Fair Scheduling for Deadline-Driven, Resource-Constrained, Multi-Analytics Workloads

In this paper, we analyze and empirically evaluate Justice, a fair-share, deadline-aware job scheduler for resource-constrained cloud deployments managed by big data resource negotiators. Justice provides admission control, which leverages historical traces and job deadlines to guide and adapt resource allocation decisions to changing cloud conditions. We evaluate Justice using different deadline types and production workloads. We find that it outperforms extant allocators in terms of fair allocation, deadline satisfaction, and useful work.