AutRM

AutRM, short for Autonomous Resource Manager, is a software framework concept designed to automate the allocation, monitoring, and optimization of computing resources in distributed systems. It aims to minimize manual intervention by making autonomous, policy-driven decisions about CPU, memory, storage, and network resources across hosts, containers, and virtual machines.

Architecture and components: A typical AutRM stack includes a resource monitor, a policy engine, a decision module, an action executor, and a feedback loop. The resource monitor gathers telemetry from compute nodes and workloads. The policy engine codifies rules and machine-learning policies. The decision module selects actions such as scaling, migration, or throttling. The action executor enforces changes, while the feedback loop measures results to refine policies.

Operation and capabilities: AutRM combines declarative policies with predictive models to anticipate demand, prevent contention, and meet quality-of-service goals. It supports multi-tenant isolation, quotas, and dynamic reconfiguration. It can integrate with existing schedulers and orchestration layers through APIs and event streams.

Applications and deployment: The concept is applied in cloud data centers, edge computing, high-performance computing clusters, and service meshes where dynamic resource contention occurs. It is used to improve utilization, reduce manual tuning, and enhance resilience.

Challenges and considerations: Implementations must address policy conflicts, explainability, security, and safety, as well as telemetry reliability and system overhead. Interoperability with existing management tools and governance of ML models are common concerns.

See also: autonomic computing, cluster management, resource scheduling, policy-based management, reinforcement learning.