DeepAalo: Auto-adjusting Demotion Thresholds for Information-agnostic Coflow Scheduling

Abstract

The existing coflow scheduling schemes minimize coflow completion time (CCT) based on the information of previous coflows, which makes them hard to use in practice. Thus, new scheduling mechanisms without considering prior knowledge are proposed, such as Aalo. In general, these algorithms demote coflows from the highest priority queue into several lower priority queues when their sent-bytes exceed predefined thresholds. However, most of the information-agnostic algorithms use static thresholds, and these coflow scheduling mechanisms may suffer a performance penalty when threshold settings mismatch traffic. In this paper, an information-agnostic coflow scheduler DeepAalo is proposed to minimize CCT by automatically adjusting thresholds of queues. DeepAalo applies Deep Reinforcement Learning(DRL) techniques to translate the design of thresholds into a continuous learning process. Specifically, DeepAalo can collect network information, learn from past decisions, and automatically update the thresholds of queues every $t$ time. Therefore, DeepAalo has better self-adaptability when traffic changes. A flow-level simulator using python is developed, and the simulation results show that DeepAalo improves the average CCT up to 1.37× over Aalo.