Alibaba Cloud said that it has been using ZooRoute in AliCloud for the last 18 months, where it has reduced outage time by 92.71%. Nezha for network performance in high-demand VMs Another software upgrade is helping Alibaba Cloud maintain network performance for high-demand virtual machines (VMs) without spending more on SmartNIC-accelerated virtual switches (vSwitches). Nezha, a distributed vSwitch load-sharing system, identifies idle SmartNICs and uses them to create a remote resource pool for high-demand virtual NICs (vNICs). Alibaba has tested the system in its data centers for a year and said in the paper that “Nezha effectively resolves vSwitch overloads and removes it as a bottleneck.” With the number of concurrent flows improved by up to 50x, and the number of vNICs by up to 40x, the bottleneck s now the VM kernel stack, the researchers wrote. Dai’s Forrester said that Nezha’s stateless offloading and cluster-wide pooling design is superior to solutions being pursued by rival cloud service providers. Separately, Alibaba’s cloud computing division has also been working on another software update that will enable it to provide better network performance for AI workloads.

Most research participants also told us they need to improve visibility into their data center network fabrics and WAN edge connectivity services. (See also: 10 network observability certifications to boost IT operations skills) The need for real-time data Observability of AI networks will require many enterprises to optimize how their tools collect network data. For instance, most observability tools rely on SNMP polling to pull metrics from network infrastructure, and these tools typically poll devices at five minute intervals. Shorter polling intervals can adversely impact network performance and tool performance. Sixty-nine percent of survey participants told EMA that AI networks require real-time infrastructure monitoring that SNMP simply cannot support. Real-time telemetry closes visibility gaps. For instance, AI traffic bursts that create congestion and packet drops may last only seconds, an issue that a five-minute polling interval would miss entirely. To achieve this level of metric granularity, network teams will have to adopt streaming network telemetry. Unfortunately, support of such technology is still uneven among network infrastructure and network observability vendors due to a lack of industry standardization and a perception among vendors that customers simply don’t need it. Well, AI is about to create a lot of demand for it.  In parallel to the need for granular infrastructure metrics, 51% of respondents told EMA that they need more real-time network flow monitoring. In general, network flow technologies such as NetFlow and IPFIX can deliver data nearly in real-time, with delays of seconds or a couple minutes depending on the implementation. However, other technologies are less timely. In particular, the VPC flow logs generated by cloud providers are do not offer the same data granularity. Network teams may need to turn to real-time packet monitoring to close cloud visibility gaps.  Smarter analysis for smarter networks Network teams also need their network

A New Chapter in Data Center Energy Strategy Equinix’s strategic investments in advanced nuclear and fuel cell technologies mark a pivotal moment in the evolution of data center energy infrastructure. By proactively securing power sources like Oklo’s fast reactors and Radiant’s microreactors, Equinix is not merely adapting to the industry’s growing energy demands but is actively shaping the future of sustainable, resilient power solutions. This forward-thinking approach is mirrored across the tech sector. Google, for instance, has partnered with Kairos Power to develop small modular reactors (SMRs) in Tennessee, aiming to supply power to its data centers by 2030 . Similarly, Amazon has committed to deploying 5 gigawatts of nuclear energy through partnerships with Dominion Energy and X-energy, underscoring the industry’s collective shift towards nuclear energy as a viable solution to meet escalating power needs . The urgency of these initiatives is underscored by projections from the U.S. Department of Energy, which anticipates data center electricity demand could rise to 6.7%–12% of total U.S. production by 2028, up from 4.4% in 2023. This surge, primarily driven by AI technologies, is straining existing grid infrastructure and prompting both public and private sectors to explore innovative solutions. Equinix’s approach, i.e. investing in both immediate and long-term energy solutions, sets a precedent for the industry. By integrating fuel cells for near-term needs and committing to advanced nuclear projects for future scalability, Equinix exemplifies a balanced strategy that addresses current challenges while preparing for future demands. As the industry moves forward, the collaboration between data center operators, energy providers, and policymakers will be crucial. The path to a sustainable, resilient energy future for data centers lies in continued innovation, strategic partnerships, and a shared commitment to meeting the digital economy’s power needs responsibly.

