Diluting the Scalability Boundaries: Exploring the Use of Disaggregated Architectures for High-Level Network Data Analysis

Traditional data centers are designed with a rigid architecture of fit-for-purpose servers that provision resources beyond the average workload in order to deal with occasional peaks of data. Heterogeneous data centers are pushing towards more cost-efficient architectures with better resource provisioning. In this paper we study the feasibility of using disaggregated architectures for intensive data applications, in contrast to the monolithic approach of server-oriented architectures. Particularly, we have tested a proactive network analysis system in which the workload demands are highly variable. In the context of the dReDBox disaggregated architecture, the results show that the overhead caused by using remote memory resources is significant, between 66\% and 80\%, but we have also observed that the memory usage is one order of magnitude higher for the stress case with respect to average workloads. Therefore, dimensioning memory for the worst case in conventional systems will result in a notable waste of resources. Finally, we found that, for the selected use case, parallelism is limited by memory. Therefore, using a disaggregated architecture will allow for increased parallelism, which, at the same time, will mitigate the overhead caused by remote memory.Comment: 8 pages, 6 figures, 2 tables, 32 references. Pre-print. The paper will be presented during the IEEE International Conference on High Performance Computing and Communications in Bangkok, Thailand. 18 - 20 December, 2017. To be published in the conference proceeding

Remote Cell Growth Sensing Using Self-Sustained Bio-Oscillations

A smart sensor system for cell culture real-time supervision is proposed, allowing for a significant reduction in human effort applied to this type of assay. The approach converts the cell culture under test into a suitable “biological” oscillator. The system enables the remote acquisition and management of the “biological” oscillation signals through a secure web interface. The indirectly observed biological properties are cell growth and cell number, which are straightforwardly related to the measured bio-oscillation signal parameters, i.e., frequency and amplitude. The sensor extracts the information without complex circuitry for acquisition and measurement, taking advantage of the microcontroller features. A discrete prototype for sensing and remote monitoring is presented along with the experimental results obtained from the performed measurements, achieving the expected performance and outcomes