The traffic bandwidth costs, primarily driven by outbound traffic, comprise a significant amount of operating expenditure in CDNs, induced by the traffic from the end-users to edge servers (edge cost) and from the edge to the center servers (midgress cost). Traffic allocation is the main approach to minimizing the total bandwidth cost. The joint optimization of the total costs is challenging, specifically when the percentile charging mechanism as well as some other practical issues are considered, such as the dynamicity of midgress traffic and the granularity of traffic allocation. In this work, based on our novel miss ratio prediction mechanism, we propose the first online framework, namedIris, jointly optimizing the edge and midgress costs under the 95th percentile charging in commercial CDNs.Iriscan theoretically achieve a competitive ratio of$1+\frac {p_{e}}{\beta \cdot p_{c}}$, when the miss ratio of all domains is set as$\beta $. Here$p_{e}$and$p_{c}$are the unit bandwidth price of the edge and midgress cost, respectively.Irisis tolerant to prediction errors which can be deployed in practical CDN systems. Extensive experiments based on real data indicate thatIriscan dramatically reduce bandwidth costs by about 8.149% compared with the SOTA schemes, potentially saving millions of dollars per month for our large-scale commercial CDN collaborator.

Cell selection and data offloading are the keys to obtaining MCS services with low sensing cost and low data processing delay. Due to the spatiotemporal correlation between data and the local-area coverage of edge servers, cell selection and data offloading will affect each other and require co-optimization. To achieve the co-optimization, we design the method OptInter based on the hierarchical reinforcement learning. OptInter can realize the interactive training between cell selection model and data offloading model. Finally, we evaluate our proposed method based on four datasets, each of which composited by real-world (e.g., NO$_{2}$concentration, AQI value, Didi order, and Didi trajectory) data and simulated data. Compared with the four baseline methods (e.g., OptMOEA/D, OptStageCD, OptStageDC, and OptWeight), the comprehensive performance of our proposed method can be improved by 11.83%, 20.48%, 10.14%, and 42.27% on average, respectively.

Efficiently obtaining the up-to-date information in the disaster-stricken area is the key to successful disaster response. Unmanned aerial vehicles (UAVs), workers and cars can collaborate to accomplish sensing tasks, such as life detection task in disaster-stricken areas. In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the sensing task completion rate. we propose a MARL-based heterogeneous multi-agent route planning algorithm called MANF-RL-RP. The algorithm has made targeted designs in terms of global-local dual information processing and model structure for heterogeneous multi-agent, making it effectively considers the collaboration among heterogeneous agents and the long-term impact of current decisions. Finally, we conducted detailed experiments based on the rich simulation data. In comparison to the baseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP has exhibited a significant performance improvement. Compared to MANF-DNN-RP and Greedy-SC-RP, the task completion rate based on MANF-RL-RP increased by an average of 8.82% and 56.8%, respectively.

The traffic bandwidth costs comprise a significant amount of operating expenditure in CDNs, induced by the traffic from the end-users to edge servers (edge cost) and from the edge to the center servers (midgress cost). Traffic allocation is the main approach to minimizing the total bandwidth cost. The joint optimization of the total costs is challenging, specifically when the percentile charging mechanism as well as some other practical issues are considered, such as the dynamicity of midgress traffic and the granularity of traffic allocation. In this work, based on our novel miss ratio prediction mechanism, we propose the first online framework, named Iris, jointly optimizing the edge and midgress costs under the 95th percentile charging in commercial CDNs. Iris can theoretically achieve a competitive ratio of$1+\frac{p_{e}}{\beta\cdot p_{c}}$, when the miss ratio of all domains is set as$\beta$. Here$p_{e}$and$p_{c}$are the unit bandwidth price of the edge and midgress cost, respectively. Iris is tolerant to prediction errors which can be deployed in practical CDN systems. Extensive experiments based on real data indicate that Iris can dramatically reduce bandwidth costs by about 8.149% compared with the SOTA schemes, potentially saving millions of dollars per month for our large-scale commercial CDN collaborator.

With the explosion of data transmitted over the Internet, Content Delivery Networks (CDNs) carry massive network traffic globally and suffer an increasingly higher bandwidth cost. A critical issue for CDN service providers is how to allocate network traffic among CDN facilities to reduce the total bandwidth cost without violating the quality of service. This work studies online traffic allocation in CDNs to minimize the bandwidth cost under the 95th percentile charging model. Specifically, we here take into account practical deployment issues in large-scale CDN systems, e.g., allocation granularity and deviation. We first theoretically prove the approximation hardness of the traffic allocation problem. We then propose a novel prediction-based algorithm named OnTPC, which effectively addresses constraints raised in practical deployment. Extensive experiments demonstrate that OnTPC outperforms state-of-the-art baselines and is expected to save over a million dollars per month for our large-scale commercial CDN collaborator. Moreover, the performance of OnTPC is consistently outstanding under various settings, and specifically robust to large allocation deviation.