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.

Software architecture is undergoing a transition from monolithic architecture to microservices to achieve resilience, agility, and scalability in the software life cycle. The complex dependability of these microservices may lead to unexpected failures, which becomes a major concern on reliability for application providers. The existing fault detection and localization algorithms for microservice systems only focus on the relationship within microservices and cannot achieve finer granularity from a layered system perspective, including microservices, containers, physical machines, and networks. To tackle this problem, we propose a multilayered method that deconstructs cloud-based microservices and connects the information from various layers to enhance the precision of fault detection and localization. The proposed Transformer encoder model can detect anomalies of containers in the resource layer, and by decomposing and analyzing invocation latency, anomalies in the service layer can be detected. After determining the faulty area of the resource layer based on the above anomalies, a multifactor root cause score is used to sort root cause metrics in the faulty area for localization. Evaluations were performed on three datasets: the Sock-Shop dataset we collected from an actual microservice system, the AIOps2020 preliminary dataset, and the SMD. Empirical investigations conducted on these datasets show that our models enhance the F1 score by approximately 0.25 for anomaly detection and improve the mean average precision by up to 0.54 for root cause localization, which underscores the utility of our models in effectively managing microservice systems in practical scenarios.

Due to the complexity of microservice architecture, it is difficult to accomplish efficient microservice anomaly detection and localization tasks and achieve the target of high system reliability. For rapid failure recovery and user satisfaction, it is significant to detect and locate anomalies fast and accurately in microservice systems. In this paper, we propose an anomaly detection and localization model based on Transformer, named TADL (Transformer-based Anomaly Detector and Locator), which models the temporal features and dynamically captures container relationships using Transformer with sandwich structure. TADL uses readily available container performance metrics, making it easy to implement in already-running container clusters. Evaluations are conducted on a sock-shop dataset collected from a real microservice system and a publicly available dataset SMD. Empirical studies on the above two datasets demonstrate that TADL can outperform baseline methods in the performance of anomaly detection, the latency of anomaly detection, and the effect of anomalous container localization, which indicates that TADL is useful in maintaining complex and dynamic microservice systems in the real world.

Internet of Things (IoT) is increasingly permeating peoples' lives, gradually revolutionizing our way of life. Due to the tight connection between people and IoT, now civil and criminal investigations or internal probes must take IoT into account. From the forensic perspective, the IoT environment contains a rich set of artifacts that could benefit investigations, while the forensic investigation in IoT paradigm may have to alter to accommodate characteristics of IoT. Therefore, in this article, we analyze the impact of IoT on digital forensics and systematize the research efforts made by previous researchers from 2010 to 2018. We sketch the landscape of IoT forensics and examine the state of IoT forensics under a 3-D framework. The 3-D framework consists of a temporal dimension, a spatial dimension, and a technical dimension. The temporal dimension walks through the standard digital forensic process while the spatial dimension explores where to identify sources of evidence in IoT environment. These two dimensions attempt to provide principles and guidelines for standardizing digital investigations in the context of IoT. The technical dimension guides a way to the exploration of tools and techniques to ensure the enforcement of digital forensics in the ever-evolving IoT environment. Put together, we present a holistic overview of digital forensics in IoT. We also highlight open issues and outline promising suggestions to inspire future study.

Performance of a current-fed bidirectional LLC resonant converter for energy storage system in DC grid applications is analyzed and tested. The well match of the voltages on two sides of the LLC circuit is ensured by varying the duty cycle of buck-boost circuit under wide battery voltage range. Hence LLC circuit is operated like a DC transformer to improve efficiency. In the battery discharging mode, the pulse-width modulation (PWM) control is employed. While in the battery charging mode, the phase-shift modulation (PSM) and PWM control is adopted. To achieve the zero-voltage-switching (ZVS) of all the MOSFETs, a frequency feed-forward loop is introduced in the battery charging mode. Compared with conventional bidirectional LLC converter, the frequency regulation range of this converter is narrowed which is beneficial for the design of magnetic components. The operation principles are introduced, the main features such as current ripple of battery, and the zero-voltage-switching (ZVS) of the two modes are analyzed and compared with conventional solutions. A 1.6-kW prototype with a battery voltage of 160V–240V and a DC-bus voltage of 400V has been built and tested to verify the operational principle analysis.