Ayush Mishra

Laurin Brandner, Ayush Mishra, Sebastiano Miano et al.

Service meshes have recently emerged as the de-facto standard for deploying microservices. Conceptually, they provide a uniform abstraction for inter-process communication (IPC) between services by implementing common networking mechanisms -- such as encryption, routing, and load balancing -- and by allowing these mechanisms to be configured and composed through high-level policies. Supporting these policies, however, comes with a significant performance cost, since service meshes interpose proxies (``sidecars'') on the data path, leading to numerous context switches. This paper presents L7FP, a fast path for service meshes which can enforce the vast majority of application-layer policies seen in the wild directly in kernel space. Given high-level policies, L7FP automatically synthesizes an eBPF-based data plane which enforces them in the kernel. L7FP accelerates existing microservices without any code modification, and transparently falls back to existing service proxies (the slow path) for the few unsupported policies. We fully implemented L7FP, with support for both TLS and HTTP/2. Compared to state-of-the-art service meshes, L7FP reduces the median request latency of realistic applications by up to $6\times$ while sustaining $3\times$ more throughput.

Ege Cem Kirci, Ayush Mishra, Laurent Vanbever

The Internet, the world's largest and most pervasive network, lacks a transparent, granular view of its traffic patterns, volumes, and growth trends, hindering the networking community's understanding of its dynamics. This paper leverages publicly available Internet Exchange Point traffic statistics to address this gap, presenting a comprehensive two-year study (2023-2024) from 472 IXPs worldwide, capturing approximately 300 Tbps of peak daily aggregate traffic by late 2024. Our analysis reveals a 49.2% global traffic increase (24.5% annualized), uncovers regionally distinct diurnal patterns and event-driven anomalies, and demonstrates stable utilization rates, reflecting predictable infrastructure scaling. By analyzing biases and confirming high self-similarity, we establish IXP traffic as a robust proxy for overall Internet growth and usage behavior. With transparent, replicable data--covering 87% of the worldwide IXP port capacity--and plans to release our dataset, this study offers a verifiable foundation for long-term Internet traffic monitoring. In particular, our findings shed light on the interplay between network design and function, providing an accessible framework for researchers and operators to explore the Internet's evolving ecosystem.

Lorenzo Satta Chiris, Ayush Mishra

As autonomous agentic AI systems see increasing adoption across organisations, persistent challenges in alignment, governance, and risk management threaten to impede deployment at scale. We present AURA (Agent aUtonomy Risk Assessment), a unified framework designed to detect, quantify, and mitigate risks arising from agentic AI. Building on recent research and practical deployments, AURA introduces a gamma-based risk scoring methodology that balances risk assessment accuracy with computational efficiency and practical considerations. AURA provides an interactive process to score, evaluate and mitigate the risks of running one or multiple AI Agents, synchronously or asynchronously (autonomously). The framework is engineered for Human-in-the-Loop (HITL) oversight and presents Agent-to-Human (A2H) communication mechanisms, allowing for seamless integration with agentic systems for autonomous self-assessment, rendering it interoperable with established protocols (MCP and A2A) and tools. AURA supports a responsible and transparent adoption of agentic AI and provides robust risk detection and mitigation while balancing computational resources, positioning it as a critical enabler for large-scale, governable agentic AI in enterprise environments.