Q-percentile Bandwidth Billing Based Geo-Scheduling Algorithm

Abstract

Current IaaS providers deploy cheaper computing resources in newly built data centers and provide cross-regional network services to improve the interoperability of computing resources in different regions. Third-party service providers can use part of their budget to purchase cross-regional communication resources to use cheaper resources in remote areas to reduce the cost of processing massive task requests. The Q-percentile charging model is widely used in cross-regional communication resources billing, but there is little task scheduling research on that billing method. Therefore, this paper studies a geo-distributed task scheduling scenario using the Q-percentile charging model. We design a geo-scheduling algorithm specifically for Q-percentile charging model to allocate resources in the two dimensions of computing resources and communication resources. Furthermore, referring to three existing communication resource allocation strategies, we design three bandwidth allocation algorithms considering the Q-percentile charging characteristics to provide suitable solutions for different scenarios. We conducted experiments based on public well-known datasets such as LIGO workflow. Results show that, compared with the baseline, the scheduling algorithm proposed in this paper can reduce the task scheduling cost between geo-distributed data centers by 10%-20% based on various task loads and show differences in the applicability of different communication resource allocation strategies.