AIORI is stepping into the next phase of networking evolution, where systems are no longer just monitored but intelligently controlled. Artificial Intelligence (AI) is enabling networks to become self-optimizing, continuously adapting to maintain Quality of Service (QoS).

Guided by frameworks from the International Telecommunication Union, AIORI is exploring how automation can transform network performance. With its distributed infrastructure, AIORI has the foundation to move from measurement toward intelligent decision-making.

AI in Network Automation

AI-driven automation introduces closed-loop systems where networks can:

• Monitor performance in real time
• Analyze data using machine learning models
• Make decisions using optimization algorithms
• Apply changes automatically

Techniques such as reinforcement learning, predictive analytics, and intent-based networking enable these capabilities. The result is a system that continuously improves itself without manual intervention.

ITU Standards and Frameworks

The ITU has introduced several key standards for AI-driven automation:

• Y.3142 defines AI-based network optimization frameworks
• Y.3192 focuses on customer-oriented QoS auto-optimization
• Y.3172 and Y.3176 provide machine learning integration models

These standards ensure that automation systems remain transparent, interoperable, and aligned with global best practices.

AIORI’s Role in Automation

AIORI’s measurement ecosystem provides the data foundation required for automation. By integrating AI models into this ecosystem, AIORI can evolve toward:

• Predictive QoS management
• Automated anomaly response
• Intelligent resource allocation
• Adaptive network optimization

Its distributed anchors allow testing these systems in real-world conditions, making solutions more robust and scalable.

Challenges and Governance

AI-driven automation must address:

• Trust and reliability of AI decisions
• Transparency and explainability
• Risk of over-automation
• Security vulnerabilities in AI systems

ITU emphasizes human oversight, auditability, and fail-safe mechanisms to ensure responsible deployment.

Expected Outcomes for AIORI

• Transition from measurement platform to AI-driven network intelligence system
• Enable development of self-optimizing network solutions
• Attract telecom operators seeking QoS automation tools
• Strengthen position in next-generation networking technologies
• Build capabilities for AI-based network control and optimization

Conclusion

AIORI is evolving from observing networks to actively shaping them. By combining AI with ITU standards, it is paving the way for intelligent, self-adaptive networks that deliver consistent performance and enhanced user experience, defining the future of automated networking

International Telecommunication Union –
ITU-T Y.3142 (AI/ML Network Optimization Framework)
https://www.itu.int/rec/T-REC-Y.3142

International Telecommunication Union –
ITU-T Y.3192 (Customer-Oriented QoS Auto-Optimization)
https://www.itu.int/rec/T-REC-Y.3192

International Telecommunication Union –
ITU-T Y.3172 (Machine Learning Framework for Networks)
https://www.itu.int/rec/T-REC-Y.3172

Ericsson –
Cognitive Network & AI-driven Assurance
https://www.ericsson.com/en/portfolio/networks/ai

O-RAN Alliance –
AI/ML in Open RAN Architecture
https://www.o-ran.org

Linux Foundation –
ONAP (Open Network Automation Platform)
https://www.onap.org

AIORI is redefining how the Internet is measured, moving beyond traditional metrics into a world where Artificial Intelligence (AI) deciphers complex traffic behavior. The Internet today resembles a constantly shifting ecosystem, where patterns evolve by the second. AI brings the capability to understand this dynamic environment at scale.

Aligned with global frameworks from the International Telecommunication Union, AIORI is building a measurement ecosystem that is both standardized and intelligent. With its distributed anchors, especially across rural India, AIORI contributes unique visibility to regions often underrepresented in global Internet datasets.

AI in Internet Measurement

Traditional Internet measurement focused on latency, packet loss, and throughput. However, modern networks require deeper insights. AI enables:

• Traffic classification, even in encrypted environments
• Detection of anomalies like congestion or cyberattacks
• Forecasting of bandwidth demand using time-series models
• Identification of usage patterns through clustering

These capabilities transform raw measurement data into actionable intelligence.

ITU Framework and Role

The ITU has laid the foundation for standardized measurement through recommendations such as Y.1540 and Y.1543, which define IP performance metrics. More recent work like Y.3658 emphasizes programmable networks capable of collecting flow-based data dynamically.

These standards ensure that measurement practices remain consistent and globally interoperable. AI builds upon these frameworks, enabling more scalable and adaptive measurement systems.

