Storage Performance Management Overview. Brett Allison, IntelliMagic, Inc.

Transcription

1 Overview Brett Allison, IntelliMagic, Inc.

2 SNIA Legal Notice The material contained in this tutorial is copyrighted by the SNIA unless otherwise noted. Member companies and individual members may use this material in presentations and literature under the following conditions: Any slide or slides used must be reproduced in their entirety without modification The SNIA must be acknowledged as the source of any material used in the body of any document containing material from these presentations. This presentation is a project of the SNIA Education Committee. Neither the author nor the presenter is an attorney and nothing in this presentation is intended to be, or should be construed as legal advice or an opinion of counsel. If you need legal advice or a legal opinion please contact your attorney. The information presented herein represents the author's personal opinion and current understanding of the relevant issues involved. The author, the presenter, and the SNIA do not assume any responsibility or liability for damages arising out of any reliance on or use of this information. NO WARRANTIES, EXPRESS OR IMPLIED. USE AT YOUR OWN RISK. 2

3 Abstract (SPM), Storage Architecture and SMI-S This session will appeal to Storage Managers, Performance and Capacity Managers, and those that are seeking a fundamental understanding of storage performance management. This session includes an overview of the processes, technology and skills required to implement SPM, as well as an overview of disk storage system architecture, and the SMI-S specification as it relates to block level performance. The focus is on block level storage systems. 3

4 (SPM) 4

5 How Large is Storage Expenditure? Where will you be in 2012? Reduce storage costs by implementing SPM IT Storage Spend Forecast (Billion) Source: [1]

6 SPM Defined SPM is the process of ensuring that users constantly receive required I/O service levels to avoid performance problems; that storage assets are efficiently configured and used to avoid over spending on hardware. Risk avoidance is more important than saving money. Logical Volume Management (SRM) Physical Disk Choices LUN LUN LUN LUN LUN LUN LUN LUN LUN workload LUN balancing 146G 15k 146G 15k 146G 15k 146G 15k 146G 15k 300G 10k 300G 10k 300G 10k 300G 10k LUN LUN LUN LUN (automated) tiering SSD/Flash SSD/Flash SSD/Flash SSD/Flash 6

7 Risks Higher Under configured: Performance issues The cost difference between these two is often 30% or more of all storage. Sweet spot: Right performance Right Cost Over configured: Efficiency issues Risk is not removed: Hot spots will still occur from time due to imbalance Performance Cost Lower size (and cost) of storage configuration Utilization levels 7

8 The Four SPM Primary Processes 8

9 SPM Maturity Stages 9

10 SMI-S 10

11 SMI-S SMI-S is a vendor independent protocol to manage storage subsystems via a HTTP/HTTPS based protocol. Common standard across all vendors Provide both topology and performance information Standard scope includes storage, switches, and servers Performance measurement is commonly supported dards/smi 11

12 SMI-S Implementation Software exploiting SMI-S SMI-S (on TCP/IP) Vendor choice SMI-S Provider SMI-S Client 12

13 What ElementTypes Does SMI Define for Performance? ElementType ElementType2 ElementType3 Component Cumulative statistics for the storage system Front-end Controllers Vendor Specific ElementType4 Peer Storage System (Mirroring) ElementType5 ElementType6 ElementType7 ElementType8 Back-end Controllers Front-end FC ports Back-end Ports Volumes ElementType9 Extent Intermediate storage ElementType10 ElementType11 Disk Drive Arbitrary Logical Units Controller commands ElementType12 Remote Replica Group Remote Mirror 13

14 Statistics Summary by ElementType Statistic Property Top Level Computer System Component Computer System (Front-end) Component Computer System (Peer) Component Computer System (Back-end) Frontend Port Backend Port Volume (LogicalDi sk) Composite Extent Disk Statistic Time R R R R R R R R R TotalIOs R R R R R R R R R Kbytes Transferred R O O O R O R R R IOTimeCounter O O O O O O O N O ReadIOs O R R N N N R N R ReadHitIOs O R R N N N R N N ReadIOTimeCounter O O O N N N R N N ReadHitIOTimeCounter O O O N N N O N N Kbytes Read O O O O N N O N O Write Ios O R R N N N R N O WriteHitIOs O R R N N N R N N WriteIOTimeCounter O O O N N N O N O WriteHitIOTimeCounter O O O N N N O N N KbytesWritten O O O O N N O N O IdleTimeCounter N N N O O N O O O MaintOp N N N N N N N O O MaintTimeCounter N N N N N N N O O R - Required O - Optional N Not Specified 14

15 Relationships A simple example Configuration data is necessary to provide the relationships between the elements: Which LUNs are defined on which extent pool Which physical drives make up an array group Which port (types) are connected to each (host adapter) Disks Extent/ Raid Group Volume Front End 15

