In the emerging microservice-based architecture of cloud HPC systems, a challenging problem of critical importance for system service capability is how we can schedule microservices to minimize the end-to-end response time for user requests while keeping cost within the specified budget. We address this problem for multi-instance microservices requested by a single application to which no existing result is known to our knowledge. We propose an effective two-stage solution of first allocating budget (resources) to microservices within the budget constraint and then deploying microservice instances on servers to minimize system operational overhead. For budget allocation, we formulate it as the Discrete Time Cost Tradeoff (DTCT) problem which is NP-hard, present a linear program (LP) based algorithm, and provide a rigorous proof of its worst-case performance guarantee of 4 from the optimal solution. For microservice deployment, we show that it is harder than the NP-hard problem of 1-D binpacking through establishing its mathematical model, and propose a heuristic algorithm of Least First Mapping that greedily places microservice instances on fewest possible servers to minimize system operation cost. The experiment results of extensive simulations on DAG-based applications of different sizes demonstrate the superior performance of our algorithm in comparison with the existing approaches.