Modern resource management frameworks for largescale analytics leave unresolved the
problematic tension between high cluster utilization and job's performance predictability …

Providing timely results in the face of rapid growth in data volumes has become important for
analytical frameworks. For this reason, frameworks increasingly operate on only a subset of …

Cloud computing promises flexibility and high performance for users and high cost-efficiency
for operators. Nevertheless, most cloud facilities operate at very low utilization, hurting both …

The 3Sigma cluster scheduling system uses job runtime histories in a new way. Knowing
how long each job will execute enables a scheduler to more effectively pack jobs with …

Scheduling diverse applications in large, shared clusters is particularly challenging. Recent
research on cluster scheduling focuses either on scheduling speed, using sampling to …

Job scheduling in Big Data clusters is crucial both for cluster operators' return on investment
and for overall user experience. In this context, we observe several anomalies in how …

The continuous shift towards data-driven approaches to business, and a growing attention to
improving return on investments (ROI) for cluster infrastructures is generating new …

Cloud applications are increasingly shifting from large monolithic services, to large numbers
of loosely-coupled, specialized microservices. Despite their advantages in terms of …

Cloud providers rent the resources they do not allocate as evictable virtual machines (VMs),
like spot instances. In this paper, we first characterize the unallocated resources in Microsoft …

Given the well-known tradeoffs between fairness, performance, and efficiency, modern
cluster schedulers often prefer instantaneous fairness as their primary objective to ensure …