IT networks generate large volumes of information in the form of security, network, system and application logs. The volume and variety of log data makes traditional network monitoring capabilities ineffective — especially for monitoring use cases that require proactive decision making. These decisions are based on things like:
- Network performance trends
- Traffic patterns
- Necessary context
All of this makes large-scale and complex enterprise IT networks a suitable use case for advanced AI and machine learning capabilities. Indeed, “predictive network technologies” can proactively identify and respond to network incidents, including performance issues and security incidents.
Traditional vs. predictive network technology
Traditional network monitoring and analytics tools typically use statistical modeling techniques to represent network behavior, predict a future state and respond accordingly.
What makes Predictive Network Technologies different? They are driven by data.
How predictive network technology works
These tools do not explicitly model a network system – which changes rapidly as you scale your resources and integrate more third-party tools, accessible by a global user base.
Instead, they may use a deep learning architecture and a data preprocessing pipeline, which learns the behavior of the network based on network parameters instead of explicit design specifications.
This is important, because it may be near-impossible to fully represent any large-scale network exactly to specification with a fixed statistical model. In a deep learning framework, the model parameters can be tuned, updated and trained to learn high-dimensional features of the network, therefore, accurately modeling dependencies between them.
Key drivers of modern & predictive network technologies
Let’s discuss the key drivers of advanced predictive network technologies:
Network data volume
Large scale networks generate large volumes of information including security, network infrastructure, application and system logs. This information is generated at every node of the network, using IoT devices and connected hardware, at regular intervals. These periodic observations capture insights on network performance, health and security.
Considering the scale of complex network operations, data generated at the network nodes quickly transforms into big data, which means that users have more information than capacity to store and analyze efficiently.
GPUs & parallel processing
Advances in computing capabilities decreased the cost of running complex machine learning models that process large volumes of log big data.
GPU technologies play an important role in machine learning use cases. Machine learning algorithms involve resource intensive numerical calculations such as matrix multiplications. GPUs contain parallel processing units that can handle these operations for large data volumes efficiently, making it a compelling business case for organizations to invest in AI-enabled predictive network use cases.
Incidents follow trends
Advanced machine learning algorithms can discover these anomalies and trigger automated control actions such as isolating the affected network nodes, revoking access to sensitive business data and balancing the load to servers with better health and performance.
IT is overwhelmed (!)
Data-driven organizations may operate on limited resources and budget. Dedicating in-house experts and resources merely to keep the network alive is not your best way to maximize ROI on technology investments.
Instead, business executives want to focus their resources on activities that lead to business process improvements, innovation and capturing a larger market share with new products and services.
Use cases for predictive network technologies
The convergence of advanced AI capabilities, GPU technologies and the business demand to optimize human resource utilization is driving the trend of predictive network technologies.
Let’s review some the key use cases of predictive network technologies in multi-cloud and on-premise private data center environments.
IT networks combine a variety of monitoring, observability and detection technologies to:
- Predict network health.
- Identify underperforming nodes.
- Balance the IT workloads to high performance network locations in real-time.
This reduces downtime incidents and network performance degradation without relying on human involvement and manual network management operations.
Predictive network security
Compelling security use cases include the following:
- Attack projection and intent recognition: what steps has the adversary taken so far and what is their next target step.
- Intrusion detection and prevention: classifying an anomalous network activity as malicious and automatically triggering appropriate security measures.
- Risk mitigation: identifying the nodes that may be compromised and isolating them from the rest of the network to contain damages.
Infrastructure management and automation
Dynamic load distribution based on a variety of factors — network health, traffic patterns, operational cost and more. A predictive network technology system analyzes these factors in real-time and uses capabilities such as Hyperautomation Intelligence to manage network operations and load balancing.
Network performance improvement and user experience
Your business performance in the digital world depends directly on the end-user experience of your online services. The performance of these services depend on the health and capacity of your network to accommodate unpredictable, varied and surging network traffic.
By adopting predictive network technologies, you can plan your network resource capacity, scale resource, balance load and manage network operations — before the surging traffic impacts end-user experience.
Intelligence capabilities for the customer experience
It is important to note that Predictive Network Technology is not a specific solution but can be seen as a set of network intelligence capabilities that help improve network performance, security and end-user experience. It relies on advancements in data and AI technologies, as well as focus on a business goal driven approach to solve the challenges facing IT teams that operate large and complex network systems at scale.
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