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

TL;DR

This paper evaluates the feasibility of disaggregated architectures for high-level network data analysis, revealing significant remote memory overhead but potential for increased parallelism and resource efficiency.

Contribution

It demonstrates the trade-offs and benefits of using disaggregated architectures over traditional monolithic servers for variable workload data analysis.

Findings

Remote memory overhead ranges from 66% to 80%.

Memory usage is much higher under stress workloads.

Disaggregated architectures can increase parallelism and resource efficiency.

Abstract

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.