Orchestration Glossary

External operation performed by a worker, separate from workflow control logic.

Policy enforcement mechanism during resource creation.

Tracking datasets, models, and outputs across workflows.

Execution model where tasks may run more than once under failure conditions.

Execution model where tasks run no more than once, risking loss on failure.

Slowing execution to prevent system overload.

Coordinating background or scheduled jobs.

Efficiently fitting workloads into available resources.

Deployment strategy using two environments to reduce downtime.

Gradual rollout of changes to a subset of users.

Saving execution progress to allow recovery after failure.

Decentralized coordination where services react to events independently.

Corrective steps executed when rollback is not possible.

Limiting the number of tasks running simultaneously.

Executing different workflow paths based on runtime conditions.

Managing lifecycle, networking, and scaling of containers.

Component responsible for orchestration logic, state, and policies.

Orchestrator overwhelmed by state or event volume.

Continuous reconciliation mechanism used in Kubernetes.

Unique identifier used to tie together logs, traces, and events for a single workflow or request across many services and tasks.

Tradeoff between automation benefits and coordination overhead.

Making execution decisions based on cost optimization.

Extension of orchestration APIs for domain-specific resources.

Ensuring required data is available before task execution.

Coordinating ingestion, transformation, and delivery of data.

Layer where actual workload execution occurs.

Storage for failed tasks requiring manual inspection.

Orchestration that accounts for per-task or per-workflow deadlines, prioritizing or skipping work to meet SLAs.

Defining what the system should achieve rather than how.

Defining execution order and relationships between tasks.

Target configuration an orchestrator continuously enforces.

Ability to replay workflow logic consistently from recorded history.

Graph structure used to model task dependencies without cycles.

Coordination mechanism used by orchestrators to serialize access to shared resources or critical sections across workers/nodes.

Identifying divergence between expected and actual execution.

Workflow structure determined at runtime rather than design time.

Coordinating workloads across edge locations.

Large-scale coordination across teams, systems, and compliance boundaries.

Managing Extract-Transform-Load workflows.

Persistent log of workflow events used for recovery, auditing, and replay.

Design where workflow and system state are reconstructed from an append-only log of events rather than mutable state snapshots, enabling replay and auditability.

Triggering workflows in response to events.

Logical guarantee ensuring tasks have a single effect, often simulated through idempotency.

Visual representation of workflow execution for debugging.

Guarantees defining how tasks are executed, such as retry behavior and delivery guarantees.

Managing parallel ML experiments.

Retry strategy where delay between retries increases (often exponentially) to avoid overload and thundering-herd failures.

Mechanisms for detecting, isolating, and recovering from failures.

Pattern where tasks split into parallel branches and later rejoin.

Coordinating feature engineering workflows.

Cleanup logic executed before resource deletion.

Linking serverless functions into workflows.

Process where workers stop accepting new tasks, finish in-flight work, and hand off leases before being terminated or upgraded.

Probes used to assess service availability.

Coordinating workloads across on-prem and cloud environments.

Workflow step that pauses for a manual approval, review, or input before continuing automated orchestration.

Periodic signal from long-running tasks to indicate liveness.

Explicitly specifying execution steps and commands.

Routing and scaling model inference workloads.

Stable token attached to an operation so that retried or duplicated orchestration steps can be safely de-duplicated.

Ability to repeat operations without unintended side effects.

Open-source container orchestration platform using declarative APIs.

Workflow that may run for hours, days, or longer, often with timers, human approvals, and durable state rather than a single process lifetime.

Distributing traffic across multiple instances.

Repeating workflow steps until conditions are met.

Coordinating data prep, training, evaluation, and deployment.

Tracing model versions back to data and code.

Managing workloads across multiple cloud providers.

Supporting multiple isolated users or teams.

Logical separation of workloads in shared platforms.

Visibility into orchestration behavior using metrics, logs, and traces.

Ability of orchestrated systems to withstand failures.

Standard procedures for handling orchestration failures.

Custom controller that encodes domain-specific orchestration logic.

Automated coordination and management of multiple services, tasks, or resources to execute complex workflows reliably.

Single workflow or controller becoming a bottleneck.

Performance cost introduced by orchestration layers.

Centralized control versus decentralized interaction model.

A system that coordinates execution, dependencies, scaling, and failure handling across components.

Running independent tasks at the same time.

Scheduling workloads based on performance characteristics.

Smallest deployable unit in Kubernetes.

Task that repeatedly fails and blocks progress.

Scheduling strategy that orders task execution based on priority classes (e.g., P0 incidents ahead of batch jobs).

Enforcing resource usage limits.

Controlling execution frequency or throughput.

Continuous process of aligning actual state with desired state.

Reverting to a previous stable state after failure.

Re-running workflows deterministically to diagnose issues.

Assigning compute, memory, and storage to tasks.

Placing workloads efficiently across available infrastructure.

Rules defining how and when failed tasks are retried.

Gradual replacement of running instances with new versions.

Orchestration pattern for long-running, multi-step workflows where each step has a compensating action instead of a global distributed transaction (2PC).

Automatic detection and recovery from failures.

Managing event-driven, function-based workflows.

Automatic detection of service endpoints.

Partitioning task queues into multiple shards to scale throughput and avoid hotspots in large-scale orchestration systems.

Workflow that completes within a single process or short time window, typically without durable state or complex recovery semantics.

Excessive workflow state growth increasing cost and latency.

Tracking current and historical execution state.

Workflow that persists execution state across steps.

Workflow that does not retain execution state between steps.

Keeping workflow execution on the same worker to improve performance.

Managing continuous data processing pipelines.

Reusable workflow invoked by another workflow.

A discrete unit of work executed as part of a workflow.

Operational data collected for monitoring and analysis.

Queue that routes tasks or activities to available workers.

Determining when and where tasks should run based on policies and resources.

Intentionally limiting resource consumption.

Running workflows at fixed intervals.

Recovery triggered when tasks stop responding.

Tracking execution paths across distributed workflows.

Process that executes tasks or activities on behalf of the orchestrator.

Automatically increasing or decreasing the number of workers based on queue depth, latency, or SLA targets to keep workflows on time.

A defined sequence of tasks executed in a specific order to achieve an outcome.

Building complex workflows by combining simpler ones.

Software that executes, tracks, and manages workflows based on defined logic.

Uncontrolled parallelism causing resource exhaustion.

Reconstructing workflow state from event history after failure or restart.

Time guarantees for workflow completion.

Uncontrolled growth of workflows increasing complexity.

Time limit after which a workflow or task is automatically failed.

Mechanism for running multiple versions of a workflow definition in parallel, while safely migrating in-flight executions between versions.