Imagine a data center humming with 100,000 cutting-edge GPUs, the backbone of the AI/ML and Gen AI revolution. The energy required for processing, cooling, and networking such a facility can easily reach 150 megawatts—enough to power 100,000-120,000 households. This staggering consumption, and the strain it places on local power grids, highlights a critical challenge: the growing environmental footprint of our digital world. U.S. Department of Energy projections paint a stark picture: data center electricity consumption in the U.S. is expected to surge to 6.7%-12% of total usage by 2028 – a dramatic increase from 58 TWh in 2014 to a projected 325-580 TWh by 2028.
Chart Source: Lawrence Berkeley National Laboratory US Data Center Energy Usage Report 2024
This exponential growth demands immediate, sustainable solutions particularly in networking infrastructure, where both performance and environmental impact are important.
The evolution of data center networks: from redundancy to responsibility
Traditional data center networks, built on a three-tier architecture of core, aggregation and access layers, prioritized maximum performance and redundancy. While this approach ensured high availability, it resulted in a constantly active infrastructure, consuming power regardless of actual utilization. Networks were over-engineered for peak capacity with systems designed to meet the needs of diverse customers, leading to unused features and redundant components consuming energy even when idle. Though reliable, this approach is increasingly at odds with today’s sustainability goals.
Revolutionizing network architecture for a sustainable future
To address these challenges, modern data centers are embracing four key pillars of energy efficiency:
- Next-generation energy-efficient hardware:
- Modern networking equipment features advanced ASICs with power-gating, selectively disabling unused circuits, and dynamic voltage and frequency scaling, adjusting processing power to workload demands.
- Manufacturers are adopting advanced packaging and silicon processes to minimize power leakage.
- Innovative cooling solutions, such as liquid and immersion cooling, and high-temperature tolerant components, further reduce energy consumption.
- These hardware-level optimizations, combined with intelligent power management firmware, maximize throughput per watt.
- Intelligent energy-aware routing:
- Traditional routing prioritizes speed, often neglecting energy consumption, but modern protocols are beginning to incorporate energy awareness.
- Based on policy definitions, networks dynamically choose routes based on power consumption, carbon intensity, cooling needs and time-of-day energy pricing.
- This approach balances performance with sustainability, reducing the overall energy footprint.
- Comprehensive energy observability:
- Traditional network energy observability involves monitoring and analyzing power consumption across the networking infrastructure, but there is no more benefit in granular monitoring and analysis of power consumption.
- Modern devices incorporate sophisticated sensors and metrics collection systems that track energy and thermal usage as granular as an individual component. That allows identification of power-hungry components and detection of anomalies in energy consumption patterns to make impactful changes.
- Leveraging machine learning and real-time analytics and thermal mapping, an organization can implement automated power management strategies to adjust active network system resources.
- Predictive energy utilization with AI:
- Forecasting network utilization and implementing dynamic topology adaptation allows for predictive energy usage.
- AI models predict power consumption based on historical data, traffic patterns, and environmental factors.
- This enables proactive topology adjustments, such as routing traffic through energy-efficient paths and consolidating network flows.
At Juniper Networks, we use machine learning to predict future power consumption patterns based on historical data, traffic patterns and environmental factors. The forecasts enable proactive topology adjustments – for example, routing traffic through energy-efficient paths, putting underutilized devices into low-power states or consolidating network flows to minimize the number of active components. By combining accurate energy predictions with dynamic topology optimization, networks can achieve substantial energy savings while maintaining performance requirements and service level agreements. This approach is particularly effective in large-scale data centers where even small improvements in energy efficiency can lead to significant operational cost reductions and environmental benefits
Juniper Energy Intelligence: leading the charge
Juniper Energy Intelligence (JEI) exemplifies the next generation of data center energy management. Currently undergoing PoCs and anticipated to be available later this year, JEI is born from innovation at Juniper Beyond Labs to deliver:
- Real-time visibility into data center energy usage, analyzing thermal, power, and bandwidth metrics.
- AI-driven predictive modeling of energy and bandwidth requirements, providing intelligent resource optimization recommendations.
- Automated energy-saving actions based on traffic forecasting.
- Comprehensive thermal management with hotspot detection, enabling efficient cooling.
- Machine learning-based capacity planning and energy optimization.
This is an important step to help customers reduce TCO and improve OPEX through new levels of monitoring to help optimize network operations and achieve sustainability goals.
The screenshots below give a glimpse into the JEI interface and its abilities. Figure 1 shows the sources of energy consumption in a network system. Figure 2 shows the power usage, thermal profile, and recommended actions to adjust the energy usage. Figure 3 shows the heat map of the entire data center up to the level of each switch, storage, and computing node including energy usage trend and a forecast of energy usage. Figure 4 shows Alarms and Alerts based on anomaly detection. More features and functionality are included. JEI delivers many tangible benefits such as extending the networking hardware lifespan by 2-3 years (due to automatically shutting off unused components), reduced operational expenses via optimized energy usage and decreased cooling costs due to thermal optimization, and direct visibility into sustainability metrics and automated adaptation.
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Figure 4
Other JEI benefits include reduced operational expenses, improved sustainability, and enhanced infrastructure ROI. For example, predictions can result in more efficient cooling systems, redirecting cold air to specific components, resulting in significant energy savings. At the same time, JEI can be used with large-scale infrastructure, balancing performance, cost, and environmental responsibility.
Visit our booth (2D12) at Mobile World Congress 2025 to see our product development in sustainable networking across the company. To learn more about Juniper Energy Intelligence, please visit the Juniper Beyond Labs webpage to stay up to date on its progress.
Call to Action
The transition to sustainable data center networks is both an environmental and business imperative. As we move towards a more connected future, solutions like JEI will form the foundation of green infrastructure.
As a community, we must collectively commit to sustainable data center networks through ongoing innovation. With continued dedication, we can harness the power of green computing to build a more sustainable digital future.
Juniper Networks may disclose information related to development and plans for future products, features or enhancements, known as a Statement of Product Direction or Plan of Record (“POR”). These details provided are based on Juniper’s current development efforts and plans. These development efforts and plans are subject to change at Juniper’s sole discretion, without notice. Except as may be set forth in a definitive agreement, Juniper Networks provides no assurances and assumes no responsibility to introduce products, features or enhancements described in this website, presentation, meeting, or publication, nor is Juniper liable for any loss arising out of reliance on the POR. Purchasing decisions by third-parties should not be based on this POR, and no purchases are contingent upon Juniper Networks delivering any feature or functionality described in this website, presentation, meeting, or publication.