AIORI’s Contribution

AIORI’s infrastructure, including platforms like v2.aori.in and its distributed anchor network, continuously collects real-world data across diverse environments. This data becomes significantly more valuable when combined with AI techniques.

By applying machine learning to its datasets, AIORI can:

• Identify hidden traffic patterns
• Predict network demand
• Detect large-scale anomalies
• Provide insights for infrastructure planning

Importantly, AIORI’s rural presence ensures that measurement is not biased toward urban, high-connectivity regions.

Challenges and Policy Considerations

AI-driven measurement introduces several challenges:

• Privacy concerns when analyzing traffic data
• Bias due to uneven data coverage
• High computational requirements
• Need for transparency in AI models

ITU emphasizes ethical AI use, standardized methodologies, and global collaboration to address these issues.

Expected Outcomes for AIORI

• Position AIORI as a leader in AI-driven Internet measurement
• Enhance its ability to convert raw data into predictive insights
• Attract collaborations with global research and measurement initiatives
• Strengthen its role in digital inclusion and rural connectivity analysis
• Enable development of advanced traffic intelligence platforms

Conclusion

AIORI is not just measuring the Internet, it is interpreting its behavior at scale. By integrating AI with ITU-aligned frameworks, AIORI is building a smarter, more inclusive understanding of global connectivity, ensuring that every region, including the most remote, is part of the digital map

International Telecommunication Union –
ITU-T Y.1540 (IP Performance Parameters)
https://www.itu.int/rec/T-REC-Y.1540

International Telecommunication Union –
ITU-T Y.1543 (Network Performance Measurement)
https://www.itu.int/rec/T-REC-Y.1543

International Telecommunication Union –
ITU-T Y.3658 (Big Data Networking & Programmable Networks)
https://www.itu.int/rec/T-REC-Y.3658

Cloudflare –
Blog: The Tricky Science of Internet Measurement
https://blog.cloudflare.com/the-tricky-science-of-measuring-the-internet/

RIPE Atlas –
Global Internet Measurement Platform
https://atlas.ripe.net

Measurement Lab –
https://www.measurementlab.net

AIORI stands at the forefront of transforming how networks are understood, measured, and optimized. In an era where digital infrastructure behaves like a living system, constantly evolving and generating massive data streams, Artificial Intelligence (AI) becomes the brain that interprets and acts on this data.

Aligned with global frameworks from the International Telecommunication Union, AIORI is shaping a future where network intelligence is proactive, inclusive, and deeply data-driven. With its distributed measurement infrastructure and strong presence in rural regions, AIORI is uniquely positioned to bring visibility to areas often left out of global network analysis.

AI in Network Monitoring & Anomaly Detection

AI enhances network monitoring by continuously analyzing telemetry, logs, and performance indicators. Advanced techniques such as machine learning, anomaly detection algorithms, and time-series forecasting allow systems to identify unusual patterns before they evolve into failures.

ITU standards like Y.3661 define architectures where real-time data flows through sensing and analytics layers to predict and prevent disruptions. Q.4081 further emphasizes the importance of monitoring AI models themselves to ensure reliability over time.

For AIORI, this creates an opportunity to leverage its anchor network across diverse geographies to detect anomalies in real-world conditions, especially in regions where connectivity challenges are more frequent and complex.

AI in Internet Measurement & Traffic Analysis

Internet measurement is no longer limited to basic metrics like latency or throughput. AI enables deeper traffic analysis, including classification, forecasting, and anomaly detection at scale.

Frameworks such as Y.1540 and Y.3658 from the ITU standardize how measurement data is collected and analyzed. AI builds on this foundation by transforming raw data into actionable insights that can inform network planning and policy decisions.

AIORI’s infrastructure, including platforms like v2.aori.in and its distributed anchors, generates valuable datasets across urban and rural environments. Applying AI to this data allows AIORI to contribute to a more accurate and inclusive understanding of Internet performance globally.

AI in Network Automation & QoS Assurance

The evolution of networking is moving toward automation. AI-driven systems can monitor, analyze, and adjust network configurations in real time to maintain Quality of Service (QoS). Techniques like reinforcement learning and intent-based networking enable closed-loop systems that continuously optimize performance.

ITU standards such as Y.3142 and Y.3192 define frameworks for AI-based optimization and customer-oriented QoS assurance. These systems ensure consistent user experience even under dynamic network conditions.

