By: Refat Al Karmi – Senior Consultant for META at Juniper Networks.
Many companies today are keen to harness the power of AI. Data centers are a convenient platform for these ambitions. However, achieving optimal performance requires a well-designed network infrastructure and architecture. AI can help to enhance network manageability and deliver the best end user experience, illustrating its dual-edged impact.
Business processes rely heavily on applications, making sure that smooth operation is a key priority for CIOs, particularly for companies integrating AI more extensively into their business processes.
These applications typically reside in businesses’ own (on-premises) data centers, highlighting the critical need for a robust network environment and associated architecture. Without these, business applications cannot function optimally and connections to devices are not possible.
Automation is Inescapable
Managing the network environment in data centers has become increasingly complex in recent years. This complexity is compounded by the diverse technologies and applications in use, often managed by separate teams, which can lead to communication challenges and isolated operations.
The large volume of data generated within data centers also complicates the quick identification and analysis of relevant management information.
Manual monitoring, along with tasks like configuration, patching and incident resolution, is typically labour-intensive, inefficient and prone to human error. Similarly, manually tracking and optimizing network performance is extremely time-consuming.
Furthermore, without advanced monitoring and analysis tools, identifying security risks and potential anomalies becomes more challenging. Manual maintenance tends to be reactive in nature, which may result in unnecessary costs and unexpected disruptions.
AI Offers a Solution
Automating data center management offers many benefits, including reducing errors and speeding up routine tasks.
AIOps plays a crucial role in elevating automated management to a higher standard. By integrating data from various sources, AIOps provides a centralized overview and uncovers patterns and correlations that are often missed by human administrators.
This fosters better collaboration among administrators, accelerates issue resolution, and offers a more ‘holistic’ view of the network environment.
Additionally, automatically filtering and analyzing data ensures that only the most important, relevant information is made available to administrators. Which means fewer burdens for IT administrators, more efficient data analysis and faster, more effective decision-making.
AIOps also continuously monitors and suggests optimizations for network management tasks, which are traditionally performed manually. It detects incidents in real-time, analyses potential causes and proactively recommends or implements solutions.
Moreover, predictive analytics enable AIOps to anticipate issues before they arise and recommend maintenance based on actual requirements, shifting management from reactive to proactive or perhaps even preventative.
Ultimately, AIOps provides a superior experience for automated data center management, enabling an exceptional, stress-free experience for administrators and employees, allowing them to focus on critical tasks such as enhancing application value and leveraging data effectively.
Data Centers and AI Workloads
AIOps not only enhances automated and proactive data center management but also positions data centers as the ideal platform for running AI workloads and processing essential data.
Therefore, it is crucial to set up these environments optimally, especially the network infrastructure, to accommodate AI as an effective technology.
However, processing AI workloads in data centers presents new challenges, primarily due to the unique demands AI traffic imposes, compared to traditional data centers.
The cluster of computers and especially GPU processors that constitute AI, for example, within the data center, require high speeds for data processing. Accordingly, bandwidth must provide robust and consistent capacities, reaching speeds of 800 Gbps and beyond.
Moreover, this capacity’s scalability is vital to meet varying business demands. It involves efficiently leveraging data center resources to reduce costs, ensure consistent performance, prevent overloads, allocate resources to critical tasks, and optimize energy consumption.
Scalability also enables data centers to swiftly adapt to evolving business requirements, enhancing redundancy and maintenance capabilities with minimal disruption to operations.
Right Network Environment for AI
Running AI workloads in data centers requires the right network environment, for example, processing AI traffic via the familiar Ethernet protocol, instead of the more niche InfiniBand.
Utilizing intent-based networking in addition to AIOps will make data centers far more suitable for processing AI workloads.
Intent-based networking architecture allows companies to describe business goals, after which the data center networking environment automatically converts them to the right configurations, based on network information, analytics and orchestration.
These configurations are continuously validated via closed-loop validation to check whether they still meet the objectives and are automatically adjusted as necessary.
Intent-based networking ensures data centers maintain the correct network configuration, optimizing them specifically for handling AI workloads.
This automated approach not only relieves administrators of manual tasks but also consistently enhances user experiences to meet desired standards.
Data center operators using intent-based networking plus AIOps to radically simplify and improve the design, deployment and ongoing operations of infrastructure will increasingly differentiate themselves from their competitors.
A Comprehensive AI-Native Networking Platform
AI is seeing widespread utilization in many organizations, increasingly shaping their applications and data processing, with data centers playing a pivotal role.
A network environment that is foundationally optimized for AI ensures that AI workloads can be processed efficiently.
AI-Native Networking Platform enables companies and administrators to manage work environments via AIOps from a single environment and optimally process AI workloads within controlled conditions.
This allows enterprises, administrators and end users to focus entirely on their business objectives without having to worry about the required network infrastructure and architecture.
