Adaptive Resource Allocation for Heterogeneous Compute Nodes
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Solution Overview
Problem
Distributed systems with heterogeneous compute nodes face suboptimal performance due to uncontrolled mapping, static resource allocation, and inadequate consideration of resource consumption profiles, especially in edge computing environments where nodes and applications have varying capacities and requirements.
Innovation Solution
An adaptive resource allocation method that dynamically monitors and adjusts resource allocation during runtime, using an allocation and migration unit to assign applications to suitable compute nodes based on changing resource demands, considering both computing and communication resources, and employing machine learning technologies for optimal mapping and scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static mapping is used for resource allocation, then device complexity is reduced, but application performance and resource utilization deteriorate
Solution Approach 1:
The patent implements dynamic resource allocation that continuously monitors application resource consumption profiles and adapts the mapping between applications and compute nodes during runtime. This allows the system to respond to changing resource demands and node availability, improving application performance while maintaining manageable complexity through automated decision-making.
Solution Approach 2:
The system employs feedback mechanisms by monitoring the actual resource consumption of applications during execution and using this information to dynamically adjust resource allocation. The monitoring component collects data on resource usage patterns, and this feedback drives the adaptive mapping decisions to optimize performance.
2Productivity
If dynamic resource allocation is implemented, then application performance improves, but device complexity increases
Solution Approach 1:
The system enables self-service by allowing applications to effectively request and receive appropriate resources based on their consumption profiles without manual intervention. The automated resource allocation system monitors application behavior and autonomously makes mapping decisions, reducing the need for complex manual configuration while improving performance.
3Adaptability or versatility
If heterogeneous compute nodes are used, then system versatility improves, but mapping difficulty increases
Solution Approach 1:
The patent applies local quality by assigning specific types of compute nodes to specific applications based on the application's resource consumption profile and the node's capabilities. Different regions of the compute pool are optimized for different workloads, allowing the system to leverage heterogeneity effectively while simplifying the mapping decision process through specialized assignments.
4Device complexity
If resource allocation does not consider execution sequence, then scheduling simplicity is maintained, but application performance deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-analyzing application resource consumption profiles and predicting execution patterns before full deployment. This allows the resource allocation system to prepare optimized mappings in advance, considering execution sequences and dependencies, thereby improving performance without adding significant runtime complexity.
Data Source
AI summary
A method for adaptive resource allocation for applications in a distributed system of heterogeneous compute nodes. The following adaptation steps are carried out repeatedly and in an automated manner by an allocation and migration unit at least partially during a runtime of the applications: carrying out monitoring of the applications and the resources of the system to ascertain a need for changes of a resource allocation of the resources of the system for the applications; adapting the resource allocation based on the ascertained need for changes.