For AIORI, this represents the next step, moving from measurement to intelligent optimization, enabling the development of adaptive and self-healing network systems.

Why AIORI is Driving This Vision

AIORI is not merely observing the evolution of networking, it is actively shaping it. By aligning its initiatives with ITU standards and integrating AI into its measurement ecosystem, AIORI aims to transition from a data collection platform to a comprehensive AI-driven network intelligence provider.

Its distributed anchor network acts as a real-world laboratory, enabling experimentation and innovation across diverse connectivity scenarios.

Expected Outcomes for AIORI

• Establish leadership in AI-driven network monitoring and measurement
• Develop predictive analytics and anomaly detection capabilities
• Expand into AI-based network automation and optimization
• Strengthen collaborations with telecom, research, and policy stakeholders
• Contribute to reducing the digital divide through data-driven insights

Conclusion

AIORI represents the convergence of measurement, intelligence, and innovation in modern networking. By leveraging AI and aligning with ITU frameworks, it is evolving from observing networks to understanding and ultimately optimizing them.

As networks become more complex and critical to global connectivity, AIORI’s vision ensures they remain resilient, efficient, and inclusive, shaping the future of intelligent networking

International Telecommunication Union –
ITU-T Y.3661 (Big Data Driven Network Operation)
https://www.itu.int/rec/T-REC-Y.3661

International Telecommunication Union –
ITU-T Q.4081 (Monitoring of AI Systems in Telecom)
https://www.itu.int/rec/T-REC-Q.4081

International Telecommunication Union –
ITU-T SG20 IoT Technical Reports (AI-based anomaly detection)
https://www.itu.int/en/ITU-T/studygroups/2022-2024/20/Pages/default.aspx

Türk Telekom AI Network Analytics Case Study
https://www.tmforum.org/resources/case-study/turk-telekom-ai-analytics/

Rakuten Mobile AI Monitoring Insights
https://www.rakutenmobile.com/en/technology/

The AIORI (Advanced Internet Operations Research in India) initiative addresses this challenge through a measurement-first approach. Instead of focusing solely on infrastructure expansion, AIORI emphasizes analyzing real-time network performance to uncover deeper insights. This perspective aligns closely with global discussions such as the Pulse Internet Measurement Forum held in Jakarta, where experts highlighted the importance of measuring school Internet connectivity to drive meaningful improvements.

Rural connectivity challenges are multi-dimensional. Limited infrastructure, high costs, geographical barriers, and gaps in digital literacy all contribute to the problem. However, one of the most critical gaps is the lack of reliable, ground-level data. Without measurable insights, it becomes difficult to assess network stability, identify congestion patterns, or determine where infrastructure investments are truly needed.

Discussions at the Pulse Internet Measurement Forum reinforced that improving connectivity requires continuous monitoring rather than one-time deployment. AIORI adopts this principle by tracking key performance indicators such as latency, packet loss, bandwidth availability, and uptime. These metrics transform vague assumptions into evidence-based decision-making.

A key strength of AIORI lies in its real-world deployment. The initiative is supported by a growing network of AIORI Anchors distributed across India, 70+numbers of 219  are located in rural and semi-urban areas. Through the platform v2.aiori.in, these anchors function as live monitoring nodes that continuously collect Internet performance data from diverse locations.

Fig: Aiori Dashboard for the Anchor locations

AIORI bridges the gap between research and reality by using anchor nodes to transform real-world rural data into actionable connectivity insights. This data-driven approach ensures that digital inclusion efforts are reliable, measurable, and tailored to the specific needs of underserved communities.

Standards such as:

• RFC 7950: YANG Data Modeling Language
• RFC 8342: Network Management Datastore Architecture

define how configuration and operational state are modeled.

AI and Structured Telemetry

YANG-based streaming telemetry enables continuous export of network state. AI systems can integrate into this pipeline by:

• Consuming structured telemetry streams
• Detecting anomalous state transitions
• Triggering automated adjustments via NETCONF or RESTCONF

Because these mechanisms are standardized, automation remains vendor-neutral. AI-driven orchestration can operate across heterogeneous environments without relying on proprietary schemas.

Security and Anomaly Detection

Security monitoring also benefits from structured protocol definitions. AI models can analyze:

• BGP routing updates
• DNS query patterns
• IPFIX flow exports

By learning normal behavior patterns, anomaly detection systems can identify route leaks, DNS tunneling, or coordinated malicious IP activity more efficiently than static filters.

