Feedback-Driven Task Distribution for Fair Multitenant Workloads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing task management frameworks lack efficient distribution of tasks across computing resources, particularly in multitenant data centers, due to insufficient visibility into the work being performed by worker nodes and reliance on external metrics like CPU utilization and memory usage, leading to inefficiencies and unfair resource allocation.
Innovation Solution
A controller gains visibility into worker node operations through feedback, learns to assess resource requirements for different task types, and uses a closed-loop mechanism to optimize task distribution, ensuring fair and efficient allocation based on historical data and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If tasks are distributed based on external metrics like CPU utilization and memory usage, then resource allocation can be simplified, but task distribution efficiency and fairness deteriorate due to insufficient visibility into actual work being performed
Solution Approach 1:
The patent implements a feedback mechanism where worker nodes report task completion status and processing information back to the controller. This feedback loop enables the controller to gain visibility into actual work being performed and make informed distribution decisions based on real processing capacity rather than just external metrics like CPU utilization.
Solution Approach 2:
Worker nodes autonomously report their task completion status and processing information without requiring external monitoring. This self-service approach allows the system to gather accurate internal state information while keeping the distribution mechanism relatively simple.
2Device complexity
If traditional task distribution methods are used without feedback loops, then system complexity is reduced, but adaptability to changing workloads and demands deteriorates
Solution Approach 1:
The controller receives feedback information from worker nodes about task completion and processing status, using this information to dynamically adjust task distribution strategies. This feedback-driven approach enables the system to adapt to changing workloads and demands while maintaining manageable complexity through automated decision-making.
Solution Approach 2:
The task distribution system transitions from static allocation based on initial metrics to dynamic allocation that continuously adjusts based on real-time feedback about worker node performance and workload characteristics.
3Productivity
If comprehensive feedback collection is implemented to improve task distribution accuracy, then resource utilization improves, but system complexity and overhead increase
Solution Approach 1:
The system implements a targeted feedback mechanism where worker nodes report specific task completion status and processing information. This focused feedback approach provides sufficient information for improved resource utilization without requiring comprehensive monitoring of all system parameters, thereby limiting the increase in system complexity.
4Ease of operation
If task distribution is based on partitioning systems without visibility into actual processing work, then initial task allocation is simplified, but fairness among tenants deteriorates in multitenant environments
Solution Approach 1:
The controller receives feedback from worker nodes about actual task processing performance and uses this information to make fairer distribution decisions. This enables the system to maintain simple initial allocation through partitioning while achieving fairness through subsequent feedback-driven adjustments.
Solution Approach 2:
The system performs preliminary task allocation using partitioning systems to simplify initial distribution, then uses feedback loops to make corrective adjustments that ensure fairness among tenants in multitenant environments.
Data Source
Figure 1
Figure 2
Figure 3
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.