  Runtime performance variability at the servers has been a major issue,
hindering the predictable and scalable performance in modern distributed
systems. Executing requests or jobs redundantly over multiple servers has been
shown to be effective for mitigating variability, both in theory and practice.
Systems that employ redundancy has drawn significant attention, and numerous
papers have analyzed the pain and gain of redundancy under various service
models and assumptions on the runtime variability. This paper presents a cost
(pain) vs. latency (gain) analysis of executing jobs of many tasks by employing
replicated or erasure coded redundancy. Tail heaviness of service time
variability is decisive on the pain and gain of redundancy and we quantify its
effect by deriving expressions for the cost and latency. Specifically, we try
to answer four questions: 1) How do replicated and coded redundancy compare in
the cost vs. latency tradeoff? 2) Can we introduce redundancy after waiting
some time and expect to reduce the cost? 3) Can relaunching the tasks that
appear to be straggling after some time help to reduce cost and/or latency? 4)
Is it effective to use redundancy and relaunching together? We validate the
answers we found for each of the questions via simulations that use empirical
distributions extracted from a Google cluster data.
