The escalating complexity of micro-services architecture in cloud-native technologies poses significant challenges for maintaining system stability and efficiency. To conduct root cause analysis (RCA) and resolution of alert events, we propose a pioneering framework, multi-Agent Blockchain-inspired Collaboration for root cause analysis in micro-services architecture (mABC), to revolutionize the AI for IT operations (AIOps) domain, where multiple agents based on the powerful large language models (LLMs) perform blockchain-inspired voting to reach a final agreement following a standardized process for processing tasks and queries provided by Agent Workflow. Specifically, seven specialized agents derived from Agent Workflow each provide valuable insights towards root cause analysis based on their expertise and the intrinsic software knowledge of LLMs collaborating within a decentralized chain. To avoid potential instability issues in LLMs and fully leverage the transparent and egalitarian advantages inherent in a decentralized structure, mABC adopts a decision-making process inspired by blockchain governance principles while considering the contribution index and expertise index of each agent. Experimental results on the public benchmark AIOps challenge dataset and our created train-ticket dataset demonstrate superior performance in accurately identifying root causes and formulating effective solutions, compared to previous strong baselines. The ablation study further highlights the significance of each component within mABC, with Agent Workflow, multi-agent, and blockchain-inspired voting being crucial for achieving optimal performance. mABC offers a comprehensive automated root cause analysis and resolution in micro-services architecture and achieves a significant improvement in the AIOps domain compared to existing baselines

Data Acquisition and working in machine learning is a challenge. Most of the companies are collecting data of customers, sales or employees from enterprise resource planning(ERP) and customer relationship management(CRM). Each tools collects data in its own ways which provides unstructured or semi-structured or structured data for consolidation stage. There are lots of variety of data in huge scale for processing. This heterogeneity of data is a roadblock during integration and understanding meaningful insight.

Mist unveiled two services launched on the Mist cloud platform: Mist business-critical Wi-Fi; and Mist's patented Virtual Bluetooth Low Energy (vBLE). Currently available, Mist products are already in use by many medium-to-large organizations globally. Mist is an extensible, programmable microservices cloud architecture for the indoor wireless technologies of Wi-Fi and Bluetooth Low Energy (BLE). A Mist wireless network understands and adapts to each user, how they are moving, the devices they are carrying and the content they are consuming at a scale never before possible. Mist's cloud platform enables a new approach to wireless in nearly a decade, applying data science and machine learning to transform and assure mobile user experience. With businesses managing more connected devices, enterprise-level IoT implementations and location-based marketing initiatives, Mist delivers products that go beyond simple connectivity, empowering businesses to deliver better mobile experiences.