Once failure is experienced in production systems, traditional approaches require human input to diagnose problems, apply fixes, and bring service back online.
Olayinka Idowu has spent most of his time perfecting infrastructure to automatically react to failures, develop systems to detect, analyse, and repair themselves before the user even experiences degradation.
Idowu’s strategy for self-healing infrastructure begins by emphasising end-to-end observability. Instead of using mere threshold-based alerts, her systems are always accumulating metrics in numerous dimensions: application performance metrics, system resource levels, network latency behaviours, and user experience metrics.
This data is the foundation of machine-based models that recognise normal operating behaviour and are capable of detecting anomalies, suggesting problems.
The fundamental architecture is developed based on three interrelated layers. The detection layer employs unsupervised learning models to establish baseline behaviour for each component of the infrastructure stack.
The models are continuously refining their knowledge to capture the rhythms of the seasons, traffic fluctuations, and characteristic rhythms of diverse services through the week and day. Upon detecting an anomaly, the system will not automatically raise an alert but will initiate an in-depth study to comprehend the underlying cause and probable effect.
The diagnosis layer utilises pattern recognition methods to determine correlations between symptoms present in multiple system parts.
Idowu has built a custom framework to define relations between services to define how a lag in a database might be expressed as elevated response times in several application services, or how pressure on memory in a single node might propagate performance problems across an entire distributed system. This correlation engine prevents the system from treating symptoms as individual anomalies but instead resolves their causes.
The most sophisticated aspect of Idowu’s work pertains to predictive models that anticipate failures prior to their occurrence. These models conduct analysis of historical failure patterns, resource consumption trends, and external factors to identify conditions that typically precede system degradation.
Instead of awaiting the manifestation of problems, the system possesses the capability to proactively adjust resource allocation, restart services exhibiting early signs of memory leaks, or redirect traffic away from nodes that are likely to fail.
The machine learning pipeline processes data in real-time using streaming analytics to maintain up-to-date system health models.
Idowu implemented a feedback loop whereby results of automated remediation actions that are both unsuccessful and successful are used as training data to improve subsequent decision-making. This approach allows the system to draw lesson outcomes from self-interventions and improve over time in choosing the appropriate repair strategies.
One of the hardest parts of this work relates to dealing with the complexity associated in today’s distributed systems. Applications running in multiple cloud regions and their myriad dependencies create situations in which an individual failure is likely to spin off cascading behaviour in the infrastructure.
The models developed by Idowu consider these interdependencies, using graph-based algorithms to understand how issues propagate and determine the best points at which to intervene.
Idowu’s system supports a cache of remediation actions associated with diverse problem types. The actions represent a continuum ranging from base-resource modifications to sophisticated sequences of orchestration using many system components.
The remediation always includes safety constraints to avoid the automation making moves that would compound the problem. For example, the system is designed to refuse to restart over a defined percentage of instances simultaneously, so as to be able to have enough capacity to absorb traffic.
The remediation engine further incorporates business logic to make choices in alignment with operating priorities. During cases of peak traffic, it might choose to supplement resources rather than restart services to avoid any potential service disruption. During low-traffic time, it might be more aggressive in restarting services to clear up built-up memory or connection leaks.
Regardless of how autonomous these systems are, Idowu built in some safety features to avoid runaway automation. The circuit breakers serve to restrict the system from doing too many steps in remediation in a short time span, a possibility that may be an underlying issue to be investigated by humans.
The system also maintains very detailed logs of all steps automated and thus offers clarity on decisions and allows the system behaviour to be traced and confirmed by the engineers.
Such critical changes still require human validation even in automated systems. Idowu’s paradigm defines differences between routine maintenance tasks, possibly triggered automatically, and significant modifications to be accompanied by human assessment. This categorisation process considers several elements such as the severity of the alteration to be made, the degree of diagnosis certainty, and the system’s current operational mode.
Effective performance of autonomous repair systems necessitates careful measurement above and beyond mere uptime metrics. Idowu monitors mean time to detection, mean time to repair, and automated diagnosis accuracy. Specifically, she monitors the false positive rate and unnecessary intervention and consequently prevents the system from getting overaggressive in repair endeavours.
The system produces periodic reports of the kinds of problems encountered, the effectiveness of the various remediation strategies tried in comparison to those that were not tried, and how the models could be enhanced. This feedback is incorporated into the developmental process to enhance the machine learning models and to enrich the repertoire of potential remediation actions.
Deployment of autonomous healing systems in production requires careful consideration of existing operating procedures and team relationships. Idowu worked closely with operating teams to ensure automated systems complemented, instead of replacing, human expertise. The aim is not to eliminate human intervention but to resolve mundane problems automatically and hence free up engineers to focus on deeper challenges and system improvement.
Cultural adoption was as critical as technical deployment. Teams needed time to come to trust automated systems and to understand how their roles would change. Idowu smoothed this transition by providing broad visibility into system decisions and maintaining manual override features, allowing teams to override when needed.
Idowu is now focusing on the expansion of the parameters of self-healing to the application level, instead of focusing on infrastructure-based problems. This task incorporates the design of models able to understand application behaviour tendencies and independently change configurations, database queries, or cache strategies to maintain optimum performance.
He is also investigating incorporating natural language processing techniques in an effort to ease application log and error message analysis in a greater degree. This would enable the system to identify and correct a wider variety of issues than are presently noticeable as individual anomalies in metrics but apparent in log patterns or user feedback.
The long-term dream of self-healing infrastructure is to design systems that are not only self-repairing but are always fine-tuning their performance. The systems would absorb knowledge gleaned from usage patterns to fine-tune resource allocation, adapt configurations to do a better job, and even recommend architectural changes to avoid coming problems.
With his work on infrastructure self-healing using machine learning, Olayinka Idowu has shown it is possible to move machine learning well beyond alerting and simple monitoring to create truly self-managing systems. His approach combines deep technical acumen with a practical understanding of operational problems and results in solutions that improve system reliability while allowing engineering teams to focus on innovation rather than on repeated maintenance.
As organisations continue to scale their infrastructure and add to system complexity, his work provides a template on how to maintain reliability through cognitive automation.
