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Cloud Aware Network Management


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What are key considerations, strategies, technologies and tactics for network management as dependence on the cloud for tools and revenue increases?

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Cloud Aware Network Management

  1. 1. Cloud-Aware Network Management Alex Henthorn-Iwane VP Marketing KentikTechnologies
  2. 2. The Cloud is a Digital Supply Chain • SaaS, PaaS, IaaS are major suppliers for your users • Enterprises are offering more cloud-based services • Mobile apps • E-commerce • Which function and depend on Web APIs • Maps, Search, Ads, etc. • The Internet is the global freight routing system • Must be high performing
  3. 3. Cloud-Aware Net Mgmt:Strategic Considerations • Assures delivery of performance and user experience • Deals with reality of Internet security • Particularly DDoS because it is as much an operational availability issue as a security challenge • Leverages redundancy via multi-homing and CDN infrastructure
  4. 4. Cloud-Aware Net Mgmt:Tactics • Collect detailed traffic flow information • Instrument key nexus servers with performance metrics collection • Utilize advanced analytics • Deploy synthetic testing to understand availability • Limited reliance on traditional deep packet capture techniques, which are cumbersome for cloud networking
  5. 5. Elements of Cloud NetworkManagement • NetFlow, sFlow, IPFIX traffic flow data export • Sampled flows are fine • Passive BGP peering • Cost-effective server-side network instrumentation • Granular, tune-able alerts for anomalies & attacks • Deep analytical visibility • Automated remediation
  6. 6. MonitoringConsiderations • Global visibility • Top-down visibility • Full details for drill-downs • More than just summaries • Not siloed • Integrate with other tools, dashboards, etc. • Data/views easily shared with many functional teams • Supports fully hybrid environments
  7. 7. Alerting Considerations • Network-wide • Scalable with detail • Host-level capable • Dynamic anomaly detection (self-learning what is normal behavior) • Flexible integration with your choice of notification as well as automated remediation • E.g. DDoS scrubbers, load balancers, network orchestration • Alerting & detection needs to be complemented by deep analytics
  8. 8. Reality of NetworkBig Data • Network data is big data • Commonplace to generate hundreds of millions of data records per day • Traditional approaches very limited • Only produced roll-up summaries • Okay for top-level views • Useless for real action • Compute/storage scale means big data analytics are now relevant • Recent announcement by Cisco on Tetration Analytics is major signal • Key is to go past BI and have operational speed
  9. 9. Big Data Challenges for NetworkAnalytics • Ingest speed • Latency to query • Time to query response • Pre-computed cubes • On the fly
  10. 10. Advanced (Big Data) NetworkAnalytics • Need to enable engineers to leverage their technical and institutional knowledge effectively • Ad-hoc queries across massive datasets in a timely manner • Multi-dimensional analytics • Combine and visualize multiple fields • Like a massive pivot table • Complemented by automated analyses that reveal complex relationships • Practically speaking, turning insightful ad-hoc queries into dashboards
  11. 11. Cloud-BasedAnalytics • SaaS network management is now becoming more common • Big data approaches: DIY or SaaS • Very easy to adopt, fast time to value, but not feasible for all
  12. 12. A Case Study: Advanced Analytics of a DDoS Attack
  13. 13. Starting from Top-Level View • Seemingly Normal Variations over Several Days….?
  14. 14. Geo-Based Analytics • Looking at only SRC=CN (China)
  15. 15. A Closer Look • Zooming in time range on Second Spike
  16. 16. Checking AnotherDimension • Number of Unique Source IP Addresses
  17. 17. Where is the Traffic Going? • Flip to: Destination Addresses
  18. 18. PullingBack to Gauge the Situation • Looking at all inbound traffic to the target victim Dest IP
  19. 19. Narrowing in on the Actual Attack • Attack details by protocol
  20. 20. The Finding: Multi-LayerAttack • Multiple simultaneous vectors at hand
  21. 21. The MitigationPlan • Finding the Necessary Details for Setting Filter Policies
  22. 22. Case Example: Summary - Unusual traffic patterns from suspect Geo - Turned out to be DNS Amplification targeting a specific dest IP - But main attack was hiding other attacks/exploits - Data harvested for mitigation - Time required to complete this analysis: 3 minutes!
  23. 23. Closing Thoughts • Cloud isn’t just an external resource, it’s a way of business • Internet traffic should be more top of mind • Summary level views are insufficient and behind the curve • Big data analytics and SaaS network management tools are now WE HAVE MET THE CLOUD AND HE IS US
  24. 24. Thank You!