The most effective strategy for balancing load and optimizing performance in a compute cluster for a big data application would be C. Distributing tasks based on the server's current load and resource availability.

Here's why:

A. Using a single powerful server to handle all tasks: This approach is not scalable. If the data grows beyond the capacity of the server, performance will degrade. Also, it creates a single point of failure.

B. Implementing a round-robin scheduling algorithm: This approach does not consider the current load or capacity of each server. It simply assigns tasks in a circular order, which could lead to some servers being overloaded while others are underutilized.

C. Distributing tasks based on the server's current load and resource availability: This approach ensures that tasks are assigned to servers that have the capacity to handle them, which optimizes performance and prevents

Question

The most effective strategy for balancing load and optimizing performance in a compute cluster for a big data application would be C. Distributing tasks based on the server's current load and resource availability.

Here's why:

A. Using a single powerful server to handle all tasks: This approach is not scalable. If the data grows beyond the capacity of the server, performance will degrade. Also, it creates a single point of failure.

B. Implementing a round-robin scheduling algorithm: This approach does not consider the current load or capacity of each server. It simply assigns tasks in a circular order, which could lead to some servers being overloaded while others are underutilized.

C. Distributing tasks based on the server's current load and resource availability: This approach ensures that tasks are assigned to servers that have the capacity to handle them, which optimizes performance and prevents

Knowee AI · Accepted Answer

The most effective strategy for balancing load and optimizing performance in a compute cluster for a big data application would be C. Distributing tasks based on the server's current load and resource availability.

Here's why:

A. Using a single powerful server to handle all tasks: This approach is not scalable. If the data grows beyond the capacity of the server, performance will degrade. Also, it creates a single point of failure.

B. Implementing a round-robin scheduling algorithm: This approach does not consider the current load or capacity of each server. It simply assigns tasks in a circular order, which could lead to some servers being overloaded while others are underutilized.

C. Distributing tasks based on the server's current load and resource availability: This approach ensures that tasks are assigned to servers that have the capacity to handle them, which optimizes performance and prevents

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