Deep neural networks (DNNs) are currently being deployed as machine learning technology
in a wide range of important real-world applications. DNNs consist of a huge number of …

Federated learning (FL) is a machine learning paradigm where a shared central model is
learned across distributed devices while the training data remains on these devices …

Modern web services use in-memory caching extensively to increase throughput and reduce
latency. There have been several workload analyses of production systems that have fueled …

The Chameleon testbed is a case study in adapting the cloud paradigm for computer
science research. In this paper, we explain how this adaptation was achieved, evaluate it …

Memory is the most contended and least elastic resource in datacenter servers today.
Applications can use only local memory—which may be scarce—even though memory …

The conventional wisdom is that CPU resources such as cores, caches, and memory
bandwidth must be partitioned to achieve performance isolation between tasks. Both the …

Disaggregated memory can address resource provisioning inefficiencies in current
datacenters. Multiple software runtimes for disaggregated memory have been proposed in …

With user-facing apps adopting serverless computing, good latency performance of
serverless platforms has become a strong fundamental requirement. However, it is difficult to …

We explore the feasibility of using Generative Adversarial Networks (GANs) to automatically
learn generative models to generate synthetic packet-and flow header traces for networking …

As a cache eviction algorithm, FIFO has a lot of attractive properties, such as simplicity,
speed, scalability, and flash-friendliness. The most prominent criticism of FIFO is its low …