Serverless computing is growing in popularity by virtue of its lightweight and simplicity of
management. It achieves these merits by reducing the granularity of the computing unit to …

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

Despite existing work in machine learning inference serving, ease-of-use and cost efficiency
remain challenges at large scales. Developers must manually search through thousands of …

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

Deep learning workloads are common in today's production clusters due to the proliferation
of deep learning driven AI services (eg, speech recognition, machine translation). A deep …

Picking the right cloud configuration for recurring big data analytics jobs running in clouds is
hard, because there can be tens of possible VM instance types and even more cluster sizes …

Data centers are widely used for big data analytics, which often involve data-parallel jobs,
including query and web service. Meanwhile, cluster frameworks are rapidly developed for …

Video cameras are pervasively deployed for security and smart city scenarios, with millions
of them in large cities worldwide. Achieving the potential of these cameras requires …

Recent workload trends indicate rapid growth in the deployment of machine learning,
genomics and scientific workloads on cloud computing infrastructure. However, efficiently …

Serverless computing is becoming increasingly popular due to its ease of programming, fast
elasticity, and fine-grained billing. However, the serverless provider still needs to provision …