Take a comprehensive look at advanced analytics and reporting capabilities in HPE GreenLake and HPE Data Services Cloud Console for HPE Alletra Storage MP B10000. – By Wiley Thrasher, WW Senior Product Manager, HPE Data Services, Manageability, AI/ML, and Analytics
Are you seeking intelligent management solutions that offer unparalleled data insights? I’m here to build the case for HPE GreenLake and HPE Data Services Cloud
This robust foundation brings your organization the power of predictive analytics, cross-stack visibility, enhanced reporting, and detailed performance insights – all integrated with seamless management and cloud operational experience. The result? Transformed workload provisioning, simplified troubleshooting, and optimized utilization, plus minimized downtime coupled with storage infrastructure reliability and resilience.
You might already be familiar with some of our past innovations, but today we have exciting updates to share. We’re thrilled to introduce the extended, enhanced AIOps capabilities now available for HPE Alletra Storage MP B10000 systems managed under HPE GreenLake cloud. These advanced capabilities are designed to make the lives of your administrators and operators easier than ever before.
AIOps enhancements to transform your IT management experience
Let’s examine the key features and benefits of these AIOps advancements – and how they work together to simplify administration and operations. In future blogs, I’ll be drilling down into some of these areas in far greater detail, but for now, let’s set the stage with a quick rundown on capabilities.
Advanced telemetry
HPE Alletra Storage MP B10000 leverages a new and enhanced on-array sensor/metric telemetry metadata collection framework with high-frequency metric delivery, empowering advancements in AI/ML-based analytics to uplift manageability, supportability, observability, and monitoring experiences, deliver anomaly detection, enable intelligent workflow automation, and proactive issue detection with prescriptive remediation.
Predictive analytics and wellness
HPE GreenLake’s signature-based wellness automation continuously analyzes incoming system telemetry metadata to predict potential issues before they occur, minimizing downtime and ensuring continuous operation. HPE Data Services Cloud Console users can access the HPE GreenLake Wellness Dashboard for a centralized view of wellness events, health insights and prescriptive recommendations.
Sustainability
HPE Sustainability Insight Center provides a single pane of glass with customizable view of total energy consumption for your IT estate including information about greenhouse gas (GHG) emissions, and calculated costs associated with energy consumption using electric grid emissions factors based on device location. Specifically, the HPE Sustainability Insight Center measures and reports the energy consumption and carbon footprint of managed HPE and third-party assets, analyzes telemetry aggregated across sites and provides trends, cumulative totals, and averages. HPE Data Ops Manager also provides a Sustainability tab with detailed reporting of power consumption for HPE Alletra Storage MP B10000 systems with trending of power consumption which can be broken down by system, enclosure, and switch.
Software update and schedule recommendations
Self-service software updates are easier than ever with HPE’s software recommendation engine working behind the screens to provide recommended OS updates, enabling new features and expanding compatibility while also protecting systems with critical patches. Data Ops Manager automatically stages the software update on the system, the user can then choose to initiate the update or to schedule the update procedure; after running necessary pre-checks, the HPE Alletra Storage MP B10000 workload patterns are analyzed to provide schedule recommendations for the optimal time to apply the update.
Intent-based provisioning
Enable self-service provisioning without the need for storage expertise, transforming the process of workload deployment from days to minutes by abstracting the details traditionally required for storage provisioning in a guided workflow. Intent-based provisioning leverages AI-driven global intelligence with real-time context to ensure workloads are deployed consistently with a best-fit on the most suitable target.
Workload planner
Streamline the process of planning for new workloads for HPE Alletra Storage MP B10000 systems with detailed ‘’what if?” scenario analysis. Workload Planner helps avoid bottlenecks caused by overutilization of storage resources, maximizes hardware utilization by using application-specific workload characterization to determine the impact to both capacity and performance headroom when placing a new workload. Detailed reporting shows the best-fit recommendation across available systems, and utilization trends with projected impact based on the simulated workload placement. In addition, reports can be downloaded in PDF format.