On the latest episode of the Data Center Frontier Show Podcast, we sat down with Brian Melka, CEO of Rehlko, to explore how the century-old mission-critical power provider is reinventing itself to support the new realities of AI-driven data center growth. Rehlko, formerly known as Kohler Energy, rebranded a year ago but continues to draw on more than a century of experience in power generation and backup systems. Melka emphasized that while the name has changed, the mission has not: delivering reliable, scalable, and flexible energy solutions to support always-on digital infrastructure. Meeting Surging AI Power Demands Asked how Rehlko is evolving to support the next wave of data center development, Melka pointed to two major dynamics shaping the market: Unprecedented capacity needs driven by AI training and inference. New, “spiky” usage patterns that strain traditional backup systems. “Power generation is something we’ve been doing longer than anyone else, starting in 1920,” Melka noted. “As we look forward, it’s not just about the scale of backup power required — it’s about responsiveness. AI has very large short-duration power demands that put real strain on traditional systems.” To address this, Rehlko is scaling its production capacity fourfold over the next three to four years, while also leveraging its global in-house EPC (engineering, procurement, construction) capabilities to design and deliver hybrid systems. These combine diesel or gas generation with battery storage and short-duration modulation, creating a more responsive power backbone for AI data centers. “We’re the only ones out there that can deliver that breadth of capability on a full turnkey basis,” Melka said. “It positions us to support customers as they navigate these new patterns of energy demand.” Speed to Power Becomes a Priority In today’s market, “speed to power” has become the defining theme. Developers and operators are increasingly considering

And with the current restrictions being placed on US manufacturers selling AI parts to China, reporting says NVIDIA is developing a Blackwell-based China chip, more capable than the current H20 but still structured to comply with U.S. export rules. Reuters reported that it would be  a single-die design (roughly half the compute of the dual-die B300), with HBM and NVLink, sampling as soon as next month. A second compliant workstation/inference product (RTX6000D) is also in development. Chinese agencies have reportedly discouraged use of NVIDIA H20 in government work, favoring Huawei Ascend. However, there have been reports describing AI training using the Ascend to be “challenging”, forcing some AI firms to revert to NVIDIA for large-scale training while using Ascend for inference. This keeps China demand alive for compliant NVIDIA/AMD parts—hence the U.S. interest in revenue-sharing. Meanwhile, AMD made its announcements at June’s “Advancing AI 2025” to set MI350 (CDNA 4) expectations and a yearly rollout rhythm that’s designed to erase NVIDIA’s time lead as much as fight on absolute perf/Watt. If MI350 systems ramp aligns with major cloud designs in 2026, AMD’s near-term objective is defending MI300X momentum while converting large customers to multi-vendor strategies (often pairing MI clusters with NVIDIA estates for redundancy and price leverage). The 15% China license fee will shape how AMD prices MI-series export SKUs and whether Chinese hyperscalers still prefer them to the domestic alternative (Huawei Ascend), which continue to face software/toolchain challenges. If Chinese buyers balk or Beijing discourages purchases, the revenue-share may be moot; if they don’t, AMD has a path to keep seats warm in China while building MI350 demand elsewhere. Beyond China export licenses, the U.S. and EU recently averted a larger trade war by settling near 15% on certain sectors, which included semiconductors, as opposed to the far more

Cooling Without the Risk Johnson Controls’ Data Center Cooling as a Service (DCCaaS) approach is designed to take cooling risk off the operator’s shoulders. The company doesn’t just provide the technology—it delivers a comprehensive, long-term service package that covers design, build, operation, maintenance, and life cycle management. The model shifts cooling from a capital expense to an operating expense, providing financial flexibility at a time when operators are pouring billions into AI-ready infrastructure. “We take on the risk of performance and uptime,” Renkis explained. “If we don’t meet the agreed-upon KPIs, there are financial consequences for us—not the customer.” The AI Advantage A key differentiator in Johnson Controls’ approach is its integration of AI, machine learning, and advanced analytics. Through its OpenBlue and Metasys platforms—supplemented by partnerships with three to four external AI providers—the company is able to continuously optimize cooling system performance. These AI-driven systems not only extend the life of equipment but also deliver financially guaranteed outcomes. “We tie our results to customer-defined KPIs,” said Renkis. “If we miss, we pay. That accountability drives everything we do.” Modularity with Flexibility While the industry is trending toward modularity and prefabricated builds, Renkis stressed that every DCCaaS project remains unique. Johnson Controls designs contracts with “detour functionality”—flexible pathways to upgrade and adapt as technology shifts. That flexibility is crucial given the rapid emergence of AI factory-scale demands. New chip architectures and ultra-dense racks—600kW, 1MW, even 1.5MW—are reshaping expectations for cooling and power. “Nobody knows exactly how this will evolve,” Renkis noted. “That uncertainty makes the as-a-service model the most prudent path forward.” Beyond Traditional Facilities Management Cooling-as-a-service is distinct from conventional facilities management in both scope and financial muscle. Johnson Controls brings to the table its own capital arm—Johnson Controls Capital—and a joint venture with Apollo Group, known as Ionic