Abstract

This paper studies the energy efficiency of composable data center (DC) infrastructures over network topologies. Using a mixed integer linear programming (MILP) model, we compare the performance of disaggregation at rack-scale and pod-scale over selected electrical, optical and hybrid network topologies relative to a traditional DC. Relative to a pod-scale DC, the results show that physical disaggregation at rack-scale is sufficient for optimal efficiency when the optical network topology is adopted, and resource components are allocated in a suitable manner. The optical network topology also enables optimal energy efficiency in composable DCs. The paper also studies logical disaggregation of traditional DC servers over an optical network topology. Relative to physical disaggregation at rack-scale, logical disaggregation of server resources within each rack enables marginal fall in the total DC power consumption (TDPC) due to improved resource demands placement. Hence, an adaptable composable infrastructure that can support both in memory (access) latency sensitive and insensitive workloads is enabled. We also conduct a study of the adoption of micro-service architecture in both traditional and composable DCs. Our results show that increasing the modularity of workloads improves the energy efficiency in traditional DCs, but disproportionate utilization of DC resources persists. A combination of disaggregation and micro-services achieved up to 23% reduction in the TDPC of the traditional DC by enabling optimal resources utilization and energy efficiencies. Finally, we propose a heuristic for energy efficient placement of workloads in composable DCs which replicates the trends produced by the MILP model formulated in this paper.

In Compute/Memory Dynamic Packet/Circuit Switch Placement for Optically Disaggregated Data Centers

Adaranijo Peters, George Oikonomou, and Georgios Zervas
J. Opt. Commun. Netw. 10(7) B164-B178 (2018)

Optically Disaggregated Data Centers With Minimal Remote Memory Latency: Technologies, Architectures, and Resource Allocation [Invited]

Georgios Zervas, Hui Yuan, Arsalan Saljoghei, Qianqiao Chen, and Vaibhawa Mishra
J. Opt. Commun. Netw. 10(2) A270-A285 (2018)

Network-aware compute and memory allocation in optically composable data centers with deep reinforcement learning and graph neural networks

Zacharaya Shabka and Georgios Zervas
J. Opt. Commun. Netw. 15(2) 133-143 (2023)

Optics enabled networks and architectures for data center cost and power efficiency [Invited]

Marc Taubenblatt, Pavlos Maniotis, and Asser Tantawi
J. Opt. Commun. Netw. 14(1) A41-A49 (2022)

Energy Efficiency of an Integrated Intra-Data-Center and Core Network With Edge Caching

Matteo Fiorani, Slavisa Aleksic, Paolo Monti, Jiajia Chen, Maurizio Casoni, and Lena Wosinska
J. Opt. Commun. Netw. 6(4) 421-432 (2014)