We show that there is a one-round randomized distributed algorithm that can 2-color cycles such that the expected fraction of monochromatic edges is less than 0...

To overcome the physical limitations of scaling monolithic quantum computers, distributed quantum computing (DQC) interconnects multiple smaller-scale quantum p...

Configuring stream processing systems for efficient performance, especially in cloud-native deployments, is a challenging and largely manual task. We present an...

Aggregate Programming (AP) is a paradigm for programming the collective behaviour of sets of distributed devices, possibly situated at the network far edge, by ...

For an offline-first collaborative application to operate in true peer-to-peer fashion, its collaborative features must function even in environments where inte...

This is the second of five papers comprising The Semantic Arrow of Time. Part I established that computing's arrow of time is semantic rather than thermodynamic...

As the convergence of cloud computing and advanced networking continues to reshape modern software development, edge-cloud-native paradigms have become essentia...

Every link disconnection or flap in a datacenter corrupts the network's self-knowledge -- its graph. We call this corruption a ghost: a node that appears reacha...

The emergence of large-scale, sparse, multimodal, and agentic AI models has coincided with a shift in hardware toward supernode architectures that integrate hun...

AI‑Assisted Coding vs. Team‑Level Bottlenecks AI‑assisted coding tools are helping developers generate code faster than ever. So why aren’t teams shipping fast...

Serverless computing and stream processing represent two dominant paradigms for event-driven data processing, yet both make assumptions that render them ineffic...

Parallel programming in high-performance computing depends on low-level APIs such as MPI, requiring users to manage synchronization and resources manually. Seve...