The proliferation of camera-enabled devices and large video repositories has led to a
diverse set of video analytics applications. These applications rely on video pipelines …

Stateful serverless analytics can be enabled using a remote memory system for inter-task
communication, and for storing and exchanging intermediate data. However, existing …

Cluster schedulers routinely face data-parallel jobs with complex task dependencies
expressed as DAGs (directed acyclic graphs). Understanding DAG structures and runtime …

Emerging ML training deployments are trending towards larger models, and hybrid-parallel
training that is not just dominated by compute-intensive all-reduce for gradient aggregation …

Modern Hybrid Transactional/Analytical Processing (HTAP) systems use an integrated data
processing engine that performs analytics on fresh data, which are ingested from a …

Adaptability is critical for stream processing systems to ensure stable, low-latency, and high-
throughput processing of long-running queries. Such adaptability is particularly challenging …

The popularity of big data and AI has led to many optimizations at different layers of
distributed computation stacks. Despite–or perhaps, because of–its role as the narrow waist …

Cloud providers have recently introduced new offerings whereby spare computing
resources are accessible at discounts compared to on-demand computing. Exploiting such …

Deep Neural Networks (DNNs) are witnessing increased adoption in multiple domains
owing to their high accuracy in solving real-world problems. However, this high accuracy …

Cloud providers auction off unallocated resources at a low cost to avoid keeping hardware
idle. One such mechanism is Harvest VMs (HVMs). These VMs grow and shrink as the …