Capacity Estimations for Distributed Job Scheduler

1. Traffic Estimate

• Job Submissions: Estimate the number of job submissions per unit of time.
• For example, if the system is expected to receive 1000 job submissions per hour during peak periods, the traffic estimate would be 1000 jobs/hour.

2. Storage Estimate

• Job Metadata: Estimate the amount of storage required for job metadata. If each job metadata entry consumes 1 KB of storage and the system is expected to handle 1 million jobs, the storage estimate would be 1 GB.
• Logs and Configuration: Estimate additional storage for logs and configuration data based on expected usage patterns and retention policies.

3. Bandwidth Estimate

• Communication Overhead: Estimate the bandwidth required for communication between the scheduler and worker nodes.
• For example, if each message sent between nodes is 1 KB in size and there are 1000 messages exchanged per second, the bandwidth estimate would be 1 MB/s.

4. Memory Estimate

• Job Queues: Estimate the memory required for storing job queues. If each job queue entry consumes 100 bytes of memory and the system is expected to handle 10,000 concurrent jobs, the memory estimate would be 1 MB.
• Execution Context: Estimate memory for storing execution contexts and system state based on the complexity of job execution and concurrency levels.

In today’s technology-driven world, it’s critical to handle computing tasks across different systems efficiently. A Distributed Job Scheduler system helps coordinate the running of tasks across multiple computers in a distributed computing environment. It manages the scheduling, distributing, and tracking of these tasks, including data processing, analysis, batch job runs, and resource assignment.