Selfadaptive

Self-adaptive refers to systems that can modify their behavior in response to changes in the environment, goals, or internal state, without human intervention. The aim is to maintain or improve quality attributes such as performance, reliability, or energy efficiency. Self-adaptive capability is a central concept in autonomic computing and self-adaptive software systems.

A common architectural pattern for self-adaptive systems is a closed-loop feedback model known as MAPE-K: Monitor, Analyze, Plan, Execute, with a shared Knowledge base. The monitor collects runtime data; the analyzer detects when adaptation is needed; the planner selects appropriate actions; the executor applies changes to the system. The knowledge base stores models, policies, and historical information that guide decisions. Adaptation decisions are typically driven by policies, optimization techniques, or learning methods.

Adaptation can be categorized into several types. Parameter adaptation tunes configuration knobs without structural changes. Structural or architectural adaptation modifies the system’s composition, such as adding or removing components or reconfiguring interfaces. Self-healing targets fault detection and recovery, while self-optimizing aims to improve performance or resource usage. Real-world examples include autoscaling in cloud services, adaptive bitrate streaming, and self-adjusting control systems. Designers must balance responsiveness with stability and safety, and manage the overhead and potential conflicts among policies.

Benefits of self-adaptive systems include improved resilience, better performance under varying conditions, and more efficient resource use. Challenges encompass ensuring correctness and safety of adaptations, preventing oscillations, handling policy conflicts, and evaluating runtime benefits. Research areas cover formal verification of adaptive behavior, policy design, and learning-based approaches to automatic adaptation.