Feedback-Based Task Distribution for Fair Multitenant Resource Allocation
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Solution Overview
Problem
Existing task management frameworks lack efficient distribution of tasks based on real-time feedback from worker nodes, leading to suboptimal resource utilization and unfairness in multitenant environments.
Innovation Solution
A controller in the task management framework receives feedback from worker nodes to learn the computing resources required for each task type, enabling it to accurately assess and distribute tasks efficiently while ensuring fairness among tenants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are distributed based on traditional metrics (CPU usage, memory usage), then task distribution can be performed, but resource utilization is suboptimal and fairness is compromised
Solution Approach 1:
The system implements a feedback mechanism where the controller receives feedback information from worker nodes about actual task processing times and resource consumption. This feedback is used to continuously update the estimated processing times for different task types, enabling the controller to make more accurate task distribution decisions that optimize resource utilization while ensuring fairness among tenants.
2Productivity
If tasks are distributed without real-time feedback, then distribution speed is maintained, but task distribution efficiency is suboptimal
Solution Approach 1:
The system performs preliminary actions by maintaining pre-calculated estimated processing times for different task types based on historical feedback data. When new tasks need to be distributed, the controller can quickly consult these pre-established estimates to make rapid distribution decisions without needing to collect and process real-time feedback for each individual task, thus minimizing time loss while improving overall efficiency.
3Ease of operation
If the controller assigns tasks without accurate workload assessment, then task assignment is simple, but resource allocation is unbalanced
Solution Approach 1:
The worker nodes autonomously provide feedback information about their actual task processing performance and resource consumption. This self-service approach enables the controller to automatically assess workload characteristics and make balanced resource allocation decisions without requiring complex manual intervention or simplified heuristics, thus maintaining ease of operation while achieving balanced resource allocation.
Data Source
AI summary
Job management solutions often involve a controller distributing tasks to worker nodes or worker nodes in an efficient manner. In one example, this disclosure describes a method that includes receiving, by a controller, a first set of tasks; assigning, by the controller, each of the tasks in the first set of tasks to worker nodes for processing by the worker nodes; receiving, by the controller and for at least some of the tasks in the first set of tasks, feedback information; determining, by the controller and based on the feedback information, an expected amount of processing associated with each task type in the plurality of task types; receiving, by the controller, a second set of tasks; and assigning, by the controller and based on the expected amount of processing associated with each task type, each of the tasks in the second set of tasks to the worker nodes for processing.