Solving the I/O Bottleneck in NextGen Data Centers & Cloud Computing. Anil Vasudeva, President & Chief Analyst, IMEX Research

Transcription

1 Solving the I/O Bottleneck in NextGen Data Centers & Cloud Computing Anil Vasudeva, President & Chief Analyst, IMEX Research

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 Solving the I/O Bottleneck in NextGen Data Centers & Cloud Computing Virtualization brings tremendous advantages to data centers of all sizes large and small. But the rapid proliferation of virtual machines (VMs) per physical server, in its wake, creates a highly randomized I/O problem, raising performance bottlenecks. To address this I/O issue, the IT industry and storage administrators in particular, have begun the adoption of a slew of newer technology solutions - ranging from faster and larger memories in cache, SSDs for higher IOPS cost effectively, high bandwidth (10GbE) networks using NPIVs for virtualized networks between VMs and shared storage systems along with embedded intelligence software to optimize various VM workloads all in order to meet various SLA metrics of performance, availability, cost etc.. Learning Objectives: This presentation provides a simple and illustrative view of the impact of Server Virtualization on existing storage I/O solutions and best practices. It delineates the roles, capabilities and cost effectiveness of emerging technologies in mitigating the I/O bottlenecks so the IT infrastructure implementers can achieve their targeted performance under various workloads, from their storage systems in virtualized data centers. The audience will obtain a clear understanding of the factors that can create side-effects from Server Virtualization and become knowledgably better prepared to ask pertinent questions of their suppliers of IT Infrastructure Equipment, Storage Virtualization and Data Storage Management software as well as in their implementation to achieve targeted results in performance, availability, scalability, interoperability and data management in their virtualized data centers 3

4 Agenda Data Centers & Cloud Infrastructure Storage Usage in NGDC & Clouds Performance Metrics by Workload Data Center I/O Bottlenecks Anatomy of Data Access I/O Gaps Role of SSD in Improving I/O Perf. Gap Improving Query Response Time Apps Benefitting from Improved I/O New Storage Economics SSD vs. HDD - IOPS/GB I/O Forensics & Use in Auto Storage-Tiering Key Takeaways 4

5 Data Centers & Cloud Infrastructure Public CloudCenter Enterprise VZ Data Center On-Premise Cloud Vertical Clouds IaaS, PaaS SaaS Servers VPN Switches: Layer 4-7, Layer 2, 10GbE, FC Stg Supplier/Partners Remote/Branch Office Cellular Wireless ISP ISP ISP ISP Internet Core Optical ISP Edge Web 2.0 Social Ntwks. Facebook, Twitter, YouTube Cable/DSL Home Networks ISP ISP Caching, Proxy, FW, SSL, IDS, DNS, LB, Web Servers Tier-1 Edge Apps Application Servers HA, File/Print, ERP, SCM, CRM Servers Tier-2 Apps Directory Security Policy Middleware Platform FC/ IPSANs Database Servers, Middleware, Data Mgmt Tier-3 Data Base Servers Management A request for data from a remote client to an enterprise data center crosses a myriad of systems and devices. Key is understanding where potential I/O bottlenecks can happen Source:: IMEX Research - Cloud Infrastructure Report

6 Cloud Computing Architecture SaaS PaaS IaaS Private Cloud Enterprise App SLA App Cloud Computing SLA SaaS Applications App Hybrid Cloud SLA Platform Tools & Services Operating Systems Virtualization Public Cloud Service Providers App SLA App Resources (Servers, Storage, Networks) SLA Management In Cloud Computing, Applications will carry SLAs as Metadata, dictating Availability, Performance, Cost, Security criterion and marshalling IaaS resources including Virtualized Tiered-Storage Elements (SSDs/HDDs/Tape/Data Placement & Mgmt. SW) required to mitigate any IO bottlenecks. 6

