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.

翻译：随着异构计算系统上运行的异构应用服务数量不断增长，资源管理问题变得愈发关键。尽管现有解决方案考虑了部分网络和时间要求，但其设计大多仅处理预定义的资源类型列表，因此无法提供可扩展的方案来评估其他需求集合或切换资源估算策略。本研究提出一种基于启发式的估算方案，可作为自评估机制支持任意计算系统，包括动态加权需求考量、计算各节点对准入请求的承载能力，并允许扩展评估所考虑的资源类型列表——这在相关研究中尚属罕见视角。该算法可被分布式和集中式资源分配协议用于确定服务部署的最佳节点。通过对各组件进行验证，结果表明该方案在资源估算方面性能直观，同时具备良好的可扩展性与可扩展能力。