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Topic: Greedy Algorithm Problem / Level: advanced
Problem:
Design a Greedy algorithm to allocate limited computing resources in a cloud environment where each task has different memory, CPU, and execution time requirements. Maximize total task completion while minimizing server usage.
More Problems
You are tasked with scheduling the deployment of rescue teams during a natural disaster where each affected region has varying levels of need and logistical constraints. Maximize the number of people rescued while minimizing resource wastage.
Design a Greedy algorithm to allocate limited research grants to scientific projects where each project has varying risks, timelines, and potential breakthroughs. Maximize total research output while minimizing financial risk.
You are given a set of marketing campaigns where each campaign has varying audience reach, engagement levels, and costs. Implement a Greedy algorithm to allocate marketing budgets across campaigns to maximize total audience engagement while minimizing expenses.
Design a Greedy algorithm to allocate limited bandwidth across multiple video streaming platforms where each platform has varying viewer engagement, data usage, and potential revenue. Maximize viewer satisfaction while minimizing bandwidth congestion.
You are tasked with scheduling repairs for high-traffic roads in a city where each road has varying traffic volumes, repair costs, and repair times. Maximize the number of roads repaired while minimizing traffic disruptions.
Design a Greedy algorithm to allocate limited public resources to environmental conservation projects where each project has varying ecological impacts, costs, and completion times. Maximize total conservation impact while minimizing resource wastage.
You are given a set of jobs in a distributed computing system where each job has different processing needs, execution times, and memory requirements. Implement a Greedy algorithm to maximize total system throughput while minimizing resource conflicts.
Design a Greedy algorithm to allocate limited research grants to university departments where each department has varying success probabilities, project timelines, and potential discoveries. Maximize total research output while minimizing financial risk.
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