Network SLA Management
Identify problems in real time, before they cause downtime.
Your private network will only function as well as your real-time decisioning engine.
Scalability
Ingest and process telemetry data at scale
Best-in-Class Performance
Trigger actions to automatically maintain SLAs
Machine Learning
Use ML to improve outcomes over time
High Availability
Achieve 99.999% uptime
The Network SLA Management Challenge
Virtualized networks are rapidly becoming the foundation of enterprise telecommunications, evolving into an essential resource for scenarios where uptime and performance are essential for the proper functioning of mission-critical services.
Each of these networks will have its own service level agreement (SLA) around latency and/or availability. Comprehensive and robust private network service-level management is essential for achieving planned business outcomes and making decisions on accurate, relevant, and consistent data. Organizations also need real-time machine learning intelligence to properly manage SLAs.
Smart Factories — A Real-World Example
A smart factory campus has a production line split across several buildings with autonomous robots performing the majority of the work. Some robots will be located in specific areas, while others may roam between buildings.
A private network would be a good solution for connectivity and security concerns, but consider the cost of any downtime both in delays to production and the cost of expensive robots that can’t communicate and end up crashing into each other.
This enterprise is likely to want “five 9s” (ie 99.999% uptime) availability with strict SLAs that require a solution to monitor the network in real time and take automated actions where possible. A five 9s SLA means a maximum of 1.31 minutes of downtime per quarter, so real-time proactive SLA management is critical to make the investment in such a private mobile network worthwhile.
Why Volt for Network SLA Management
Your private network will only function as well as your real-time decisioning engine. The core issue is the need to quickly ingest and understand colossal amounts of telemetry data from across the network to spot problems, take immediate decisions, and trigger actions in real time, which becomes a problem when a centralized data platform does the processing.
ML models are a great tool to spot anomalies and self-learn from actual outcomes to ensure better responses over time without the need for human intervention to update business logic.
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Volt is a distributed real-time data processing platform that removes the layers inherent in other systems. Volt’s smaller footprint is optimized to be deployed around the network edge, allowing you to strip out irrelevant and unimportant data, minimize data sent to a central processing facility, and make low-latency decisions at the edge while the data, and any actions resulting from it, are still relevant.
