High tech professional in various roles that include GTM Director, Consulting, Channel Management, Product Management, and Marketing from HPE, Aruba, and channel organizations. Technology areas include networking, storage and data management, high availability, cloud, and SaaS/NaaS, for enterprise, SMB, and channel business. Industries include healthcare, higher education, retail, financial services, ISVs, and MSPs.
An effective, modern approach to AIOps derives root cause symptoms for most common network problems in pristine network conditions. Machine Learning (ML) can then be used to determine how to recognize and interpret those conditions when they occur in a real customer environment. Root cause analysis (RCA) using this approach can be efficient and accurate, using a knowledge tree capturing most-common problems as well as an expanding set of use-cases based on the actual events and performance of the network.
Our Blue Planet Enterprise Automation Suite (BPE) takes this further, providing detailed, step-by-step remediation that is proven to address the exact root-cause conditions – and presents this information for push-button selection in a closed-loop automation approach which is simple for IT administrators. Ease-of-use is critical to ensure that AIOps is practical and delivers real benefits when it is needed most.
With BPE, administrators are asked to validate that the remediation worked as expected to fix the problem, enabling the knowledge base to constantly learn the specific network – creating opportunities for predictive and preemptive remediation with very high accuracy.
Figure 1. Use of Aggregated Symptoms, RCA Diagnosis and Validated Remediation can Accelerate Benefits for Network Operations
The way in which this approach to AIOps can accelerate benefits is illustrated in Figure 1. The collection and aggregation of network symptoms collected from many sources, combined with fast RCA correlation using AI/ML-based pattern recognition, enables accurate troubleshooting that simplifies the remediation process by cutting through the clutter. Likewise, administrator validation that the suggested guided remediation paths corrected the root problems strengthens and accelerates the approach for the specific network.
Let’s also consider how critical these benefits can be towards enabling IT teams and their constituents.
What’s in it for the stakeholders?
For organizations that depend on networks – and let’s face it, that’s virtually every organization – it’s essential that Network Operations Center (NOC) teams automate routine tasks, use AI/ML for routine troubleshooting and network correction, and can enforce network configuration policies to ensure secure operations and cost efficiencies. This approach not only drops costs but ensures that business teams will not be disrupted as they pivot to serve new customers, to develop new products, and to lead within their communities.
For NOC teams
A key benefit for the NOC team is to avoid burdening their experienced network administrators with routine tasks by enabling less experienced operators to address common issues and keep the network running smoothly. NOC teams utilizing AIOps can operate with better efficiency – and with a proactive stance to avoid network slowdowns and outages. Shortening the mean-time-to-knowledge – which we could also call the mean-time-to-action – is how AIOps brings this needed efficiency.
For any network administrator or operator, the benefit is that if something goes wrong with the network, with AIOps they can easily and effectively fix it. This alleviates stress, cuts through complexity, and helps networks administrators to do a better job to keep the network operational.
For operators, working with AIOps and guided remediation can help to accelerate their individual learning-curve on how to manage large networks with a large variety of brands and devices.
BPE Brings It All Together with Outcome-Focused Capabilities
With BPE, if anything goes wrong on and network devices can’t rely on the network to report the problem, the associated applications and services will be down or slowed to the brink of a failure. This could have a huge cost in terms of time and money, depending on the use-cases being used. With AIOps, this entire scenario can be easily prevented. Companies need to be proactive, they can’t wait for failure, and AIOps provides just that.