16 Storage System Architectures and Measurement with SMI-S 16

17 Architectural Trends Impacting Performance Spinning disks are getting bigger faster than they are getting faster! Requests per GB now exceeds access density capability of spinning drives for many workloads Trends driving density increase include De-duplication Thin provisioning SSD could remove disk as bottleneck in the future Commodity hardware 17

18 Disk Storage System (DSS) Architecture All vendors agree: Frontend (host) and Backend (disk) and Cache and Volumes are required Do the metrics provided tell us what we need to know? SMI-S has a well defined model for performance metrics and relationships. High level storage system metrics are not enough! We can only rely on these if the I/O is evenly spread across components Front-end Adapter Back-end Adapter/Ports Extent Pool Disks Front-end Adapter Cache Back-end Adapter/Ports Volume 18

19 System Level Response Time and Throughput 19

20 Processors and Cache Different implementations use different approaches All use cache to store Recently used tracks and records Recently written records Pre-loaded tracks for sequential read Some form of track descriptor tables to facilitate write operations without a disk access Async copy information SCSI SMP SMP SCSI HA RIO-G memory RIO-G DA Midrange: centralized cache management FC controller control Direct Matrix Direct Matrix Cache control Direct Matrix Direct Matrix Back-end FC Cache shared between engines Fixed cache assignment 20

21 Read Cache Hit % by DSS 21

22 Front-end Adapter Fibre Channel Protocol Engine Processor Host Adapter (generic) Data Mover ASIC Fibre Channel Protocol Engine Buffer Provides connectivity between disk subsystem and hosts Cards support ESCON, SCSI, FICON Fibre, SAS and/or iscsi sometimes FICON and Fibre with one card Implementations differ greatly in maximum data handling capability, especially for FICON and Fibre Even though ports are rated as (e.g.) 8 Gbit/s, no implementation achieves this speed due to overhead. 22

23 Front-end Controller/Adapter Read Response Time 23

24 Front-end Ports 24

25 Device Adapters DA Buffer Fibre Channel Protocol Engine Data Mover RAID ASIC Processor Fibre Channel Protocol Engine Connect HDDs to internal disk system resources Manage RAID operations, sometimes using cache memory for RAID computations Configured in pairs to provide redundancy if one adapter fails HDD interfaces include various generations of SCSI, SSA, FC-AL, SATA and SSD FC-AL switched back-end are gradually being replaced by SAS back-ends 25

26 Backend Adapter Throughput 26

27 Logical Components: RAID Group Sets/Storage Pools RAID Groups are created from Physical Disks RAID Group Sets created from RAID Groups Volumes created from RAID Group Sets Disk Drives Disk 1 Disk 2 Disk 3 Disk 4 Disk 5 Disk 6 Disk 7 Disk 8 RAID Group 1 RAID Group 2 RAID Group Set 1 Volume 1 Volume 2 Volume 3 27

28 Extent Pool/RAID Group Sets: Merging Topology and Performance 28

29 Interpreting Disk Response Service Levels 29

30 Logical Volumes Disk Drives Volume 1 RAID 5 Disk 1 Disk 2 Disk 3 Disk 4 Parity 123 Data 1 Data 2 Data 3 Data 4 Parity 456 Data 5 Data 6 Volume Data 71 Data 8 Parity 789 Data 9 Data a Data b Data c Parity abc Parity def Data d Data e Data e Volume 2 RAID 5 Metavolume 1 Disk Drives Disk 5 Disk 6 Disk 7 Disk 8 Parity 123 Data 1 Data 2 Data 3 Data 4 Parity 456 Data 5 Data 6 Volume Data 75 Data 8 Parity 789 Data 9 Data a Data b Data c Parity abc Parity def Data d Data e Data e 30

31 Volume Performance Metrics 31

32 Volume Fast Write Bypass % 32

33 Summary The goal of storage performance management (SPM) is to reduce storage costs while maintaining performance SLAs. SPM consists of: Processes Measurement Skills SMI-S provides a solid foundation for obtaining the necessary measurements to implement SPM 33

34 Q&A / Feedback Please send any questions or comments on this presentation to SNIA: tracktutorials@snia.org Brett Allison Many thanks to the following individuals for their contributions to this tutorial. Gilbert Hautekamer - 34

35 References [1] User Spending on Enterprise Storage Systems Reached $31 Billion in enterprise-storage-systems

36 Bridging the Visibility Gap An effective (SPM) solution must: Provide visibility inside the storage system where 70% of the bottlenecks occur, and Automatically correlate your workload metrics with the specific hardware component capabilities. 36