Distributed computing has become a common approach for large-scale computation tasks
due to benefits such as high reliability, scalability, computation speed, and cost …

Summary The Internet of Things (IoT) interconnects billions of physical objects to collect and
exchange information and makes available various applications. Despite all the advantages …

High-demand LLM inference services (eg, ChatGPT and BARD) support a wide range of
requests from short chat conversations to long document reading. To ensure that all client …

Efficiently scheduling data processing jobs on distributed compute clusters requires complex
algorithms. Current systems use simple, generalized heuristics and ignore workload …

We introduce Gandiva, a new cluster scheduling framework that utilizes domain-specific
knowledge to improve latency and efficiency of training deep learning models in a GPU …

Existing deep learning systems commonly parallelize deep neural network (DNN) training
using data or model parallelism, but these strategies often result in suboptimal …

Resource management problems in systems and networking often manifest as difficult
online decision making tasks where appropriate solutions depend on understanding the …

With widespread advances in machine learning, a number of large enterprises are
beginning to incorporate machine learning models across a number of products. These …

Distributed computing remains inaccessible to a large number of users, in spite of many
open source platforms and extensive commercial offerings. While distributed computation …

Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from
many thousands of different applications, across a number of clusters each with up to tens of …