A pragmatic analysis of scheduling environments on new computing platforms
Today, large scale parallel systems are available at relatively low cost. Many powerful such systems have been installed all over the world and the number of users is always increasing. The use of such clusters requires special administration tools, which have been developed recently and most frequently rely on intuitive heuristics to solve the underlying difficult scheduling problems. On the other hand, recent theoretical work has designed a number of models, such as the Parallel Task model, specifically aimed at cluster environments. The objective of this work is to study and analyze the path from theoretical models and algorithms to their implementation on an actual environment on a real platform. We outline in detail the divergences between classical models and a real batch scheduling system, and propose solutions to adapt a theoretically well-founded algorithm to such a system. Experimental results show that this implementation performs about as well as FCFS with backfilling, and much better in the difficult instances. We hope that further usage of this algorithm in a live system will show that interaction between theory and practice can be fruitful.
parallel tasks cluster computing job scheduling