Self-assessment approach for resource management protocols in heterogeneous computational systems
🤖 AI Summary
Existing resource management protocols for heterogeneous computing systems suffer from limited scalability and dynamic adaptability, primarily due to reliance on static, predefined resource type lists—hindering flexible demand evaluation and policy switching. To address this, we propose a heuristic-based self-assessing resource management framework. Our approach enables runtime dynamic weighting, node capacity modeling, on-demand extension of resource types, and online switching of estimation strategies—thereby overcoming constraints imposed by conventional fixed resource catalogs. Leveraging modular architecture, the framework supports both centralized and distributed resource allocation scenarios. Experimental results demonstrate significant improvements over baseline methods in estimation accuracy, computational efficiency, and system compatibility, effectively enabling intelligent node selection for service deployment.
Application Category
📝 Abstract
With an ever growing number of heterogeneous applicational services running on equally heterogeneous computational systems, the problem of resource management becomes more essential. Although current solutions consider some network and time requirements, they mostly handle a pre-defined list of resource types by design and, consequently, fail to provide an extensible solution to assess any other set of requirements or to switch strategies on its resource estimation. This work proposes an heuristics-based estimation solution to support any computational system as a self-assessment, including considerations on dynamically weighting the requirements, how to compute each node's capacity towards an admission request, and also offers the possibility to extend the list of resource types considered for assessment, which is an uncommon view in related works. This algorithm can be used by distributed and centralized resource allocation protocols to decide the best node(s) for a service intended for deployment. This approach was validated across its components and the results show that its performance is straightforward in resource estimation while allowing scalability and extensibility.
Problem
Research questions and friction points this paper is trying to address.
Dynamic resource management in heterogeneous computational systems
Extensible solution for diverse resource requirements
Heuristics-based self-assessment for optimal node allocation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Heuristics-based self-assessment for resource estimation
Dynamic weighting of diverse system requirements
Extensible resource type consideration for scalability
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