Mapping workflow resource requests for bandwidth efficiency in data centers

Representing a large class of coarse-grained distributed applications, workflows require large computing and bandwidth resources for their execution. With specific resource requirements due to data precedence and time disjointness among workflow tasks, mapping workflow resource requests in data centers is a challenging problem for cloud providers. While existing approaches only focus on satisfying computing resources and ignore the impact of mapping on bandwidth usage, we consider both computing and network resources to improve bandwidth efficiency in data centers while guaranteeing the performance of users' applications. We first formulate an integer programming optimization model for the mapping problem that minimizes the bandwidth allocated to workflows. We then develop two algorithms namely Critical Path Workflow Mapping (CPWM) and Edge Priority Workflow Mapping (EPWM) to solve this problem. We evaluate CPWM and EPWM through comprehensive simulations. The results show that CPWM and EPWM significantly reduce the bandwidth allocated for a workflow request by up to 66% for random workflows and 80% for realisticapplication workflows compared to baseline algorithms.