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Traditional data centers are designed with a rigid architecture offit-for-purpose servers that provision resources beyond the average workload inorder to deal with occasional peaks of data. Heterogeneous data centers arepushing towards more cost-efficient architectures with better resourceprovisioning. In this paper we study the feasibility of using disaggregatedarchitectures for intensive data applications, in contrast to the monolithicapproach of server-oriented architectures. Particularly, we have tested aproactive network analysis system in which the workload demands are highlyvariable. In the context of the dReDBox disaggregated architecture, the resultsshow 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 oneorder of magnitude higher for the stress case with respect to averageworkloads. Therefore, dimensioning memory for the worst case in conventionalsystems will result in a notable waste of resources. Finally, we found that,for the selected use case, parallelism is limited by memory. Therefore, using adisaggregated architecture will allow for increased parallelism, which, at thesame time, will mitigate the overhead caused by remote memory.

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