Deep reinforcement learning: a survey
Deep reinforcement learning (RL) has become one of the most popular topics in artificial
intelligence research. It has been widely used in various fields, such as end-to-end control …
intelligence research. It has been widely used in various fields, such as end-to-end control …
Machine learning (ML)-centric resource management in cloud computing: A review and future directions
Cloud computing has rapidly emerged as a model for delivering Internet-based utility
computing services. Infrastructure as a Service (IaaS) is one of the most important and …
computing services. Infrastructure as a Service (IaaS) is one of the most important and …
Benchmarking graph neural networks
In the last few years, graph neural networks (GNNs) have become the standard toolkit for
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …
Learning to dispatch for job shop scheduling via deep reinforcement learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
Bao: Making learned query optimization practical
Recent efforts applying machine learning techniques to query optimization have shown few
practical gains due to substantive training overhead, inability to adapt to changes, and poor …
practical gains due to substantive training overhead, inability to adapt to changes, and poor …
Neo: A learned query optimizer
Query optimization is one of the most challenging problems in database systems. Despite
the progress made over the past decades, query optimizers remain extremely complex …
the progress made over the past decades, query optimizers remain extremely complex …
{Heterogeneity-Aware} cluster scheduling policies for deep learning workloads
Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs have been
increasingly deployed to train deep learning models. These accelerators exhibit …
increasingly deployed to train deep learning models. These accelerators exhibit …
Sinan: ML-based and QoS-aware resource management for cloud microservices
Cloud applications are increasingly shifting from large monolithic services, to large numbers
of loosely-coupled, specialized microservices. Despite their advantages in terms of …
of loosely-coupled, specialized microservices. Despite their advantages in terms of …
[HTML][HTML] {SONIC}: Application-aware data passing for chained serverless applications
The conference papers and full proceedings are available to registered attendees now and
will be available to everyone beginning Wednesday, July 14, 2021. Paper abstracts and …
will be available to everyone beginning Wednesday, July 14, 2021. Paper abstracts and …
Can far memory improve job throughput?
As memory requirements grow, and advances in memory technology slow, the availability of
sufficient main memory is increasingly the bottleneck in large compute clusters. One solution …
sufficient main memory is increasingly the bottleneck in large compute clusters. One solution …