However, these capabilities depend on consistent protocol semantics. The IETF’s role in defining packet structures and routing attributes ensures that AI interpretations remain technically grounded.

Emerging Governance Discussions

Recent IETF meetings have included discussions on autonomous AI agents and machine-to-machine communication frameworks. Additionally, initiatives such as the AIPREF Working Group are examining mechanisms for expressing publisher preferences toward AI crawlers.

These discussions reflect a forward-looking governance model. AI integration is addressed within open, consensus-based processes rather than closed ecosystems.

Conclusion

AI in networking does not replace the foundations built by the IETF and IRTF. It operates on top of them. Standards provide structured telemetry and interoperable protocols. Research expands analytical methodologies. AI converts standardized inputs into predictive and adaptive outcomes.

The result is a technically accountable evolution of the Internet, where automation and intelligence remain aligned with open documentation and public review.

Within this ecosystem, the Measurement and Analysis Research Group focuses on empirical analysis of Internet infrastructure, routing patterns, and traffic evolution. The Applied Networking Research Workshop provides a peer-reviewed venue for publishing cutting-edge research.

AI in Measurement Research

Publicly archived workshop papers frequently include:

• Machine learning for traffic classification
• Time-series anomaly detection 
• Statistical modeling of infrastructure identifiers
• Large-scale routing behavior analysis 

For example, predictive modeling techniques have been applied to infer patterns in IP identifier behavior. These studies demonstrate how statistical and machine learning tools can enhance traditional measurement methodologies.

The significance lies in openness. Research presented in IRTF forums is publicly documented, debated, and reviewed. AI techniques are transparent and replicable rather than proprietary.

Transparency and Replicability

The IETF and IRTF processes emphasize:

• Open mailing lists
• Archived proceedings
• Public draft revisions
• Consensus-driven evaluation

This environment ensures that AI-driven measurement research can be independently validated. The integration of AI into Internet analysis is therefore not experimental in isolation. It is community-reviewed and technically scrutinized.

In this layered structure, standards define measurable parameters. Research explores innovative analytical methods. AI serves as the computational engine that extracts patterns from large-scale data.

Together, they transform raw network telemetry into structured insight while maintaining technical accountability.

The IETF does not standardize AI models. Instead, it defines interoperable metrics, protocols, and data structures. AI systems rely on these standardized foundations to operate reliably across different networks and vendors.

Why Standardized Metrics Matter

Networking performance metrics are formally defined through the IP Performance Metrics Working Group. Foundational RFCs such as:

• RFC 2330: Framework for IP Performance Metrics
• RFC 3393: IP Packet Delay Variation
• RFC 7680: IPPM Metric Registry

establish precise definitions for delay, packet loss, and jitter.

This precision ensures that a latency measurement in one network is equivalent to the same metric in another. For AI systems, this uniformity is critical. Models trained on standardized metrics can be transferred across environments without semantic ambiguity.

Proof of Concept: Predictive Monitoring Architecture

A standards-aligned AI measurement system may follow this structure:

1. Deploy distributed probes collecting IPPM-defined metrics.
2. Store telemetry in a time-series database.
3. Apply forecasting algorithms to predict congestion or degradation.
4. Trigger automated network policies via SDN controllers.

The essential factor is compliance with open definitions. Because the data conforms to RFC-defined semantics, the resulting AI outputs remain auditable and reproducible.

This standards-driven approach prevents fragmentation. Instead of vendor-specific counters, AI systems consume universally defined metrics. That alignment ensures interoperability and long-term sustainability.

In this architecture, the IETF provides the language of measurement. AI interprets that language to forecast behavior and optimize performance. Intelligence is not replacing standards. It is amplifying them.

Operationalizing Internet Resilience:

The Role of Measurement in Strengthening India’s DNS Ecosystem

High Level Thematic Dialogue | ICANN85 Event by NIXI

At ICANN85 in Mumbai, the National Internet Exchange of India (NIXI) hosted a high-level thematic panel discussion on AIORI (Advanced Internet Operations Research in India), an initiative focused on strengthening India’s Internet resilience through structured measurement and research.

The panel discussion was driven by Anupam Agrawal, who facilitated dialogue among key stakeholders from the government, technical community, and infrastructure operators on the importance of institutionalizing Internet measurement practices in India.

