13th September, 20 05 CSE 598B: Fall2005 Presen tation 1 Automated administration for storage system Presentation by Amitayu Das
Dec 14, 2015
13th September, 2005 CSE 598B: Fall2005 Presentation1
Automated administration for storage system
Presentation by Amitayu Das
13th September, 2005 CSE 598B: Fall2005 Presentation2
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
(Dynamic) business
requirementsDesign/redesign
Configure/reconfigure
MonitorStorage devices
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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
(Dynamic) business
requirementsDesign/redesign
Configure/reconfigure
MonitorStorage devices
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Application
ApplicationApplication
System design and assignment problem
Application
Assignment engine Storage
System
Storage
System configuration
Storage device abilities
Workload
Workload requirements
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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
. siz
e
Capacity
I/O rate
How many drives? Holding which data?
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Initial system design –> 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
13th September, 2005 CSE 598B: Fall2005 Presentation12Minerva Control flow. The array designer is called as a subroutine by allocator.
Minerva’s role in storage system life cycle. Input and output are shown.
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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
(Dynamic) business
requirementsDesign/redesign
Configure/reconfigure
MonitorStorage devices
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System redesign/reconfiguration
Running System
Reconfigured System
• new application added• new users added
• system load increases• hardware/software upgraded• device fails• new storage arrives• performance tuning
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 workload’s 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)
2
1
4
3
workload
summary
candidate design
dsgn
utilzn (dsgn)
finalized design
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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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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
requirementsDesign/redesign
Configure/reconfigure
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
Examples: LVM, xFS, StorageTank Hides Physical details from high-level applications
ApplicationStorage
management
Operating System
StorageVirtualization
Disks,Controllers
Hardware
resources
AbstractInterface
Physical Disks
Virtual Disks
Clients
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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
Storage
manager
Storage
serverDiskarray
Kernel
ClientApplicati
on
Storage
Clerk
Kernel
ClientApplicati
on
Storage
Clerk
Storage
serverDiskarray
Storage
serverDiskarray
Control mesg Data/cmds
Gigabit network
Object interface
Object interface
client client
File interface
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A 2-level CVC Scheduler
Client
Storage Manager
Storage Server
Storage Server
Storage Server
52
7
3
6
14
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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