Distributed Resource Management Optimizing Hardware Diversity
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Solution Overview
Problem
Data centers and large hardware clusters face low resource utilization, leading to increased energy costs and reduced profitability due to inefficient management of low-level hardware resources, which current resource management systems fail to optimize effectively.
Innovation Solution
A distributed resource management system that determines usage metrics for hardware resources and assigns applications to clusters to maximize diversity of resource usage, using hardware performance counters and micro-benchmarking to optimize resource allocation and reduce interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If applications are allocated to hardware resources based on CPU and memory scheduling at a high level, then resource allocation decisions are simplified, but the efficiency of the use of other system resources is not optimized
Solution Approach 1:
The patent segments resource management into multiple levels: high-level CPU/memory scheduling and low-level hardware resource monitoring. The low-level resource usage is measured separately using performance counters, and this information is then integrated back into the scheduling decisions, allowing each level to operate independently while contributing to overall optimization.
Solution Approach 2:
The system implements feedback by continuously monitoring low-level hardware resource usage through performance counters and using this information to adjust application allocation decisions. The resource manager receives feedback about cache usage, memory bandwidth consumption, and other hardware metrics, then uses this feedback to make informed scheduling decisions that optimize overall system efficiency.
2Productivity
If applications are packed together to maximize hardware resource utilization, then overall resource utilization improves, but application performance may be penalized due to resource interference
Solution Approach 1:
The patent applies local quality by making allocation decisions specific to each application's hardware usage characteristics. Instead of treating all applications uniformly, the system measures and identifies the specific hardware resources each application uses (cache, memory bandwidth, pipelines) and makes targeted allocation decisions that consider these local characteristics, thereby reducing interference while maximizing utilization.
Solution Approach 2:
The system changes parameters by measuring multiple hardware metrics (cache hit rates, memory bandwidth usage, pipeline utilization) and using these parameter measurements to dynamically adjust application allocation. The resource manager modifies allocation decisions based on measured parameter values, optimizing the balance between resource utilization and application performance.
3Adaptability or versatility
If low-level hardware resources are shared amongst all applications using a given machine, then hardware resource availability is maximized, but resource interference increases and reduces application performance
Solution Approach 1:
The patent applies preliminary action by measuring and characterizing each application's hardware usage patterns before making allocation decisions. The system uses performance counters to pre-assess which hardware resources each application will consume, then uses this preliminary information to make informed allocation decisions that minimize interference while maintaining high resource availability.
Data Source
AI summary
A method of distributed resource management in a distributed computing system includes determining usage of respective hardware resources by an application and generating usage metrics for the application, and assigning the application to a cluster of hardware resources to optimize diversity of usage of hardware resources in the cluster and to enhance utilization of the hardware resources by applications running in that cluster. The diversity of usage of the hardware resources is determined from respective usage metrics of the respective applications running in that cluster. The diversity of usage of the hardware resources in the cluster is optimized by assigning the application to a diversity pool of hardware resources adapted to minimize interference when applications assigned to the diversity pool of hardware resources access the hardware resources in the diversity pool and assigning applications from different diversity pools to the cluster of hardware resources.