7 Performance Metrics by Workload IOPS* (*Latency-1) 1000 K 100 K 10K 1K Transaction Processing (RAID - 1, 5, 6) OLTP ecommerce Data Warehousing Business Intelligence OLAP Scientific Computing (RAID - 0, 3) HPC 100 TP HPC Imaging Audio Video Web *IOPS for a required response time ( ms) *=(#Channels*Latency-1) Source:: IMEX Research - Cloud Infrastructure Report MB/sec 500 7

8 Anatomy of Data Access Performance I/O Gap For the time it takes to do each Disk Operation: - Millions of CPU Operations can be done - Hundreds of Thousands of Memory Operations can be accomplished Anatomy of Data Access Time taken by CPU, Memory, Network, Disk for a typical I/O Operation during a Data Access Source: IMEX Research SSD Industry Report 2011 A 7.2K/15k rpm HDD can only do 100/140 IOPS 8

9 Data Center Performance Bottlenecks Applications Excessive Locking Data Contention I/O Delays/Errors Network I/O Network Congestion Dropped packets Data Retransmissions Timeouts Component Failures Storage I/O Connect Lack of Bandwidth Overloaded PCIe Connect Storage Device Contention LAN Access Networks Storage I/O Access Clients Windows, Linux, Unix Servers Web Servers Application Servers Database Servers Storage Web, Application, Database User Bottlenecks Connectivity Timeouts, Workload Surges Server Bottlenecks Lack of Srvr Power IO Wait & Queuing CPU Overhead I/O Timeouts Device Bottlenecks Device I/O Hotspots Cache Flush Lack of Storage Capacity Source: IMEX Research SSD Industry Report

10 Improving Query Response Time in OLTP 8 Query Response Time (ms) HDDs 14 Drives $$ HDDs 112 Drives w short stroking $$$$$$$$ Hybrid HDDs 36 Drives + SSDs $$$$ SSDs 12 Drives $$$ 0 IOPS (or Number of Concurrent Users) Conceptual Only -Not to Scale 0 10,000 20,000 30,000 40,000 For a targeted query response time, many more concurrent users can be added cost-effectively when using SSDs or SSD + HDDs storage vs. forcing more HDDs or short-stroking HDDs particularly when running DB & OLTP Apps Source: IMEX Research SSD Industry Report

11 Role of SSD in Improving I/O Perf. Gap Price $/GB NAND NOR SCM DRAM CPU SDRAM PCIe SSD DRAM getting Faster (to feed faster CPUs) & Larger (to feed Multi-cores & Multi-VMs from Virtualization) Tape HDD SATA SSD HDD becoming Cheaper, not faster SSD segmenting into PCIe SSD Cache - as backend to DRAM & SATA SSD - as front end to HDD Drawing Not to Scale Source: IMEX Research SSD Industry Report 2011 Source: IMEX Research SSD Industry Report 2011 Performance I/O Access Latency 11

12 SCM: A New Storage Class Memory SCM (Storage Class Memory) Solid State Memory filling the gap between DRAMs & HDDs Marketplace segmenting SCMs into SATA and PCIe based SSDs Key Metrics Required of Storage Class Memories Device - Capacity (GB), Cost ($/GB), Performance - Latency (Random/Block RW Access-ms); Bandwidth BW(R/W- GB/sec) Data Integrity - BER (Better than 1 in 10^17) Reliability - Write Endurance (30K PE Cycles No. of writes before death); - Data Retention (5 Years); MTBF (2 millions of Hrs), Environment - Power Consumption (Watts); Volumetric Density (TB/cu.in.); Power On/Off Time (sec), Resistance - Shock/Vibration (g-force); Temp./Voltage Extremes 4-Corner (oc,v); Radiation (Rad) Source: IMEX Research SSD Industry Report

