Energy Efficient Placement of Workloads in Composable Data Center Networks

TL;DR

This paper evaluates energy-efficient workload placement in composable data centers using MILP models, highlighting the benefits of rack-scale disaggregation and optical topologies for optimal efficiency, and proposing a heuristic for practical deployment.

Contribution

It introduces a MILP-based analysis of disaggregation scales and network topologies in composable data centers, and proposes a heuristic for energy-efficient workload placement.

Findings

Rack-scale disaggregation with optical networks achieves optimal efficiency.

Logical disaggregation offers marginal power savings over physical disaggregation.

Combining disaggregation with micro-services reduces total data center power consumption by up to 23%.

Abstract

This paper studies the energy efficiency of composable datacentre (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.