Adaptive resource scheduling

AI-driven scheduling for real-time optimization of compute, memory, and bandwidth.

The Scheduling Gap

Our Approach

What We Schedule

Measurable Gains

How To Start

The scheduling gap

Rules can't see the future

Most schedulers are:

Our approach

Demand-aware, cost-aware, SLA-aware

Calsoft’s intelligent scheduler uses:

Predictive load forecasting using ML (ARIMA, Prophet, LSTM)

Dynamic priority queues based on SLA urgency

Cost-efficiency heuristics (spot vs reserved vs burstable)

Temporal shift recommendations (off-peak rescheduling)

Node affinity and anti-affinity awareness for clusters

Feedback loops for continuous optimization

What we schedule

Across cloud and edge

Optimizations applied to:

Batch and real-time jobs (Airflow, Azure Data Factory, Step Functions)

ML model training workloads

CI/CD pipelines

Container orchestration (Kubernetes, ECS, AKS, EKS)

Streaming systems (Kafka, Flink)

Measurable gains

Save cloud. Save time. Deliver faster

Metric	Before	After
Cloud cost per run	High	↓ by 30–45%
Job SLA adherence	Inconsistent	↑ to 98%
Compute idle time	Wasted	↓ by 50%
Queue length	Long	Shortened by 60%
Manual interventions	Frequent	Rare

How to start

Smart scheduling in 4 steps

Identify Target Workloads

Start with workloads that have high compute cost or frequent delay.

Ingest Historical Run Data

Pull logs, usage metrics, SLA breaches from your existing orchestrators.

Enable Prediction & Prioritization

Use ML models to forecast load and dynamically adjust job priorities.

Embed AI Scheduler

Integrate with existing orchestration layer to auto-assign resources, reschedule, or throttle workloads.