Scalability and Systems Resource Management Questions
Design and operational practices for managing compute and platform resources as systems scale. Covers autoscaling, resource pooling, orchestration, cost trade offs between always on versus on demand provisioning, and architectural choices that affect resource utilization and performance. Candidates should be prepared to discuss capacity planning for infrastructure, metrics and alerts for autoscaling, and cost versus performance decisions for high availability systems.
MediumTechnical
87 practiced
Discuss the operational and architectural trade-offs of a multi-cloud autoscaling strategy where an application's capacity can be provisioned across two cloud providers. Cover latency, consistency, disaster recovery, pricing, and complexity of autoscaling coordination.
HardTechnical
115 practiced
You observe frequent oscillations in your autoscaling system where replica counts rapidly increase and decrease, causing instability. Describe a comprehensive engineering response covering metric smoothing, hysteresis, cooldowns, predictive scaling, and safeguards. Explain why each part helps stabilize the system.
HardSystem Design
74 practiced
Design an admission controller (Kubernetes webhook) that enforces resource quota limits and a tag-based policy that prevents pods from disabling autoscaling labels. Describe required inputs, validation logic, performance considerations, and how you would test it safely in production.
MediumTechnical
80 practiced
Implement a simple greedy bin-packing heuristic in Python that assigns a list of containers (each with CPU and memory requirements) to nodes with fixed capacity. The function should return a mapping of node -> assigned containers minimizing number of nodes used. Aim for clarity; not optimality. Provide a brief explanation of complexity.
MediumTechnical
76 practiced
Compare the operational implications of performing a rolling cluster upgrade (in-place) versus reprovisioning a new cluster and migrating workloads (blue/green) for a critical platform that cannot tolerate more than 0.01% downtime per month. Include factors like network configuration, immutable infrastructure, and rollback complexity.
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