April 2018 Summaries

Harness provides a solution for transitioning from monolithic to microservices architectures by offering deployment strategies and Continuous Verification, which simplifies the process of deploying, testing, and verifying microservices. As organizations move towards cloud, DevOps, and Continuous Delivery, migrating to microservices becomes essential, despite the challenges posed by their implicit dependencies. Harness automates the deployment and health verification of microservices, allowing for independent and collective assessment through machine learning-based verification and integration with tools like Splunk and AppDynamics. A recent feature, "Barriers," enhances pipeline flow control by ensuring that verification only begins once all microservices are deployed, thereby addressing the complexity of testing and rollback in microservices environments. This feature enables teams to deploy and verify multiple microservices simultaneously while managing dependencies, improving consistency and reliability in production environments.

Harness has been utilizing neural networks to improve anomaly detection in application log analysis, achieving a 50% reduction in false positives, though with a slight increase in false negatives and performance challenges. Their approach, which initially relied on textual similarity and occurrence frequencies, has evolved to incorporate unsupervised machine learning and natural language processing (NLP) techniques. Despite the noise inherent in log data compared to natural language text, neural networks have shown promise by transforming variable-size log messages into fixed-size vectors, enhancing clustering and anomaly prediction accuracy. While these advancements have improved detection capabilities, challenges remain in real-time processing and maintaining low false negative rates. Harness plans to gradually implement this neural network-based solution with select customers to further refine their models and address existing drawbacks.

Harness has significantly enhanced its platform by introducing Smart Automation, Continuous Verification, and Continuous Security features, which include Kubernetes automation, AWS service support, and integrations with Dynatrace and Datadog, as well as Role-Based Access Control (RBAC). These updates enable rapid, secure deployments and improved anomaly detection using neural networks. The Smart Automation feature allows for quick deployment pipeline creation through native integrations and Configuration-As-Code in YAML, alongside enhanced Kubernetes support and tools for infrastructure provisioning like HashiCorp Terraform. Continuous Verification leverages machine learning for deployment health checks, supporting tools like AppDynamics and AWS CloudWatch, and includes a new real-time dashboard for deployment verifications. Continuous Security focuses on auditing and controlling deployment pipelines, offering features like Audit Trails, Secrets Management, and a new on-premises deployment option, along with RBAC and IP White Listing for enhanced security and governance.