BTP: automatic identification and prediction of tasks in data center networks

Abstract

Abstract Modern data centers have widely deployed lots of cluster computing applications such as MapReduce and Spark. Since the coflow/task abstraction can exactly express the requirements of cluster computing applications, various task-based solutions have been proposed to improve application-level performance. However, most of solutions require modification of the applications to obtain task information, making them impractical in many scenarios. In this paper, we propose a Bayesian decision-based Task Prediction mechanism named BTP to identify task and predict the task-size category. First, we design an automatic identification mechanism to identify tasks without manually modifying the applications. Then we leverage bayesian decision to predict the task-size category. Through a series of large-scale NS2 simulations, we demonstrate that BTP can accurately identify task and predict the task-size category. More specifically, BTP achieves 96% precision and 92% recall while obtaining accuracy by up to 98%.

Keywords

• Data center
• Task
• Cloud computing