Enhancing Cloud Availability via Intelligent Monitoring using Time Series Database and Machine Learning

Every cloud provider, wishes to provide 99.9999% availabil- ity for the systems provisioned and operated by them for the customer i.e. may it be SaaS or PaaS or IaaS model, the availability of the system must be greater than 99.9999%.It becomes vital for the provider to mon- itor the systems and take proactive measures to reduce the downtime.In an ideal scenario, the support colleagues (24*7 technical support) must be aware of the on-going issues in the production systems before it is raised as an incident by the customer. But currently, there is no effective alert monitoring solutions for the same. The proposed solution presented in this paper is to have a central alert monitoring tool for all cloud so- lutions offered by the cloud provider. The central alert monitoring tool constantly observes the time series database which contains metric val- ues populated by HA and compares the incoming metric values with the defined thresholds. When a metric value exceeds the defined threshold, using machine learning techniques the monitoring tool decides & takes actions.

A Survey of AIOps Methods for Failure Management

Modern society is increasingly moving toward complex and distributed computing systems. The increase in scale and complexity of these systems challenges O&M teams that perform daily monitoring and repair operations, in contrast with the increasing demand for reliability and scalability of modern applications. For this reason, the study of automated and intelligent monitoring systems has recently sparked much interest across applied IT industry and academia. Artificial Intelligence for IT Operations (AIOps) has been proposed to tackle modern IT administration challenges thanks to Machine Learning, AI, and Big Data. However, AIOps as a research topic is still largely unstructured and unexplored, due to missing conventions in categorizing contributions for their data requirements, target goals, and components. In this work, we focus on AIOps for Failure Management (FM), characterizing and describing 5 different categories and 14 subcategories of contributions, based on their time intervention window and the target problem being solved. We review 100 FM solutions, focusing on applicability requirements and the quantitative results achieved, to facilitate an effective application of AIOps solutions. Finally, we discuss current development problems in the areas covered by AIOps and delineate possible future trends for AI-based failure management.

Design of Remote Environmental Monitoring Network Based on Intelligent Sensor Network Address Allocation and Addressing

Based on the wireless sensor network, this paper combines node monitoring data with intelligent network address management. Users can view real-time environmental data through a computer or mobile phone and can manually remotely manage the environmental adjustment equipment of the network address through the mobile phone. This article first discusses the research background of the subject, introduces the current domestic and foreign research status of WSN in environmental detection, and analyzes the reasons for choosing ZigBee network as the wireless transmission environment of the intelligent monitoring system. Secondly, the structure, layered model, and key technologies of wireless sensor networks are introduced, and it is pointed out that ZigBee technology, which has the characteristics of low power consumption, reliable communication, self-organization of the network, strong self-healing ability, and low cost, is very suitable for application in the environment. Then, it analyzes TI’s protocol stack Z-Stack based on the ZigBee2006 standard and analyzes the network address assignment and addressing in Z-Stack, the process and steps of node binding, the routing mechanism and routing maintenance, and channel configuration. The realization of other functions was discussed in depth. During the simulation experiment, in the hardware design of the intelligent monitoring system, the network node was divided into two parts: the core board and the backplane. The crystal oscillator, power supply, antenna, and I/O port circuits of the core board were designed, and the data acquisition, relay, and power supply of the backplane were designed. Finally, this paper studies the data security issues in the environmental monitoring network and proposes two solutions to control network access and data encryption. Experimental results show that in terms of low-power design, the energy of the entire system is calculated to determine the factors that affect the power consumption of the system and methods such as increasing the node sleep time to ensure that the system can work for a long time.

Application of Intelligent Ultrasound in Real-Time Monitoring of Postoperative Analgesic Nerve Block

In order to monitor the effect of nerve block in postoperative analgesia more accurately, this paper puts forward the application research of ultrasonic real-time intelligent monitoring of nerve block in postoperative analgesia. Ultrasonic real-time intelligent monitoring of nerve block in upper limb surgery, lower limb surgery, and abdominal surgery combined with the nerve stimulator. The experiments show that there are 5 cases of adverse reactions when the nerve stimulator is only used, but no adverse reactions occur when combined with ultrasound-guided block. Continuous subclavian brachial plexus block with the ultrasound-guided nerve stimulator can clearly see the subclavian brachial plexus and its surrounding tissue structure, the direction of needle insertion in the plane, and the diffusion of narcotic drugs. The average success rate of block was up to 95.2%, which was significantly higher than that of nerve stimulator alone, and the success rate of recatheterization after the first failure was also improved. The average postoperative analgesia satisfaction was 85.6%, the average operation time was only 20 min, and the subclavian artery and pleura were avoided effectively. No pneumothorax and other complications occurred. The average success rate of ultrasound-guided subclavicular brachial plexus block in 1-2-year-old children was 97%, which was much higher than the average success rate of nerve stimulator localization with 63%. Ultrasound-guided nerve block not only directly blocks nerves under visual conditions but also helps to observe the structures around nerves and dynamically observe the diffusion of local anesthetics, which can significantly improve the accuracy and success rate of nerve block and reduce the incidence of complications.