Towards Disaggregation-Native Data Streaming between Devices
Nils Asmussen, Michael Roitzsch

TL;DR
This paper proposes a disaggregation-native data streaming approach that allows direct device-to-device data flows, reducing latency in datacenter workloads by bypassing CPU staging.
Contribution
It introduces a novel data streaming facility that enables direct device-to-device communication in disaggregated datacenter architectures.
Findings
Reduced processing latencies in datacenter workloads
Enabling direct device-to-device data flows improves efficiency
Supports flexible resource pooling in disaggregated systems
Abstract
Disaggregation is an ongoing trend to increase flexibility in datacenters. With interconnect technologies like CXL, pools of CPUs, accelerators, and memory can be connected via a datacenter fabric. Applications can then pick from those pools the resources necessary for their specific workload. However, this vision becomes less clear when we consider data movement. Workloads often require data to be streamed through chains of multiple devices, but typically, these data streams physically do not directly flow device-to-device, but are staged in memory by a CPU hosting device protocol logic. We show that augmenting devices with a disaggregation-native and device-independent data streaming facility can improve processing latencies by enabling data flows directly between arbitrary devices.
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Taxonomy
TopicsData Stream Mining Techniques · Caching and Content Delivery · Peer-to-Peer Network Technologies
