As microservice architecture is evolving toward Logical Data Center (LDC), accurate forecasting of the microservices indicators can support reasonable resource allocation, thereby ensuring the availability and reliability of cloud service. From a macro perspective, due to the architecture hierarchy, microservices exhibit: 1) collaborative relationships derived from shared functionalities, 2) backup relationships between replicas, and 3) dynamic correlation driven by cooperation. From a micro perspective, there exist causal relationships among indicators within a microservice. That is, workload will first impact system consumption, such as CPU and memory usage, then affect service quality like system latency. Based on these insights, we propose MaMiClif, a macro-micro collaborative learning framework for LDC microservice indicators forecasting. MaMiClif constructs Macro Graph and Micro Matrix to model the microservices dependencies and the causality of indicators. To learn fine-grained indicator dependencies, Indicator-Centric Embedding is leveraged to generate representations for indicator series. We use Heterogeneous Graph Convolution to update workload representations based on the Macro Graph, and adopt Causal Sparse Self-attention to integrate causal strength into the self-attention calculation, enabling a comprehensive exploration of dependencies among indicators. Experiments on two datasets, including LDC_MS, which was collected from the LDC system of Ant Group, demonstrate the effectiveness of MaMiClif.

Recently, Multi-modal Large Language Models (MLLMs) have shown remarkable effectiveness for multi-modal tasks due to their abilities to generate and understand cross-modal data. However, processing long sequences of visual tokens extracted from visual backbones poses a challenge for deployment in real-time applications. To address this issue, we introduce FOLDER, a simple yet effective plug-and-play module designed to reduce the length of the visual token sequence, mitigating both computational and memory demands during training and inference. Through a comprehensive analysis of the token reduction process, we analyze the information loss introduced by different reduction strategies and develop FOLDER to preserve key information while removing visual redundancy. We showcase the effectiveness of FOLDER by integrating it into the visual backbone of several MLLMs, significantly accelerating the inference phase. Furthermore, we evaluate its utility as a training accelerator or even performance booster for MLLMs. In both contexts, FOLDER achieves comparable or even better performance than the original models, while dramatically reducing complexity by removing up to 70% of visual tokens.

Carpooling Route Planning(CRP) has become an important issue with the growth of low-carbon traffic systems. We investigate a novel, meaningful and challenging scenario for CRP in industry, calledMulti-Candidate Carpooling Route Planning(MCRP) problem, where each passenger may have several potential positions to get on and off the car. We surprisingly notice that this problem can be easily generalized for similar services such as express, takeout, or crowdsensing services, which means MCRP is a new fundamental combinatorial optimization problem. Traditional graph search algorithms or indexing methods are usually time and space consuming or perform poorly, which are not suitable for solving the problem. In this paper, we propose an end-to-end encoder-decoder model to plan a route for each many-to-one carpooling order with various data-driven mechanisms such as graph partitioning and feature crossover. The encoder is a filter-integrated Graph Convolution Network with external information fusion combining a supervised pre-training classification task, while the latter mimics a pointer network with a rule-based mask mechanism and a domain feature crossover module. We validate the effectiveness and efficiency of our model based on both synthetic and real-world datasets.

Traffic flow prediction is a critical issue in transportation engineering and presents distinct challenges when handling large-scale datasets in the real world. Existing complex spatio-temporal forecasting paradigms use the same parameters to fit traffic sequences with varying spatio-temporal features, and tend to train an average performance model over different time series. This approach greatly reduces their accuracy when applied to larger road networks. Moreover, the significant differences in traffic data distribution from one city to another can also pose great challenges. The same model may be excellent for one city and mediocre when applied to another. To this end, we propose a Meta Backbone Agnostic Spatio-Temporal Clustering Framework for Traffic Forecasting on Large-Scale Road Networks named MetaSTC. We tackle the disparities of spatio-temporal features of traffic flow through a spatio-temporal clustering-based strategy. We design meta-learner for large-scale road network that dynamically extracts the shared information across roads in the same sub-task. In this way, the model can represent task-specific details with a simpler model and make quick and accurate predictions. Our paradigm is backbone-agnostic and can be combined with different traffic prediction models, solving the problem caused by the difference in data distribution. Extensive experimental results conducted on real-world traffic dataset demonstrate the high accuracy and computational efficiency of our model over SOTA approaches.

Microservice architecture has become a driving force in enhancing the modularity and scalability of web applications, as evidenced by the Alipay platform's operational success. However, a prevalent issue within such infrastructures is the suboptimal utilization of CPU resources due to inflexible resource allocation policies. This inefficiency necessitates the development of dynamic, accurate workload prediction methods to improve resource allocation. In response to this challenge, we present STAMP, a Spatio Temporal Graph Network for Microservice Workload Prediction. STAMP is designed to comprehensively address the multifaceted interdependencies between microservices, the temporal variability of workloads, and the critical role of system state in resource utilization. Through a graph-based representation, STAMP effectively maps the intricate network of microservice interactions. It employs time series analysis to capture the dynamic nature of workload changes and integrates system state insights to enhance prediction accuracy. Our empirical analysis, using three distinct real-world datasets, establishes that STAMP exceeds baselines by achieving an average boost of 5.72% in prediction precision, as measured by RMSE. Upon deployment in Alipay's microservice environment, STAMP achieves a 33.10% reduction in resource consumption, significantly outperforming existing online methods. This research solidifies STAMP as a validated framework, offering meaningful contributions to the field of resource management in microservice architecture-based applications.