13 SSDs - Price Erosion & IOPS/GB HDD IOPS/GB 300 SSD 4 Units (Millions) 0 IOPS/GB HDDs Database Note: 2U storage Applications rack, 2.5 HDD max cap constitute = 400GB / 24 HDDs, dominant de-stroked to 20%, usage 2.5 SSD in max IT cap. = Key 800GB / to 36 SSDs DB performance are random IOPS. SSDs outshine HDD in IOPS price/performance a mjor reason, besides better space and power, for their explosive growth. 0 Source: IMEX Research SSD Industry Report

14 Improving TPS: Saving Memory with SSDs Use 2500 Transactions per second (TPS) x 2.5 x 2.5 x 4.0 x Buffer Pool (Memory) GB To achieve a certain TPS improvement, its cheaper to deploy SSD vs increased buffer memory (in GB costs) needed with using HDDs alone. Source: IMEX Research SSD Industry Report

15 New Storage Hierarchy in NGDC & Clouds I/O Access Frequency vs. Percent of Corporate Data % 75% 65% % of I/O Accesses SSD Logs Journals Temp Tables Hot Tables FCoE/ SAS Arrays Tables Indices Hot Data Primary Storage SATA/ Cloud Storage Primary Capacity Storage Back Up Data Archived Data Offsite DataVault 1% 2% 10% 50% 100% % of Corporate Data Data: IMEX Research - Cloud Infrastructure Report

16 IO Bottleneck Mitigation in Virtualized Servers Virtualized Server VM Client 1 VM Client 2 VM Client n XCL Mgr. I/O Reg. XCL Driver I/O Reg. XCL Driver I/O Reg. XCL Driver Disk Controller. XCL VLUN SSD Driver ESX Kernel Offloading IOPS from Primary Storage Primary Storage Source: IMEX Research SSD Industry Report 2011 Both Applications & Storage Run Faster SSD Drive w/virtualization Server Driver 16

17 I/O Forensics for Auto Storage-Tiering Storage-Tiered Virtualization Storage-Tiering at LBA/Sub-LUN Level Physical Storage Logical Volume SSDs Arrays Hot Data HDDs Arrays Cold Data LBA Monitoring and Tiered Placement Every workload has unique I/O access signature Historical performance data for a LUN can identify performance skews & hot data regions by Logical Block Addresses IBM & IMEX Research SSD Industry Report IMEX

18 Apps Benefitting from Improved I/O VDI Commercial Visualization Bioinformatics & Diagnostics Decision Support Bus. Intelligence Entertainment- VoD / U-Tube Data: IMEX Research & Panasas 18 Instant On Boot Ups Rugged, Low Power 1GB/s, ms Rendering (Texture & Polygons) Very Read Intensive, Small Block I/O Data Warehousing Random IO, High OLTPM 10 GB/s, ms 1GB/s, ms 4 GB/s, ms Most Accessed Videos Very Read Intensive 18

19 Key Takeaways Solving I/O Problems I/O Bottlenecks occur at multiple places in the Compute Stack, the largest being at Storage I/O SSD comes out cheaper/iop for IO Intensive Apps To get of Reads Improve Indexing, archive out old data Minimize the impact of writes Get rid of temp tables/filesorts on slow disks. Compress big varchar/text/blobs Data Forensics and Tiered Placement Every workload has unique I/O access signature Historical performance data for a LUN can identify performance skews & hot data regions by LBAs Use Smart Tiering to identify hot LBA regions and non-disruptively migrate hot data from HDD to SSDs. Typically 4-8% of data becomes a candidate and when migrated to SSDs can provide response time reduction of ~65% at peak loads 19

20 Acknowledgements Many thanks to the following individuals for their industry vision and leadership in identifying the I/O Bottlenecks Late Jim Gray, Research Fellow, Microsoft Joshua Tseng Joseph White and the SNIA Education Committee Author: Anil Vasudeva, President & Chief Analyst, IMEX Research.com 20

21 Q&A / Feedback Please send any questions or comments on this presentation to SNIA: tracksolidstate@snia.org 21