International Journal of Science, Engineering and TechnologyAuthors: Ramani Teegala
Abstract: By March 2022, microservices architectures had become the dominant paradigm for building large scale distributed systems in industries such as banking, healthcare, e-commerce, and telecommunications. While microservices enabled independent deployment, horizontal scalability, and organizational agility, they also introduced systemic fragility arising from network failures, partial outages, dependency misalignment, and operational complexity. Traditional fault handling mechanisms based on manual intervention, static alerting, and reactive incident management proved increasingly inadequate as systems grew in size and deployment frequency accelerated. As a result, the concept of self healing microservices emerged as a critical evolution in distributed system design, emphasizing the ability of systems to detect, diagnose, and remediate failures autonomously while maintaining service continuity and correctness. Self healing in microservices does not imply the elimination of failure, but rather the capacity to respond intelligently to failure conditions without human intervention wherever possible. By early 2022, this capability was increasingly realized through the combination of resilience patterns, runtime observability, automated orchestration, policy driven remediation, and data driven decision mechanisms. Health checks, circuit breakers, retries, bulkheads, and auto scaling had matured into standard practices, but their effectiveness depended on static thresholds and predefined rules. Emerging self healing approaches sought to extend these mechanisms by incorporating adaptive control loops that respond dynamically to changing system conditions rather than relying solely on predefined assumptions. This paper examines self healing microservices as understood and practicable by March 2022, focusing on architectural patterns, control mechanisms, and operational strategies that enable autonomous recovery in distributed environments. The analysis emphasizes the distinction between reactive resilience and proactive healing, highlighting how feedback loops, runtime telemetry, and orchestration platforms enable systems to transition from simple failure detection to intelligent remediation. Particular attention is given to the interaction between service meshes, container orchestration platforms, and observability pipelines, which together form the technical substrate for self healing behavior. The paper further situates self healing microservices within enterprise and regulated contexts, where automated recovery must coexist with requirements for auditability, change control, and operational transparency. Rather than presenting self healing as a fully autonomous paradigm, the discussion frames it as a human supervised system capability in which automation handles routine failure modes while preserving escalation paths and control boundaries for high risk scenarios. Through a synthesis of industry practice and academic research available up to early 2022, the paper proposes conceptual and layered models for designing self healing microservices that improve availability and resilience without compromising governance or correctness.
DOI: http://doi.org/10.5281/zenodo.18680202
