Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

Large-scale software systems are currently designed as distributed entities and deployed in
cloud data centers. To overcome the limitations inherent to this type of deployment …

Public cloud providers seek to meet stringent performance requirements and low hardware
cost. A key driver of performance and cost is main memory. Memory pooling promises to …

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

In many public and private Cloud systems, users need to specify a limit for the amount of
resources (CPU cores and RAM) to provision for their workloads. A job that exceeds its limits …

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 …

Search-based methods for hard combinatorial optimization are often guided by heuristics.
Tuning heuristics in various conditions and situations is often time-consuming. In this paper …

As machine learning (ML) techniques are applied to a widening range of applications, high
throughput ML inference serving has become critical for online services. Such ML inference …

Many networking tasks now employ deep learning (DL) to solve complex prediction and
optimization problems. However, current design philosophy of DL-based algorithms entails …