Review and Analysis of Failure Detection and Prevention Techniques in IT Infrastructure Monitoring

Maintaining the health of IT infrastructure components for improved reliability and availability is a research and innovation topic for many years. Identification and handling of failures are crucial and challenging due to the complexity of IT infrastructure. System logs are the primary source of information to diagnose and fix failures. In this work, we address three essential research dimensions about failures, such as the need for failure handling in IT infrastructure, understanding the contribution of system-generated log in failure detection and reactive & proactive approaches used to deal with failure situations. This study performs a comprehensive analysis of existing literature by considering three prominent aspects as log preprocessing, anomaly & failure detection, and failure prevention. With this coherent review, we (1) presume the need for IT infrastructure monitoring to avoid downtime, (2) examine the three types of approaches for anomaly and failure detection such as a rule-based, correlation method and classification, and (3) fabricate the recommendations for researchers on further research guidelines. As far as the authors\u27 knowledge, this is the first comprehensive literature review on IT infrastructure monitoring techniques. The review has been conducted with the help of meta-analysis and comparative study of machine learning and deep learning techniques. This work aims to outline significant research gaps in the area of IT infrastructure failure detection. This work will help future researchers understand the advantages and limitations of current methods and select an adequate approach to their problem

Message queue telemetry transport and lightweight machine-to-machine comparison based on performance efficiency under various scenarios

Internet of things (IoT) is been advancing over a long period of time in many aspects. For data transfer between IoT devices in a wireless sensor network, various IoT protocols are proposed. Among them, the most widely used are constrained application protocol (CoAP) and message queue telemetry transport (MQTT). Overcoming the limitations of CoAP, lightweight machine-to-machine (LwM2M) framework was designed above CoAP. Recent statistics show that LwM2M and MQTT are the widely used, but LwM2M is still less used than MQTT. Our paper is aimed at comparing both MQTT and LwM2M on the basis of performance efficiency, which will be achieved by sending same file through both protocols to the server. Performance efficiency will be calculated in two scenarios, i) when the client makes a connection with the server i.e., while initial connection and ii) while sending data file to server i.e., while data transfer. Both the protocols will be tested on the number of packets sent and the variability of packet size throughout the session. Experimental results indicated that LwM2M outperformed MQTT in both above scenarios by almost 69%. Therefore, we concluded by stating that LwM2M is best choice over MQTT, but MQTT can still be used in some situations if necessary

H-Prop and H-Prop-News: Computational Propaganda Datasets in Hindi

In this digital era, people rely on the internet for their news consumption. As people are free to express their opinions on social media, much information shared on the internet is loaded with propaganda. Propagandist contents are intended to influence public opinion. In the mainstream media or prominent news agencies, the authors’ and news agencies’ own bias may impact in the news contents. Hence, it is required to detect such propaganda spread through news articles. Detection and classification of propagandist text require standard, high-quality, annotated datasets. A few datasets are available for propaganda classification. However, these datasets are mostly in English. Hindi is the most spoken language in India, and efforts are needed to detect its propagandist contents. This research work introduces two new datasets: H-Prop and H-Prop-News, which consist of news articles in Hindi annotated as propaganda or non-propaganda. The H-Prop dataset is generated by translating 28,630 news articles from the QProp dataset. The H-Prop-News dataset contains 5500 news articles collected from 32 prominent Hindi news websites. We experiment with the proposed datasets using four supervised machine learning models combined with different feature vectors and word embeddings. Our experiments achieve 87% accuracy using Logistic Regression with TF-IDF feature vectors. The datasets provide high-quality labeled news articles in Hindi and open new avenues for researchers to explore techniques for analyzing and classifying propaganda in Hindi text

H-Prop and H-Prop-News: Computational Propaganda Datasets in Hindi

In this digital era, people rely on the internet for their news consumption. As people are free to express their opinions on social media, much information shared on the internet is loaded with propaganda. Propagandist contents are intended to influence public opinion. In the mainstream media or prominent news agencies, the authors’ and news agencies’ own bias may impact in the news contents. Hence, it is required to detect such propaganda spread through news articles. Detection and classification of propagandist text require standard, high-quality, annotated datasets. A few datasets are available for propaganda classification. However, these datasets are mostly in English. Hindi is the most spoken language in India, and efforts are needed to detect its propagandist contents. This research work introduces two new datasets: H-Prop and H-Prop-News, which consist of news articles in Hindi annotated as propaganda or non-propaganda. The H-Prop dataset is generated by translating 28,630 news articles from the QProp dataset. The H-Prop-News dataset contains 5500 news articles collected from 32 prominent Hindi news websites. We experiment with the proposed datasets using four supervised machine learning models combined with different feature vectors and word embeddings. Our experiments achieve 87% accuracy using Logistic Regression with TF-IDF feature vectors. The datasets provide high-quality labeled news articles in Hindi and open new avenues for researchers to explore techniques for analyzing and classifying propaganda in Hindi text

Failure Detection Using Semantic Analysis and Attention-Based Classifier Model for IT Infrastructure Log Data

The improvement in the reliability, availability, and maintenance of the IT infrastructure components is paramount to ensure uninterrupted services in large-scale IT Infrastructures. The massive system logs generated by infrastructures have proved to be advantageous to pursue the runtime circumstances and behavior of the system. Existing literature has log-based failure detection techniques carrying semantic analysis but on limited log features, reflecting ineffectiveness in anomaly detection for unstable and unseen log records. We have proposed in this paper a semantic log analysis model with three log features to apprehend the gist of the log message. BERT pre-trained model is employed to adapt the feature embedding. The generated numerical vectors are further furnished to train an attention-based OLSTM (Optimized Long Short-Term Memory Networks) classifier to detect failures in diverse infrastructures. The proposed model is evaluated on five different infrastructures: Apache from a server application, OpenStack from the Distributed Systems, Windows from the Operating System, BGL from a Supercomputer, and Android from the Mobile System. The findings illustrate that the proposed system delivers improved and stable results, considering the varied IT infrastructures