Blue Planet Prevents Up To 95 Percent of Network Outages with New Proactive Network Operations Solution
Closed-loop automation solution uses artificial intelligence to significantly reduce service disruptions
14 May 2019
/ HANOVER, Md.
As networks become more complex and software-driven due to the rise of 5G, IoT, and cloud-based services, service providers have an urgent need to use more proactive methods for addressing service disruptions.
To address this challenge, Blue Planet, a division of Ciena (NYSE: CIEN), is harnessing the power of automation and artificial intelligence (AI) to help providers not only predict but to also proactively avoid outages. Blue Planet’s new Proactive Network Operations (PNO) solution enables AI-assisted operations to significantly improve the trouble-to-resolve process, forging a path toward a more adaptive network.
- According to Ovum, communications service providers (CSPs) indicate that their top priority use case for AI is network fault prediction, detection and correction, with over 40 percent prioritizing the implementation of this use case within the next 18 months1.
- Colt Technology Services has seen the benefits of Blue Planet’s predictive capabilities that collect and process telemetry data from multiple sources across its global production network and identify faults with strong accuracy.
- Using advanced machine learning algorithms, Blue Planet’s PNO can predict potential service disruptions with exceptional accuracy, foreseeing up to 95 percent of unplanned network outages based on analysis of Ethernet and optical loss of signal (LOS) anomalies. PNO can quickly pinpoint the root cause and then prescribe the best actions to preemptively resolve the issue.
- Blue Planet has also created an OpEx savings calculator to estimate the potential savings by deploying PNO. According to Blue Planet’s estimates, providers on average can save up to 38 percent in trouble-to-resolve OpEx per year.
- PNO can be integrated into existing provider environments regardless of which vendor systems and networking equipment are in place. Additionally, Blue Planet’s service professionals can help fine-tune and tailor PNO based on specific business objectives and network environment.
1 Ovum: “Artificial Intelligence: Impact and Opportunities” by Tom Pringle, published March 27, 2019.
- “AI works best when applied to real-world use cases that improve our network operations by eliminating time-consuming manual tasks. Blue Planet provides the capability to move beyond the hype to deliver real business benefits from predictive and eventually prescriptive analytics. This allows us to plan for the future, preparing our network for 5G, IoT, cloud and other emerging technologies.”
- Mirko Voltolini, Global Head of Network on Demand, Colt Technology Services
- “One of the biggest challenges CSPs face is the ability to predict and proactively resolve network outages. Blue Planet’s PNO solution implements AI techniques such as machine and deep learning to predict and enable automatic resolution of network failures, allowing CSPs to drive efficient operations and improve the overall customer experience.”
- Adaora Okeleke, Senior Analyst, Telecoms Operations and IT, Ovum
- “More providers are realizing that AI is key to facilitating a more automated way of running their business. Blue Planet’s goal is to enable customers to apply intelligent, closed-loop automation to all aspects of their operations with the goal of realizing Ciena’s vision of a fully adaptive network.”
- Kailem Anderson, Vice President of Portfolio and Engineering, Blue Planet, a division of Ciena
About Blue Planet
Blue Planet, a division of Ciena (NYSE: CIEN), provides market-leading intelligent automation software and specialized professional services to help clients modernize their IT and network operations. Driven by policy and AI-based insights, Blue Planet delivers the closed-loop automation tools needed to align IT and networking processes—critical to facilitating digital transformation. For updates on Blue Planet, visit www.blueplanet.com.
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