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Controlling CPU and memory utilization in servers and data centers have been extensively addressed using control theory.
For example, the authors in Ref. [14] proposed to use a MIMO controller for CPU and memory utilization in a web server.
In this research, a computational model based on stored logic is described and interesting issues concerned with memory utilization in processing are analyzed.
The experimental results reveal that the memory utilization in the section cache is 3.4 times higher than in the decompressed cache.
In order to handle large PSM datasets, we use the Cholesky factorization technique to improve memory utilization in model training.
We use the Cholesky factorization technique to improve memory utilization in model training for large PSM datasets.
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Our solution achieves guaranteed minimal CPU consumption, while keeping the memory utilization linear in the number of objects in the window.
For example the measure processing time is expressed in minutes while the measure memory utilization is expressed in Mbytes.
The Memory utilization is given in (6) as M u /M T, and is related to λ via N a in (8) and N c in (9), which are in turn used in the expression of M u in (13).
Our comprehensive experimental study, using both synthetic and real data from domains of moving object monitoring and stock trades, demonstrates superiority of our proposed strategies over alternate methods in both CPU processing resources and in memory utilization.
Simulation results using different network settings and traffic load conditions demonstrate meaningful improvement in memory utilization efficiency.
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