Task Allocation With Geography-Context-Capacity Awareness in Distributed Burstable Billing Edge-Cloud Systems

Shihao Shen, Chenfei Gu, Yuanze Li, Chao Qiu, Xiaofei Wang, Rui Tan, Cheng Zhang, Wenyu Wang

Abstract: The new real-time interactive services, such as virtual and augmented reality, demand significantly higher network bandwidth and quality, which the traditional centralized cloud struggles to meet. In addition, centralized optimization management becomes inefficient as the scale of the scene continues to expand. In response, edge cloud systems have emerged, but distributed geographic locations, burstable billing business models, and large numbers of servers in large-scale scenarios pose new challenges for resource management. In this article, we propose GeoCC, a novel strategy to save bandwidth overhead in burstable billing edge cloud systems. GeoCC addresses challenges through a dual approach. First, a geography-aware graph construction and partitioning algorithm is used to organize server resources, and a large number of servers are reasonably divided into multiple server pools for parallel processing. Second, it introduces an enhanced burstable billing optimization mechanism that considers contextual factors and adaptive bandwidth capacity. Experiments based on real data from an edge cloud operator demonstrate the effectiveness of GeoCC. Compared with the baseline, GeoCC can effectively reduce bandwidth peaks, decreasing bandwidth costs by an average of 28.30% and up to 81.83% at the 95th percentile billing.