The Xangati virtual appliance features in-memory database architecture designed for speed and scale that delivers real-time, cross-silo intelligence to optimize virtual infrastructure workloads.
The Xangati virtual appliance, which deploys as an OVF (open virtual file) guest-VM format in the user’s hypervisor of choice, collects data without software agents using push/pull APIs and protocols from an extensive set of disparate data sources. Xangati then immediately begins an auto-discover and mapping process by which objects of various types are polled in an ongoing manner.
After that, Xangati interlocks collected consumptive object metrics and interactional inter-object metrics via an in-memory database within a high-fidelity timeline. Contiguously, Xangati continually stores objects and metrics in an historical database at 2-minute intervals for long-term trend analysis and reporting.
Xangati also computes dynamic thresholds for consumptive and interactional metrics by applying machine learning profiling techniques to historical data, which generates performance and efficiency alerts as and when needed when observed data significantly differs from dynamic thresholds or from best practice thresholds. By continually analyzing alerts using root cause analysis (RCA), Xangati employs excusive troubleshooting techniques that identify resources in contention, triggering clustered alerts.
Xangati also analyzes whether extant contention storms are correlated to resource capacity saturation issues and uses that as a guide to automated, prescriptive remediation. Finally, Xangati provides configuration, performance, efficiency and capacity data, and analysis to Xangati UIs to facilitate powerful streaming application contextual interfaces.
Storm-tracker contention analysis utility: anticipate problems before they impact operational agility.
Track Resource Contention Storms: From when they first appear to when they begin to impact operations
Provides IT Guidance to Avoid Future Storms: See areas of impact, sources of contention
Analyzes incoming metrics second by second
Continually adjusted based on historial metrics, heuristics, best practices
The Xangati Service Assurance Analytics framework delivers the fastest and most scalable way to locate, resolve, and/or prevent end-to-end performance issues in your Hybrid-Cloud Infrastructure, and the most optimal way to balance performance, capacity and efficiency.
Time to install – agentless, probe-less, auto-discovery
Time to visibility – live and continuous, end-to-end, cross-silo intelligence
Time to value – intelligent alerts with dynamic thresholds
Time to root-cause analysis – real-time analytics with in-memory architecture, post-mortem conclusions with DVR-like recording and VTT (visual trouble ticket)
Time to resolution – storm-tracker utility provides 300x RCA granularity
Fastest, more scalable way to locate, resolve, and/or prevent end-to-end performance issues across Clouds
Most optimal way to balance Performance, Capacity, Efficient
In-memory architecture scales to 10000s of endpoints/objects
Unique control analytics
–Network layer insights
–In-silo metrics and cross-silo dependency metrics
Control based on correlation analytics balancing Performance Capacity, Efficiency