Shri Alkesh Sharma, in his keynote address, emphasized that as the Internet becomes the core national infrastructure supporting governance, finance, healthcare, education, and commerce, resilience must move beyond reactive measures. He highlighted the need for proactive approaches, stating, “Modern Internet infrastructure requires a shift toward anticipation and preparedness.” He further underlined that structured and data-driven Internet measurement frameworks are essential to enable real-time visibility and support evidence-based decision making.

Dr. Devesh Tyagi, CEO of the National Internet Exchange of India (NIXI), reinforced that strengthening Internet resilience begins with understanding operational realities. He stressed the foundational role of measurement, noting, “One important dimension of strengthening resilience is measurement. To improve what we operate and manage, we must first be able to observe and understand it.” Continuous measurement, he added, helps identify vulnerabilities, improve operational readiness, and guide informed policy decisions.

During the session, Mr. Anand Raje presented on the AIORI initiative, an indigenous Internet measurement and research platform designed to bridge operations, research, and policy. AIORI enables structured observation of network and DNS ecosystem performance through distributed measurement infrastructure, edge measurement devices, and collaborative research initiatives.

He also discussed various initiatives such as L-Root deployments across multiple Indian cities, measurement of DNS traffic distribution patterns, and resilience stress-testing experiments. These highlighted the ongoing efforts of the Government of India, to strengthen resilience through localized measurement capabilities, better understanding regional traffic dynamics, and improving preparedness for disaster scenarios.

The discussion concluded with a shared understanding that institutionalizing Internet measurement work in India is essential for strengthening DNS stability and overall Internet resilience. The AIORI initiative represents a forward-looking effort to build national capability while contributing to global knowledge on Internet operations.

Through continued collaboration among government bodies, technical communities, academia, and global institutions, India is taking meaningful steps toward operationalizing Internet resilience and ensuring a more secure, stable, and measurable digital future.

Presentation

Strengthening India’s Internet Research Ecosystem Through Academia–Industry Collaboration

The India Internet Foundation (IIFON) proudly announces the inauguration of the Advanced Research Laboratory on Internet Research Network at Chandigarh University, established in collaboration with the AIORI Project and the National Internet Exchange of India (NIXI).

This milestone represents more than the launch of a laboratory. It reflects a strategic step toward strengthening India’s Internet research backbone through a structured academia–industry–standards partnership model. In an era where the Internet forms the foundation of digital governance, economic growth, and national resilience, building indigenous research and operational capability is both timely and essential.

A Strategic Platform for Internet Research

The newly inaugurated laboratory has been designed as a hands-on research and experimentation hub focused on advancing India’s expertise in:

• Internet operations and critical infrastructure
• DNS ecosystem research and resilience mechanisms
• Protocol experimentation aligned with global standards
• Internet measurement and performance analysis
• Next-generation networking technologies

By creating a structured environment for applied research, the laboratory ensures that students and researchers engage directly with real-world Internet infrastructure challenges, fostering practical competence alongside academic rigor.

Alignment with Global Internet Ecosystems

The laboratory’s research vision aligns with global Internet standards and governance ecosystems, including:

• IETF (Internet Engineering Task Force)
• ICANN (Internet Corporation for Assigned Names and Numbers)
• Regional Internet Registries (RIRs)

This alignment ensures that research outcomes contribute meaningfully to international technical discussions and standards development processes, strengthening India’s voice in global Internet engineering forums.

Strengthening Indigenous Measurement & Resilience

A core objective of this collaboration is to enhance India’s indigenous Internet measurement and resilience capabilities. Robust measurement frameworks are fundamental to understanding network behavior, DNS stability, routing efficiency, and infrastructure reliability.

Through AIORI-aligned experimentation and structured research programs, the laboratory will:

• Generate data-driven insights into Internet performance
• Strengthen national resilience initiatives
• Build capacity in critical Internet technologies
• Develop a pipeline of researchers contributing to global standards

Leadership & Institutional Support

• IIFON extends its sincere appreciation to the Chandigarh University leadership for their vision and commitment:
• Dr. Raviraja N Seetharam, Vice Chancellor
• Raghuveer VR, Pro-Vice Chancellor (Academic Affairs)
• Dr. Satbir S Sehgal, Pro-Vice Chancellor (Operations)
• Dr. Sachin Ahuja, Executive Director (UIE)

We also acknowledge the leadership and contributions of Dr. John Jose (IITG) and Dr. Venkatesh Tamarapalli (IITG), along with the faculty members and organizing team whose efforts made this initiative possible.

