Bag-of-Tasks Scheduling under Time and Budget Constraints

Commercial cloud offerings, such as Amazon’s EC2, let users allocate compute resources on demand, charging based on reserved time intervals. While this gives great flexibility to elastic applications, users lack guidance for choosing between multiple offerings, in order to complete their computations within given time and budget constraints. In this work, we present BaTS, our budget and timeconstrained scheduler. BaTS can schedule large bags of tasks onto multiple clouds with different CPU performance and cost, minimizing completion time while respecting an upper bound for the budget to be spent. BaTS requires no apriori information about task completion times, and learns task completion time distributions at run time. We evaluate BaTS by emulating a cloud environment on the DAS-3 multi-cluster system and compare to task schedulers that have a-priori completion time information.