The efficiency of distributed shared memory (DSM) has been greatly improved by recent
hardware technologies. But, the difficulty of distributed memory management can still be a …

The global scale and challenging requirements of modern cloud applications have led to the
development of complex, widely distributed, service-oriented applications. One enabler of …

Serverless workflows are characterized as multi-stage computing, while downstream
functions require accessing intermediate states or the output of upstream functions for …

The modern computing scenario of the Computing Continuum exhibits large and complex
applications with heterogeneous requirements running on distributed infrastructure. Still …

Modern datacenter applications are increasingly being built using a microservices
architecture. These microservices communicate with each other using datacenter RPCs …

With full resource disaggregation on the horizon, it is unclear what the most suitable
programming model is that enables dataflow developers to fully harvest the potential that …

Datacenter applications often rely on remote procedure calls (RPCs) for fast, efficient, and
secure communication. However, RPCs are slow, inefficient, and hard to use as they require …

Shuffle is one of the most expensive communication primitives in distributed data processing
and is difficult to scale. Prior work addresses the scalability challenges of shuffle by building …

As data becomes increasingly distributed, traditional RPC and data serialization limits
performance, result in rigidity, and hamper expressivity. We believe that technology trends …

Serverless computing has emerged as a popular cloud deployment paradigm. In serverless,
the developers implement their application as a set of chained functions that form a workflow …