TOWARDS HIGH-SPEED NETWORKING IN THE POST-MOORE ERA
The motivation behind this dissertation stems from two polarizing trends inside modern datacenters—on the one hand, the bandwidth demand and link speed within datacenters keep increasing rapidly as applications keep getting more distributed, and resources (e.g., storage) keep getting disaggregated; but on the other hand, the processing and switching speeds of the underlying networking infrastructure, comprising primarily of general-purpose CPUs and packet switches, is scaling much more slowly due to the slowdown in Moore’s law and the end of Dennard scaling. Thus, at the end-hosts, it is becoming increasingly difficult to saturate the high-speed links using CPU-based network stack, and in the network core, it is becoming increasingly difficult to build high-speed switching fabric in a cost and power effective manner using packet switches. To that end, this dissertation looks beyond the general-purpose CPUs and the packet switches as the building blocks for datacenter networks, and instead presents two promising alternatives that demonstrate that it is possible to build high-performance, high-speed end-host network stacks and switching fabrics at low cost and power, even as Moore’s law continues to slow down. First, with regards to the end-host network stack, this dissertation demonstrates the need to complement general-purpose CPUs with domain-specific processors for network processing, to keep up with increasing link speeds. However, one of the key challenges with designing a domain-specific processor is providing the right balance between programmability and performance. Using packet scheduling as the target network processing application, our main contribution here is a new packet scheduling primitive, called Push-In-Extract-Out (PIEO), and the corresponding hardware architecture implementing the primitive, that together form an idealistic packet scheduler which is simultaneously programmable, scalable, and high-speed—PIEO primitive is more expressive (programmable) than any state-of-the-art packet scheduling primitive, and PIEO’s hardware architecture could easily scale to 10s of thousands of flows (>30x more scalable than state-of-the-art) while making scheduling decisions in O(1) time (up to 300x faster than a single CPU core). Second, with regards to the switching fabric, this dissertation demonstrates that by using fast circuit switches (that could reconfigure within nanoseconds) as building blocks, one could build extremely high-speed and high-performance switching fabrics in a cost and power effective manner. The key challenge here is designing a high-speed control plane for the circuit-switched network that could complement the fine-grained reconfiguration technology by being able to schedule the right set of circuits every few nanoseconds. In that regard, our main contribution here is a new control plane architecture for circuit switching which is a stark departure from the traditional (slow) centralized controller-based architectures, and is in fact a fully de-centralized and traffic agnostic design that could schedule high-performing circuit configurations at nanosecond granularity. Further, our design is agnostic to the underlying circuit switching technology, and can operate with electrical, optical, or wireless technologies alike. We demonstrate that the resulting circuit-switched network, called Shoal, can effectively scale to high switching speeds while achieving comparable or better throughput and latency than several state-of-the-art packet-switched network designs at significantly lower power and cost. Overall, through PIEO and Shoal, this dissertation demonstrates that by effectively leveraging the promise of domain-specific processing and fast circuit switching, one can indeed build efficient high-speed end-host network stacks and switching fabrics even in light of continued slowdown in Moore’s law and the end of Dennard scaling.
Circuit Switching; Computer Networks; Datacenter Networks; High-speed Networking; Network Hardware; Programmable Networks
Foster, Nate; Delimitrou, Christina; Agarwal, Rachit
Ph. D., Computer Science
Doctor of Philosophy
Attribution 4.0 International
dissertation or thesis
Except where otherwise noted, this item's license is described as Attribution 4.0 International
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