Bag-of-Tasks Scheduling under Budget Constraints

Abstract

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¿exibility to elastic applications, users lack guidance for choosing between multiple offerings, in order to complete their computations within given budget constraints. In this work, we present BaTS, our budget-constrained 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 a-priori information about task completion times, and learns to estimate them at runtime. We evaluate BaTS by emulating different cloud environments on the DAS-3 multi-cluster system. Our results show that BaTS is able to schedule within a user-defined-budget (if such a schedule is possible at all.) At the expense of extra compute time, significant cost savings can be achieved when comparing to a cost-oblivious round-robin scheduler.