment of stores
  and streams to devices
• One approach constraint-based multi-dimensional
  bin-packing
• Sample constraints of device 1
• - Sum of store sizes ? capacity
• - Sum of stream utilizations ? 1.0
• Sample objective functions
• - Minimize cost
• - Balance load

Req. size
Capacity
I/O rate
How many drives? Holding which data?
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Initial system design gt disk arrays

• Problem
• extending the single disk solution to disk arrays
• The space of array designs is potentially huge
• LUN sizes and RAID levels, stripe unit sizes,
  disks in LUNs
• More work needed before the solver can run

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Minerva Control flow. The array designer is
called as a subroutine by allocator.
Minervas role in storage system life cycle.
Input and output are shown.
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Minerva running a sample workload
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Merits/demerits

• Merits
• Reasonable automation
• Demerits
• Requires accurate models of workloads,
  performance requirements, and devices
• Address only the mechanisms, not the policy

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Storage System life-cycle redesign/reconfigure
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System redesign/reconfiguration

• new application added

• new users added

• system load increases

• hardware/software upgraded

• device fails

• new storage arrives

Reconfigured System

• performance tuning

Running System
Events triggering redesign/reconfiguration
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Iterative storage management loop
Design new system
Implement design
Analyze workload
Events triggering reconfiguration
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Hippodrome

• Two objectives
• The automated loop must converge on a viable
  design that meets the workloads requirements
  without over- or under-provisioning.
• It must converge to a stable final system as
  quickly as possible, with as little as input from
  its users.

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Components of Hippodrome

• Analysis component (1)
• Performance model component (2)
• Solver components (3)
• Migration component (4)

candidate design
2
utilzn (dsgn)
4
workload
1
summary
dsgn
finalized design
3
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Issues in system design and allocation

• What optimization algorithms are most effective?
• What optimization objectives and constraints
  produce reasonable designs?
• ex cost of reconfiguring system
• What's the right part of the storage design space
  to explore?
• ex RAID level vs. stripe unit size vs. cache
  management parameters
• What are reasonable general guidelines for
  tagging a store's RAID level?
• What (other) decompositions of the design and
  allocation problem are reasonable?
• How to generalize system design?
• for SAN environment
• for host and applications

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Issues in reconfiguration

• How to do system discovery?
• e.g., existing state, presence of new devices
• Dealing with inconsistent information
• In a scalable fashion
• How to abstractly describe storage devices?
• For system discovery output
• For input to tools that perform changes
• How to automate the physical redesign process?
• e.g., physical space allocation etc.
• Events trigger redesign decision
• How do we decide when to reconfigure?
• Reconfiguration inputs
• current system configuration/assignment
• desired system configuration/assignment

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Self- storage architecture
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Administration and organization

• Administrative interface
• Supervisors
• Administrative assistants
• Data access and storage
• Routers
• Workers

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Merits

• Simpler storage administration
• Data protection
• Performance tuning
• Planning and deployment
• Monitoring and record-keeping
• Diagnosis and repair

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Demerits

• The proposed solution is too simplistic to handle
  the issues raised.
• Authors have provided solution from a high-level
  viewpoint, but the solution is not complete in
  any sense.
• The implementation and evaluation is not
  convincing enough.
• All the aspects of self- has not been
  addressed as claimed.

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Storage System life-cycle virtualization
(Dynamic) business requirements
Configure/ reconfigure
Design/ redesign
Monitor
Performance tuning
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Runtime management problem

• Often, enterprise customers outsource their
  storage needs to data centers.
• At data centers, different workload /application
  /services share the underlying storage
  infrastructure.
• Sharing (of disk drives, storage caches, network
  links, controllers etc.) can lead to interference
  between the users/applications leading to
  possible violations in performance-based QoS
  guarantees.
• To prevent that, data centers needs to insulate
  the users from each other virtualization.

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Need for virtualization

• At data centers, many different enterprise
  servers that support different business
  processes, such as, Web servers, file servers,
  database serves may have very different
  performance requirements on their backend storage
  server.
• Sophisticated resource allocation and scheduling
  technology is required to effectively isolate
  these logical storage servers as if they are
  separate physical storage servers.
• Storage Virtualization refers to the technology
  that allows creation of a set of logical storage
  devices from a single physical storage structure.

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Storage virtualization
Storage management
Application
Clients
Abstract Interface
Virtual Disks
Storage Virtualization
Operating System
Hardware resources
Disks, Controllers
Physical Disks

• Examples LVM, xFS, StorageTank
• Hides Physical details from high-level
  applications

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Dimensions of virtualization

• Commercial storage virtualization systems are
  rather limited because they can virtualize
  storage capacity.
• However, from the standpoint of storage clients
  or enterprise servers, the virtual storage
  devices are desired to be as tangible as physical
  disks.
• Need to virtualize efficiently any standard
  attribute associated with a physical disk, such
  as capacity, bandwidth, latency, availability
  etc.

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Hardware Organization
client
client
Object interface
File interface
Object interface
Storage manager
Data/cmds
Control mesg
Gigabit network
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A 2-level CVC Scheduler
Storage Server
Storage Server
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1
Storage Manager
Client
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2
7
Storage Server
3
6
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References

• Hippodrome running circles around storage
  administration. Eric Anderson et. al., FAST 02,
  pp. 175-188, January 2002.
• Minerva an automated resource provisioning tool
  for large-scale storage systems. G. Alveraz et.
  al., ACM Transactions on Computer Systems 19 (4)
  483-518, November 2001
• Ergastulum quickly finding near-optimal storage
  system designs. Eric Anderson et. al., Technical
  Report from HP Laboratories.
• Disk Array Models in Minerva. Arif Merchant et.
  al., Technical Report, HP Laboratories.
• Self- Storage Brick-based Storage with
  Automated Administration. G. Ganger et. al.,
  Technical report,2003

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References

• SIGMETRICS 00 Tutorial, HP Laboratories.
• Optimization algorithms
• Bin-packing Heuristics Coffman84
• Toyoda Gradient Toyoda75
• Simulated Annealing Drexl88
• Relaxation Approaches Pattipati90, Trick92
• Genetic Algorithms Chu97
• Multidimensional Storage Virtualization. Lan
  Huang et. al., SIGMETRICS 04, New York, June
  2004.
• An Interposed 2-Level I/O Scheduling Framework
  for Performance Virtualization. J. Zhang et. al.,
  SIGMETRICS 05
• Efficiency-aware disk scheduler
• - Cello, Prism, YFQ

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