Hitachi Data Center Analytics

Transcription

1 Hitachi Data Center Analytics

2 Agenda Storage analytics challenges Introducing Hitachi Data Center Analytics Storage analytics use cases and solutions Q&A

3 Storage Analytics Challenges

4 Storage Pain Points Driven by rapid capacity growth, storage analytics is required to address key storage pain points of delivering storage performance, forecasting and reporting

5 Leading Performance Management Challenges Business Alignment, Risk, and Cost Most Pressing Storage Challenges Meeting SLAs on performance, availability or recovery 42.0 Successfully troubleshooting potentially storage-related problems Time and/or budget to implement advanced storage features Time in planning/doing storage migrations/technology refreshes Quickly fulfilling storage provisioning requests 26.4 Complexity in managing too many storage product architectures 23.8 Other % of respondents Source: IDC General Storage Quick Poll #243511

6 Hitachi Command Suite Across all storage platforms Across management functions Across file, block, and object Across global storage virtualization Automate Control Analyze Service Levels Optimize Protect Unified Management Framework Compute Unified Content Appliance Hitachi Blade Server VSP G1000, VSP, VSP Midrange, HUS VM, HUS, HNAS HCP HDI

7 Key Storage Management Capabilities: Analyze CONTROL UNIFY ALL DATA TYPES AGILE DEPLOYMENT MAXIMIZE, SIMPLIFY ANALYZE GAIN INSIGHT IMPROVE PERFORMANCE AVOID PROBLEMS INTEGRATION INTELLIGENCE AUTOMATION OPTIMIZE PROTECT REDUCE RISK BUSINESS CONTINUITY HIGH AVAILABILITY INCREASE ROI GAIN EFFICIENCY ALIGN RESOURCES

8 Storage Analytics Approach KEY Points SINGLE DATA COLLECTOR KEY Customer Value HITACHI STORAGE PERFORMANCE ANALYTICS CLOUD DEPLOYED SAAS MODEL STORAGE ANALYTICS APPROACH BIG DATA ALIGNED SERVICES ATTACHED SINGLE DATA REPOSITORY NEW STORAGE ANALYTICS APPROACH

9 Introducing Hitachi Data Center Analytics

10 What Is Hitachi Data Center Analytics (HDCA)? Hitachi Data Center Analytics (HDCA) provides data center managers with useful insights about their Hitachi storage infrastructure using sophisticated analytics On-demand analytics Tree view of the environment Correlation capabilities Near real-time reporting Advanced interactive UI using HTML5 and Javascript Customizable reports through report builder External business intelligence integration Scalable solution Powered by proven NoSQL technology Ability to store highly granular data for years Easy and lightweight deployment BI Integration Baselines REST API Near Real Time Tree View Hitachi Data Center Analytics No SQL Advanced Analytics Interactive UI Custom Reporting

11 Hitachi Data Center Analytics Tree Shows hierarchical representation of the storage system objects Interactive Reporting Select a time duration Compare different time durations Select an object to be analyzed Zoom In on a specified time Select or deselect metrics to be displayed

12 Hitachi Data Center Analytics Zoom-in Reports Compare Timelines Select First time duration Select Second time duration View the Zoom- In report Both values are plotted (primary in bold and secondary in dash) Apply Zoom to other reports Reset Zoom to go back to original time interval Zoom-In/Zoom-out bar : Apply zoom and reset zoom icon appears

13 Hitachi Data Center Analytics: Lightweight Deployment Model Data Center Analytics has just 2 software components; both are installed as virtual machines Probes: gather performance and configuration data from targets (extract, transform and load) Server: receives data from probes for processing, analysis and reporting Hitachi Storage RIAT Probe VM Data Center Analytics Server RMLIB TMEA Collector Interactive Reports Custom Reports End Users Hitachi Storage RMLIB TMEA Collector User Interface

14 Hitachi Data Center Analytics: Scalability Input Data Database Java, C#, SQL Analysis Traditional performance analysis Input Data Dehydrate data Database Rehydrate data Procedural Language (e.g., Swazall, Hive) New approach (i.e. Google Tools) Input Data Dehydrate data Proprietary No-SQL DB MARS Query Language Hitachi Data Center Analytics (HDCA)

15 Hitachi Data Center Analytics Scalability and granularity Highly scalable, granular enterprise class performance data collection Trend analysis Historical trend reports spanning multiple years Performance data warehouse For Hitachi storage environments EFFICIENT AND SCALABLE ANALYTICS FOR TODAY S DATA CENTER

16 Storage Analytics Use Case and Solutions

17 Storage Analytics Scalable analytics to properly analyze performance trends Business scenario Collecting storage performance data doesn t properly scale across the data center Inadequate performance statistics doesn t facilitate historical trend analysis for proper planning Customer requirements Historical performance data collection that properly scale as the storage infrastructure grows Granular performance statistics for deep performance analysis Scalable analytics with Hitachi Data Center Analytics Highly scalable and granular performance data warehouse solution for storage analytics reporting, to properly plan future storage infrastructure growth

18 Storage Analytics How We Do It Measure and store configuration and performance data from storage, hypervisors, and operating systems Correlate and analyze data center performance issues from virtual machines to storage down to 1-second intervals [Currently only 1 second intervals on Linux platforms] Trend and scale performance data long term across the data center infrastructure Report and solve the most difficult performance issues quickly

19 Hitachi Storage Analytics Solutions Hitachi Tuning Manager (HTnM) End-to-end performance monitoring and reporting From applications (Oracle, Microsoft SQL Server, Micrsoft Exchange) to logical storage devices Troubleshooting Excellent for deep dive analysis of data path problem areas for all Hitachi storage environments Alarms Provides granular monitoring and SNMP alarms for all Hitachi storage platforms Third-party management integrations REST-based API and CLI for 3rd-party integration Used for custom reporting Data interchange for custom-built applications or other familiar reporting tools Hitachi Data Center Analytics (HDCA) Scalability large-scale enterprise-class data collection for historical reporting and analysis Complementary extension for Tuning Manager when longer range data collection is required Granularity near real time, fine-granularity data collection Data warehouse performance data warehouse for Hitachi storage environments Flexible reporting includes both standard, out-of-the-box reports and custom reporting capabilities

20 Hitachi Data Center Analytics Advantages Scalable performance data warehouse for large enterprise data growth Utilize historical trend analysis for future infrastructure requirement planning Generate both standard and customized reports Provide deep performance monitoring for efficient problem identification and management Storage Analytics Simplified

21 Questions and Discussion

22 Thank You