  Modern Hybrid Transactional/Analytical Processing (HTAP) systems use an
integrated data processing engine that performs analytics on fresh data, which
are ingested from a transactional engine. HTAP systems typically consider data
freshness at design time, and are optimized for a fixed range of freshness
requirements, addressed at a performance cost for either OLTP or OLAP. The data
freshness and the performance requirements of both engines, however, may vary
with the workload.
  We approach HTAP as a scheduling problem, addressed at runtime through
elastic resource management. We model an HTAP system as a set of three
individual engines: an OLTP, an OLAP and a Resource and Data Exchange (RDE)
engine. We devise a scheduling algorithm which traverses the HTAP design
spectrum through elastic resource management, to meet the data freshness
requirements of the workload. We propose an in-memory system design which is
non-intrusive to the current state-of-art OLTP and OLAP engines, and we use it
to evaluate the performance of our approach. Our evaluation shows that the
performance benefit of our system for OLAP queries increases over time,
reaching up to 50% compared to static schedules for 100 query sequences, while
maintaining a small, and controlled, drop in the OLTP throughput.
