  Today's big data clusters based on the MapReduce paradigm are capable of
executing analysis jobs with multiple priorities, providing differential
latency guarantees. Traces from production systems show that the latency
advantage of high-priority jobs comes at the cost of severe latency degradation
of low-priority jobs as well as daunting resource waste caused by repetitive
eviction and re-execution of low-priority jobs. We advocate a new resource
management design that exploits the idea of differential approximation and
sprinting. The unique combination of approximation and sprinting avoids the
eviction of low-priority jobs and its consequent latency degradation and
resource waste. To this end, we designed, implemented and evaluated DiAS, an
extension of the Spark processing engine to support deflate jobs by dropping
tasks and to sprint jobs. Our experiments on scenarios with two and three
priority classes indicate that DiAS achieves up to 90% and 60% latency
reduction for low- and high-priority jobs, respectively. DiAS not only
eliminates resource waste but also (surprisingly) lowers energy consumption up
to 30% at only a marginal accuracy loss for low-priority jobs.
