  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.
