Cloud Bursting Glossary

Running large ETL or BI workloads in cloud when demand exceeds local capacity.

Controlling throughput to avoid saturating hybrid links during bursts.

Running batch ETL, analytics, or rendering workloads in cloud during peaks.

Moving block volumes or snapshots to cloud for burst workloads.

Comparing long-term cost of on-prem expansion vs periodic bursting.

Automated provisioning of cloud resources when triggers activate.

Financial limit placed on cloud bursting activities.

High-speed intermediate storage (often NVMe/SSD) placed close to burst compute nodes to absorb I/O spikes and hide latency/bandwidth limits between on-prem storage and cloud.

Temporary compute, storage, or GPU resources provisioned in cloud during peak workload periods.

Monitoring spend attributable only to burst sessions.

Gradually shifting workloads back on-prem when burst ends.

The length of time a workload remains in cloud burst capacity.

Measures whether bursting is cost-effective vs buying more hardware.

Classification of bursts as synchronous (tied to live user traffic and strict latency) or asynchronous (offline jobs), which guides data movement, routing, and cost strategy.

Predicting future spikes to pre-stage cloud capacity.

Organization-level rules defining which teams and workloads may burst, allowed regions, tagging requirements, budget limits, and required approvals or reviews.

High-speed link specifically designed to carry burst traffic.

A pre-hardened cloud environment (accounts, VPCs, subnets, IAM, logging, baseline security) specifically prepared to receive burst workloads safely and consistently.

Additional delay from running workloads across hybrid environments.

A set of rules controlling when, how far, and how long workloads may burst.

Automated health and configuration checks before allowing a burst.

Automated guardrail that halts or throttles further bursting when budgets, error rates, security checks, or latency exceed predefined thresholds.

Explicit service-level objectives/agreements for bursting, such as maximum time to acquire burst capacity, acceptable added latency, and cost ceilings per burst event.

The metric level (CPU, RAM, queue depth, latency) that triggers bursting.

The event, metric, or condition that activates a cloud burst.

Examining peak times when bursts commonly occur.

Application design optimized for hybrid overflow into cloud environments.

A hybrid cloud strategy where applications run on-premises but automatically expand into public cloud during demand spikes.

A controller or service that decides when, where, and how to burst (which cloud/region/instance types) based on metrics, policies, and cost signals, then drives the end-to-end workflow.

The connectivity model (hub-and-spoke, mesh, direct connect) enabling bursting.

Temporary cloud-side cache accelerating burst workloads.

Automated failover strategy when primary hybrid link fails mid-burst.

Returning workloads to on-prem for cost or compliance reasons.

Autoscaler that adds cloud nodes when pods cannot be scheduled locally.

Coordinating bursts across multiple clusters or clouds.

Cold: provisioned on demand; warm: staged; hot: ready and idle.

Delay when cloud resources must scale up from zero during bursts.

Scheduler that selects burst locations based on compliance constraints.

Bursting only into compliant regions based on regulatory constraints.

Overflow of CPU-heavy workloads into cloud compute nodes.

Bursting decisions influenced by real-time or forecasted cloud cost.

Scaling that takes cost signals into account, not just performance.

Mechanism enabling services running across on-prem and cloud to locate each other.

Uniform security policy across hybrid burst environments.

Routing logic enabling traffic to flow between on-prem and cloud nodes.

Limits and controls (quotas, alerts, policies) on cross-environment data transfer during bursts to cap egress costs and comply with data movement restrictions.

Physical distance between compute and data; major factor in burst performance.

Copying data to cloud storage to prepare for stateful bursting.

Regulations governing where data used during bursts is allowed to reside.

Ensuring data consistency between on-prem and cloud before/after bursts.

Preloading data into cloud storage before a burst to reduce latency.

Applications burst directly into cloud compute without intermediaries.

Bursting enabled by DNS routing policies to cloud endpoints.

Low-risk test bursts (reduced scale, synthetic data or mirrored traffic) used to validate orchestration, routing, data paths, and cost assumptions before real production bursts.

Reactive bursting based on real-time usage spikes.

Handling spikes during sales seasons by shifting load to cloud.

Extending workloads from edge locations into centralized cloud resources.

Ensuring data moving between environments is protected in transit.

Preventing bursting when security or compliance checks fail.

Running compute-intensive simulations during market volatility.

Bursting only into allowed cloud regions due to compliance requirements.

Temporary use of cloud GPUs when on-prem GPUs are fully utilized.

Using global load balancing to route users between on-prem and cloud based on demand.

Running HPC workloads locally but bursting to cloud nodes when job queues overflow.

Combining on-prem infrastructure with cloud resources to provide elastic overflow capacity.

Links (VPN, Direct Connect, ExpressRoute, Interconnect) between on-prem and cloud.

Consistent identity and access control across on-prem and cloud.

A single logical cluster containing both on-prem and cloud worker nodes.

Scheduler logic deciding which jobs stay on-prem vs which burst.

Integrating schedulers (Slurm, PBS, Airflow) with cloud bursting.

Dynamic cluster scaling using Karpenter to add cloud capacity rapidly during bursts.

Extending an on-prem cluster into cloud nodes when capacity is exceeded.

Acceptable delay between on-prem and cloud resources during bursting.

Bursting constrained by software licensing limits (e.g., simulation engines).

Using load balancers to distribute traffic between on-prem and cloud.

Using cloud GPUs for training or inference surges.

A mesh routing layer distributing traffic across hybrid clusters.

Pods spilled from on-prem node pools to cloud node pools during bursts.

Syncing datasets to cloud object stores for burst readiness.

Workloads run on-prem by default and burst only into cloud.

Sequence controlling provisioning, data sync, scheduling, and draining.

Paying only for cloud resources consumed during bursts.

Affinity/anti-affinity and policy rules (e.g., “run near dataset X”, “avoid region Y”) used by the scheduler to choose suitable cloud nodes or regions for burst workloads.

Cloud instances kept warm for immediate bursting.

Time needed to spin up cloud burst resources.

Routing requests through an intermediary service or proxy during bursts.

When HPC or batch job queues exceed local cluster capacity and overflow to cloud.

Offloading CGI or video rendering jobs to cloud GPU fleets.

Discounted precommitted cloud capacity for predictable bursts.

Moving workloads from cloud back to on-prem during outages, cost spikes, or latency requirements.

Planned bursting for predictable workload peaks (e.g., month-end closes).

Using SD-WAN to dynamically route workloads into cloud.

Sending mirrored production traffic to cloud to warm systems before a full burst.

Extending NFS/Lustre/FSx-like file systems across hybrid environments.

Risk of inconsistent routing tables between on-prem and cloud systems.

Using spot/preemptible instances to reduce burst cost.

Bursting workloads requiring synchronized storage or session state.

Bursting workloads that do not require persistent data on-prem.

Using metrics, logs, and events to decide when to burst.

Prioritizing or deprioritizing burst traffic based on policy.

Workloads can burst either direction based on cost, latency, or capacity.

A Kubernetes abstraction enabling pods to run on cloud compute as if they were local.

Redirection of excess jobs or traffic to cloud when local resources saturate.

Writes stay in cache temporarily; faster but riskier in hybrid models.

Writes go to both cache and primary storage; used for safe bursting.

Applying zero-trust models to dynamically created burst resources.