In mobile edge computing (MEC) environment, effective microservices deployment significantly reduces vendor costs and minimizes application latency. However, existing literatures overlook the impact of dynamic characteristics such as the frequency of user requests and geographical location, and lack in-depth consideration of the types of microservices and their interaction frequencies. To address these issues, we propose TP-MDU, a novel two-stage deployment framework for microservices. This framework is designed to learn users’ dynamic behaviors and introduces, for the first time, a minimal deployment unit. Initially, TP-MDU generates minimal deployment units online, tailored to the types of microservices and their interaction frequencies. In the initial deployment phase, aiming for load balancing, it employs a simulated annealing algorithm to achieve a superior deployment plan. During the optimization scheduling phase, it utilizes reinforcement learning algorithms and introduces dynamic information and new optimization objectives. Previous deployment plans serve as the initial state for policy learning, thus facilitating more optimal deployment decisions. This paper evaluates the performance of TP-MDU using a real dataset from Australia’s EUA and some related synthetic data. The experimental results indicate that TP-MDU outperforms other representative algorithms in performance.

Microservice architectures, characterized by their loosely coupled services and complex call patterns, have become predominant in cloud applications, benefiting from elastic scalability and development agility. However, they face challenges in anomaly propagation and root cause analysis (RCA), often depending on the operational knowledge and system familiarity. FlowRCA, a non-invasive RCA framework, addresses these challenges by leveraging common monitoring metrics like CPU load, memory usage, and container latency to facilitate RCA in microservice environments. By analyzing causal relationships between metrics, FlowRCA clarifies fault propagation complexities, enabling accurate and comprehensive failure diagnosis. Experimental evidence shows FlowRCA’s superiority over existing algorithms, effectively identifying faulty microservices and root cause metrics in simulated environments.

Artificial Intelligence for IT Operations (AIOps) for microservice systems has attracted much attention in academia and industry, aiming to reduce the burden of operations developers and improve the reliability of microservices. However, due to mostly private datasets and unique data requirements of different studies, researchers are forced to invest considerable effort in tedious tasks such as data simulation and data collection, which prevents them from concentrating on model development. To tackle this dilemma, we introduce MicroOps, a microservice data simulation and AIOps model development platform. MicroOps provides full-process automation support for microservice AIOps research, with key roles for rapid dataset generation and intuitive model testing. Based on MicroOps, we release two multimodal datasets collected from two widely used microservice systems. A user survey is conducted on MicroOps, evaluating its usability and practicality through the System Usability Scale (SUS) and open-ended questions. The results show that both are highly positively rated. Platform: https://github.com/OpenNetAI/MicroOps.

Recent graph-based methods achieve significant success in multivariate time series modeling and forecasting due to their ability to handle relationships among time series variables. However, only pairwise relationships are considered in most existing works. They ignore beyond-pairwise relationships and their potential categories in practical scenarios, which leads to incomprehensive relationship learning for multivariate time series forecasting. In this paper, we present ReMo, a Relational Modeling-based method, to promote fine-grained relational learning among multivariate time series data. Firstly, by treating time series variables and complex relationships as nodes and hyperedges, we extract multi-view hypergraphs from data to capture beyond-pairwise relationships. Secondly, a novel hypergraph message passing strategy is designed to characterize both nodes and hyperedges by inferring the potential categories of relationships and further distinguishing their impacts on time series variables. By integrating these two modules into the time series forecasting framework, ReMo effectively improves the performance of multivariate time series forecasting. The experimental results on seven commonly used datasets from different domains demonstrate the superiority of our model.