Adaptive Resource Management in Distributed Systems
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Solution Overview
Problem
In distributed computing systems, existing resource management techniques lead to underutilization of resources due to manual monitoring and configuration requirements, which are tedious and costly, especially in large clusters with varied applications, resulting in inefficient resource allocation and increased hardware deployment costs.
Innovation Solution
An adaptive resource management system that automatically monitors and allocates resources based on historical application run statistics, using soft and hard limits to optimize resource utilization, allowing dynamic resizing and eliminating the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and configuration is used for resource allocation, then resource utilization can be controlled, but operational costs increase and the process becomes tedious and complex
Solution Approach 1:
The system enables self-service automation where the resource manager automatically monitors resource usage statistics, analyzes historical data, and adjusts resource allocation without manual intervention. The system serves itself by autonomously performing tasks that previously required human operators to monitor and tune resource configurations.
Solution Approach 2:
The system implements continuous feedback loops where resource usage statistics are collected, analyzed, and used to automatically adjust resource allocation. The resource manager monitors actual resource consumption and feeds this information back into the allocation decisions, creating a closed-loop control system that optimizes resource utilization dynamically.
2Reliability
If resources are allocated based on requested amounts rather than actual usage, then tasks can avoid being killed, but resource under-utilization occurs
Solution Approach 1:
The system transitions from static resource allocation (fixed requested amounts) to dynamic allocation based on actual usage patterns. Resource limits are adjusted dynamically according to monitored statistics and historical data, allowing the system to adapt resource allocation in real-time rather than maintaining fixed allocations throughout task execution.
Solution Approach 2:
The system changes the parameters of resource allocation from fixed requested values to variable limits based on actual usage statistics. By modifying resource limits as parameters based on monitored consumption patterns, the system optimizes both task stability and resource utilization efficiency.
3Quantity of substance
If more nodes with higher configuration hardware are deployed to handle resource under-utilization, then resource capacity increases, but hardware deployment costs increase
Solution Approach 1:
Instead of changing the physical quantity of hardware resources, the system changes the utilization parameters through automated monitoring and dynamic allocation. By optimizing how existing resources are allocated and utilized based on actual usage statistics, the system achieves better resource capacity utilization without requiring additional hardware deployment.
Data Source
AI summary
The present disclosure provides a framework for adaptive resource handling of applications being executed in distributed systems so as to ensure efficient resource utilization. The present disclosure provides a framework: to enable a client to participate in identifying application uniquely using tags such that resource adaptation is more effective, to collect and store resource statistics for an application task against various parameters, to monitor resources done based on historical statistics collected, to resource management is done based on historically identified resource usage limits, and if exact match for the records are not found then based on confidence score the limits are identified, and to enable each resource manager (RM) agent to receives the workers to be launched with both limits: given by the application (hard limit) and by the historical statistics (soft limit).