Representing IIFON at the inauguration, Mr. Anand Raje, CTO and Co-Founder of IIFON, was present to reaffirm the Foundation’s commitment to advancing Internet research, measurement, and standards engagement in India.

Our sincere appreciation also goes to Ruchika Gupta and Navneet Sehgal for their valuable inputs, and to Dr. Navpreet Kaur Walia, Dr. Vikas Wasson, Dr. Puneet Kumar, Tripti Sharma, and Dr. Jasneet Kaur for gracing the occasion with their presence.

A Shared Vision for India’s Internet Future

This collaboration reflects a broader national vision: strengthening India’s Internet engineering capabilities through sustained partnerships between academia, industry, and standards communities.

By fostering applied research, enabling standards participation, and building measurement-driven infrastructure insight, the Advanced Research Laboratory stands as a catalyst for long-term impact.

India’s digital future depends not only on adopting global technologies, but on contributing to their architecture and evolution. Through this initiative, IIFON reaffirms its commitment to building capacity, capability, and meaningful global contributions from India in Internet engineering and operations.

Together, we are shaping a resilient and research-driven Internet ecosystem for the nation.

In the ever-accelerating race for internet speed, the “last mile” of performance often hides in the DNS resolution process. During the AIORI-2 Hackathon, our team from Sharda University tackled this bottleneck head-on. We didn’t just build a resolver; we engineered a Hyperfast DNS Load Balancer that leverages the parallel processing power of the AWS Cloud to slash latency and fortify reliability.

1. The Core Innovation: “First to Finish” Resolution

Traditional DNS forwarders are sequential and fragile—if one upstream server lags, your request hangs. Our system flips the script. Using a parallel query mechanism, our load balancer sends a single incoming request to multiple upstream giants (Google, Cloudflare, Quad9) simultaneously. The first valid response to cross the finish line is served back to the user, while slower packets are discarded.

This approach, rooted in RFC 1035, resulted in a staggering 60% reduction in average response time during our stress tests.

2. Cloud-Native Architecture on AWS

To move from a prototype to a production-grade service, we anchored our logic in the AWS Well-Architected Framework. By deploying across multiple Availability Zones (AZs), we ensured that even a localized data center failure wouldn’t interrupt global DNS resolution.

The Stack:

• Traffic Entry: Amazon Route 53 acts as the intelligent gateway, utilizing health checks to ensure queries only hit active balancers.
• Distribution: An Application Load Balancer (ALB) spreads the load across a fleet of EC2 instances.
• Intelligence: Our Python-based resolver logic (utilizing asyncio and dnspython) lives on EC2, dynamically scaling via Auto Scaling Groups as traffic spikes.
• Persistence: Amazon RDS (Master-Standby) maintains our query logs and cache hit data, ensuring zero data loss during failovers.

3. Security without the Sluggishness

Speed is meaningless if the data is compromised. We implemented DNS-over-TLS (DoT) as per RFC 7858, ensuring that every parallel query is wrapped in a secure tunnel. By utilizing AWS Certificate Manager (ACM), we automated the heavy lifting of SSL/TLS rotations, allowing our team to focus on performance tuning.

Technical Insight: To combat the inherent latency of TLS handshakes, we optimized the system for connection reuse and session caching, effectively neutralizing the “security tax” usually associated with encrypted DNS.

4. Measuring Success: The Metrics

Our 48-hour stress test proved that high-performance DNS is achievable in the cloud:

5. Lessons from the Trenches

This sprint taught us that observability is as important as code. Tools like AWS CloudWatch and Wireshark were our eyes and ears, helping us identify a “database replication lag” early on that could have throttled our logging speed. Furthermore, working with RFC 2308 (Negative Caching) taught us that knowing “what doesn’t exist” is just as crucial for speed as knowing “what does.”

6. The Future: AI and the Edge

We aren’t stopping here. The next phase of the Hyperfast Load Balancer involves:

• Machine Learning: Predicting the fastest upstream server based on real-time regional network health.
• Edge Evolution: Using AWS Lambda@Edge to move the load balancer even closer to the user, potentially bringing resolution times into the single digits.

Read the full report