Technology

In-memory architecture

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.

Root Cause Analysis (RCA)

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

Leveraging dynamic thresholds

Sophisticated analytics engine

Analyzes incoming metrics second by second

Machine-learned dynamic thresholds

Continually adjusted based on historial metrics, heuristics, best practices

Virtual infrastructure deployment architecture

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.

Fastest value
  • 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

Service Assurance
  • 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

Most scalable
  • In-memory architecture scales to 10000s of endpoints/objects

Most optimal
  • Unique control analytics
    –Network layer insights
    –In-silo metrics and cross-silo dependency metrics

  • Control based on correlation analytics balancing Performance Capacity, Efficiency

Xangati Virtual Infrastructure

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