AMRC: an algebraic model for reconfiguration of high performance cluster computing systems at runtime

High Performance Cluster Computing Systems (HPCSs) represent the best performance because their configuration is customized regarding the features of the problem to be solved at design time. Therefore, if the problem has static nature and features, the best customized configuration can be done. New generations of scientific and industrial problems usually have dynamic nature and behavior. A drawback of this dynamicity is that the customized HPCSs face challenges at runtime, and consequently show the worse performance. The reason for this might be due to the fact that dynamic problems are not adapted to configuration of the HPCS. Hence, requests of the dynamic problem are not in the direction of the HPCS configuration. The main proposed solutions for this challenge are dynamic load balancing or using reconfigurable platforms. In this paper, a vector algebra-based model for HPCS reconfiguration at runtime is presented and named AMRC. This model determines the element causing the dynamic behavior and analyzes the reason regarding both software and hardware at runtime. Some results of the presented model show that by defining a general state vector whose direction is toward reaching high performance computing and whose weight is based on the initial features and explicit requirements of the problem, as well as by defining a vector for each process in the problem at runtime, we can trace changes in the directions and uncover the reason for them.