Topology insights and cross stack analytics
Provide storage administrators with comprehensive VM and Volume topology views, detailing dependencies between HPE Alletra Storage MP B10000 systems and their volumes, datastores, hosts, and VMs, with KPIs and integrated analytics. VM and Datastore utilization monitoring, resource contention and latency attribution charting to help identify performance bottlenecks, optimize resource allocation, and enable prompt troubleshooting of related issues beyond storage.
Resource contention insights
Identify CPU and Disk saturation trends which can contribute to bottlenecks, slowdowns, and performance degradation, shows the associated resources and workload details and provides recommendations on how to address and alleviate contention issues by moving workloads, application of QoS limits, and suggesting hardware upgrades to increase system headroom. In addition, reports can be downloaded in CSV and PDF format.
Latency with correlated system and workload factors
Annotations to the system-level latency charts help pinpoint underlying factors contributing to latency spikes, providing insight by highlighting corresponding changes in system or workload which align with fluctuations in latency, allowing for targeted troubleshooting and optimization.
Headroom trend analysis with top contributors
Expose HPE Alletra Storage B10000 headroom utilization trends over time, to understand current utilization and anticipate future capacity requirements. With detailed breakdowns by top volume and volume set contributors, administrators can accurately identify specific contributors to system utilization and proactively allocate resources to prevent overutilization and potential bottlenecks.
Workload drift insights
Identify changes in HPE Alletra Storage MP B10000 workload patterns as annotation overlays on the volume throughput chart and provides related insights into how these deviations impact storage performance. This experience also provides a detailed distribution view to visualize the I/O size buckets before, during and after a period of drift. By detecting workload drift, administrators can adapt resource allocations and configurations to ensure optimal performance and maintain service levels over time.
Latency variation insights
Provide visibility into changes to storage latency by leveraging the advanced sensor/metric collection capabilities of HPE Alletra Storage MP B10000. When there are high variation of I/O latency for a period, annotation overlays are presented on the volume latency chart, enabling administrators to identify outliers with performance issues outside of the reported averages.
Top volume and volume set hotspots
This advanced anomaly detection capability for HPE Alletra Storage MP B10000 highlights volumes and volume set outliers experiencing the highest levels of activity or resource utilization, enabling administrators to prioritize optimization efforts and ensure optimal performance for critical workloads. Drill down directly from the hotspot to explore volume-level Workload Drift and Latency Variation Insights. By identifying hotspots, organizations can focus related troubleshooting efforts to maintain consistent performance across their storage infrastructure.
Capacity forecast
Extend capacity planning capabilities for HPE Alletra Storage MP B10000 by leveraging detailed historical trending and seasonality modeling for advanced visibility of usable capacity consumption and time-until-full projection. Analysis models updated each day to assure proactive notifications in rapidly changing environments.
Enhanced reporting
Provide a customizable framework for performance reporting and detailed analysis, with charting and trending of key performance metrics for Volumes, Volume Sets, Virtual Machines, Host Ports, Peer Ports, Hosts, Remote Copy links and Physical Drives. Users can choose from Top-6 or Select-10 resources with the option to sort-by metric.
Dramatically simplify your storage management experience
By combining the advanced AIOps capabilities of HPE GreenLake cloud and Data Services Cloud Console, you gain a comprehensive and dramatically simplified storage management solution for HPE Alletra Storage MP B10000. With this level of AI-driven self-service, predictive analytics, and cross-stack visibility, your organization can unlock the full potential of your data.
Count on HPE AIOps to help accelerate time to value – without the need for storage expertise for the line-of-business owners and developers, freeing IT resources to work on strategic, higher-value initiatives.
Ready to learn more?
Check out these resources for a deeper dive on this topic:
- User Guide: HPE Data Services Cloud Console / Data Ops Manager
- Getting Started Guide: HPE GreenLake for Data Ops Manager
- Release Notes: HPE Data Service Cloud Console - Data Ops Manager
- Release Notes: HPE Data Service Cloud Console – Block Storage
- Welcome Center: HPE Alletra Storage MP B10000
- Blog: Stay ahead of IT issues with HPE’s AIOps-driven wellness dashboard
- Blog: HPE Sustainability Insight Center
Meet Storage Experts blogger Wiley Thrasher, WW Senior Product Manager, HPE Data Services, Manageability, AI/ML, and Analytics
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