Cloud computing is ubiquitous: more and more companies are moving the workloads into the Cloud. However, this rise in popularity challenges Cloud service providers, as they need to monitor the quality of their ever-growing offerings effectively. To address the challenge, we designed and implemented an automated monitoring system for the IBM Cloud Platform. This monitoring system utilizes deep learning neural networks to detect anomalies in near-real-time in multiple Platform components simultaneously.After running the system for a year, we observed that the proposed solution frees the DevOps team's time and human resources from manually monitoring thousands of Cloud components. Moreover, it increases customer satisfaction by reducing the risk of Cloud outages.In this paper, we share our solutions' architecture, implementation notes, and best practices that emerged while evolving the monitoring system. They can be leveraged by other researchers and practitioners to build anomaly detectors for complex systems.
Logs contain critical information about the quality of the rendered services on the Cloud and can be used as digital evidence. Hence, we argue that the critical nature of logs calls for immutability and verification mechanism without a presence of a single trusted party. In this paper, we propose a blockchain-based log system, called Logchain, which can be integrated with existing private and public blockchains. To validate the mechanism, we create Logchain as a Service (LCaaS) by integrating it with Ethereum public blockchain network. We show that the solution is scalable (being able to process 100 log files per second) and fast (being able to "seal" a log file in 23 seconds, on average).
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