Oligolithic Cross-task Optimizations across Isolated Workloads.

Enterprises collect data in large volumes and leverage them to drive numerous concurrent decisions and business processes. Their teams deploy multiple applications that often operate concurrently on the same data and infrastructure but have widely different performance requirements. To meet these requirements, enterprises enforce resource boundaries between applications, isolating them from one another. However, boundaries necessitate separate resources per application, making processing increasingly resource-hungry and expensive as concurrency increases. While cross-task optimizations, such as data and work sharing, are known to curb the increase in total resource requirements, resource boundaries render them inapplicable. We propose the principle of functional isolation: cross-task optimizations can and should be combined with performance isolation. Systems should permit cross-optimization as long as participating tasks achieve indistinguishable or improved performance compared to isolated execution. The outcome is faster, more cost-effective, and more sustainable data processing. We make an initial step toward our vision by addressing functional isolation for work sharing and propose GroupShare, a strategy that reduces both total CPU consumption and the latency of all queries.