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Marco Cali

Publications and source records attributed to Marco Cali.

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Nexus: Transparent I/O Offloading for High-Density Serverless Computing

Serverless computing relies on extreme multi-tenancy to remain economically viable, driving providers to rely on virtual machines (VMs) that ensure strong isolation and seamless ecosystem compatibility with the FaaS programming model. However, current architectures tightly couple application processing logic with I/O processing, forcing every VM to duplicate a heavy communication fabric (cloud SDK, RPC, and TCP/IP). Our analysis reveals this duplication consumes over 25% of a function's memory footprint, and may double the CPU cycles in VMs compared to bare-metal execution. While prior systems attempt to solve this using WebAssembly or library OSes, they naively sacrifice ecosystem compatibility, forcing developers to migrate code and dependencies to new languages. We introduce Nexus, a serverless-native KVM-based hypervisor that transparently decouples compute from I/O. Nexus shifts the execution model by intercepting communication fabric at the API boundary and offloading it to an always-on host shared backend via zero-copy shared memory. This removes the heavyweight communication fabric from the guest VM, while preserving the conventional serverless programming model. By structurally separating these domains, Nexus unlocks asynchronous I/O optimizations: overlapping input payload prefetching with VM restoration from a snapshot and writing output payloads back to storage off the critical path. Compared to the production baseline, Nexus reduces overall node-level CPU and memory consumption by up to 44% and 31%, respectively, thus increasing deployment density by 37%. Also, Nexus reduces warm- and cold-start latency by 39% and 10%, respectively, bringing the response time within 20% of that of a WASM-based, ecosystem-incompatible hypervisor.

cs.DC

Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition

In the field of robotics many different approaches ranging from classical planning over optimal control to reinforcement learning (RL) are developed and borrowed from other fields to achieve reliable control in diverse tasks. In order to get a clear understanding of their individual strengths and weaknesses and their applicability in real world robotic scenarios is it important to benchmark and compare their performances not only in a simulation but also on real hardware. The '2nd AI Olympics with RealAIGym' competition was held at the IROS 2024 conference to contribute to this cause and evaluate different controllers according to their ability to solve a dynamic control problem on an underactuated double pendulum system with chaotic dynamics. This paper describes the four different RL methods submitted by the participating teams, presents their performance in the swing-up task on a real double pendulum, measured against various criteria, and discusses their transferability from simulation to real hardware and their robustness to external disturbances.

cs.RO