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Cisco Crosswork Situation Manager 1.0 v1 now available on dCloud!

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Overview

Cisco® Crosswork Situation Manager uses patented artificial intelligence technology to automatically reduce your alert volume, get proactive insights into the health of your technology stack, and collaborate quickly to resolve any incident that might arise. The main benefits are noise reduction and event correlation and aggregation, increasing efficiency of operations.

This demo walks you through the features of Cisco Crosswork Situation Manager focusing on the user interface and using a pre-recorded set of events, allows you to experience the work flow of events to situations, and using collaboration features.

Scenarios

  • Scenario 1: Crosswork Situation Manager UI Dashboard
  • Scenario 2: System Settings
  • Scenario 3: Inject Events to Trigger New Situations
  • Scenario 4: View the Newly Created Situations and Alerts
  • Scenario 5: Resolving Situations
  • Scenario 6: Optional - Restart Crosswork Situation Manager to Repeat Demo

Requirements

  • Required:  Laptop
  • Optional:  Cisco AnyConnect®

Components

  • Crosswork Situation Manager (based on moogsoft AIOps 6.4.0.2)

Features

Crosswork Situation Manager

Automatic noise reduction:

  • Rank monitoring events by significance, enabling IT and network operations teams to focus only on relevant alerts. Up to 99 percent noise reduction without rules or filters, avoiding both false positives (useless alerts that distract operators) and false negatives (useful alerts that are filtered out before operators see them), as well as minimizing operator fatigue from excessive alert volumes.
  • Avoids issues being lost in the noise and helps operators focus on relevant events.

Algorithmic correlation:

  • A variety of patented algorithmic and machine-learning techniques build clusters of related alerts automatically, identifying unique situations without the need for laborious development and time-intensive maintenance of rules, filters, or inventory-based service maps. Reduce incidents being created in ticketing systems (existing customers have seen 62 percent reduction of tickets), avoiding the corresponding direct and indirect costs.
  • Delivers situational context around an issue, enabling correct prioritization, without requiring laborious creation and maintenance of rules, filters, or service maps.

Cross-silo collaboration:

  • Automatically assemble cross-disciplinary teams to work on identified situations, while avoiding distraction effect of involving unnecessarily large teams in issues where they are not required. New participants can be invited automatically (based on skills required) or manually and receive notification by email or SMS or through integration with existing notification systems.
  • Streamlines both diagnosis and remediation by limiting size of incident teams and providing powerful collaboration tools (situation room). Existing customers have seen 30 percent reduction in customer-identified incidents and 25 percent reduction in MTTR, leading to greater quality of service for less effort.
  • Integrated ChatOps functionality further accelerates diagnosis and remediation by integrating Cisco or external automation tools.

Probable root cause:

  • Machine-learning functionality to identify events that might be the root cause of a situation and rank them according to the confidence of that identification.
  • Enable IT and network operations teams to start their investigation from events that have the greatest chance of being the cause of the incident, without requiring manual dependency mapping.

Algorithmic knowledge capture:

  • Suggest resolving actions captured automatically from past situations in order to speed resolution times and identify recurring issues. Assemble knowledge based on proven solutions to past situations, with inline ratings by operators to help flag useful suggestions.
  • Provide IT and network operations teams with proven remediation actions from past situations, without requiring manual documentation to be compiled or consulted.

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