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

Datacenter operations today provide a plethora of new queueing and scheduling problems.
The notion of a “job” has become more general and multi-dimensional. The ways in which …

In this paper, we explore how a natural generalization of Shortest Remaining Processing
Time (SRPT) can be a powerful meta-algorithm for online scheduling. The meta-algorithm …

Deep Neural Networks (DNNs) have been widely deployed in mobile applications. DNN
inference latency is a critical metric to measure the service quality of those applications …

Modern parallel platforms, such as clouds or servers, are often shared among many different
jobs. However, existing parallel programming runtime systems are designed and optimized …

We propose a unifying framework based on configuration linear programs and randomized
rounding, for different energy optimization problems in the dynamic speed-scaling setting …

Modern data centers are tasked with processing heterogeneous workloads consisting of
various classes of jobs. These classes differ in their arrival rates, size distributions, and job …

Problem definition: Cloud computing is recognized as a critical driver of information
technology–enabled innovations. The operations management (OM) community, however …

Modern data analytics frameworks often integrate with external storage services, which can
lead to storage bottlenecks. Existing caching and prefetching solutions utilize high-level …

We consider the classic problem of scheduling jobs with precedence constraints on a set of
identical machines to minimize the makespan objective function. Understanding the exact …